WorldWideScience

Sample records for sample learning activities

  1. Windowed active sampling for reliable neural learning

    NARCIS (Netherlands)

    Barakova, E.I; Spaanenburg, L

    The composition of the example set has a major impact on the quality of neural learning. The popular approach is focused on extensive pre-processing to bridge the representation gap between process measurement and neural presentation. In contrast, windowed active sampling attempts to solve these

  2. Active learning and adaptive sampling for non-parametric inference

    NARCIS (Netherlands)

    Castro, R.M.

    2007-01-01

    This thesis presents a general discussion of active learning and adaptive sampling. In many practical scenarios it is possible to use information gleaned from previous observations to focus the sampling process, in the spirit of the "twenty-questions" game. As more samples are collected one can

  3. Less is more: Sampling chemical space with active learning

    Science.gov (United States)

    Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas; Isayev, Olexandr; Roitberg, Adrian E.

    2018-06-01

    The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble's prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach, we develop the COmprehensive Machine-learning Potential (COMP6) benchmark (publicly available on GitHub) which contains a diverse set of organic molecules. Active learning-based ANI potentials outperform the original random sampled ANI-1 potential with only 10% of the data, while the final active learning-based model vastly outperforms ANI-1 on the COMP6 benchmark after training to only 25% of the data. Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1x) that provides accurate energy and force predictions on the entire COMP6 benchmark. This universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecules or materials, while remaining applicable to the general class of organic molecules composed of the elements CHNO.

  4. Active learning for clinical text classification: is it better than random sampling?

    Science.gov (United States)

    Figueroa, Rosa L; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

    2012-01-01

    Objective This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Design Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Measurements Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. Results The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. Conclusion For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty. PMID:22707743

  5. Minimax bounds for active learning

    NARCIS (Netherlands)

    Castro, R.M.; Nowak, R.

    2008-01-01

    This paper analyzes the potential advantages and theoretical challenges of "active learning" algorithms. Active learning involves sequential sampling procedures that use information gleaned from previous samples in order to focus the sampling and accelerate the learning process relative to "passive

  6. Active Learning Using Hint Information.

    Science.gov (United States)

    Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hsuan-Tien

    2015-08-01

    The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

  7. Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

    Science.gov (United States)

    Andrews, T. M.; Leonard, M. J.; Colgrove, C. A.; Kalinowski, S. T.

    2011-01-01

    Previous research has suggested that adding active learning to traditional college science lectures substantially improves student learning. However, this research predominantly studied courses taught by science education researchers, who are likely to have exceptional teaching expertise. The present study investigated introductory biology courses randomly selected from a list of prominent colleges and universities to include instructors representing a broader population. We examined the relationship between active learning and student learning in the subject area of natural selection. We found no association between student learning gains and the use of active-learning instruction. Although active learning has the potential to substantially improve student learning, this research suggests that active learning, as used by typical college biology instructors, is not associated with greater learning gains. We contend that most instructors lack the rich and nuanced understanding of teaching and learning that science education researchers have developed. Therefore, active learning as designed and implemented by typical college biology instructors may superficially resemble active learning used by education researchers, but lacks the constructivist elements necessary for improving learning. PMID:22135373

  8. Learning in later life: participation in formal, non-formal and informal activities in a nationally representative Spanish sample.

    Science.gov (United States)

    Villar, Feliciano; Celdrán, Montserrat

    2013-06-01

    This article examines the participation of Spanish older people in formal, non-formal and informal learning activities and presents a profile of participants in each kind of learning activity. We used data from a nationally representative sample of Spanish people between 60 and 75 years old ( n  = 4,703). The data were extracted from the 2007 Encuesta sobre la Participación de la Población Adulta en Actividades de Aprendizaje (EADA, Survey on Adult Population Involvement in Learning Activities). Overall, only 22.8 % of the sample participated in a learning activity. However, there was wide variation in the participation rates for the different types of activity. Informal activities were far more common than formal ones. Multivariate logistic regression indicated that education level and involvement in social and cultural activities were associated with likelihood of participating, regardless of the type of learning activity. When these variables were taken into account, age did not predict decreasing participation, at least in non-formal and informal activities. Implications for further research, future trends and policies to promote older adult education are discussed.

  9. Scene recognition based on integrating active learning with dictionary learning

    Science.gov (United States)

    Wang, Chengxi; Yin, Xueyan; Yang, Lin; Gong, Chengrong; Zheng, Caixia; Yi, Yugen

    2018-04-01

    Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.

  10. Active-Learning versus Teacher-Centered Instruction for Learning Acids and Bases

    Science.gov (United States)

    Sesen, Burcin Acar; Tarhan, Leman

    2011-01-01

    Background and purpose: Active-learning as a student-centered learning process has begun to take more interest in constructing scientific knowledge. For this reason, this study aimed to investigate the effectiveness of active-learning implementation on high-school students' understanding of "acids and bases". Sample: The sample of this…

  11. Manifold Regularized Experimental Design for Active Learning.

    Science.gov (United States)

    Zhang, Lining; Shum, Hubert P H; Shao, Ling

    2016-12-02

    Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

  12. Learning outcomes between Socioscientific Issues-Based Learning and Conventional Learning Activities

    OpenAIRE

    Piyaluk Wongsri; Prasart Nuangchalerm

    2010-01-01

    Problem statement: Socioscientific issues-based learning activity is essential for scientific reasoning skills and it could be used for analyzing problems be applied to each situation for more successful and suitable. The purposes of this research aimed to compare learning achievement, analytical thinking and moral reasoning of seventh grade students who were organized between socioscientific issues-based learning and conventional learning activities. Approach: The samples used in research we...

  13. Unsupervised active learning based on hierarchical graph-theoretic clustering.

    Science.gov (United States)

    Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

    2009-10-01

    Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

  14. Applying Active Learning to Assertion Classification of Concepts in Clinical Text

    Science.gov (United States)

    Chen, Yukun; Mani, Subramani; Xu, Hua

    2012-01-01

    Supervised machine learning methods for clinical natural language processing (NLP) research require a large number of annotated samples, which are very expensive to build because of the involvement of physicians. Active learning, an approach that actively samples from a large pool, provides an alternative solution. Its major goal in classification is to reduce the annotation effort while maintaining the quality of the predictive model. However, few studies have investigated its uses in clinical NLP. This paper reports an application of active learning to a clinical text classification task: to determine the assertion status of clinical concepts. The annotated corpus for the assertion classification task in the 2010 i2b2/VA Clinical NLP Challenge was used in this study. We implemented several existing and newly developed active learning algorithms and assessed their uses. The outcome is reported in the global ALC score, based on the Area under the average Learning Curve of the AUC (Area Under the Curve) score. Results showed that when the same number of annotated samples was used, active learning strategies could generate better classification models (best ALC – 0.7715) than the passive learning method (random sampling) (ALC – 0.7411). Moreover, to achieve the same classification performance, active learning strategies required fewer samples than the random sampling method. For example, to achieve an AUC of 0.79, the random sampling method used 32 samples, while our best active learning algorithm required only 12 samples, a reduction of 62.5% in manual annotation effort. PMID:22127105

  15. Sampling Memories: Using Hip-Hop Aesthetics to Learn from Urban Schooling Experiences

    Science.gov (United States)

    Petchauer, Emery

    2012-01-01

    This article theorizes and charts the implementation of a learning activity designed from the hip-hop aesthetic of sampling. The purpose of this learning activity was to enable recent urban school graduates to reflect upon their previous schooling experiences as a platform for future learning in higher education. This article illustrates what…

  16. Exploring Representativeness and Informativeness for Active Learning.

    Science.gov (United States)

    Du, Bo; Wang, Zengmao; Zhang, Lefei; Zhang, Liangpei; Liu, Wei; Shen, Jialie; Tao, Dacheng

    2017-01-01

    How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified best-versus-second-best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms.

  17. Active Learning in the Era of Big Data

    Energy Technology Data Exchange (ETDEWEB)

    Jamieson, Kevin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Davis, IV, Warren L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-10-01

    Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for realworld, reproducible active learning research. This paper details the challenges of building the system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.

  18. Applying active learning to supervised word sense disambiguation in MEDLINE.

    Science.gov (United States)

    Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu; Zheng, Kai; Xu, Hua

    2013-01-01

    This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models. We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation. Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements. This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models.

  19. ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

    Energy Technology Data Exchange (ETDEWEB)

    Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J. [Astronomy Department, University of California, Berkeley, CA 94720-7450 (United States); Brink, Henrik [Dark Cosmology Centre, Juliane Maries Vej 30, 2100 Copenhagen O (Denmark); Long, James P.; Rice, John, E-mail: jwrichar@stat.berkeley.edu [Statistics Department, University of California, Berkeley, CA 94720-7450 (United States)

    2012-01-10

    Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL-where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up-is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

  20. ACTIVE LEARNING TO OVERCOME SAMPLE SELECTION BIAS: APPLICATION TO PHOTOMETRIC VARIABLE STAR CLASSIFICATION

    International Nuclear Information System (INIS)

    Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Berian James, J.; Brink, Henrik; Long, James P.; Rice, John

    2012-01-01

    Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

  1. Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

    Science.gov (United States)

    Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

    2012-01-01

    Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

  2. Distribution-Preserving Stratified Sampling for Learning Problems.

    Science.gov (United States)

    Cervellera, Cristiano; Maccio, Danilo

    2017-06-09

    The need for extracting a small sample from a large amount of real data, possibly streaming, arises routinely in learning problems, e.g., for storage, to cope with computational limitations, obtain good training/test/validation sets, and select minibatches for stochastic gradient neural network training. Unless we have reasons to select the samples in an active way dictated by the specific task and/or model at hand, it is important that the distribution of the selected points is as similar as possible to the original data. This is obvious for unsupervised learning problems, where the goal is to gain insights on the distribution of the data, but it is also relevant for supervised problems, where the theory explains how the training set distribution influences the generalization error. In this paper, we analyze the technique of stratified sampling from the point of view of distances between probabilities. This allows us to introduce an algorithm, based on recursive binary partition of the input space, aimed at obtaining samples that are distributed as much as possible as the original data. A theoretical analysis is proposed, proving the (greedy) optimality of the procedure together with explicit error bounds. An adaptive version of the algorithm is also introduced to cope with streaming data. Simulation tests on various data sets and different learning tasks are also provided.

  3. Applying active learning to supervised word sense disambiguation in MEDLINE

    Science.gov (United States)

    Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu; Zheng, Kai; Xu, Hua

    2013-01-01

    Objectives This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models. Methods We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation. Results Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements. Conclusions This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models. PMID:23364851

  4. Diverse Expected Gradient Active Learning for Relative Attributes.

    Science.gov (United States)

    You, Xinge; Wang, Ruxin; Tao, Dacheng

    2014-06-02

    The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called Diverse Expected Gradient Active Learning (DEGAL). This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multi-class distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

  5. Learning by Doing: Twenty Successful Active Learning Exercises for Information Systems Courses

    Directory of Open Access Journals (Sweden)

    Alanah Mitchell

    2017-01-01

    Full Text Available Aim/Purpose: This paper provides a review of previously published work related to active learning in information systems (IS courses. Background: There are a rising number of strategies in higher education that offer promise in regards to getting students’ attention and helping them learn, such as flipped classrooms and offering courses online. These learning strategies are part of the pedagogical technique known as active learning. Active learning is a strategy that became popular in the early 1990s and has proven itself as a valid tool for helping students to be engaged with learning. Methodology: This work follows a systematic method for identifying and coding previous research based on an aspect of interest. The authors identified and assessed research through a search of ABI/Inform scholarly journal abstracts and keywords, as well as additional research databases, using the search terms “active learning” and “information systems” from 2000 through June 2016. Contribution: This synthesis of active learning exercises provides guidance for information technology faculty looking to implement active learning strategies in their classroom by demonstrating how IS faculty might begin to introduce more active learning techniques in their teaching as well as by presenting a sample teaching agenda for a class that uses a mix of active and passive learning techniques to engage student learning. Findings: Twenty successful types of active learning exercises in IS courses are presented. Recommendations for Practitioners\t: This paper offers a “how to” resource of successful active learning strategies for IS faculty interested in implementing active learning in the classroom. Recommendation for Researchers: This work provides an example of a systematic literature review as a means to assess successful implementations of active learning in IS. Impact on Society: An updated definition of active learning is presented as well as a meaningful

  6. Active learning reduces annotation time for clinical concept extraction.

    Science.gov (United States)

    Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

    2017-10-01

    To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. From learning objects to learning activities

    DEFF Research Database (Denmark)

    Dalsgaard, Christian

    2005-01-01

    This paper discusses and questions the current metadata standards for learning objects from a pedagogical point of view. From a social constructivist approach, the paper discusses how learning objects can support problem based, self-governed learning activities. In order to support this approach......, it is argued that it is necessary to focus on learning activities rather than on learning objects. Further, it is argued that descriptions of learning objectives and learning activities should be separated from learning objects. The paper presents a new conception of learning objects which supports problem...... based, self-governed activities. Further, a new way of thinking pedagogy into learning objects is introduced. It is argued that a lack of pedagogical thinking in learning objects is not solved through pedagogical metadata. Instead, the paper suggests the concept of references as an alternative...

  8. Localization-Aware Active Learning for Object Detection

    OpenAIRE

    Kao, Chieh-Chi; Lee, Teng-Yok; Sen, Pradeep; Liu, Ming-Yu

    2018-01-01

    Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification. However, the use of active learning for object detection is still largely unexplored as determining informativeness of an object-location hypothesis is more difficult. In this paper, we address this issue and present two metrics for measuring the informativeness of an object hypothesis,...

  9. Interpretable Active Learning

    OpenAIRE

    Phillips, Richard L.; Chang, Kyu Hyun; Friedler, Sorelle A.

    2017-01-01

    Active learning has long been a topic of study in machine learning. However, as increasingly complex and opaque models have become standard practice, the process of active learning, too, has become more opaque. There has been little investigation into interpreting what specific trends and patterns an active learning strategy may be exploring. This work expands on the Local Interpretable Model-agnostic Explanations framework (LIME) to provide explanations for active learning recommendations. W...

  10. [Purity Detection Model Update of Maize Seeds Based on Active Learning].

    Science.gov (United States)

    Tang, Jin-ya; Huang, Min; Zhu, Qi-bing

    2015-08-01

    Seed purity reflects the degree of seed varieties in typical consistent characteristics, so it is great important to improve the reliability and accuracy of seed purity detection to guarantee the quality of seeds. Hyperspectral imaging can reflect the internal and external characteristics of seeds at the same time, which has been widely used in nondestructive detection of agricultural products. The essence of nondestructive detection of agricultural products using hyperspectral imaging technique is to establish the mathematical model between the spectral information and the quality of agricultural products. Since the spectral information is easily affected by the sample growth environment, the stability and generalization of model would weaken when the test samples harvested from different origin and year. Active learning algorithm was investigated to add representative samples to expand the sample space for the original model, so as to implement the rapid update of the model's ability. Random selection (RS) and Kennard-Stone algorithm (KS) were performed to compare the model update effect with active learning algorithm. The experimental results indicated that in the division of different proportion of sample set (1:1, 3:1, 4:1), the updated purity detection model for maize seeds from 2010 year which was added 40 samples selected by active learning algorithm from 2011 year increased the prediction accuracy for 2011 new samples from 47%, 33.75%, 49% to 98.89%, 98.33%, 98.33%. For the updated purity detection model of 2011 year, its prediction accuracy for 2010 new samples increased by 50.83%, 54.58%, 53.75% to 94.57%, 94.02%, 94.57% after adding 56 new samples from 2010 year. Meanwhile the effect of model updated by active learning algorithm was better than that of RS and KS. Therefore, the update for purity detection model of maize seeds is feasible by active learning algorithm.

  11. Automatic Earthquake Detection by Active Learning

    Science.gov (United States)

    Bergen, K.; Beroza, G. C.

    2017-12-01

    In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

  12. Active Learning Through Discussion in E-Learning

    OpenAIRE

    Daru Wahyuningsih

    2016-01-01

    Active learning is generally made by a lecturer in learning face to face. In the face to face learning, lecturer can implement a variety of teaching methods to make students actively involved in learning. This is different from learning that is actuating in e-learning. The main characteristic of e-learning is learning that can take place anytime and anywhere. Special strategies are needed so that lecturer can make students play an active role in the course of e-learning. Research in order to ...

  13. Active-learning versus teacher-centered instruction for learning acids and bases

    Science.gov (United States)

    Acar Sesen, Burcin; Tarhan, Leman

    2011-07-01

    Background and purpose: Active-learning as a student-centered learning process has begun to take more interest in constructing scientific knowledge. For this reason, this study aimed to investigate the effectiveness of active-learning implementation on high-school students' understanding of 'acids and bases'. Sample The sample of this study was 45 high-school students (average age 17 years) from two different classes, which were randomly assigned to the experimental (n = 21) and control groups (n = 25), in a high school in Turkey. Design and methods A pre-test consisting of 25 items was applied to both experimental and control groups before the treatment in order to identify student prerequisite knowledge about their proficiency for learning 'acids and bases'. A one-way analysis of variance (ANOVA) was conducted to compare the pre-test scores for groups and no significant difference was found between experimental (ME = 40.14) and control groups (MC = 41.92) in terms of mean scores (F 1,43 = 2.66, p > 0.05). The experimental group was taught using an active-learning curriculum developed by the authors and the control group was taught using traditional course content based on teacher-centered instruction. After the implementation, 'Acids and Bases Achievement Test' scores were collected for both groups. Results ANOVA results showed that students' 'Acids and Bases Achievement Test' post-test scores differed significantly in terms of groups (F 1,43 = 102.53; p acid and base theories'; 'metal and non-metal oxides'; 'acid and base strengths'; 'neutralization'; 'pH and pOH'; 'hydrolysis'; 'acid-base equilibrium'; 'buffers'; 'indicators'; and 'titration'. Based on the achievement test and individual interview results, it was found that high-school students in the experimental group had fewer misconceptions and understood the concepts more meaningfully than students in control group. Conclusion The study revealed that active-learning implementation is more effective at

  14. Sparse feature learning for instrument identification: Effects of sampling and pooling methods.

    Science.gov (United States)

    Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu

    2016-05-01

    Feature learning for music applications has recently received considerable attention from many researchers. This paper reports on the sparse feature learning algorithm for musical instrument identification, and in particular, focuses on the effects of the frame sampling techniques for dictionary learning and the pooling methods for feature aggregation. To this end, two frame sampling techniques are examined that are fixed and proportional random sampling. Furthermore, the effect of using onset frame was analyzed for both of proposed sampling methods. Regarding summarization of the feature activation, a standard deviation pooling method is used and compared with the commonly used max- and average-pooling techniques. Using more than 47 000 recordings of 24 instruments from various performers, playing styles, and dynamics, a number of tuning parameters are experimented including the analysis frame size, the dictionary size, and the type of frequency scaling as well as the different sampling and pooling methods. The results show that the combination of proportional sampling and standard deviation pooling achieve the best overall performance of 95.62% while the optimal parameter set varies among the instrument classes.

  15. Reinforcement learning or active inference?

    Science.gov (United States)

    Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

    2009-07-29

    This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

  16. Reinforcement learning or active inference?

    Directory of Open Access Journals (Sweden)

    Karl J Friston

    2009-07-01

    Full Text Available This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

  17. MLS student active learning within a "cloud" technology program.

    Science.gov (United States)

    Tille, Patricia M; Hall, Heather

    2011-01-01

    In November 2009, the MLS program in a large public university serving a geographically large, sparsely populated state instituted an initiative for the integration of technology enhanced teaching and learning within the curriculum. This paper is intended to provide an introduction to the system requirements and sample instructional exercises used to create an active learning technology-based classroom. Discussion includes the following: 1.) define active learning and the essential components, 2.) summarize teaching methods, technology and exercises utilized within a "cloud" technology program, 3.) describe a "cloud" enhanced classroom and programming 4.) identify active learning tools and exercises that can be implemented into laboratory science programs, and 5.) describe the evaluation and assessment of curriculum changes and student outcomes. The integration of technology in the MLS program is a continual process and is intended to provide student-driven active learning experiences.

  18. Learning to reason from samples

    NARCIS (Netherlands)

    Ben-Zvi, Dani; Bakker, Arthur; Makar, Katie

    2015-01-01

    The goal of this article is to introduce the topic of learning to reason from samples, which is the focus of this special issue of Educational Studies in Mathematics on statistical reasoning. Samples are data sets, taken from some wider universe (e.g., a population or a process) using a particular

  19. Active learning: a step towards automating medical concept extraction.

    Science.gov (United States)

    Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

    2016-03-01

    This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined. The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The effect of incremental learning vs standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. The following 2 clinical data sets were used for evaluation: the Informatics for Integrating Biology and the Bedside/Veteran Affairs (i2b2/VA) 2010 natural language processing challenge and the Shared Annotated Resources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) 2013 eHealth Evaluation Lab. The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared with the random sampling baseline, the saving is at least doubled. Incremental active learning is a promising approach for building effective and robust medical concept extraction models while significantly reducing the burden of manual annotation. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  20. The Influence of Problem Based Learning Model toward Students’ Activities and Learning Outcomes on Financial Management Subject

    Directory of Open Access Journals (Sweden)

    Han Tantri Hardini

    2016-12-01

    Full Text Available This research aims to know the influence of problem based learning model toward students’ activities and achievement on Financial Management subject for undergraduate program students of Accounting Education. It was a quantitative research that used true experimental design. Samples of this study were undergraduate program students of Accounting Education in the year of 2014. Class A were control class and class B were experimental class. Data were analyzed by using t-test in order to determine the differences of learning outcomes between control class and experimental class. Then, questionnaires were distributed to gather students’ activities information in their students’ learning model. Findings show that there is an influence of Problem Based Learning model toward students’ activities and learning outcomes on Financial Management subject for undergraduate program students of Accounting Education since t-count ≥ t-table. It is 6.120 ≥ 1.9904. Students’ learning activities with Problem Based Learning model are better than students who are taught by conventional learning model.

  1. Per-Sample Multiple Kernel Approach for Visual Concept Learning

    Directory of Open Access Journals (Sweden)

    Ling-Yu Duan

    2010-01-01

    Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

  2. Per-Sample Multiple Kernel Approach for Visual Concept Learning

    Directory of Open Access Journals (Sweden)

    Tian Yonghong

    2010-01-01

    Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

  3. Understanding active sampling strategies: Empirical approaches and implications for attention and decision research.

    Science.gov (United States)

    Gottlieb, Jacqueline

    2018-05-01

    In natural behavior we actively gather information using attention and active sensing behaviors (such as shifts of gaze) to sample relevant cues. However, while attention and decision making are naturally coordinated, in the laboratory they have been dissociated. Attention is studied independently of the actions it serves. Conversely, decision theories make the simplifying assumption that the relevant information is given, and do not attempt to describe how the decision maker may learn and implement active sampling policies. In this paper I review recent studies that address questions of attentional learning, cue validity and information seeking in humans and non-human primates. These studies suggest that learning a sampling policy involves large scale interactions between networks of attention and valuation, which implement these policies based on reward maximization, uncertainty reduction and the intrinsic utility of cognitive states. I discuss the importance of using such paradigms for formalizing the role of attention, as well as devising more realistic theories of decision making that capture a broader range of empirical observations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Theoretical Foundations of Active Learning

    Science.gov (United States)

    2009-05-01

    I study the informational complexity of active learning in a statistical learning theory framework. Specifically, I derive bounds on the rates of...convergence achievable by active learning , under various noise models and under general conditions on the hypothesis class. I also study the theoretical...advantages of active learning over passive learning, and develop procedures for transforming passive learning algorithms into active learning algorithms

  5. Machine learning of molecular properties: Locality and active learning

    Science.gov (United States)

    Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

    2018-06-01

    In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

  6. Active learning for semi-supervised clustering based on locally linear propagation reconstruction.

    Science.gov (United States)

    Chang, Chin-Chun; Lin, Po-Yi

    2015-03-01

    The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Re-imagining Active Learning

    DEFF Research Database (Denmark)

    Dall'Alba, Gloria; Bengtsen, Søren Smedegaard

    2018-01-01

    is largely lacking in the literature on active learning. In this article, we explore the possibility of re-imagining, or at least extending, the meaning of active learning by drawing out dimensions that are neither readily visible nor instrumental, as much of this literature implies. Drawing from educational......Ample attention is being paid in the higher education literature to promoting active learning among students. Where studies on active learning report student outcomes, they indicate improved or equivalent outcomes when compared with traditional lectures, which are considered more passive...... philosophy and, in particular, existential philosophies, we argue that active learning may also be partly invisible, unfocused, unsettling, and not at all instrumentalsometimes even leaving the learner more confused and (temporarily) incompetent. However, such forms of undisclosed or ‘dark’ learning, we...

  8. Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning.

    Directory of Open Access Journals (Sweden)

    Anne Hsu

    Full Text Available A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning.

  9. Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning

    Science.gov (United States)

    2016-01-01

    A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning. PMID:27310576

  10. A Studi on High Plant Systems Course with Active Learning in Higher Education Through Outdoor Learning to Increase Student Learning Activities

    OpenAIRE

    Nur Rokhimah Hanik, Anwari Adi Nugroho

    2015-01-01

    Biology learning especially high plant system courses needs to be applied to active learning centered on the student (Active Learning In Higher Education) to enhance the students' learning activities so that the quality of learning for the better. Outdoor Learning is one of the active learning invites students to learn outside of the classroom by exploring the surrounding environment. This research aims to improve the students' learning activities in the course of high plant systems through t...

  11. A study of active learning methods for named entity recognition in clinical text.

    Science.gov (United States)

    Chen, Yukun; Lasko, Thomas A; Mei, Qiaozhu; Denny, Joshua C; Xu, Hua

    2015-12-01

    Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they often require large amounts of annotated samples, which are expensive to build due to the requirement of domain experts in annotation. Active learning (AL), a sample selection approach integrated with supervised ML, aims to minimize the annotation cost while maximizing the performance of ML-based models. In this study, our goal was to develop and evaluate both existing and new AL methods for a clinical NER task to identify concepts of medical problems, treatments, and lab tests from the clinical notes. Using the annotated NER corpus from the 2010 i2b2/VA NLP challenge that contained 349 clinical documents with 20,423 unique sentences, we simulated AL experiments using a number of existing and novel algorithms in three different categories including uncertainty-based, diversity-based, and baseline sampling strategies. They were compared with the passive learning that uses random sampling. Learning curves that plot performance of the NER model against the estimated annotation cost (based on number of sentences or words in the training set) were generated to evaluate different active learning and the passive learning methods and the area under the learning curve (ALC) score was computed. Based on the learning curves of F-measure vs. number of sentences, uncertainty sampling algorithms outperformed all other methods in ALC. Most diversity-based methods also performed better than random sampling in ALC. To achieve an F-measure of 0.80, the best method based on uncertainty sampling could save 66% annotations in sentences, as compared to random sampling. For the learning curves of F-measure vs. number of words, uncertainty sampling methods again outperformed all other methods in ALC. To achieve 0.80 in F-measure, in comparison to random

  12. Active and Adaptive Learning from Biased Data with Applications in Astronomy

    DEFF Research Database (Denmark)

    Kremer, Jan

    This thesis addresses the problem of machine learning from biased datasets in the context of astronomical applications. In astronomy there are many cases in which the training sample does not follow the true distribution. The thesis examines different types of biases and proposes algorithms...... set. Against this background, the thesis begins with a survey of active learning algorithms for the support vector machine. If the cost of additional labeling is prohibitive, unlabeled data can often be utilized instead and the sample selection bias can be overcome through domain adaptation, that is...... to handle them. During learning and when applying the predictive model, active learning enables algorithms to select training examples from a pool of unlabeled data and to request the labels. This allows for selecting examples that maximize the algorithm's accuracy despite an initial bias in the training...

  13. Active Learning Strategies and Academic Achievement among Some Psychology Undergraduates in Barbados

    OpenAIRE

    Grace Adebisi Fayombo

    2013-01-01

    This study investigated the relationships between the active learning strategies (discussion, video clips, game show, role– play, five minute paper, clarification pauses, and small group) and academic achievement among a sample of 158 undergraduate psychology students in The University of the West Indies (UWI), Barbados. Results revealed statistically significant positive correlations between active learning strategies and students’ academic achievement; so also the activ...

  14. Reinforcement active learning in the vibrissae system: optimal object localization.

    Science.gov (United States)

    Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

    2013-01-01

    Rats move their whiskers to acquire information about their environment. It has been observed that they palpate novel objects and objects they are required to localize in space. We analyze whisker-based object localization using two complementary paradigms, namely, active learning and intrinsic-reward reinforcement learning. Active learning algorithms select the next training samples according to the hypothesized solution in order to better discriminate between correct and incorrect labels. Intrinsic-reward reinforcement learning uses prediction errors as the reward to an actor-critic design, such that behavior converges to the one that optimizes the learning process. We show that in the context of object localization, the two paradigms result in palpation whisking as their respective optimal solution. These results suggest that rats may employ principles of active learning and/or intrinsic reward in tactile exploration and can guide future research to seek the underlying neuronal mechanisms that implement them. Furthermore, these paradigms are easily transferable to biomimetic whisker-based artificial sensors and can improve the active exploration of their environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

  15. Pupils' Activities in a Multimaterial Learning Environment in Craft subject A Pilot Study using an Experience Sampling Method based on a Mobile Application in Classroom Settings

    Directory of Open Access Journals (Sweden)

    Juha Jaatinen

    2017-12-01

    Full Text Available This study investigates holistic craft processes in craft education with an instrument for data-collection and self-assessment. Teaching in a study context is based on co-teaching and a design process, highlighted by the Finnish Basic Education Core Curriculum 2014. The school architecture and web-based learning environment is combined. Division for textiles and technical work is no longer supported in this multimaterial learning environment. The aim of the study is to 1 make pupils’ holistic craft processes visible in everyday classroom practices with information collected by a mobile-application and 2 point out the curriculum topics that are covered during everyday classroom practices as defined by the teachers. The data is collected using an Experience Sampling Method with a gamified learning analytics instrument. Teachers’ classroom activities were used as the backbone for the thematic mapping of the craft curriculum. Preliminary measurements were carried out in a Finnish primary school in grades 5–6 (age 10–12, n = 125 during a four-week period in October-November 2016. The list of classroom activities was updated after the four weeks’ experiment and was tested in March-May 2017 with all the pupils of the pilot school (N = 353. The key findings were that a for pupils the self-assessment was easy as a technical process but there were several factors in the everyday classroom settings that made the process challenging and b it was relatively difficult for teachers to describe the classroom activities in terms of the new curriculum; however, after four weeks they could not only described the activities in more details but had also developed new activities that supported the ideas of the new curriculum better.Keywords: multi-material craft, learning environment, holistic craft process, experience sampling method

  16. Active Learning Methods

    Science.gov (United States)

    Zayapragassarazan, Z.; Kumar, Santosh

    2012-01-01

    Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…

  17. Active Self-Paced Learning for Cost-Effective and Progressive Face Identification.

    Science.gov (United States)

    Lin, Liang; Wang, Keze; Meng, Deyu; Zuo, Wangmeng; Zhang, Lei

    2018-01-01

    This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://hcp.sysu.edu.cn/projects/aspl/.

  18. Modelization of cognition, activity and motivation as indicators for Interactive Learning Environment

    Directory of Open Access Journals (Sweden)

    Asmaa Darouich

    2017-06-01

    Full Text Available In Interactive Learning Environment (ILE, the cognitive activity and behavior of learners are the center of the researchers’ concerns. The improvement of learning through combining these axes as a structure of indicators for well-designed learning environment, encloses the measurement of the educational activity as a part of the learning process. In this paper, we propose a mathematical modeling approach based on learners actions to estimate the cognitive activity, learning behavior and motivation, in accordance with a proposed course content structure. This Cognitive indicator includes the study of knowledge, memory and reasoning. While, activity indicator aims to study effort, resistance and intensity. The results recovered on a sample of students with different levels of education, assume that the proposed approach presents a relation among all these indicators which is relatively reliable in the term of cognitive system.

  19. Use of Active Learning to Design Wind Tunnel Runs for Unsteady Cavity Pressure Measurements

    Directory of Open Access Journals (Sweden)

    Ankur Srivastava

    2014-01-01

    Full Text Available Wind tunnel tests to measure unsteady cavity flow pressure measurements can be expensive, lengthy, and tedious. In this work, the feasibility of an active machine learning technique to design wind tunnel runs using proxy data is tested. The proposed active learning scheme used scattered data approximation in conjunction with uncertainty sampling (US. We applied the proposed intelligent sampling strategy in characterizing cavity flow classes at subsonic and transonic speeds and demonstrated that the scheme has better classification accuracies, using fewer training points, than a passive Latin Hypercube Sampling (LHS strategy.

  20. Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring.

    Science.gov (United States)

    Guo, Junqi; Zhou, Xi; Sun, Yunchuan; Ping, Gong; Zhao, Guoxing; Li, Zhuorong

    2016-06-01

    Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges.

  1. Active Learning with Statistical Models.

    Science.gov (United States)

    1995-01-01

    Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with

  2. Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach.

    Science.gov (United States)

    McCoy, Lise; Pettit, Robin K; Kellar, Charlyn; Morgan, Christine

    2018-01-01

    Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction.

  3. Active-constructive-interactive: a conceptual framework for differentiating learning activities.

    Science.gov (United States)

    Chi, Michelene T H

    2009-01-01

    Active, constructive, and interactive are terms that are commonly used in the cognitive and learning sciences. They describe activities that can be undertaken by learners. However, the literature is actually not explicit about how these terms can be defined; whether they are distinct; and whether they refer to overt manifestations, learning processes, or learning outcomes. Thus, a framework is provided here that offers a way to differentiate active, constructive, and interactive in terms of observable overt activities and underlying learning processes. The framework generates a testable hypothesis for learning: that interactive activities are most likely to be better than constructive activities, which in turn might be better than active activities, which are better than being passive. Studies from the literature are cited to provide evidence in support of this hypothesis. Moreover, postulating underlying learning processes allows us to interpret evidence in the literature more accurately. Specifying distinct overt activities for active, constructive, and interactive also offers suggestions for how learning activities can be coded and how each kind of activity might be elicited. Copyright © 2009 Cognitive Science Society, Inc.

  4. Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach

    Science.gov (United States)

    McCoy, Lise; Pettit, Robin K; Kellar, Charlyn; Morgan, Christine

    2018-01-01

    Background: Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Methods: Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Results: Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. Conclusions: The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction. PMID:29707649

  5. Sample-Based Extreme Learning Machine with Missing Data

    Directory of Open Access Journals (Sweden)

    Hang Gao

    2015-01-01

    Full Text Available Extreme learning machine (ELM has been extensively studied in machine learning community during the last few decades due to its high efficiency and the unification of classification, regression, and so forth. Though bearing such merits, existing ELM algorithms cannot efficiently handle the issue of missing data, which is relatively common in practical applications. The problem of missing data is commonly handled by imputation (i.e., replacing missing values with substituted values according to available information. However, imputation methods are not always effective. In this paper, we propose a sample-based learning framework to address this issue. Based on this framework, we develop two sample-based ELM algorithms for classification and regression, respectively. Comprehensive experiments have been conducted in synthetic data sets, UCI benchmark data sets, and a real world fingerprint image data set. As indicated, without introducing extra computational complexity, the proposed algorithms do more accurate and stable learning than other state-of-the-art ones, especially in the case of higher missing ratio.

  6. A fast learning method for large scale and multi-class samples of SVM

    Science.gov (United States)

    Fan, Yu; Guo, Huiming

    2017-06-01

    A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

  7. Bounds on the sample complexity for private learning and private data release

    Energy Technology Data Exchange (ETDEWEB)

    Kasiviswanathan, Shiva [Los Alamos National Laboratory; Beime, Amos [BEN-GURION UNIV.; Nissim, Kobbi [BEN-GURION UNIV.

    2009-01-01

    Learning is a task that generalizes many of the analyses that are applied to collections of data, and in particular, collections of sensitive individual information. Hence, it is natural to ask what can be learned while preserving individual privacy. [Kasiviswanathan, Lee, Nissim, Raskhodnikova, and Smith; FOCS 2008] initiated such a discussion. They formalized the notion of private learning, as a combination of PAC learning and differential privacy, and investigated what concept classes can be learned privately. Somewhat surprisingly, they showed that, ignoring time complexity, every PAC learning task could be performed privately with polynomially many samples, and in many natural cases this could even be done in polynomial time. While these results seem to equate non-private and private learning, there is still a significant gap: the sample complexity of (non-private) PAC learning is crisply characterized in terms of the VC-dimension of the concept class, whereas this relationship is lost in the constructions of private learners, which exhibit, generally, a higher sample complexity. Looking into this gap, we examine several private learning tasks and give tight bounds on their sample complexity. In particular, we show strong separations between sample complexities of proper and improper private learners (such separation does not exist for non-private learners), and between sample complexities of efficient and inefficient proper private learners. Our results show that VC-dimension is not the right measure for characterizing the sample complexity of proper private learning. We also examine the task of private data release (as initiated by [Blum, Ligett, and Roth; STOC 2008]), and give new lower bounds on the sample complexity. Our results show that the logarithmic dependence on size of the instance space is essential for private data release.

  8. Sample-efficient Strategies for Learning in the Presence of Noise

    DEFF Research Database (Denmark)

    Cesa-Bianchi, N.; Dichterman, E.; Fischer, Paul

    1999-01-01

    In this paper, we prove various results about PAC learning in the presence of malicious noise. Our main interest is the sample size behavior of learning algorithms. We prove the first nontrivial sample complexity lower bound in this model by showing that order of &egr;/&Dgr;2 + d/&Dgr; (up...... to logarithmic factors) examples are necessary for PAC learning any target class of {#123;0,1}#125;-valued functions of VC dimension d, where &egr; is the desired accuracy and &eegr; = &egr;/(1 + &egr;) - &Dgr; the malicious noise rate (it is well known that any nontrivial target class cannot be PAC learned...... with accuracy &egr; and malicious noise rate &eegr; &egr;/(1 + &egr;), this irrespective to sample complexity). We also show that this result cannot be significantly improved in general by presenting efficient learning algorithms for the class of all subsets of d elements and the class of unions of at most d...

  9. Bidirectional Active Learning: A Two-Way Exploration Into Unlabeled and Labeled Data Set.

    Science.gov (United States)

    Zhang, Xiao-Yu; Wang, Shupeng; Yun, Xiaochun

    2015-12-01

    In practical machine learning applications, human instruction is indispensable for model construction. To utilize the precious labeling effort effectively, active learning queries the user with selective sampling in an interactive way. Traditional active learning techniques merely focus on the unlabeled data set under a unidirectional exploration framework and suffer from model deterioration in the presence of noise. To address this problem, this paper proposes a novel bidirectional active learning algorithm that explores into both unlabeled and labeled data sets simultaneously in a two-way process. For the acquisition of new knowledge, forward learning queries the most informative instances from unlabeled data set. For the introspection of learned knowledge, backward learning detects the most suspiciously unreliable instances within the labeled data set. Under the two-way exploration framework, the generalization ability of the learning model can be greatly improved, which is demonstrated by the encouraging experimental results.

  10. Active Learning Using Arbitrary Binary Valued Queries

    Science.gov (United States)

    1990-10-01

    active learning in the sense that the learner has complete choice in the information received. Specifically, we allow the learner to ask arbitrary yes...no questions. We consider both active learning under a fixed distribution and distribution-free active learning . In the case of active learning , the...a concept class is actively learnable iff it is finite, so that active learning is in fact less powerful than the usual passive learning model. We

  11. Predict-share-observe-explain learning activity for the Torricelli's tank experiment

    Science.gov (United States)

    Panich, Charunya; Puttharugsa, Chokchai; Khemmani, Supitch

    2018-01-01

    The purpose of this research was to study the students' scientific concept and achievement on fluid mechanics before and after the predict-share-observe-explain (PSOE) learning activity for the Torricelli's tank experiment. The 24 participants, who were selected by purposive sampling, were students at grade 12 at Nannakorn School, Nan province. A one group pre-test/post-test design was employed in the study. The research instruments were 1) the lesson plans using the PSOE learning activity and 2) two-tier multiple choice question and subjective tests. The results indicated that students had better scientific concept about Torricelli's tank experiment and the post-test mean score was significantly higher than the pre-test mean score at a 0.05 level of significance. Moreover, the students had retention of knowledge after the PSOE learning activity for 4 weeks at a 0.05 level of significance. The study showed that the PSOE learning activity is suitable for developing students' scientific concept and achievement.

  12. Social Trust and Types of Classroom Activities: Predictors of Language Learning Motivation

    Directory of Open Access Journals (Sweden)

    Hossein Khodabakhshzadeh

    2017-01-01

    Full Text Available The present study examined the role of social trust and types of classroom activities as some probable significant predictors of language learning motivation on a sample of 200 Iranian EFL upper-intermediate learners who have been selected randomly. Consequently, the participants completed three questionnaires, Language Learning Motivation Inventory, Classroom and school Community Inventory, and Classroom Activities Inventory, the reliability and validity of each have been checked previously. After running Multiple Regression through SPSS Software, the results revealed that social trust and types of classroom activities accounted for 16.7% of the variance in language learning motivation. Although each of them had a unique impact on language learning motivation, "Deep Language Use" as one of the types of classroom activities had a greater contribution to English as a foreign language learning motivation (002< .05, outweighing social trust as a more important predictor, (.005 < .05. Finally, pedagogical implications along with suggestions for further studies are discussed.

  13. Nondestructive neutron activation analysis of volcanic samples: Hawaii

    International Nuclear Information System (INIS)

    Zoller, W.H.; Finnegan, D.L.; Crowe, B.

    1986-01-01

    Samples of volcanic emissions have been collected between and during eruptions of both Kilauea and Mauna Loa volcanoes during the last three years. Airborne particles have been collected on Teflon filters and acidic gases on base-impregnated cellulose filters. Chemically neutral gas-phase species are collected on charcoal-coated cellulose filters. The primary analytical technique used is nondestructive neutron activation analysis, which has been used to determine the quantities of up to 35 elements on the different filters. The use of neutron activation analysis makes it possible to analyze for a wide range of elements in the different matrices used for the collection and to learn about the distribution between particles and gas phases for each of the elements

  14. Support vector machine incremental learning triggered by wrongly predicted samples

    Science.gov (United States)

    Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

    2018-05-01

    According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

  15. An Experience Sampling Study of Learning, Affect, and the Demands Control Support Model

    Science.gov (United States)

    Daniels, Kevin; Boocock, Grahame; Glover, Jane; Hartley, Ruth; Holland, Julie

    2009-01-01

    The demands control support model (R. A. Karasek & T. Theorell, 1990) indicates that job control and social support enable workers to engage in problem solving. In turn, problem solving is thought to influence learning and well-being (e.g., anxious affect, activated pleasant affect). Two samples (N = 78, N = 106) provided data up to 4 times per…

  16. Active sensing associated with spatial learning reveals memory-based attention in an electric fish.

    Science.gov (United States)

    Jun, James J; Longtin, André; Maler, Leonard

    2016-05-01

    Active sensing behaviors reveal what an animal is attending to and how it changes with learning. Gymnotus sp, a gymnotiform weakly electric fish, generates an electric organ discharge (EOD) as discrete pulses to actively sense its surroundings. We monitored freely behaving gymnotid fish in a large dark "maze" and extracted their trajectories and EOD pulse pattern and rate while they learned to find food with electrically detectable landmarks as cues. After training, they more rapidly found food using shorter, more stereotyped trajectories and spent more time near the food location. We observed three forms of active sensing: sustained high EOD rates per unit distance (sampling density), transient large increases in EOD rate (E-scans) and stereotyped scanning movements (B-scans) were initially strong at landmarks and food, but, after learning, intensified only at the food location. During probe (no food) trials, after learning, the fish's search area and intense active sampling was still centered on the missing food location, but now also increased near landmarks. We hypothesize that active sensing is a behavioral manifestation of attention and essential for spatial learning; the fish use spatial memory of landmarks and path integration to reach the expected food location and confine their attention to this region. Copyright © 2016 the American Physiological Society.

  17. ict and quality of teaching–learning related activities in primary ...

    African Journals Online (AJOL)

    Irene

    (ICT) enhance teaching learning related activities in primary schools in Ogoja education zone of Cross. River State ... A sample of six hundred and twenty ... Based on the findings of the study, it was recommended that ... video conferencing).

  18. Enhancing students' learning in problem based learning: validation of a self-assessment scale for active learning and critical thinking.

    Science.gov (United States)

    Khoiriyah, Umatul; Roberts, Chris; Jorm, Christine; Van der Vleuten, C P M

    2015-08-26

    Problem based learning (PBL) is a powerful learning activity but fidelity to intended models may slip and student engagement wane, negatively impacting learning processes, and outcomes. One potential solution to solve this degradation is by encouraging self-assessment in the PBL tutorial. Self-assessment is a central component of the self-regulation of student learning behaviours. There are few measures to investigate self-assessment relevant to PBL processes. We developed a Self-assessment Scale on Active Learning and Critical Thinking (SSACT) to address this gap. We wished to demonstrated evidence of its validity in the context of PBL by exploring its internal structure. We used a mixed methods approach to scale development. We developed scale items from a qualitative investigation, literature review, and consideration of previous existing tools used for study of the PBL process. Expert review panels evaluated its content; a process of validation subsequently reduced the pool of items. We used structural equation modelling to undertake a confirmatory factor analysis (CFA) of the SSACT and coefficient alpha. The 14 item SSACT consisted of two domains "active learning" and "critical thinking." The factorial validity of SSACT was evidenced by all items loading significantly on their expected factors, a good model fit for the data, and good stability across two independent samples. Each subscale had good internal reliability (>0.8) and strongly correlated with each other. The SSACT has sufficient evidence of its validity to support its use in the PBL process to encourage students to self-assess. The implementation of the SSACT may assist students to improve the quality of their learning in achieving PBL goals such as critical thinking and self-directed learning.

  19. STEM learning activity among home-educating families

    Science.gov (United States)

    Bachman, Jennifer

    2011-12-01

    Science, technology, engineering, and mathematics (STEM) learning was studied among families in a group of home-educators in the Pacific Northwest. Ethnographic methods recorded learning activity (video, audio, fieldnotes, and artifacts) which was analyzed using a unique combination of Cultural-Historical Activity Theory (CHAT) and Mediated Action (MA), enabling analysis of activity at multiple levels. Findings indicate that STEM learning activity is family-led, guided by parents' values and goals for learning, and negotiated with children to account for learner interests and differences, and available resources. Families' STEM education practice is dynamic, evolves, and influenced by larger societal STEM learning activity. Parents actively seek support and resources for STEM learning within their home-school community, working individually and collectively to share their funds of knowledge. Home-schoolers also access a wide variety of free-choice learning resources: web-based materials, museums, libraries, and community education opportunities (e.g. afterschool, weekend and summer programs, science clubs and classes, etc.). A lesson-heuristic, grounded in Mediated Action, represents and analyzes home STEM learning activity in terms of tensions between parental goals, roles, and lesson structure. One tension observed was between 'academic' goals or school-like activity and 'lifelong' goals or everyday learning activity. Theoretical and experiential learning was found in both activity, though parents with academic goals tended to focus more on theoretical learning and those with lifelong learning goals tended to be more experiential. Examples of the National Research Council's science learning strands (NRC, 2009) were observed in the STEM practices of all these families. Findings contribute to the small but growing body of empirical CHAT research in science education, specifically to the empirical base of family STEM learning practices at home. It also fills a

  20. Active learning in practice: Implementation of the principles of active learning in an engineering course

    DEFF Research Database (Denmark)

    Rützou, C.

    2017-01-01

    The most common form of teaching is still the form where a teacher presents the subject of the lecture to a listening audience. During teaching history this has proved to be an effective way of teaching, however the probability of students being inactive is high and the learning outcome may...... through the same curriculum as usual during a term? • Will Active Learning reduce failure rate? • Will Active Learning give a higher learning outcome than traditional teaching? This paper deals with the results of this experiment, answers the mentioned questions and presents a way to implement Active...

  1. Incorporating active learning in psychiatry education.

    Science.gov (United States)

    Kumar, Sonia; McLean, Loyola; Nash, Louise; Trigwell, Keith

    2017-06-01

    We aim to summarise the active learning literature in higher education and consider its relevance for postgraduate psychiatry trainees, to inform the development of a new Formal Education Course (FEC): the Master of Medicine (Psychiatry) at the University of Sydney. We undertook a literature search on 'active learning', 'flipped classroom', 'problem-based learning' and 'psychiatry education'. The effectiveness of active learning pedagogy in higher education is well supported by evidence; however, there have been few psychiatry-specific studies. A new 'flipped classroom' format was developed for the Master of Medicine (Psychiatry). Postgraduate psychiatry training is an active learning environment; the pedagogical approach to FECs requires further evaluation.

  2. Instructional Utility and Learning Efficacy of Common Active Learning Strategies

    Science.gov (United States)

    McConell, David A.; Chapman, LeeAnna; Czaijka, C. Douglas; Jones, Jason P.; Ryker, Katherine D.; Wiggen, Jennifer

    2017-01-01

    The adoption of active learning instructional practices in college science, technology, engineering, and mathematics (STEM) courses has been shown to result in improvements in student learning, contribute to increased retention rates, and reduce the achievement gap among different student populations. Descriptions of active learning strategies…

  3. Doing physical activity – not learning

    DEFF Research Database (Denmark)

    Jensen, Jens-Ole

    2017-01-01

    Introduction In recent years there have been a raising critique concerning PE as a subject which is more concerned with keeping pupils physically active than insuring that they learn something (Annerstedt, 2008). In Denmark, this issue has been actualized in a new sense. In 2014, a new school...... reform with 45 minutes of daily physical activity was introduced to enhance the pupils’ health, well-being and learning capabilities. Instead of focusing on learning bodily skills, physical activities has become an instrument to improve learning in the academic subjects. Physical activities.......g. Biesta, 2010; Standal, 2015) I will argue that the focus on learning outcome and effects on physical activity has gone too far in order to reach the objectives. If the notion of ‘keeping pupils physically active’ is understood as a representation of the core quality of physical activity, it seems...

  4. The ICAP Active Learning Framework Predicts the Learning Gains Observed in Intensely Active Classroom Experiences

    Directory of Open Access Journals (Sweden)

    Benjamin L. Wiggins

    2017-05-01

    Full Text Available STEM classrooms (science, technology, engineering, and mathematics in postsecondary education are rapidly improved by the proper use of active learning techniques. These techniques occupy a descriptive spectrum that transcends passive teaching toward active, constructive, and, finally, interactive methods. While aspects of this framework have been examined, no large-scale or actual classroom-based data exist to inform postsecondary education STEM instructors about possible learning gains. We describe the results of a quasi-experimental study to test the apex of the ICAP framework (interactive, constructive, active, and passive in this ecological classroom environment. Students in interactive classrooms demonstrate significantly improved learning outcomes relative to students in constructive classrooms. This improvement in learning is relatively subtle; similar experimental designs without repeated measures would be unlikely to have the power to observe this significance. We discuss the importance of seemingly small learning gains that might propagate throughout a course or departmental curriculum, as well as improvements with the necessity for faculty to develop and implement similar activities.

  5. Using IMS Learning Design to model collaborative learning activities

    NARCIS (Netherlands)

    Tattersall, Colin

    2006-01-01

    IMS Learning Design provides a counter to the trend towards designing for lone-learners reading from screens. It guides staff and educational developers to start not with content, but with learning activities and the achievement of learning objectives. It recognises that learning can happen without

  6. Create a good learning environment and motivate active learning enthusiasm

    Science.gov (United States)

    Bi, Weihong; Fu, Guangwei; Fu, Xinghu; Zhang, Baojun; Liu, Qiang; Jin, Wa

    2017-08-01

    In view of the current poor learning initiative of undergraduates, the idea of creating a good learning environment and motivating active learning enthusiasm is proposed. In practice, the professional tutor is allocated and professional introduction course is opened for college freshman. It can promote communication between the professional teachers and students as early as possible, and guide students to know and devote the professional knowledge by the preconceived form. Practice results show that these solutions can improve the students interest in learning initiative, so that the active learning and self-learning has become a habit in the classroom.

  7. Active Learning to Develop Motor Skills and Teamwork

    Directory of Open Access Journals (Sweden)

    Johanna Lorena Aristizabal-Almanza

    2017-12-01

    Full Text Available This action-research project was conducted to determine how the use of principles of active learning, specifically collaboration, had an effect on psychomotor performance and achievement in teamwork. The research setting included 20 students of first grade from a private school located in Bogota, Colombia. The students were selected through not randomized sampling based on criteria. The methodological process included observation, interviews, and a scale based on standardized tests to measure skills; the latter was applied before and after the intervention. Data analysis was performed using a triangulation of qualitative data, and through comparative analysis of the initial and final student profile for quantitative inputs. The results showed that, after the intervention with collaborative techniques based on action learning, students achieved a positive variation in their performance. Being part of a team positively affected the achievement of the objectives. Systematical reflection on their practices fostered their capacity to identify strengths and weaknesses to build knowledge in interaction with others. Knowledge construction was nurtured based in their previous experiences. Students showed more accountability and self-directed learning behaviors, according to their age. Overall the experience showed the importance of research and innovation in the classroom in order to provide meaningful data, so teachers and researchers can engage in providing learning experiences based in active learning.

  8. Face-to-Face Activities in Blended Learning

    DEFF Research Database (Denmark)

    Kjærgaard, Annemette

    While blended learning combines online and face-to-face teaching, research on blended learning has primarily focused on the role of technology and the opportunities it creates for engaging students. Less focus has been put on face-to-face activities in blended learning. This paper argues...... that it is not only the online activities in blended learning that provide new opportunities for rethinking pedagogy in higher education, it is also imperative to reconsider the face-to-face activities when part of the learning is provided online. Based on a review of blended learning in business and management...... education, we identify what forms of teaching and learning are suggested to take place face-to-face when other activities are moved online. We draw from the Community of Inquiry framework to analyze how face-to-face activities contribute to a blended learning pedagogy and discuss the implications...

  9. A Learning Activity Design Framework for Supporting Mobile Learning

    Directory of Open Access Journals (Sweden)

    Jalal Nouri

    2016-01-01

    Full Text Available This article introduces the Learning Activity Design (LEAD framework for the development and implementation of mobile learning activities in primary schools. The LEAD framework draws on methodological perspectives suggested by design-based research and interaction design in the specific field of technology-enhanced learning (TEL. The LEAD framework is grounded in four design projects conducted over a period of six years. It contributes a new understanding of the intricacies and multifaceted aspects of the design-process characterizing the development and implementation of mobile devices (i.e. smart phones and tablets in curricular activities conducted in Swedish primary schools. This framework is intended to provide both designers and researchers with methodological tools that take account of the pedagogical foundations of technologically-based educational interventions, usability issues related to the interaction with the mobile application developed, multiple data streams generated during the design project, multiple stakeholders involved in the design process and sustainability aspects of the mobile learning activities implemented in the school classroom.

  10. Out-of-Sample Generalizations for Supervised Manifold Learning for Classification.

    Science.gov (United States)

    Vural, Elif; Guillemot, Christine

    2016-03-01

    Supervised manifold learning methods for data classification map high-dimensional data samples to a lower dimensional domain in a structure-preserving way while increasing the separation between different classes. Most manifold learning methods compute the embedding only of the initially available data; however, the generalization of the embedding to novel points, i.e., the out-of-sample extension problem, becomes especially important in classification applications. In this paper, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with an iterative process. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.

  11. Do International Students Appreciate Active Learning in Lectures?

    Directory of Open Access Journals (Sweden)

    Mauricio Marrone

    2018-03-01

    Full Text Available Active learning has been linked with increased student motivation, engagement and understanding of course material. It promotes deep learning, helping to develop critical thinking and writing skills in students. Less well understood, however, are the responses of international students to active learning. Using social constructivist theory, the purpose of this study is to examine domestic and international student perceptions of active learning introduced into large undergraduate Accounting Information Systems lectures. Several active learning strategies were implemented over one semester and examined through the use of semi-structured interviews as well as pre- and post- implementation surveys. Our results suggest broad improvements for international students in student engagement and understanding of unit material when implementing active learning strategies. Other key implications include international student preference for active learning compared with passive learning styles, and that international students may receive greater benefits from active learning strategies than domestic students due to social factors. Based on these findings this paper proposes that educators should seek to implement active learning to better assist and integrate students of diverse backgrounds.

  12. Active Learning versus Traditional Teaching

    Directory of Open Access Journals (Sweden)

    L.A. Azzalis

    2009-05-01

    Full Text Available In traditional teaching most of the class time is spent with the professor lecturing and the students watching and listening. The students work individually, and cooperation is discouraged. On the other hand,  active learning  changes the focus of activity from the teacher to the learners, in which students solve problems, answer questions, formulate questions of their own, discuss, explain, debate during class;  moreover, students work in teams on problems and projects under conditions that assure positive interdependence and individual accountability. Although student-centered methods have repeatedly been shown to be superior to the traditional teacher-centered approach to instruction, the literature regarding the efficacy of various teaching methods is inconclusive. The purpose of this study was to compare the student perceptions of course and instructor effectiveness, course difficulty, and amount learned between the active learning and lecture sections  in Health Sciences´ courses by statistical data from Anhembi Morumbi University. Results indicated significant  difference between active  learning and traditional  teaching. Our conclusions were that strategies promoting  active  learning to  traditional lectures could increase knowledge and understanding.

  13. Active learning strategies for the deduplication of electronic patient data using classification trees.

    Science.gov (United States)

    Sariyar, M; Borg, A; Pommerening, K

    2012-10-01

    Supervised record linkage methods often require a clerical review to gain informative training data. Active learning means to actively prompt the user to label data with special characteristics in order to minimise the review costs. We conducted an empirical evaluation to investigate whether a simple active learning strategy using binary comparison patterns is sufficient or if string metrics together with a more sophisticated algorithm are necessary to achieve high accuracies with a small training set. Based on medical registry data with different numbers of attributes, we used active learning to acquire training sets for classification trees, which were then used to classify the remaining data. Active learning for binary patterns means that every distinct comparison pattern represents a stratum from which one item is sampled. Active learning for patterns consisting of the Levenshtein string metric values uses an iterative process where the most informative and representative examples are added to the training set. In this context, we extended the active learning strategy by Sarawagi and Bhamidipaty (2002). On the original data set, active learning based on binary comparison patterns leads to the best results. When dropping four or six attributes, using string metrics leads to better results. In both cases, not more than 200 manually reviewed training examples are necessary. In record linkage applications where only forename, name and birthday are available as attributes, we suggest the sophisticated active learning strategy based on string metrics in order to achieve highly accurate results. We recommend the simple strategy if more attributes are available, as in our study. In both cases, active learning significantly reduces the amount of manual involvement in training data selection compared to usual record linkage settings. Copyright © 2012 Elsevier Inc. All rights reserved.

  14. Active Learning in Engineering Education: a (re)introduction

    DEFF Research Database (Denmark)

    Lima, Rui M.; Andersson, Pernille Hammar; Saalman, Elisabeth

    2017-01-01

    The informal network ‘Active Learning in Engineering Education’ (ALE) has been promoting Active Learning since 2001. ALE creates opportunity for practitioners and researchers of engineering education to collaboratively learn how to foster learning of engineering students. The activities in ALE...... were reviewed by the European Journal of Engineering Education community and this theme issue ended up with eight contributions, which are different both in their research and Active Learning approaches. These different Active Learning approaches are aligned with the different approaches that can...

  15. An Innovative Teaching Method To Promote Active Learning: Team-Based Learning

    Science.gov (United States)

    Balasubramanian, R.

    2007-12-01

    Traditional teaching practice based on the textbook-whiteboard- lecture-homework-test paradigm is not very effective in helping students with diverse academic backgrounds achieve higher-order critical thinking skills such as analysis, synthesis, and evaluation. Consequently, there is a critical need for developing a new pedagogical approach to create a collaborative and interactive learning environment in which students with complementary academic backgrounds and learning skills can work together to enhance their learning outcomes. In this presentation, I will discuss an innovative teaching method ('Team-Based Learning (TBL)") which I recently developed at National University of Singapore to promote active learning among students in the environmental engineering program with learning abilities. I implemented this new educational activity in a graduate course. Student feedback indicates that this pedagogical approach is appealing to most students, and promotes active & interactive learning in class. Data will be presented to show that the innovative teaching method has contributed to improved student learning and achievement.

  16. Is Peer Interaction Necessary for Optimal Active Learning?

    Science.gov (United States)

    Linton, Debra L.; Farmer, Jan Keith; Peterson, Ernie

    2014-01-01

    Meta-analyses of active-learning research consistently show that active-learning techniques result in greater student performance than traditional lecture-based courses. However, some individual studies show no effect of active-learning interventions. This may be due to inexperienced implementation of active learning. To minimize the effect of…

  17. Student Perceptions of Active Learning

    Science.gov (United States)

    Lumpkin, Angela; Achen, Rebecca M.; Dodd, Regan K.

    2015-01-01

    A paradigm shift from lecture-based courses to interactive classes punctuated with engaging, student-centered learning activities has begun to characterize the work of some teachers in higher education. Convinced through the literature of the values of using active learning strategies, we assessed through an action research project in five college…

  18. Practical iterative learning control with frequency domain design and sampled data implementation

    CERN Document Server

    Wang, Danwei; Zhang, Bin

    2014-01-01

    This book is on the iterative learning control (ILC) with focus on the design and implementation. We approach the ILC design based on the frequency domain analysis and address the ILC implementation based on the sampled data methods. This is the first book of ILC from frequency domain and sampled data methodologies. The frequency domain design methods offer ILC users insights to the convergence performance which is of practical benefits. This book presents a comprehensive framework with various methodologies to ensure the learnable bandwidth in the ILC system to be set with a balance between learning performance and learning stability. The sampled data implementation ensures effective execution of ILC in practical dynamic systems. The presented sampled data ILC methods also ensure the balance of performance and stability of learning process. Furthermore, the presented theories and methodologies are tested with an ILC controlled robotic system. The experimental results show that the machines can work in much h...

  19. Are students' impressions of improved learning through active learning methods reflected by improved test scores?

    Science.gov (United States)

    Everly, Marcee C

    2013-02-01

    To report the transformation from lecture to more active learning methods in a maternity nursing course and to evaluate whether student perception of improved learning through active-learning methods is supported by improved test scores. The process of transforming a course into an active-learning model of teaching is described. A voluntary mid-semester survey for student acceptance of the new teaching method was conducted. Course examination results, from both a standardized exam and a cumulative final exam, among students who received lecture in the classroom and students who had active learning activities in the classroom were compared. Active learning activities were very acceptable to students. The majority of students reported learning more from having active-learning activities in the classroom rather than lecture-only and this belief was supported by improved test scores. Students who had active learning activities in the classroom scored significantly higher on a standardized assessment test than students who received lecture only. The findings support the use of student reflection to evaluate the effectiveness of active-learning methods and help validate the use of student reflection of improved learning in other research projects. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. Active Learning in the Middle Grades

    Science.gov (United States)

    Edwards, Susan

    2015-01-01

    What is active learning and what does it look like in the classroom? If students are participating in active learning, they are playing a more engaged role in the learning process and are not overly reliant on the teacher (Bransford, Brown, & Cocking, 2003; Petress, 2008). The purpose of this article is to propose a framework to describe and…

  1. Faculty motivations to use active learning among pharmacy educators.

    Science.gov (United States)

    Rockich-Winston, Nicole; Train, Brian C; Rudolph, Michael J; Gillette, Chris

    2018-03-01

    Faculty motivations to use active learning have been limited to surveys evaluating faculty perceptions within active learning studies. Our objective in this study was to evaluate the relationship between faculty intrinsic motivation, extrinsic motivation, and demographic variables and the extent of active learning use in the classroom. An online survey was administered to individual faculty members at 137 colleges and schools of pharmacy across the United States. The survey assessed intrinsic and extrinsic motivations, active learning strategies, classroom time dedicated to active learning, and faculty development resources. Bivariate associations and multivariable stepwise linear regression were used to analyze the results. In total, 979 faculty members completed the questionnaire (23.6% response rate). All motivation variables were significantly correlated with percent active learning use (p active learning methods used in the last year (r = 0.259, p active learning use. Our results suggest that faculty members who are intrinsically motivated to use active learning are more likely to dedicate additional class time to active learning. Furthermore, intrinsic motivation may be positively associated with encouraging faculty members to attend active learning workshops and supporting faculty to use various active learning strategies in the classroom. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Active learning methods for interactive image retrieval.

    Science.gov (United States)

    Gosselin, Philippe Henri; Cord, Matthieu

    2008-07-01

    Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

  3. Active Learning in Engineering Education: A (Re)Introduction

    Science.gov (United States)

    Lima, Rui M.; Andersson, Pernille Hammar; Saalman, Elisabeth

    2017-01-01

    The informal network "Active Learning in Engineering Education" (ALE) has been promoting Active Learning since 2001. ALE creates opportunity for practitioners and researchers of engineering education to collaboratively learn how to foster learning of engineering students. The activities in ALE are centred on the vision that learners…

  4. The double-loop feedback for active learning with understanding

    DEFF Research Database (Denmark)

    Christensen, Hans Peter

    2004-01-01

    Learning is an active process, and in engineering education authentic projects is often used to activate the students and promote learning. However, it is not all activity that leads to deep learning; and in a rapid changing society deep understanding is necessary for life-long learning. Empirical...... findings at DTU question the direct link between high activity and a deep approach to learning. Active learning is important to obtain engineering competencies, but active learning requires more than activity. Feedback and reflection is crucial to the learning process, since new knowledge is built...... on the student’s existing understanding. A model for an active learning process with a double-loop feedback is suggested - the first loop gives the student experience through experimentation, the second conceptual understanding through reflection. Students often miss the second loop, so it is important...

  5. Using Electronic Communication Tools in Online Group Activities to Develop Collaborative Learning Skills

    Science.gov (United States)

    Khalil, Hanan; Ebner, Martin

    2017-01-01

    The purpose of this study was to investigate the effect of using synchronous and asynchronous communication tools in online group activities to develop collaborative learning skills. An experimental study was implemented on a sample of faculty of education students in Mansoura University. The sample was divided into two groups, a group studied…

  6. Incorporation of Socio-scientific Content into Active Learning Activities

    Science.gov (United States)

    King, D. B.; Lewis, J. E.; Anderson, K.; Latch, D.; Sutheimer, S.; Webster, G.; Moog, R.

    2014-12-01

    Active learning has gained increasing support as an effective pedagogical technique to improve student learning. One way to promote active learning in the classroom is the use of in-class activities in place of lecturing. As part of an NSF-funded project, a set of in-class activities have been created that use climate change topics to teach chemistry content. These activities use the Process Oriented Guided Inquiry Learning (POGIL) methodology. In this pedagogical approach a set of models and a series of critical thinking questions are used to guide students through the introduction to or application of course content. Students complete the activities in their groups, with the faculty member as a facilitator of learning. Through assigned group roles and intentionally designed activity structure, process skills, such as teamwork, communication, and information processing, are developed during completion of the activity. Each of these climate change activities contains a socio-scientific component, e.g., social, ethical and economic data. In one activity, greenhouse gases are used to explain the concept of dipole moment. Data about natural and anthropogenic production rates, global warming potential and atmospheric lifetimes for a list of greenhouse gases are presented. The students are asked to identify which greenhouse gas they would regulate, with a corresponding explanation for their choice. They are also asked to identify the disadvantages of regulating the gas they chose in the previous question. In another activity, where carbon sequestration is used to demonstrate the utility of a phase diagram, students use economic and environmental data to choose the best location for sequestration. Too often discussions about climate change (both in and outside the classroom) consist of purely emotional responses. These activities force students to use data to support their arguments and hypothesize about what other data could be used in the corresponding discussion to

  7. From Tootsie Rolls to Composites: Assessing a Spectrum of Active Learning Activities in Engineering Mechanics

    Science.gov (United States)

    2009-05-01

    The introduction of active learning exercises into a traditional lecture has been shown to improve students’ learning. Hands-on learning...opportunities in labs and projects provide are additional tools in the active learning toolbox. This paper presents a series of innovative hands-on active ... learning activities for mechanics of materials topics. These activities are based on a Methodology for Developing Hands-on Active Learning Activities, a

  8. Active Discriminative Dictionary Learning for Weather Recognition

    Directory of Open Access Journals (Sweden)

    Caixia Zheng

    2016-01-01

    Full Text Available Weather recognition based on outdoor images is a brand-new and challenging subject, which is widely required in many fields. This paper presents a novel framework for recognizing different weather conditions. Compared with other algorithms, the proposed method possesses the following advantages. Firstly, our method extracts both visual appearance features of the sky region and physical characteristics features of the nonsky region in images. Thus, the extracted features are more comprehensive than some of the existing methods in which only the features of sky region are considered. Secondly, unlike other methods which used the traditional classifiers (e.g., SVM and K-NN, we use discriminative dictionary learning as the classification model for weather, which could address the limitations of previous works. Moreover, the active learning procedure is introduced into dictionary learning to avoid requiring a large number of labeled samples to train the classification model for achieving good performance of weather recognition. Experiments and comparisons are performed on two datasets to verify the effectiveness of the proposed method.

  9. Strategies for active learning in online continuing education.

    Science.gov (United States)

    Phillips, Janet M

    2005-01-01

    Online continuing education and staff development is on the rise as the benefits of access, convenience, and quality learning are continuing to take shape. Strategies to enhance learning call for learner participation that is self-directed and independent, thus changing the educator's role from expert to coach and facilitator. Good planning of active learning strategies promotes optimal learning whether the learning content is presented in a course or a just-in-time short module. Active learning strategies can be used to enhance online learning during all phases of the teaching-learning process and can accommodate a variety of learning styles. Feedback from peers, educators, and technology greatly influences learner satisfaction and must be harnessed to provide effective learning experiences. Outcomes of active learning can be assessed online and implemented conveniently and successfully from the initiation of the course or module planning to the end of the evaluation process. Online learning has become accessible and convenient and allows the educator to track learner participation. The future of online education will continue to grow, and using active learning strategies will ensure that quality learning will occur, appealing to a wide variety of learning needs.

  10. Active learning of Pareto fronts.

    Science.gov (United States)

    Campigotto, Paolo; Passerini, Andrea; Battiti, Roberto

    2014-03-01

    This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built. The computational effort in generating the supervised information is reduced by an active learning strategy. In particular, the model is learned from a set of informative training objective vectors. The training objective vectors are approximated Pareto-optimal vectors obtained by solving different scalarized problem instances. The experimental results show that ALP achieves an accurate Pareto front approximation with a lower computational effort than state-of-the-art estimation of distribution algorithms and widely known genetic techniques.

  11. Students' Satisfaction on Their Learning Process in Active Learning and Traditional Classrooms

    Science.gov (United States)

    Hyun, Jung; Ediger, Ruth; Lee, Donghun

    2017-01-01

    Studies have shown Active Learning Classrooms [ALCs] help increase student engagement and improve student performance. However, remodeling all traditional classrooms to ALCs entails substantial financial burdens. Thus, an imperative question for institutions of higher education is whether active learning pedagogies can improve learning outcomes…

  12. Group-Based Active Learning of Classification Models.

    Science.gov (United States)

    Luo, Zhipeng; Hauskrecht, Milos

    2017-05-01

    Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.

  13. Integrative Student Learning: An Effective Team Learning Activity in a Learner-Centered Paradigm

    Directory of Open Access Journals (Sweden)

    Reza Karimi, RPh, PhD

    2011-01-01

    Full Text Available Purpose: An Integrative Student Learning (ISL activity was developed with the intent to enhance the dynamic of student teamwork and enhance student learning by fostering critical-thinking skills, self-directed learning skills, and active learning. Case Study: The ISL activity consists of three portions: teambuilding, teamwork, and a facilitator driven “closing the loop” feedback discussion. For teambuilding, a set of clue sheets or manufacturer‘s drug containers were distributed among student pairs who applied their pharmaceutical knowledge to identify two more student pairs with similar clues or drugs, thus building a team of six. For teamwork, each team completed online exams, composed of integrated pharmaceutical science questions with clinical correlates, using only selected online library resources. For the feedback discussion, facilitators evaluated student impressions, opened a discussion about the ISL activity, and provided feedback to teams’ impressions and questions. This study describes three different ISL activities developed and implemented over three days with first year pharmacy students. Facilitators’ interactions with students and three surveys indicated a majority of students preferred ISL over traditional team activities and over 90% agreed ISL activities promoted active learning, critical-thinking, self-directed learning, teamwork, and student confidence in online library searches. Conclusions: The ISL activity has proven to be an effective learning activity that promotes teamwork and integration of didactic pharmaceutical sciences to enhance student learning of didactic materials and confidence in searching online library resources. It was found that all of this can be accomplished in a short amount of class time with a very reasonable amount of preparation.

  14. Integrative Student Learning: An Effective Team Learning Activity in a Learner-Centered Paradigm

    Directory of Open Access Journals (Sweden)

    Reza Karimi

    2011-01-01

    Full Text Available Purpose: An Integrative Student Learning (ISL activity was developed with the intent to enhance the dynamic of student teamwork and enhance student learning by fostering critical-thinking skills, self-directed learning skills, and active learning. Case Study: The ISL activity consists of three portions: teambuilding, teamwork, and a facilitator driven "closing the loop" feedback discussion. For teambuilding, a set of clue sheets or manufacturer's drug containers were distributed among student pairs who applied their pharmaceutical knowledge to identify two more student pairs with similar clues or drugs, thus building a team of six. For teamwork, each team completed online exams, composed of integrated pharmaceutical science questions with clinical correlates, using only selected online library resources. For the feedback discussion, facilitators evaluated student impressions, opened a discussion about the ISL activity, and provided feedback to teams' impressions and questions. This study describes three different ISL activities developed and implemented over three days with first year pharmacy students. Facilitators' interactions with students and three surveys indicated a majority of students preferred ISL over traditional team activities and over 90% agreed ISL activities promoted active learning, critical-thinking, self-directed learning, teamwork, and student confidence in online library searches. Conclusions: The ISL activity has proven to be an effective learning activity that promotes teamwork and integration of didactic pharmaceutical sciences to enhance student learning of didactic materials and confidence in searching online library resources. It was found that all of this can be accomplished in a short amount of class time with a very reasonable amount of preparation.   Type: Case Study

  15. Iterative learning control with sampled-data feedback for robot manipulators

    Directory of Open Access Journals (Sweden)

    Delchev Kamen

    2014-09-01

    Full Text Available This paper deals with the improvement of the stability of sampled-data (SD feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more, while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached

  16. Active Learning for Player Modeling

    DEFF Research Database (Denmark)

    Shaker, Noor; Abou-Zleikha, Mohamed; Shaker, Mohammad

    2015-01-01

    Learning models of player behavior has been the focus of several studies. This work is motivated by better understanding of player behavior, a knowledge that can ultimately be employed to provide player-adapted or personalized content. In this paper, we propose the use of active learning for player...... experience modeling. We use a dataset from hundreds of players playing Infinite Mario Bros. as a case study and we employ the random forest method to learn mod- els of player experience through the active learning approach. The results obtained suggest that only part of the dataset (up to half the size...... that the method can be used online during the content generation process where the mod- els can improve and better content can be presented as the game is being played....

  17. Research on Mobile Learning Activities Applying Tablets

    Science.gov (United States)

    Kurilovas, Eugenijus; Juskeviciene, Anita; Bireniene, Virginija

    2015-01-01

    The paper aims to present current research on mobile learning activities in Lithuania while implementing flagship EU-funded CCL project on application of tablet computers in education. In the paper, the quality of modern mobile learning activities based on learning personalisation, problem solving, collaboration, and flipped class methods is…

  18. Active Learning with Irrelevant Examples

    Science.gov (United States)

    Wagstaff, Kiri; Mazzoni, Dominic

    2009-01-01

    An improved active learning method has been devised for training data classifiers. One example of a data classifier is the algorithm used by the United States Postal Service since the 1960s to recognize scans of handwritten digits for processing zip codes. Active learning algorithms enable rapid training with minimal investment of time on the part of human experts to provide training examples consisting of correctly classified (labeled) input data. They function by identifying which examples would be most profitable for a human expert to label. The goal is to maximize classifier accuracy while minimizing the number of examples the expert must label. Although there are several well-established methods for active learning, they may not operate well when irrelevant examples are present in the data set. That is, they may select an item for labeling that the expert simply cannot assign to any of the valid classes. In the context of classifying handwritten digits, the irrelevant items may include stray marks, smudges, and mis-scans. Querying the expert about these items results in wasted time or erroneous labels, if the expert is forced to assign the item to one of the valid classes. In contrast, the new algorithm provides a specific mechanism for avoiding querying the irrelevant items. This algorithm has two components: an active learner (which could be a conventional active learning algorithm) and a relevance classifier. The combination of these components yields a method, denoted Relevance Bias, that enables the active learner to avoid querying irrelevant data so as to increase its learning rate and efficiency when irrelevant items are present. The algorithm collects irrelevant data in a set of rejected examples, then trains the relevance classifier to distinguish between labeled (relevant) training examples and the rejected ones. The active learner combines its ranking of the items with the probability that they are relevant to yield a final decision about which item

  19. Active Learning by Querying Informative and Representative Examples.

    Science.gov (United States)

    Huang, Sheng-Jun; Jin, Rong; Zhou, Zhi-Hua

    2014-10-01

    Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.

  20. Assessing Student Behaviors and Motivation for Actively Learning Biology

    Science.gov (United States)

    Moore, Michael Edward

    Vision and Change states that one of the major changes in the way we design biology courses should be a switch in approach from teacher-centered learning to student-centered learning and identifies active learning as a recommended methods. Studies show performance benefits for students taking courses that use active learning. What is unknown is why active learning is such an effective instructional tool and the limits of this instructional method’s ability to influence performance. This dissertation builds a case in three steps for why active learning is an effective instructional tool. In step one, I assessed the influence of different types of active learning (clickers, group activities, and whole class discussions) on student engagement behavior in one semester of two different introductory biology courses and found that active learning positively influenced student engagement behavior significantly more than lecture. For step two, I examined over four semesters whether student engagement behavior was a predictor of performance and found participation (engagement behavior) in the online (video watching) and in-class course activities (clicker participation) that I measure were significant predictors of performance. In the third, I assessed whether certain active learning satisfied the psychological needs that lead to students’ intrinsic motivation to participate in those activities when compared over two semesters and across two different institutions of higher learning. Findings from this last step show us that student’s perceptions of autonomy, competency, and relatedness in doing various types of active learning are significantly higher than lecture and consistent across two institutions of higher learning. Lastly, I tie everything together, discuss implications of the research, and address future directions for research on biology student motivation and behavior.

  1. The International Active Learning Space

    DEFF Research Database (Denmark)

    Manners, Ian James

    2015-01-01

    -Danish students receive the basic international and intercultural skills and knowledge they need in current society. The English-language masters’ seminars I teach at the Department of Political Science are international in terms of students and teacher, but they are also Active Learning seminars......-Danish students (and sometimes teachers) rarely speak to each other or learn each other’s names. In the international AL spaces I create, students must work together on joint tasks which require interaction to address tasks and integration in order to benefit from the multinational activity groups. Planning AL...... that complete the seminar soon become vocal advocates of international AL. Ultimately, enriching student learning through immersing Danish and international students in an international AL space is, for me, the best way of ensuring an internationalised learning outcome, rather than just international mobility....

  2. The SAMPLE experience: The development of a rich media online mathematics learning environment

    OpenAIRE

    Chang, Jen

    2006-01-01

    This report documents the development of Sample Architecture for Mathematically Productive Learning Experiences (SAMPLE), a rich media, online, mathematics learning environment created to meet the needs of middle school educators. It explores some of the current pedagogical challenges in mathematics education, and their amplified impacts when coupled with under-prepared teachers, a decidedly wide-spread phenomenon. The SAMPLE publishing experience is discussed in terms of its instructional de...

  3. Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

    Science.gov (United States)

    Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong

    2014-01-01

    Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

  4. IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING

    Data.gov (United States)

    National Aeronautics and Space Administration — IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING ISAAC PERSING AND VINCENT NG Abstract. Active learning has been successfully applied to many natural language...

  5. Using Active Learning to Identify Health Information Technology Related Patient Safety Events.

    Science.gov (United States)

    Fong, Allan; Howe, Jessica L; Adams, Katharine T; Ratwani, Raj M

    2017-01-18

    The widespread adoption of health information technology (HIT) has led to new patient safety hazards that are often difficult to identify. Patient safety event reports, which are self-reported descriptions of safety hazards, provide one view of potential HIT-related safety events. However, identifying HIT-related reports can be challenging as they are often categorized under other more predominate clinical categories. This challenge of identifying HIT-related reports is exacerbated by the increasing number and complexity of reports which pose challenges to human annotators that must manually review reports. In this paper, we apply active learning techniques to support classification of patient safety event reports as HIT-related. We evaluated different strategies and demonstrated a 30% increase in average precision of a confirmatory sampling strategy over a baseline no active learning approach after 10 learning iterations.

  6. The Activity Theory Approach to Learning

    Directory of Open Access Journals (Sweden)

    Ritva Engeström

    2014-12-01

    Full Text Available In this paper the author offers a practical view of the theory-grounded research on education action. She draws on studies carried out at the Center for Research on Activity, Development and Learning (CRADLE at the University of Helsinki in Finland. In its work, the Center draws on cultural-historical activity theory (CHAT and is well-known for the theory of Expansive Learning and its more practical application called Developmental Work Research (DWR. These approaches are widely used to understand professional learning and have served as a theoreticaland methodological foundation for studies examining change and professional development in various human activities.

  7. History and Evolution of Active Learning Spaces

    Science.gov (United States)

    Beichner, Robert J.

    2014-01-01

    This chapter examines active learning spaces as they have developed over the years. Consistently well-designed classrooms can facilitate active learning even though the details of implementing pedagogies may differ.

  8. GeoMapApp Learning Activities: Enabling the democratisation of geoscience learning

    Science.gov (United States)

    Goodwillie, A. M.; Kluge, S.

    2011-12-01

    GeoMapApp Learning Activities (http://serc.carleton.edu/geomapapp) are step-by-step guided inquiry geoscience education activities that enable students to dictate the pace of learning. They can be used in the classroom or out of class, and their guided nature means that the requirement for teacher intervention is minimised which allows students to spend increased time analysing and understanding a broad range of geoscience data, content and concepts. Based upon GeoMapApp (http://www.geomapapp.org), a free, easy-to-use map-based data exploration and visualisation tool, each activity furnishes the educator with an efficient package of downloadable documents. This includes step-by-step student instructions and answer sheet; a teacher's edition annotated worksheet containing teaching tips, additional content and suggestions for further work; quizzes for use before and after the activity to assess learning; and a multimedia tutorial. The activities can be used by anyone at any time in any place with an internet connection. In essence, GeoMapApp Learning Activities provide students with cutting-edge technology, research-quality geoscience data sets, and inquiry-based learning in a virtual lab-like environment. Examples of activities so far created are student calculation and analysis of the rate of seafloor spreading, and present-day evidence on the seafloor for huge ancient landslides around the Hawaiian islands. The activities are designed primarily for students at the community college, high school and introductory undergraduate levels, exposing students to content and concepts typically found in those settings.

  9. Active Learning Environment with Lenses in Geometric Optics

    Science.gov (United States)

    Tural, Güner

    2015-01-01

    Geometric optics is one of the difficult topics for students within physics discipline. Students learn better via student-centered active learning environments than the teacher-centered learning environments. So this study aimed to present a guide for middle school teachers to teach lenses in geometric optics via active learning environment…

  10. Large Sample Neutron Activation Analysis of Heterogeneous Samples

    International Nuclear Information System (INIS)

    Stamatelatos, I.E.; Vasilopoulou, T.; Tzika, F.

    2018-01-01

    A Large Sample Neutron Activation Analysis (LSNAA) technique was developed for non-destructive analysis of heterogeneous bulk samples. The technique incorporated collimated scanning and combining experimental measurements and Monte Carlo simulations for the identification of inhomogeneities in large volume samples and the correction of their effect on the interpretation of gamma-spectrometry data. Corrections were applied for the effect of neutron self-shielding, gamma-ray attenuation, geometrical factor and heterogeneous activity distribution within the sample. A benchmark experiment was performed to investigate the effect of heterogeneity on the accuracy of LSNAA. Moreover, a ceramic vase was analyzed as a whole demonstrating the feasibility of the technique. The LSNAA results were compared against results obtained by INAA and a satisfactory agreement between the two methods was observed. This study showed that LSNAA is a technique capable to perform accurate non-destructive, multi-elemental compositional analysis of heterogeneous objects. It also revealed the great potential of the technique for the analysis of precious objects and artefacts that need to be preserved intact and cannot be damaged for sampling purposes. (author)

  11. Collegewide Promotion of E-Learning/Active Learning and Faculty Development

    Science.gov (United States)

    Ogawa, Nobuyuki; Shimizu, Akira

    2016-01-01

    Japanese National Institutes of Technology have revealed a plan to strongly promote e-Learning and active learning under the common schematization of education in over 50 campuses nationwide. Our e-Learning and ICT-driven education practiced for more than fifteen years were highly evaluated, and is playing a leading role in promoting e-Learning…

  12. Pedagogical Distance: Explaining Misalignment in Student-Driven Online Learning Activities Using Activity Theory

    Science.gov (United States)

    Westberry, Nicola; Franken, Margaret

    2015-01-01

    This paper provides an Activity Theory analysis of two online student-driven interactive learning activities to interrogate assumptions that such groups can effectively learn in the absence of the teacher. Such an analysis conceptualises learning tasks as constructed objects that drive pedagogical activity. The analysis shows a disconnect between…

  13. PENERAPAN METODE STUDENT ACTIVE LEARNING (SAL MELALUI MULTI MEDIA POWER POINT UNTUK MENINGKATKAN KEAKTIFAN, KETERAMPILAN BERDISKUSI, DAN HASIL BELAJAR MATEMATIKA

    Directory of Open Access Journals (Sweden)

    Rustinah Rustinah

    2017-08-01

    Full Text Available The purpose of this study is find math learning scenarios format with active student learning method of learning mathematics by using multimedia power point to determine how much influence can enhance the activity, discuss the skills and student learning outcomes. Subjects examined or samples studied were students who study at grade students geometry IX.2 SMP Negeri 3 Batanghari, East Lampung. This study occurred during the three months using three cycles. The variables measured in the study include the involvement of the student in the learning process, skills in using media power point and student learning outcomes. Conclusions of this research is that it can increase the creativity of teachers using a variety of learning resources and selection methods that can encourage the creation of a learning process student active learning with contextual approach through multimedia. Can enhance the activity, and fun atusiasme students during the learning process, improve students' skills in solving problems and improve learning outcomes, especially the material geometry.

  14. A Novel Teaching Tool Combined With Active-Learning to Teach Antimicrobial Spectrum Activity.

    Science.gov (United States)

    MacDougall, Conan

    2017-03-25

    Objective. To design instructional methods that would promote long-term retention of knowledge of antimicrobial pharmacology, particularly the spectrum of activity for antimicrobial agents, in pharmacy students. Design. An active-learning approach was used to teach selected sessions in a required antimicrobial pharmacology course. Students were expected to review key concepts from the course reader prior to the in-class sessions. During class, brief concept reviews were followed by active-learning exercises, including a novel schematic method for learning antimicrobial spectrum of activity ("flower diagrams"). Assessment. At the beginning of the next quarter (approximately 10 weeks after the in-class sessions), 360 students (three yearly cohorts) completed a low-stakes multiple-choice examination on the concepts in antimicrobial spectrum of activity. When data for students was pooled across years, the mean number of correct items was 75.3% for the items that tested content delivered with the active-learning method vs 70.4% for items that tested content delivered via traditional lecture (mean difference 4.9%). Instructor ratings on student evaluations of the active-learning approach were high (mean scores 4.5-4.8 on a 5-point scale) and student comments were positive about the active-learning approach and flower diagrams. Conclusion. An active-learning approach led to modestly higher scores in a test of long-term retention of pharmacology knowledge and was well-received by students.

  15. Agnostic Active Learning Without Constraints

    OpenAIRE

    Beygelzimer, Alina; Hsu, Daniel; Langford, John; Zhang, Tong

    2010-01-01

    We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate hypotheses is maintained throughout learning, and only hypotheses from this set are ever returned. By avoiding this version space approach, our algorithm sheds the computational burden and brittleness associated with maintaining version spaces, yet still allows for substantial improvements over supervised learning f...

  16. Using assistive technology adaptations to include students with learning disabilities in cooperative learning activities.

    Science.gov (United States)

    Bryant, D P; Bryant, B R

    1998-01-01

    Cooperative learning (CL) is a common instructional arrangement that is used by classroom teachers to foster academic achievement and social acceptance of students with and without learning disabilities. Cooperative learning is appealing to classroom teachers because it can provide an opportunity for more instruction and feedback by peers than can be provided by teachers to individual students who require extra assistance. Recent studies suggest that students with LD may need adaptations during cooperative learning activities. The use of assistive technology adaptations may be necessary to help some students with LD compensate for their specific learning difficulties so that they can engage more readily in cooperative learning activities. A process for integrating technology adaptations into cooperative learning activities is discussed in terms of three components: selecting adaptations, monitoring the use of the adaptations during cooperative learning activities, and evaluating the adaptations' effectiveness. The article concludes with comments regarding barriers to and support systems for technology integration, technology and effective instructional practices, and the need to consider technology adaptations for students who have learning disabilities.

  17. Using Oceanography to Support Active Learning

    Science.gov (United States)

    Byfield, V.

    2012-04-01

    Teachers are always on the lookout for material to give their brightest students, in order to keep them occupied, stimulated and challenged, while the teacher gets on with helping the rest. They are also looking for material that can inspire and enthuse those who think that school is 'just boring!' Oceanography, well presented, has the capacity to do both. As a relatively young science, oceanography is not a core curriculum subject (possibly an advantage), but it draws on the traditional sciences of biology, chemistry, physic and geology, and can provide wonderful examples for teaching concepts in school sciences. It can also give good reasons for learning science, maths and technology. Exciting expeditions (research cruises) to far-flung places; opportunities to explore new worlds, a different angle on topical debates such as climate change, pollution, or conservation can bring a new life to old subjects. Access to 'real' data from satellites or Argo floats can be used to develop analytical and problem solving skills. The challenge is to make all this available in a form that can easily be used by teachers and students to enhance the learning experience. We learn by doing. Active teaching methods require students to develop their own concepts of what they are learning. This stimulates new neural connections in the brain - the physical manifestation of learning. There is a large body of evidence to show that active learning is much better remembered and understood. Active learning develops thinking skills through analysis, problem solving, and evaluation. It helps learners to use their knowledge in realistic and useful ways, and see its importance and relevance. Most importantly, properly used, active learning is fun. This paper presents experiences from a number of education outreach projects that have involved the National Oceanography Centre in Southampton, UK. All contain some element of active learning - from quizzes and puzzles to analysis of real data from

  18. Classification and authentication of unknown water samples using machine learning algorithms.

    Science.gov (United States)

    Kundu, Palash K; Panchariya, P C; Kundu, Madhusree

    2011-07-01

    This paper proposes the development of water sample classification and authentication, in real life which is based on machine learning algorithms. The proposed techniques used experimental measurements from a pulse voltametry method which is based on an electronic tongue (E-tongue) instrumentation system with silver and platinum electrodes. E-tongue include arrays of solid state ion sensors, transducers even of different types, data collectors and data analysis tools, all oriented to the classification of liquid samples and authentication of unknown liquid samples. The time series signal and the corresponding raw data represent the measurement from a multi-sensor system. The E-tongue system, implemented in a laboratory environment for 6 numbers of different ISI (Bureau of Indian standard) certified water samples (Aquafina, Bisleri, Kingfisher, Oasis, Dolphin, and McDowell) was the data source for developing two types of machine learning algorithms like classification and regression. A water data set consisting of 6 numbers of sample classes containing 4402 numbers of features were considered. A PCA (principal component analysis) based classification and authentication tool was developed in this study as the machine learning component of the E-tongue system. A proposed partial least squares (PLS) based classifier, which was dedicated as well; to authenticate a specific category of water sample evolved out as an integral part of the E-tongue instrumentation system. The developed PCA and PLS based E-tongue system emancipated an overall encouraging authentication percentage accuracy with their excellent performances for the aforesaid categories of water samples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Introduction of active learning method in learning physiology by MBBS students.

    Science.gov (United States)

    Gilkar, Suhail Ahmad; Lone, Shabiruddin; Lone, Riyaz Ahmad

    2016-01-01

    Active learning has received considerable attention over the past several years, often presented or perceived as a radical change from traditional instruction methods. Current research on learning indicates that using a variety of teaching strategies in the classroom increases student participation and learning. To introduce active learning methodology, i.e., "jigsaw technique" in undergraduate medical education and assess the student and faculty response to it. This study was carried out in the Department of Physiology in a Medical College of North India. A topic was chosen and taught using one of the active learning methods (ALMs), i.e., jigsaw technique. An instrument (questionnaire) was developed in English through an extensive review of literature and was properly validated. The students were asked to give their response on a five-point Likert scale. The feedback was kept anonymous. Faculty also provided their feedback in a separately provided feedback proforma. The data were collected, compiled, and analyzed. Of 150 students of MBBS-first year batch 2014, 142 participated in this study along with 14 faculty members of the Physiology Department. The majority of the students (>90%) did welcome the introduction of ALM and strongly recommended the use of such methods in teaching many more topics in future. 100% faculty members were of the opinion that many more topics shall be taken up using ALMs. This study establishes the fact that both the medical students and faculty want a change from the traditional way of passive, teacher-centric learning, to the more active teaching-learning techniques.

  20. Active Math Learning

    DEFF Research Database (Denmark)

    The presentation is concerned with general course planning philosophy and a specific case study (boomerang flight geometro-dynamics) for active learning of mathematics via computer assisted and hands-on unfolding of first principles - in this case the understanding of rotations and Eulers equatio...

  1. Sampling algorithms for validation of supervised learning models for Ising-like systems

    Science.gov (United States)

    Portman, Nataliya; Tamblyn, Isaac

    2017-12-01

    In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ;ID-MH; that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new ;block-ID; sampling strategy: it decomposes the given structure into square blocks with lattice dimension N ≤ 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).

  2. Minimization of annotation work: diagnosis of mammographic masses via active learning

    Science.gov (United States)

    Zhao, Yu; Zhang, Jingyang; Xie, Hongzhi; Zhang, Shuyang; Gu, Lixu

    2018-06-01

    The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance. Our proposed method is different from the existing active learning methods designed for the general problem as it is specifically designed for mammographic images. Through its modified discriminant functions and improved sample query criteria, the proposed method can fully utilize the pairing of mammographic images and select the most valuable images from both the mediolateral and craniocaudal views. Moreover, in order to extend active learning to the ordinal regression problem, which has no precedent in existing studies, but is essential for mammographic diagnosis (mammographic diagnosis is not only a classification task, but also an ordinal regression task for predicting an ordinal variable, viz. the malignancy risk of lesions), multiple sample query criteria need to be taken into consideration simultaneously. We formulate it as a criteria integration problem and further present an algorithm based on self-adaptive weighted rank aggregation to achieve a good solution. The efficacy of the proposed method was demonstrated on thousands of mammographic images from the digital database for screening mammography. The labeling costs of obtaining optimal performance in the classification and ordinal regression task respectively fell to 33.8 and 19.8 percent of their original costs. The proposed method also generated 1228 wins, 369 ties and 47 losses for the classification task, and 1933 wins, 258 ties and 185 losses for the ordinal regression task compared to the other state-of-the-art active learning algorithms. By taking the

  3. Measuring strategies for learning regulation in medical education: scale reliability and dimensionality in a Swedish sample.

    Science.gov (United States)

    Edelbring, Samuel

    2012-08-15

    The degree of learners' self-regulated learning and dependence on external regulation influence learning processes in higher education. These regulation strategies are commonly measured by questionnaires developed in other settings than in which they are being used, thereby requiring renewed validation. The aim of this study was to psychometrically evaluate the learning regulation strategy scales from the Inventory of Learning Styles with Swedish medical students (N = 206). The regulation scales were evaluated regarding their reliability, scale dimensionality and interrelations. The primary evaluation focused on dimensionality and was performed with Mokken scale analysis. To assist future scale refinement, additional item analysis, such as item-to-scale correlations, was performed. Scale scores in the Swedish sample displayed good reliability in relation to published results: Cronbach's alpha: 0.82, 0.72, and 0.65 for self-regulation, external regulation and lack of regulation scales respectively. The dimensionalities in scales were adequate for self-regulation and its subscales, whereas external regulation and lack of regulation displayed less unidimensionality. The established theoretical scales were largely replicated in the exploratory analysis. The item analysis identified two items that contributed little to their respective scales. The results indicate that these scales have an adequate capacity for detecting the three theoretically proposed learning regulation strategies in the medical education sample. Further construct validity should be sought by interpreting scale scores in relation to specific learning activities. Using established scales for measuring students' regulation strategies enables a broad empirical base for increasing knowledge on regulation strategies in relation to different disciplinary settings and contributes to theoretical development.

  4. Administrator skills: a study with academics of the administration course in the context of active learning

    Directory of Open Access Journals (Sweden)

    Sabrina Gorges

    2018-01-01

    Full Text Available The constant oscillations in society and the labor market require the management professional to evolve and develop their competencies, organizations are looking for people who are capable and flexible, who adapt quickly to changes. In this way, developing competencies has become paramount in the learning process, and higher education institutions play an important role in this construction, applying learning strategies that provide the academic with the competencies demanded by the market. Thus, it is feasible to use active learning in the Administration course, since it allows the integration between theory and practice and the experience of real situations in the classroom. Active learning is a set of pedagogical practices that address the issue of student learning from a different perspective of the classic learning techniques. In active learning, it is understood that the student should not be merely a receiver of information, but must actively engage in the acquisition of knowledge. This article aims to identify and analyze the skills of the Administrator desired and developed by the undergraduate students in Administration in the context of Active Learning. In this study, a descriptive research was carried out in a sample of 54 students from the Administration courses of three universities in Santa Catarina. Among the results, the research revealed that for students, the most important competences to be developed are: self-criticism and strategic thinking regarding opportunities.

  5. Architecture for Collaborative Learning Activities in Hybrid Learning Environments

    OpenAIRE

    Ibáñez, María Blanca; Maroto, David; García Rueda, José Jesús; Leony, Derick; Delgado Kloos, Carlos

    2012-01-01

    3D virtual worlds are recognized as collaborative learning environments. However, the underlying technology is not sufficiently mature and the virtual worlds look cartoonish, unlinked to reality. Thus, it is important to enrich them with elements from the real world to enhance student engagement in learning activities. Our approach is to build learning environments where participants can either be in the real world or in its mirror world while sharing the same hybrid space in a collaborative ...

  6. The Effectiveness of Cooperative Learning Activities in Enhancing EFL Learners' Fluency

    Science.gov (United States)

    Alrayah, Hassan

    2018-01-01

    This research-paper aims at examining the effectiveness of cooperative learning activities in enhancing EFL learners' fluency. The researcher has used the descriptive approach, recorded interviews for testing fluency as tools of data collection and the software program SPSS as a tool for the statistical treatment of data. Research sample consists…

  7. Active Ageing, Active Learning: Policy and Provision in Hong Kong

    Science.gov (United States)

    Tam, M.

    2011-01-01

    This paper discusses the relationship between ageing and learning, previous literature having confirmed that participation in continued learning in old age contributes to good health, satisfaction with life, independence and self-esteem. Realizing that learning is vital to active ageing, the Hong Kong government has implemented policies and…

  8. Experiential Learning and Learning Environments: The Case of Active Listening Skills

    Science.gov (United States)

    Huerta-Wong, Juan Enrique; Schoech, Richard

    2010-01-01

    Social work education research frequently has suggested an interaction between teaching techniques and learning environments. However, this interaction has never been tested. This study compared virtual and face-to-face learning environments and included active listening concepts to test whether the effectiveness of learning environments depends…

  9. Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples

    Directory of Open Access Journals (Sweden)

    Mingchen Yao

    2015-01-01

    Full Text Available Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.. However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM principle for sequences of time-dependent samples (TDS. In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.

  10. Developing metacognition: a basis for active learning

    NARCIS (Netherlands)

    Vos, Henk; de Graaff, E.

    2004-01-01

    The reasons to introduce formats of Active Learning in Engineering (ALE) like project work, problem based learning, use of cases, etc., are mostly based on practical experience and sometimes from applied research on teaching and learning. Such research shows that students learn more and different

  11. Active Learning Innovations in Knowledge Management Education Generate Higher Quality Learning Outcomes

    Directory of Open Access Journals (Sweden)

    Arthur Shelley

    2014-01-01

    Full Text Available Innovations in how a postgraduate course in knowledge management is delivered have generated better learning outcomes and made the course more engaging for learners. Course participant feedback has shown that collaborative active learning is preferred and provides them with richer insights into how knowledge is created and applied to generate innovation and value. The course applies an andragogy approach in which students collaborate in weekly dialogue of their experiences of the content, rather than learn the content itself. The approach combines systems thinking, learning praxis, and active learning to explore the interdependencies between topics and how they impact outcomes in real world situations. This has stimulated students to apply these ideas in their own workplaces.

  12. Annotating smart environment sensor data for activity learning.

    Science.gov (United States)

    Szewcyzk, S; Dwan, K; Minor, B; Swedlove, B; Cook, D

    2009-01-01

    The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track the activities that people perform at home. Machine learning techniques can perform this task, but the software algorithms rely upon large amounts of sample data that is correctly labeled with the corresponding activity. Labeling, or annotating, sensor data with the corresponding activity can be time consuming, may require input from the smart home resident, and is often inaccurate. Therefore, in this paper we investigate four alternative mechanisms for annotating sensor data with a corresponding activity label. We evaluate the alternative methods along the dimensions of annotation time, resident burden, and accuracy using sensor data collected in a real smart apartment.

  13. The strategic use of lecture recordings to facilitate an active and self-directed learning approach.

    Science.gov (United States)

    Topale, Luminica

    2016-08-12

    New learning technologies have the capacity to dramatically impact how students go about learning and to facilitate an active, self-directed learning approach. In U. S. medical education, students encounter a large volume of content, which must be mastered at an accelerated pace. The added pressure to excel on the USMLE Step 1 licensing exam and competition for residency placements, require that students adopt an informed approach to the use of learning technologies so as to enhance rather than to detract from the learning process. The primary aim of this study was to gain a better understanding of how students were using recorded lectures in their learning and how their study habits have been influenced by the technology. Survey research was undertaken using a convenience sample. Students were asked to voluntarily participate in an electronic survey comprised of 27 closed ended, multiple choice questions, and one open ended item. The survey was designed to explore students' perceptions of how recorded lectures affected their choices regarding class participation and impacted their learning and to gain an understanding of how recorded lectures facilitated a strategic, active learning process. Findings revealed that recorded lectures had little influence on students' choices to participate, and that the perceived benefits of integrating recorded lectures into study practices were related to their facilitation of and impact on efficient, active, and self-directed learning. This study was a useful investigation into how the availability of lecture capture technology influenced medical students' study behaviors and how students were making valuable use of the technology as an active learning tool.

  14. Active Learning and Cooperative Learning in the Organic Chemistry Lecture Class

    Science.gov (United States)

    Paulson, Donald R.

    1999-08-01

    Faculty in the physical sciences are one of the academic groups least receptive to the use of active learning strategies and cooperative learning in their classrooms. This is particularly so in traditional lecture classes. It is the objective of this paper to show how effective these techniques can be in improving student performance in classes. The use of active learning strategies and cooperative learning groups in my organic chemistry lecture classes has increased the overall pass rate in my classes by an astounding 20-30% over the traditional lecture mode. This has been accomplished without any reduction in "standards". The actual methods employed are presented as well as a discussion of how I came to radically change the way I teach my classes.

  15. Active Learning and Teaching: Improving Postsecondary Library Instruction.

    Science.gov (United States)

    Allen, Eileen E.

    1995-01-01

    Discusses ways to improve postsecondary library instruction based on theories of active learning. Topics include a historical background of active learning; student achievement and attitudes; cognitive development; risks; active teaching; and instructional techniques, including modified lectures, brainstorming, small group work, cooperative…

  16. Neutron activation analysis of geochemical samples

    International Nuclear Information System (INIS)

    Rosenberg, R.; Zilliacus, R.; Kaistila, M.

    1983-06-01

    The present paper will describe the work done at the Technical Research Centre of Finland in developing methods for the large-scale activation analysis of samples for the geochemical prospecting of metals. The geochemical prospecting for uranium started in Finland in 1974 and consequently a manually operated device for the delayed neutron activation analysis of uranium was taken into use. During 1974 9000 samples were analyzed. The small capacity of the analyzer made it necessary to develop a completely automated analyzer which was taken into use in August 1975. Since then 20000-30000 samples have been analyzed annually the annual capacity being about 60000 samples when running seven hours per day. Multielemental instrumental neutron activation analysis is used for the analysis of more than 40 elements. Using instrumental epithermal neutron activation analysis 25-27 elements can be analyzed using one irradiation and 20 min measurement. During 1982 12000 samples were analyzed for mining companies and Geological Survey of Finland. The capacity is 600 samples per week. Besides these two analytical methods the analysis of lanthanoids is an important part of the work. 11 lanthanoids have been analyzed using instrumental neutron activation analysis. Radiochemical separation methods have been developed for several elements to improve the sensitivity of the analysis

  17. Resource Letter ALIP-1: Active-Learning Instruction in Physics

    Science.gov (United States)

    Meltzer, David E.; Thornton, Ronald K.

    2012-06-01

    This Resource Letter provides a guide to the literature on research-based active-learning instruction in physics. These are instructional methods that are based on, assessed by, and validated through research on the teaching and learning of physics. They involve students in their own learning more deeply and more intensely than does traditional instruction, particularly during class time. The instructional methods and supporting body of research reviewed here offer potential for significantly improved learning in comparison to traditional lecture-based methods of college and university physics instruction. We begin with an introduction to the history of active learning in physics in the United States, and then discuss some methods for and outcomes of assessing pedagogical effectiveness. We enumerate and describe common characteristics of successful active-learning instructional strategies in physics. We then discuss a range of methods for introducing active-learning instruction in physics and provide references to those methods for which there is published documentation of student learning gains.

  18. Empathy and feedback processing in active and observational learning.

    Science.gov (United States)

    Rak, Natalia; Bellebaum, Christian; Thoma, Patrizia

    2013-12-01

    The feedback-related negativity (FRN) and the P300 have been related to the processing of one's own and other individuals' feedback during both active and observational learning. The aim of the present study was to elucidate the role of trait-empathic responding with regard to the modulation of the neural correlates of observational learning in particular. Thirty-four healthy participants completed an active and an observational learning task. On both tasks, the participants' aim was to maximize their monetary gain by choosing from two stimuli the one that showed the higher probability of reward. Participants gained insight into the stimulus-reward contingencies according to monetary feedback presented after they had made an active choice or by observing the choices of a virtual partner. Participants showed a general improvement in learning performance on both learning tasks. P200, FRN, and P300 amplitudes were larger during active, as compared with observational, learning. Furthermore, nonreward elicited a significantly more negative FRN than did reward in the active learning task, while only a trend was observed for observational learning. Distinct subcomponents of trait cognitive empathy were related to poorer performance and smaller P300 amplitudes for observational learning only. Taken together, both the learning performance and event-related potentials during observational learning are affected by different aspects of trait cognitive empathy, and certain types of observational learning may actually be disrupted by a higher tendency to understand and adopt other people's perspectives.

  19. Measuring strategies for learning regulation in medical education: Scale reliability and dimensionality in a Swedish sample

    Directory of Open Access Journals (Sweden)

    Edelbring Samuel

    2012-08-01

    Full Text Available Abstract Background The degree of learners’ self-regulated learning and dependence on external regulation influence learning processes in higher education. These regulation strategies are commonly measured by questionnaires developed in other settings than in which they are being used, thereby requiring renewed validation. The aim of this study was to psychometrically evaluate the learning regulation strategy scales from the Inventory of Learning Styles with Swedish medical students (N = 206. Methods The regulation scales were evaluated regarding their reliability, scale dimensionality and interrelations. The primary evaluation focused on dimensionality and was performed with Mokken scale analysis. To assist future scale refinement, additional item analysis, such as item-to-scale correlations, was performed. Results Scale scores in the Swedish sample displayed good reliability in relation to published results: Cronbach’s alpha: 0.82, 0.72, and 0.65 for self-regulation, external regulation and lack of regulation scales respectively. The dimensionalities in scales were adequate for self-regulation and its subscales, whereas external regulation and lack of regulation displayed less unidimensionality. The established theoretical scales were largely replicated in the exploratory analysis. The item analysis identified two items that contributed little to their respective scales. Discussion The results indicate that these scales have an adequate capacity for detecting the three theoretically proposed learning regulation strategies in the medical education sample. Further construct validity should be sought by interpreting scale scores in relation to specific learning activities. Using established scales for measuring students’ regulation strategies enables a broad empirical base for increasing knowledge on regulation strategies in relation to different disciplinary settings and contributes to theoretical development.

  20. Captivate: Building Blocks for Implementing Active Learning

    Science.gov (United States)

    Kitchens, Brent; Means, Tawnya; Tan, Yinliang

    2018-01-01

    In this study, the authors propose a set of key elements that impact the success of an active learning implementation: content delivery, active learning methods, physical environment, technology enhancement, incentive alignment, and educator investment. Through a range of metrics the authors present preliminary evidence that students in courses…

  1. Faculty Perceptions about Barriers to Active Learning

    Science.gov (United States)

    Michael, Joel

    2007-01-01

    Faculty may perceive many barriers to active learning in their classrooms. Four groups of participants in a faculty development workshop were asked to list their perceived barriers to active learning. Many of the problems identified were present on more than one list. The barriers fall into three categories: student characteristics, issues…

  2. Active Multi-Field Learning for Spam Filtering

    OpenAIRE

    Wuying Liu; Lin Wang; Mianzhu Yi; Nan Xie

    2015-01-01

    Ubiquitous spam messages cause a serious waste of time and resources. This paper addresses the practical spam filtering problem, and proposes a universal approach to fight with various spam messages. The proposed active multi-field learning approach is based on: 1) It is cost-sensitive to obtain a label for a real-world spam filter, which suggests an active learning idea; and 2) Different messages often have a similar multi-field text structure, which suggests a multi-field learning idea. The...

  3. Active Learning: The Importance of Developing a Comprehensive Measure

    Science.gov (United States)

    Carr, Rodney; Palmer, Stuart; Hagel, Pauline

    2015-01-01

    This article reports on an investigation into the validity of a widely used scale for measuring the extent to which higher education students employ active learning strategies. The scale is the active learning scale in the Australasian Survey of Student Engagement. This scale is based on the Active and Collaborative Learning scale of the National…

  4. Age-related impairments in active learning and strategic visual exploration

    Directory of Open Access Journals (Sweden)

    Kelly L Brandstatt

    2014-02-01

    Full Text Available Old age could impair memory by disrupting learning strategies used by younger individuals. We tested this possibility by manipulating the ability to use visual-exploration strategies during learning. Subjects controlled visual exploration during active learning, thus permitting the use of strategies, whereas strategies were limited during passive learning via predetermined exploration patterns. Performance on tests of object recognition and object-location recall was matched for younger and older subjects for objects studied passively, when learning strategies were restricted. Active learning improved object recognition similarly for younger and older subjects. However, active learning improved object-location recall for younger subjects, but not older subjects. Exploration patterns were used to identify a learning strategy involving repeat viewing. Older subjects used this strategy less frequently and it provided less memory benefit compared to younger subjects. In previous experiments, we linked hippocampal-prefrontal co-activation to improvements in object-location recall from active learning and to the exploration strategy. Collectively, these findings suggest that age-related memory problems result partly from impaired strategies during learning, potentially due to reduced hippocampal-prefrontal co-engagement.

  5. Age-related impairments in active learning and strategic visual exploration.

    Science.gov (United States)

    Brandstatt, Kelly L; Voss, Joel L

    2014-01-01

    Old age could impair memory by disrupting learning strategies used by younger individuals. We tested this possibility by manipulating the ability to use visual-exploration strategies during learning. Subjects controlled visual exploration during active learning, thus permitting the use of strategies, whereas strategies were limited during passive learning via predetermined exploration patterns. Performance on tests of object recognition and object-location recall was matched for younger and older subjects for objects studied passively, when learning strategies were restricted. Active learning improved object recognition similarly for younger and older subjects. However, active learning improved object-location recall for younger subjects, but not older subjects. Exploration patterns were used to identify a learning strategy involving repeat viewing. Older subjects used this strategy less frequently and it provided less memory benefit compared to younger subjects. In previous experiments, we linked hippocampal-prefrontal co-activation to improvements in object-location recall from active learning and to the exploration strategy. Collectively, these findings suggest that age-related memory problems result partly from impaired strategies during learning, potentially due to reduced hippocampal-prefrontal co-engagement.

  6. Strategies influence neural activity for feedback learning across child and adolescent development.

    Science.gov (United States)

    Peters, Sabine; Koolschijn, P Cédric M P; Crone, Eveline A; Van Duijvenvoorde, Anna C K; Raijmakers, Maartje E J

    2014-09-01

    Learning from feedback is an important aspect of executive functioning that shows profound improvements during childhood and adolescence. This is accompanied by neural changes in the feedback-learning network, which includes pre-supplementary motor area (pre- SMA)/anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), superior parietal cortex (SPC), and the basal ganglia. However, there can be considerable differences within age ranges in performance that are ascribed to differences in strategy use. This is problematic for traditional approaches of analyzing developmental data, in which age groups are assumed to be homogenous in strategy use. In this study, we used latent variable models to investigate if underlying strategy groups could be detected for a feedback-learning task and whether there were differences in neural activation patterns between strategies. In a sample of 268 participants between ages 8 to 25 years, we observed four underlying strategy groups, which were cut across age groups and varied in the optimality of executive functioning. These strategy groups also differed in neural activity during learning; especially the most optimal performing group showed more activity in DLPFC, SPC and pre-SMA/ACC compared to the other groups. However, age differences remained an important contributor to neural activation, even when correcting for strategy. These findings contribute to the debate of age versus performance predictors of neural development, and highlight the importance of studying individual differences in strategy use when studying development. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Assessing Student Behaviors and Motivation for Actively Learning Biology

    Science.gov (United States)

    Moore, Michael Edward

    2017-01-01

    Vision and Change states that one of the major changes in the way we design biology courses should be a switch in approach from teacher-centered learning to student-centered learning and identifies active learning as a recommended methods. Studies show performance benefits for students taking courses that use active learning. What is unknown is…

  8. PENGARUH MODEL ACTIVE LEARNING BERBANTUAN MEDIA FLASH TERHADAP PEMAHAMAN KONSEP DAN AKTIVITAS BELAJAR SISWA SMP KELAS VII PADA TEMA KALOR DAN PERPINDAHANNYA

    Directory of Open Access Journals (Sweden)

    Bagus Addin Hutomo

    2017-02-01

    Full Text Available Abstrak ____________________________________________________________________ Telah dilakukan penelitian yang bertujuan untuk mengetahui pengaruh model active learning berbantuan media flash terhadap pemahaman konsep dan aktivitas belajar siswa pada tema kalor dan perpindahannya. Jenis penelitian ini yaitu quasi experiment dengan desain non-equivalent control group design. Sampel diambil dengan teknik purposive sampling. Sampel dalam penelitian ini adalah kelas VII C (kelas eksperimen dan VII A (kelas kontrol SMPN 1 Ungaran. Data diambil dengan metode tes (pemahaman konsep dan observasi (aktivitas belajar siswa.Hasil penelitian menunjukkan bahwa rata-rata pemahaman konsep (posttest kelas eksperimen (87,22 lebih tinggi dari kelas kontrol (75,83. Besarnya rata-rata aktivitas belajar siswa kelas eksperimen (83,90 juga lebih besar daripada kelas kontrol (76,28. Berdasarkan hasil penelitian dapat disimpulkan model active learning berbantuan media flash pada tema kalor dan perpindahannya berpengaruh positif terhadap pemahaman konsep siswa sebesar 54,06 % dengan nilai koefisen korelasi sebesar 0,74 (kategori kuat dan berpengaruh positif terhadap aktivitas belajar siswa sebesar 85,54 % dengan nilai koefisen korelasi sebesar 0,92 (kategori sangat kuat.   Abstract Studies have been conducted to determine the effect of active learning model of flash media aided the understanding of concepts and learning activities of students on the theme of heat and displacement. This type of research is a quasi-experimental design with non-equivalent control group design. The sample was taken by purposive sampling technique. The sample in this research is class VII C (experimental class and VII A (control group SMPN 1 Ungaran. Data taken with test method (understanding of the concept and observation (student activity. The results showed that the average understanding of the concept (posttest experimental class (87.22 higher than the control class (75.83. The average size

  9. Active Learning and Self-Regulation Enhance Student Teachers’ Professional Competences

    OpenAIRE

    Virtanen, Päivi; Niemi, Hannele M.; Nevgi, Anne

    2017-01-01

    The study identifies the relationships between active learning, student teachers’ self-regulated learning and professional competences. Further, the aim is to investigate how active learning promotes professional competences of student teachers with different self-regulation profiles. Responses from 422 student teachers to an electronic survey were analysed using statistical methods. It was found that the use of active learning methods, such as goal-oriented and intentional learning as well a...

  10. Active learning for noisy oracle via density power divergence.

    Science.gov (United States)

    Sogawa, Yasuhiro; Ueno, Tsuyoshi; Kawahara, Yoshinobu; Washio, Takashi

    2013-10-01

    The accuracy of active learning is critically influenced by the existence of noisy labels given by a noisy oracle. In this paper, we propose a novel pool-based active learning framework through robust measures based on density power divergence. By minimizing density power divergence, such as β-divergence and γ-divergence, one can estimate the model accurately even under the existence of noisy labels within data. Accordingly, we develop query selecting measures for pool-based active learning using these divergences. In addition, we propose an evaluation scheme for these measures based on asymptotic statistical analyses, which enables us to perform active learning by evaluating an estimation error directly. Experiments with benchmark datasets and real-world image datasets show that our active learning scheme performs better than several baseline methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Using machine learning to accelerate sampling-based inversion

    Science.gov (United States)

    Valentine, A. P.; Sambridge, M.

    2017-12-01

    In most cases, a complete solution to a geophysical inverse problem (including robust understanding of the uncertainties associated with the result) requires a sampling-based approach. However, the computational burden is high, and proves intractable for many problems of interest. There is therefore considerable value in developing techniques that can accelerate sampling procedures.The main computational cost lies in evaluation of the forward operator (e.g. calculation of synthetic seismograms) for each candidate model. Modern machine learning techniques-such as Gaussian Processes-offer a route for constructing a computationally-cheap approximation to this calculation, which can replace the accurate solution during sampling. Importantly, the accuracy of the approximation can be refined as inversion proceeds, to ensure high-quality results.In this presentation, we describe and demonstrate this approach-which can be seen as an extension of popular current methods, such as the Neighbourhood Algorithm, and bridges the gap between prior- and posterior-sampling frameworks.

  12. An integrative review of in-class activities that enable active learning in college science classroom settings

    Science.gov (United States)

    Arthurs, Leilani A.; Kreager, Bailey Zo

    2017-10-01

    Engaging students in active learning is linked to positive learning outcomes. This study aims to synthesise the peer-reviewed literature about 'active learning' in college science classroom settings. Using the methodology of an integrative literature review, 337 articles archived in the Educational Resources Information Center (ERIC) are examined. Four categories of in-class activities emerge: (i) individual non-polling activities, (ii) in-class polling activities, (iii) whole-class discussion or activities, and (iv) in-class group activities. Examining the collection of identified in-class activities through the lens of a theoretical framework informed by constructivism and social interdependence theory, we synthesise the reviewed literature to propose the active learning strategies (ALSs) model and the instructional decisions to enable active learning (IDEAL) theory. The ALS model characterises in-class activities in terms of the degrees to which they are designed to promote (i) peer interaction and (ii) social interdependence. The IDEAL theory includes the ALS model and provides a framework for conceptualising different levels of the general concept 'active learning' and how these levels connect to instructional decision-making about using in-class activities. The proposed ALS model and IDEAL theory can be utilised to inform instructional decision-making and future research about active learning in college science courses.

  13. ONLINE EDUCATION, ACTIVE LEARNING, AND ANDRAGOGY: An approach for Student Engagement

    OpenAIRE

    CARUTH, Gail D.

    2015-01-01

    Online learning opportunities have become essential for today’s colleges and universities. Online technology can support active learning approaches to learning. The purpose of the paper was to investigate why active learning in online classes has a positive effect on student engagement. A review of the literature revealed that research studies have been conducted to investigate the benefits of active learning. There exists extensive evidence to support the notion that active learning enhances...

  14. Lectures Abandoned: Active Learning by Active Seminars

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak; Corry, Aino Vonge

    2012-01-01

    Traditional lecture-based courses are widely criticised for be- ing less eective in teaching. The question is of course what should replace the lectures and various active learning tech- niques have been suggested and studied. In this paper, we report on our experiences of redesigning a software ......- tive seminars as a replacement of traditional lectures, an activity template for the contents of active seminars, an ac- count on how storytelling supported the seminars, as well as reports on our and the students' experiences....

  15. Levels of Engagement and Barriers to Physical Activity in a Population of Adults with Learning Disabilities

    Science.gov (United States)

    Hawkins, Andrew; Look, Roger

    2006-01-01

    This study examined levels of, and barriers to, physical activity in a population of 19 adults with learning disabilities living in community supported accommodation, using diary records and semi-structured interviews with staff. The levels of physical activity were higher in the sample population than previous figures for adults with learning…

  16. StreamAR: incremental and active learning with evolving sensory data for activity recognition

    OpenAIRE

    Abdallah, Z.; Gaber, M.; Srinivasan, B.; Krishnaswamy, S.

    2012-01-01

    Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel cluster-based classification for activity recognition Systems, termed StreamAR. The system incorporates incremental and active learning for mining user ...

  17. Student Buy-In to Active Learning in a College Science Course

    Science.gov (United States)

    Cavanagh, Andrew J.; Aragón, Oriana R.; Chen, Xinnian; Couch, Brian; Durham, Mary; Bobrownicki, Aiyana; Hanauer, David I.; Graham, Mark J.

    2016-01-01

    The benefits of introducing active learning in college science courses are well established, yet more needs to be understood about student buy-in to active learning and how that process of buy-in might relate to student outcomes. We test the exposure–persuasion–identification–commitment (EPIC) process model of buy-in, here applied to student (n = 245) engagement in an undergraduate science course featuring active learning. Student buy-in to active learning was positively associated with engagement in self-regulated learning and students’ course performance. The positive associations among buy-in, self-regulated learning, and course performance suggest buy-in as a potentially important factor leading to student engagement and other student outcomes. These findings are particularly salient in course contexts featuring active learning, which encourage active student participation in the learning process. PMID:27909026

  18. Exploring Characteristics of Fine-Grained Behaviors of Learning Mathematics in Tablet-Based E-Learning Activities

    Science.gov (United States)

    Yeung, Cheuk Yu; Shum, Kam Hong; Hui, Lucas Chi Kwong; Chu, Samuel Kai Wah; Chan, Tsing Yun; Kuo, Yung Nin; Ng, Yee Ling

    2017-01-01

    Attributes of teaching and learning contexts provide rich information about how students participate in learning activities. By tracking and analyzing snapshots of these attributes captured continuously throughout the duration of the learning activities, teachers can identify individual students who need special attention and apply different…

  19. Active teaching methods, studying responses and learning

    DEFF Research Database (Denmark)

    Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain

    2010-01-01

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed....

  20. Lifeguard Final Exam—Encouraging the Use of Active Learning

    Directory of Open Access Journals (Sweden)

    Elise N. Griswold

    2015-08-01

    Full Text Available To anyone familiar with the extensive literature on teaching and learning, there is little question that active learning is more effective than passive learning. Thus, we are not directing this letter to that particular audience. Instead, we are attempting to address the question of the best way to convince instructors who have not tried to incorporate elements of active learning into their courses to make such an attempt. There are numerous examples where it becomes immediately clear that active learning is preferable to a lecture/note-taking approach. Here, we provide a question for group discussion that can be used as one such illustration.

  1. Learning shapes spontaneous activity itinerating over memorized states.

    Directory of Open Access Journals (Sweden)

    Tomoki Kurikawa

    Full Text Available Learning is a process that helps create neural dynamical systems so that an appropriate output pattern is generated for a given input. Often, such a memory is considered to be included in one of the attractors in neural dynamical systems, depending on the initial neural state specified by an input. Neither neural activities observed in the absence of inputs nor changes caused in the neural activity when an input is provided were studied extensively in the past. However, recent experimental studies have reported existence of structured spontaneous neural activity and its changes when an input is provided. With this background, we propose that memory recall occurs when the spontaneous neural activity changes to an appropriate output activity upon the application of an input, and this phenomenon is known as bifurcation in the dynamical systems theory. We introduce a reinforcement-learning-based layered neural network model with two synaptic time scales; in this network, I/O relations are successively memorized when the difference between the time scales is appropriate. After the learning process is complete, the neural dynamics are shaped so that it changes appropriately with each input. As the number of memorized patterns is increased, the generated spontaneous neural activity after learning shows itineration over the previously learned output patterns. This theoretical finding also shows remarkable agreement with recent experimental reports, where spontaneous neural activity in the visual cortex without stimuli itinerate over evoked patterns by previously applied signals. Our results suggest that itinerant spontaneous activity can be a natural outcome of successive learning of several patterns, and it facilitates bifurcation of the network when an input is provided.

  2. Active-learning strategies: the use of a game to reinforce learning in nursing education. A case study.

    Science.gov (United States)

    Boctor, Lisa

    2013-03-01

    The majority of nursing students are kinesthetic learners, preferring a hands-on, active approach to education. Research shows that active-learning strategies can increase student learning and satisfaction. This study looks at the use of one active-learning strategy, a Jeopardy-style game, 'Nursopardy', to reinforce Fundamentals of Nursing material, aiding in students' preparation for a standardized final exam. The game was created keeping students varied learning styles and the NCLEX blueprint in mind. The blueprint was used to create 5 categories, with 26 total questions. Student survey results, using a five-point Likert scale showed that they did find this learning method enjoyable and beneficial to learning. More research is recommended regarding learning outcomes, when using active-learning strategies, such as games. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. Cooperative activity and its potential for learning in tertiary education

    Directory of Open Access Journals (Sweden)

    Cirila Peklaj

    2007-01-01

    Full Text Available A learning situation can be structured in different ways, as an individual, competitive, or cooperative activity. Each of these structures can be used for different purposes and can lead to different learning outcomes. This paper focuses on cooperative activity and its potential for learning in tertiary education. After defining cooperative activity (or, in a broader sense, learning in interaction and introducing the CAMS theoretical framework to analyse cooperative activity, the main discussion focuses on the theoretical reasons for the usefulness of group learning and on the research of effects of cooperative learning on cognitive (metacognitive, affective-motivational and social processes in university students. The key elements that should be established for successful cooperation are also discussed. At the end, a new direction in using cooperative activity in learning—computer supported collaborative learning (CSCL, which emerged with rapid technology development in the last two decades—is presented and discussed.

  4. Telling Active Learning Pedagogies Apart: From Theory to Practice

    Science.gov (United States)

    Cattaneo, Kelsey Hood

    2017-01-01

    Designing learning environments to incorporate active learning pedagogies is difficult as definitions are often contested and intertwined. This article seeks to determine whether classification of active learning pedagogies (i.e., project-based, problem-based, inquiry-based, case-based, and discovery-based), through theoretical and practical…

  5. Active-learning implementation in an advanced elective course on infectious diseases.

    Science.gov (United States)

    Hidayat, Levita; Patel, Shreya; Veltri, Keith

    2012-06-18

    To describe the development, implementation, and assessment of an advanced elective course on infectious diseases using active-learning strategies. Pedagogy for active learning was incorporated by means of mini-lecture, journal club, and debate with follow-up discussion. Forty-eight students were enrolled in this 4-week elective course, in which 30% of course time was allocated for active-learning exercises. All activities were fundamentally designed as a stepwise approach in complementing each active-learning exercise. Achievement of the course learning objectives was assessed using a 5-point Likert scale survey instrument. Students' awareness of the significance of antimicrobial resistance was improved (p ≤ 0.05). Students' ability to critically evaluate the infectious-disease literature and its application in informed clinical judgments was also enhanced through these active-learning exercises (p ≤ 0.05). Students agreed that active learning should be part of the pharmacy curriculum and that active-learning exercises improved their critical-thinking, literature-evaluation, and self-learning skills. An elective course using active-learning strategies allowed students to combine information gained from the evaluation of infectious-disease literature, critical thinking, and informed clinical judgment. This blended approach ultimately resulted in an increased knowledge and awareness of infectious diseases.

  6. Student Activity and Learning Outcomes in a Virtual Learning Environment

    Science.gov (United States)

    Romanov, Kalle; Nevgi, Anne

    2008-01-01

    The aim of the study was to explore the relationship between degree of participation and learning outcomes in an e-learning course on medical informatics. Overall activity in using course materials and degree of participation in the discussion forums of an online course were studied among 39 medical students. Students were able to utilise the…

  7. Active inference and learning.

    Science.gov (United States)

    Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni

    2016-09-01

    This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  8. DASL-Data and Activities for Solar Learning

    Science.gov (United States)

    Jones, Harrison P.; Henney, Carl; Hill, Frank; Gearen, Michael; Pompca, Stephen; Stagg, Travis; Stefaniak, Linda; Walker, Connie

    2004-01-01

    DASL-Data and Activities for Solar Learning Data and Activities for Solar Learning (DASL) provides a classroom learning environment based on a twenty-five year record of solar magnetograms from the National Solar Observatory (NSO) at Kitt Peak, AZ. The data, together with image processing software for Macs or PCs, can be used to learn basic facts about the Sun and astronomy at the middle school level. At the high school level, students can study properties of the Sun's magnetic cycle with classroom exercises emphasizing data and error analysis and can participate in a new scientific study, Research in Active Solar Longitudes (RASL), in collaboration with classrooms throughout the country and scientists at NSO and NASA. We present a half-day course to train teachers in the scientific content of the project and its classroom use. We will provide a compact disc with the data and software and will demonstrate software installation and use, classroom exercises, and participation in RASL with computer projection.

  9. Large sample neutron activation analysis of a reference inhomogeneous sample

    International Nuclear Information System (INIS)

    Vasilopoulou, T.; Athens National Technical University, Athens; Tzika, F.; Stamatelatos, I.E.; Koster-Ammerlaan, M.J.J.

    2011-01-01

    A benchmark experiment was performed for Neutron Activation Analysis (NAA) of a large inhomogeneous sample. The reference sample was developed in-house and consisted of SiO 2 matrix and an Al-Zn alloy 'inhomogeneity' body. Monte Carlo simulations were employed to derive appropriate correction factors for neutron self-shielding during irradiation as well as self-attenuation of gamma rays and sample geometry during counting. The large sample neutron activation analysis (LSNAA) results were compared against reference values and the trueness of the technique was evaluated. An agreement within ±10% was observed between LSNAA and reference elemental mass values, for all matrix and inhomogeneity elements except Samarium, provided that the inhomogeneity body was fully simulated. However, in cases that the inhomogeneity was treated as not known, the results showed a reasonable agreement for most matrix elements, while large discrepancies were observed for the inhomogeneity elements. This study provided a quantification of the uncertainties associated with inhomogeneity in large sample analysis and contributed to the identification of the needs for future development of LSNAA facilities for analysis of inhomogeneous samples. (author)

  10. Prevalence of learned grapheme-color pairings in a large online sample of synesthetes.

    Directory of Open Access Journals (Sweden)

    Nathan Witthoft

    Full Text Available In this paper we estimate the minimum prevalence of grapheme-color synesthetes with letter-color matches learned from an external stimulus, by analyzing a large sample of English-speaking grapheme-color synesthetes. We find that at least 6% (400/6588 participants of the total sample learned many of their matches from a widely available colored letter toy. Among those born in the decade after the toy began to be manufactured, the proportion of synesthetes with learned letter-color pairings approaches 15% for some 5-year periods. Among those born 5 years or more before it was manufactured, none have colors learned from the toy. Analysis of the letter-color matching data suggests the only difference between synesthetes with matches to the toy and those without is exposure to the stimulus. These data indicate learning of letter-color pairings from external contingencies can occur in a substantial fraction of synesthetes, and are consistent with the hypothesis that grapheme-color synesthesia is a kind of conditioned mental imagery.

  11. Improving active Mealy machine learning for protocol conformance testing

    NARCIS (Netherlands)

    Aarts, F.; Kuppens, H.; Tretmans, J.; Vaandrager, F.; Verwer, S.

    2014-01-01

    Using a well-known industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn a model M

  12. Moments of movement: active learning and practice development.

    Science.gov (United States)

    Dewing, Jan

    2010-01-01

    As our understanding of practice development becomes more sophisticated, we enhance our understanding of how the facilitation of learning in and from practice, can be more effectively achieved. This paper outlines an approach for enabling and maximizing learning within practice development known as 'Active Learning'. It considers how, given establishing a learning culture is a prerequisite for the sustainability of PD within organisations, practice developers can do more to maximize learning for practitioners and other stakeholders. Active Learning requires that more attention be given by organisations committed to PD, at a corporate and strategic level for how learning strategies are developed in the workplace. Specifically, a move away from a heavy reliance on training may be required. Practice development facilitators also need to review: how they organise and offer learning, so that learning strategies are consistent with the vision, aims and processes of PD; have skills in the planning, delivery and evaluation of learning as part of their role and influence others who provide more traditional methods of training and education.

  13. It takes biking to learn: Physical activity improves learning a second language.

    Science.gov (United States)

    Liu, Fengqin; Sulpizio, Simone; Kornpetpanee, Suchada; Job, Remo

    2017-01-01

    Recent studies have shown that concurrent physical activity enhances learning a completely unfamiliar L2 vocabulary as compared to learning it in a static condition. In this paper we report a study whose aim is twofold: to test for possible positive effects of physical activity when L2 learning has already reached some level of proficiency, and to test whether the assumed better performance when engaged in physical activity is limited to the linguistic level probed at training (i.e. L2 vocabulary tested by means of a Word-Picture Verification task), or whether it extends also to the sentence level (which was tested by means of a Sentence Semantic Judgment Task). The results show that Chinese speakers with basic knowledge of English benefited from physical activity while learning a set of new words. Furthermore, their better performance emerged also at the sentential level, as shown by their performance in a Semantic Judgment task. Finally, an interesting temporal asymmetry between the lexical and the sentential level emerges, with the difference between the experimental and control group emerging from the 1st testing session at the lexical level but after several weeks at the sentential level.

  14. Medical Student Perspectives of Active Learning: A Focus Group Study.

    Science.gov (United States)

    Walling, Anne; Istas, Kathryn; Bonaminio, Giulia A; Paolo, Anthony M; Fontes, Joseph D; Davis, Nancy; Berardo, Benito A

    2017-01-01

    Phenomenon: Medical student perspectives were sought about active learning, including concerns, challenges, perceived advantages and disadvantages, and appropriate role in the educational process. Focus groups were conducted with students from all years and campuses of a large U.S. state medical school. Students had considerable experience with active learning prior to medical school and conveyed accurate understanding of the concept and its major strategies. They appreciated the potential of active learning to deepen and broaden learning and its value for long-term professional development but had significant concerns about the efficiency of the process, the clarity of expectations provided, and the importance of receiving preparatory materials. Most significantly, active learning experiences were perceived as disconnected from grading and even as impeding preparation for school and national examinations. Insights: Medical students understand the concepts of active learning and have considerable experience in several formats prior to medical school. They are generally supportive of active learning concepts but frustrated by perceived inefficiencies and lack of contribution to the urgencies of achieving optimal grades and passing United States Medical Licensing Examinations, especially Step 1.

  15. Teacher feedback during active learning: current practices in primary schools.

    Science.gov (United States)

    van den Bergh, Linda; Ros, Anje; Beijaard, Douwe

    2013-06-01

    Feedback is one of the most powerful tools, which teachers can use to enhance student learning. It appears difficult for teachers to give qualitatively good feedback, especially during active learning. In this context, teachers should provide facilitative feedback that is focused on the development of meta-cognition and social learning. The purpose of the present study is to contribute to the existing knowledge about feedback and to give directions to improve teacher feedback in the context of active learning. The participants comprised 32 teachers who practiced active learning in the domain of environmental studies in the sixth, seventh, or eighth grade of 13 Dutch primary schools. A total of 1,465 teacher-student interactions were examined. Video observations were made of active learning lessons in the domain of environmental studies. A category system was developed based on the literature and empirical data. Teacher-student interactions were assessed using this system. Results. About half of the teacher-student interactions contained feedback. This feedback was usually focused on the tasks that were being performed by the students and on the ways in which these tasks were processed. Only 5% of the feedback was explicitly related to a learning goal. In their feedback, the teachers were directing (rather than facilitating) the learning processes. During active learning, feedback on meta-cognition and social learning is important. Feedback should be explicitly related to learning goals. In practice, these kinds of feedback appear to be scarce. Therefore, giving feedback during active learning seems to be an important topic for teachers' professional development. © 2012 The British Psychological Society.

  16. When I Grow Up: The Relationship of "Science Learning Activation" to STEM Career Preferences

    Science.gov (United States)

    Dorph, Rena; Bathgate, Meghan E.; Schunn, Christian D.; Cannady, Matthew A.

    2018-01-01

    This paper proposes three new measures of components STEM career preferences (affinity, certainty, and goal), and then explores which dimensions of "science learning activation" (fascination, values, competency belief, and scientific sensemaking) are predictive of STEM career preferences. Drawn from the ALES14 dataset, a sample of 2938…

  17. An Analysis of Learning Activities in a Technology Education Textbook for Teachers : Learning Process Based on Contents Framework and Learning Scene to Develop Technological Literacy

    OpenAIRE

    Yata, Chikahiko; Hamamoto, Kengo; Oguri, Takenori

    2014-01-01

    This study analyzed the learning activities in a textbook on technology education for teachers, in order to examine the learning processes and learning scenes detailed therein. Results of analyzing learning process, primary learning activity found each contents framework. Other learning activities designated to be related to complementary in learning process. Results of analyzing learning scene, 14 learning scenes, among them "Scene to recognize the impact on social life and progress of techn...

  18. Child Development: An Active Learning Approach

    Science.gov (United States)

    Levine, Laura E.; Munsch, Joyce

    2010-01-01

    Within each chapter of this innovative topical text, the authors engage students by demonstrating the wide range of real-world applications of psychological research connected to child development. In particular, the distinctive Active Learning features incorporated throughout the book foster a dynamic and personal learning process for students.…

  19. Generation of Tutorial Dialogues: Discourse Strategies for Active Learning

    Science.gov (United States)

    1998-05-29

    AND SUBTITLE Generation of Tutorial Dialogues: Discourse Strategies for active Learning AUTHORS Dr. Martha Evens 7. PERFORMING ORGANI2ATION NAME...time the student starts in on a new topic. Michael and Rovick constantly attempt to promote active learning . They regularly use hints and only resort...Controlling active learning : How tutors decide when to generate hints. Proceedings of FLAIRS 󈨣. Melbourne Beach, FL. 157-161. Hume, G., Michael

  20. [Supporting an Academic Society with the Active Learning Tool Clica].

    Science.gov (United States)

    Arai, Kensuke; Mitsubori, Masahiro

    2018-01-01

     Within school classrooms, Active Learning has been receiving unprecedented attention. Indeed, Active Learning's popularity does not stop in the classroom. As more and more people argue that the Japanese government needs to renew guidelines for education, Active Learning has surfaced as a method capable of providing the necessary knowledge and training for people in all areas of society, helping them reach their full potential. It has become accepted that Active Learning is more effective over the passive listening of lectures, where there is little to no interaction. Active Learning emphasizes that learners explain their thoughts, ask questions, and express their opinions, resulting in a better retention rate of the subject at hand. In this review, I introduce an Active Learning support tool developed at Digital Knowledge, "Clica". This tool is currently being used at many educational institutions. I will also introduce an online questionnaire that Digital Knowledge provided at the 10th Annual Meeting of the Japanese Society for Pharmaceutical Palliative Care and Sciences.

  1. Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework

    Directory of Open Access Journals (Sweden)

    Juan Carlos Davila

    2017-06-01

    Full Text Available The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

  2. Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

    Science.gov (United States)

    Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek

    2017-06-07

    The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

  3. Telling Active Learning Pedagogies Apart: from theory to practice

    Directory of Open Access Journals (Sweden)

    Kelsey Hood Cattaneo

    2017-07-01

    Full Text Available Designing learning environments to incorporate active learning pedagogies is difficult as definitions are often contested and intertwined. This article seeks to determine whether classification of active learning pedagogies (i.e., project-based, problem-based, inquiry-based, case-based, and discovery-based, through theoretical and practical lenses, could function as a useful tool for researchers and practitioners in comparing pedagogies. This article classified five active learning pedagogies based on six constructivist elements. The comparison was completed through a comparative analysis and a content analysis informed by a systematic literature review. The findings were that learner-centeredness is a primary goal of all pedagogies; however, there is a strong dissonance between each pedagogy’s theoretical underpinnings and implementation realities. This dissonance complicates differentiating active learning pedagogies and classification as a comparative tool has proved to have limited usefulness.

  4. Challenges Encountered in Creating Personalised Learning Activities to Suit Students Learning Preferences

    OpenAIRE

    O'Donnell, Eileen; Wade, Vincent; Sharp, Mary; O'Donnell, Liam

    2013-01-01

    This book chapter reviews some of the challenges encountered by educators in creating personalised e-learning activities to suit students learning preferences. Technology-enhanced learning (TEL) alternatively known as e-learning has not yet reached its full potential in higher education. There are still many potential uses as yet undiscovered and other discovered uses which are not yet realisable by many educators. TEL is still predominantly used for e-dissemination and e-administration. This...

  5. An Active Learning Activity to Reinforce the Design Components of the Corticosteroids.

    Science.gov (United States)

    Slauson, Stephen R; Mandela, Prashant

    2018-02-05

    Despite the popularity of active learning applications over the past few decades, few activities have been reported for the field of medicinal chemistry. The purpose of this study is to report a new active learning activity, describe participant contributions, and examine participant performance on the assessment questions mapped to the objective covered by the activity. In this particular activity, students are asked to design two novel corticosteroids as a group (6-8 students per group) based on the design characteristics of marketed corticosteroids covered in lecture coupled with their pharmaceutics knowledge from the previous semester and then defend their design to the class through an interactive presentation model. Although class performance on the objective mapped to this material on the assessment did not reach statistical significance, use of this activity has allowed fruitful discussion of misunderstood concepts and facilitated multiple changes to the lecture presentation. As pharmacy schools continue to emphasize alternative learning pedagogies, publication of previously implemented activities demonstrating their use will help others apply similar methodologies.

  6. Sample container for neutron activation analysis

    International Nuclear Information System (INIS)

    Lersmacher, B.; Verheijke, M.L.; Jaspers, H.J.

    1983-01-01

    The sample container avoids contaminating the sample substance by diffusion of foreign matter from the wall of the sample container into the sample. It cannot be activated, so that the results of measurements are not falsified by a radioactive container wall. It consists of solid carbon. (orig./HP) [de

  7. Changing University Students' Alternative Conceptions of Optics by Active Learning

    Science.gov (United States)

    Hadžibegovic, Zalkida; Sliško, Josip

    2013-01-01

    Active learning is individual and group participation in effective activities such as in-class observing, writing, experimenting, discussion, solving problems, and talking about to-be-learned topics. Some instructors believe that active learning is impossible, or at least extremely difficult to achieve in large lecture sessions. Nevertheless, the…

  8. Active Learning Strategies in Face-to-Face Courses. IDEA Paper #53

    Science.gov (United States)

    Millis, Barbara J.

    2012-01-01

    As numerous research studies suggest, teachers who desire increased student learning should adopt active learning. This article explores the research, defines active learning, discusses its value, offers suggestions for implementing it, and provides six concrete examples of active learning approaches: Thinking-Aloud Pair Problem-Solving;…

  9. IMPROVEMENT EFFORTS TO LEARN LESSONS ACTIVITIES CHASSIS POWER TRANSFER STANDARD COMPETENCE AND CORRECT STEERING SYSTEM WITH LEARNING METHOD DISCOVERY INQUIRY CLASS XIB SMK MUHAMMADIYAH GAMPING ACADEMIC YEAR 2013/2014

    Directory of Open Access Journals (Sweden)

    Harry Suharto

    2013-12-01

    Full Text Available The purpose of the study to determine the increase learners' learning activities subjects chassis and power transfer competency standard steering system repair discovery learning through the implementation of class XI inquiry Lightweight Vehicle Technology SMK Muhammadiyah Gamping, Sleman academic year 2013/2014. This research including action research   Research conducted at SMK Muhammadiyah Gamping XIB class academic year 2013/2014 with a sample of 26 students. Techniques of data collection using questionnaire sheet, observation sheets and documentation to determine the increase in student activity. Instrument validation study using experts judgment. Analysis using descriptive statistics using the technique .   The results showed that the increased activity of the first cycle to the second cycle include an increase of 57.7 % Visual activities; Oral activities amounted to 61.6 %; Listening activities amounted to 23.04 %; Writing activities by 8.7 %; Mental activities of 73.1 %; Emotional activities of 42.3 % ( for the spirit of the students in learning activities ; Motor activities amounted to -7.7 % ( decrease negative activity . Based on these results can be known to most students in SMK Muhammadiyah Gamping gave a positive opinion on the use of inquiry and discovery learning method has a view that the use of inquiry discovery learning methods can be useful for students and schools themselves. Learners who have a good perception of the use of discovery learning method of inquiry he has known and fully aware of the standards of achievement of competence theory fix the steering system. Learning discovery learning methods on achievement of competency standards inquiry repair steering systems theory pleased with the learning process, they are also able to: 1 increase the motivation to learn, 2 improving learning achievement; 3 enhancing creativity; 4 listen, respect, and accept the opinion of the participants other students; 5 reduce boredom

  10. Teaching Engineering with Autonomous Learning Activities

    Science.gov (United States)

    Otero, Beatriz; Rodríguez, Eva; Royo, Pablo

    2015-01-01

    This paper proposes several activities that encourage self-learning in engineering courses. For each activity, the context and the pedagogical issues addressed are described emphasizing strengths and weaknesses. Specifically, this work describes and implements five activities, which are: questionnaires, conceptual maps, videos, jigsaw and…

  11. Sequence learning in differentially activated dendrites

    DEFF Research Database (Denmark)

    Nielsen, Bjørn Gilbert

    2003-01-01

    . It is proposed that the neural machinery required in such a learning/retrieval mechanism could involve the NMDA receptor, in conjunction with the ability of dendrites to maintain differentially activated regions. In particular, it is suggested that such a parcellation of the dendrite allows the neuron......Differentially activated areas of a dendrite permit the existence of zones with distinct rates of synaptic modification, and such areas can be individually accessed using a reference signal which localizes synaptic plasticity and memory trace retrieval to certain subregions of the dendrite...... to participate in multiple sequences, which can be learned without suffering from the 'wash-out' of synaptic efficacy associated with superimposition of training patterns. This is a biologically plausible solution to the stability-plasticity dilemma of learning in neural networks....

  12. Opportunities to Create Active Learning Techniques in the Classroom

    Science.gov (United States)

    Camacho, Danielle J.; Legare, Jill M.

    2015-01-01

    The purpose of this article is to contribute to the growing body of research that focuses on active learning techniques. Active learning techniques require students to consider a given set of information, analyze, process, and prepare to restate what has been learned--all strategies are confirmed to improve higher order thinking skills. Active…

  13. CAMKII activation is not required for maintenance of learning-induced enhancement of neuronal excitability.

    Directory of Open Access Journals (Sweden)

    Ori Liraz

    Full Text Available Pyramidal neurons in the piriform cortex from olfactory-discrimination trained rats show enhanced intrinsic neuronal excitability that lasts for several days after learning. Such enhanced intrinsic excitability is mediated by long-term reduction in the post-burst after-hyperpolarization (AHP which is generated by repetitive spike firing. AHP reduction is due to decreased conductance of a calcium-dependent potassium current, the sI(AHP. We have previously shown that learning-induced AHP reduction is maintained by persistent protein kinase C (PKC and extracellular regulated kinase (ERK activation. However, the molecular machinery underlying this long-lasting modulation of intrinsic excitability is yet to be fully described. Here we examine whether the CaMKII, which is known to be crucial in learning, memory and synaptic plasticity processes, is instrumental for the maintenance of learning-induced AHP reduction. KN93, that selectively blocks CaMKII autophosphorylation at Thr286, reduced the AHP in neurons from trained and control rat to the same extent. Consequently, the differences in AHP amplitude and neuronal adaptation between neurons from trained rats and controls remained. Accordingly, the level of activated CaMKII was similar in pirifrom cortex samples taken form trained and control rats. Our data show that although CaMKII modulates the amplitude of AHP of pyramidal neurons in the piriform cortex, its activation is not required for maintaining learning-induced enhancement of neuronal excitability.

  14. Virk: An Active Learning-based System for Bootstrapping Knowledge Base Development in the Neurosciences

    Directory of Open Access Journals (Sweden)

    Kyle H. Ambert

    2013-12-01

    Full Text Available The frequency and volume of newly-published scientific literature is quickly making manual maintenance of publicly-available databases of primary data unrealistic and costly. Although machine learning can be useful for developing automated approaches to identifying scientific publications containing relevant information for a database, developing such tools necessitates manually annotating an unrealistic number of documents. One approach to this problem, active learning, builds classification models by iteratively identifying documents that provide the most information to a classifier. Although this approach has been shown to be effective for related problems, in the context of scientific databases curation, it falls short. We present Virk, an active learning system that, while being trained, simultaneously learns a classification model and identifies documents having information of interest for a knowledge base. Our approach uses a support vector machine classifier with input features derived from neuroscience-related publications from the primary literature. Using our approach, we were able to increase the size of the Neuron Registry, a knowledge base of neuron-related information, by a factor of 90%, a knowledge base of neuron-related information, in 3 months. Using standard biocuration methods, it would have taken between 1-2 years to make the same number of contributions to the Neuron Registry. Here, we describe the system pipeline in detail, and evaluate its performance against other approaches to sampling in active learning.

  15. Musical Peddy-Paper: A Collaborative Learning Activity Suported by Augmented Reality

    Science.gov (United States)

    Gomes, José Duarte Cardoso; Figueiredo, Mauro Jorge Guerreiro; Amante, Lúcia da Graça Cruz Domingues; Gomes, Cristina Maria Cardoso

    2014-01-01

    Gaming activities are an integral part of the human learning process, in particular for children. Game-based learning focuses on motivation and children's engagement towards learning. Educational game-based activities are becoming effective strategies to enhance the learning process. This paper presents an educational activity focusing to merge…

  16. Attention Cueing and Activity Equally Reduce False Alarm Rate in Visual-Auditory Associative Learning through Improving Memory.

    Science.gov (United States)

    Nikouei Mahani, Mohammad-Ali; Haghgoo, Hojjat Allah; Azizi, Solmaz; Nili Ahmadabadi, Majid

    2016-01-01

    In our daily life, we continually exploit already learned multisensory associations and form new ones when facing novel situations. Improving our associative learning results in higher cognitive capabilities. We experimentally and computationally studied the learning performance of healthy subjects in a visual-auditory sensory associative learning task across active learning, attention cueing learning, and passive learning modes. According to our results, the learning mode had no significant effect on learning association of congruent pairs. In addition, subjects' performance in learning congruent samples was not correlated with their vigilance score. Nevertheless, vigilance score was significantly correlated with the learning performance of the non-congruent pairs. Moreover, in the last block of the passive learning mode, subjects significantly made more mistakes in taking non-congruent pairs as associated and consciously reported lower confidence. These results indicate that attention and activity equally enhanced visual-auditory associative learning for non-congruent pairs, while false alarm rate in the passive learning mode did not decrease after the second block. We investigated the cause of higher false alarm rate in the passive learning mode by using a computational model, composed of a reinforcement learning module and a memory-decay module. The results suggest that the higher rate of memory decay is the source of making more mistakes and reporting lower confidence in non-congruent pairs in the passive learning mode.

  17. Learning plan applicability through active mental entities

    International Nuclear Information System (INIS)

    Baroni, Pietro; Fogli, Daniela; Guida, Giovanni

    1999-01-01

    This paper aims at laying down the foundations of a new approach to learning in autonomous mobile robots. It is based on the assumption that robots can be provided with built-in action plans and with mechanisms to modify and improve such plans. This requires that robots are equipped with some form of high-level reasoning capabilities. Therefore, the proposed learning technique is embedded in a novel distributed control architecture featuring an explicit model of robot's cognitive activity. In particular, cognitive activity is obtained by the interaction of active mental entities, such as intentions, persuasions and expectations. Learning capabilities are implemented starting from the interaction of such mental entities. The proposal is illustrated through an example concerning a robot in charge of reaching a target in an unknown environment cluttered with obstacles

  18. Neurofeedback Training Effects on Inhibitory Brain Activation in ADHD: A Matter of Learning?

    Science.gov (United States)

    Baumeister, Sarah; Wolf, Isabella; Holz, Nathalie; Boecker-Schlier, Regina; Adamo, Nicoletta; Holtmann, Martin; Ruf, Matthias; Banaschewski, Tobias; Hohmann, Sarah; Brandeis, Daniel

    2018-05-15

    Neurofeedback training (NF) is a promising non-pharmacological treatment for ADHD that has been associated with improvement of attention-deficit/hyperactivity disorder (ADHD)-related symptoms as well as changes in electrophysiological measures. However, the functional localization of neural changes following NF compared to an active control condition, and of successful learning during training (considered to be the critical mechanism for improvement), remains largely unstudied. Children with ADHD (N=16, mean age: 11.81, SD: 1.47) were randomly assigned to either slow cortical potential (SCP, n=8) based NF or biofeedback control training (electromyogram feedback, n=8) and performed a combined Flanker/NoGo task pre- and post-training. Effects of NF, compared to the active control, and of learning in transfer trials (approximating successful transfer to everyday life) were examined with respect to clinical outcome and functional magnetic resonance imaging (fMRI) changes during inhibitory control. After 20 sessions of training, children in the NF group presented reduced ADHD symptoms and increased activation in areas associated with inhibitory control compared to baseline. Subjects who were successful learners (n=9) also showed increased activation in an extensive inhibitory network irrespective of the type of training. Activation increased in an extensive inhibitory network following NF training, and following successful learning through NF and control biofeedback. Although this study was only powered to detect large effects and clearly requires replication in larger samples, the results suggest a crucial role for learning effects in biofeedback trainings. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  19. The Validation of the Active Learning in Health Professions Scale

    Science.gov (United States)

    Kammer, Rebecca; Schreiner, Laurie; Kim, Young K.; Denial, Aurora

    2015-01-01

    There is a need for an assessment tool for evaluating the effectiveness of active learning strategies such as problem-based learning in promoting deep learning and clinical reasoning skills within the dual environments of didactic and clinical settings in health professions education. The Active Learning in Health Professions Scale (ALPHS)…

  20. Predicting sample size required for classification performance

    Directory of Open Access Journals (Sweden)

    Figueroa Rosa L

    2012-02-01

    Full Text Available Abstract Background Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target. Methods We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method. Results A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p Conclusions This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.

  1. Development of active learning modules in pharmacology for small group teaching.

    Science.gov (United States)

    Tripathi, Raakhi K; Sarkate, Pankaj V; Jalgaonkar, Sharmila V; Rege, Nirmala N

    2015-01-01

    Current teaching in pharmacology in undergraduate medical curriculum in India is primarily drug centered and stresses imparting factual knowledge rather than on pharmacotherapeutic skills. These skills would be better developed through active learning by the students. Hence modules that will encourage active learning were developed and compared with traditional methods within the Seth GS Medical College, Mumbai. After Institutional Review Board approval, 90 second year undergraduate medical students who consented were randomized into six sub-groups, each with 15 students. Pre-test was administered. The three sub-groups were taught a topic using active learning modules (active learning groups), which included problems on case scenarios, critical appraisal of prescriptions and drug identification. The remaining three sub-groups were taught the same topic in a conventional tutorial mode (tutorial learning groups). There was crossover for the second topic. Performance was assessed using post-test. Questionnaires with Likert-scaled items were used to assess feedback on teaching technique, student interaction and group dynamics. The active and tutorial learning groups differed significantly in their post-test scores (11.3 ± 1.9 and 15.9 ± 2.7, respectively, P active learning session as interactive (vs. 37/90 students in tutorial group) and enhanced their understanding vs. 56/90 in tutorial group), aroused intellectual curiosity (47/90 students of active learning group vs. 30/90 in tutorial group) and provoked self-learning (41/90 active learning group vs. 14/90 in tutorial group). Sixty-four students in the active learning group felt that questioning each other helped in understanding the topic, which was the experience of 25/90 students in tutorial group. Nevertheless, students (55/90) preferred tutorial mode of learning to help them score better in their examinations. In this study, students preferred an active learning environment, though to pass examinations, they

  2. Enhancing Learning Outcomes through Application Driven Activities in Marketing

    Science.gov (United States)

    Stegemann, Nicole; Sutton-Brady, Catherine

    2013-01-01

    This paper introduces an activity used in class to allow students to apply previously acquired information to a hands-on task. As the authors have previously shown active learning is a way to effectively facilitate and improve students' learning outcomes. As a result to improve learning outcomes we have overtime developed a series of learning…

  3. Students’ mathematical learning in modelling activities

    DEFF Research Database (Denmark)

    Kjeldsen, Tinne Hoff; Blomhøj, Morten

    2013-01-01

    Ten years of experience with analyses of students’ learning in a modelling course for first year university students, led us to see modelling as a didactical activity with the dual goal of developing students’ modelling competency and enhancing their conceptual learning of mathematical concepts i...... create and help overcome hidden cognitive conflicts in students’ understanding; that reflections within modelling can play an important role for the students’ learning of mathematics. These findings are illustrated with a modelling project concerning the world population....

  4. Mind and activity. Psychic mechanism of learning

    Directory of Open Access Journals (Sweden)

    Zoya A. Reshetova

    2017-09-01

    Full Text Available The paper is devoted to the issue of mechanisms of learning for understanding the nature of the human mind. Learning is regarded as a special activity that is important for developing the human mind in a specific cultural and historical setting and indirect activity. The author’s understanding of the ideas developed by the psychological theory of activity for establishing the principles of developing the human mind is highlighted. Interpretation of dialectical connections of brain processes and mind, and also the objective activity that emerges them is provided. According to the activity theory, the causes of the students’ psychological difficulties and the low efficacy of learning within predominant reproductive method or the use of the trial and error method are revealed. Thus, a new understanding of the renowned didactic principles of scientific rigour, accessibility, objectivity, the connection of learning with life and others is offered. The contribution of the psychological theory in organizing and managing the studies, increasing teaching activity and awareness, and the growth of the internal causes of motivation are shown. Particular attention is paid to the issue of intellectual development and creative abilities. The author believes the creative abilities of the student and the way the latter are taught are interconnected. At the same time, the developers and educators should make efforts to develop in the students a systemic orientation in the subject, primarily mastering the method of system analysis. Once the method of system analysis has been mastered, it becomes a general intellectual and developing tool through which activities are organized to solve any teaching problems with whatever type of content and difficulty level. Summing up, the organization and disclosure to the student of the process of learning as an activity with its social, consciously transformative and sense shaping meaning, the conditions of its development

  5. Enhancing learning in geosciences and water engineering via lab activities

    Science.gov (United States)

    Valyrakis, Manousos; Cheng, Ming

    2016-04-01

    This study focuses on the utilisation of lab based activities to enhance the learning experience of engineering students studying Water Engineering and Geosciences. In particular, the use of modern highly visual and tangible presentation techniques within an appropriate laboratory based space are used to introduce undergraduate students to advanced engineering concepts. A specific lab activity, namely "Flood-City", is presented as a case study to enhance the active engagement rate, improve the learning experience of the students and better achieve the intended learning objectives of the course within a broad context of the engineering and geosciences curriculum. Such activities, have been used over the last few years from the Water Engineering group @ Glasgow, with success for outreach purposes (e.g. Glasgow Science Festival and demos at the Glasgow Science Centre and Kelvingrove museum). The activity involves a specific setup of the demonstration flume in a sand-box configuration, with elements and activities designed so as to gamely the overall learning activity. Social media platforms can also be used effectively to the same goals, particularly in cases were the students already engage in these online media. To assess the effectiveness of this activity a purpose designed questionnaire is offered to the students. Specifically, the questionnaire covers several aspects that may affect student learning, performance and satisfaction, such as students' motivation, factors to effective learning (also assessed by follow-up quizzes), and methods of communication and assessment. The results, analysed to assess the effectiveness of the learning activity as the students perceive it, offer a promising potential for the use of such activities in outreach and learning.

  6. Flipped Classroom, active Learning?

    DEFF Research Database (Denmark)

    Andersen, Thomas Dyreborg; Levinsen, Henrik; Philipps, Morten

    2015-01-01

    Action research is conducted in three physics classes over a period of eighteen weeks with the aim of studying the effect of flipped classroom on the pupils agency and learning processes. The hypothesis is that flipped classroom teaching will potentially allocate more time to work actively...

  7. Changing University Students’ Alternative Conceptions of Optics by Active Learning

    Directory of Open Access Journals (Sweden)

    Zalkida Hadžibegović

    2013-01-01

    Full Text Available Active learning is individual and group participation in effective activities such as in-class observing, writing, experimenting, discussion, solving problems, and talking about to-be-learned topics. Some instructors believe that active learning is impossible, or at least extremely difficult to achieve in large lecture sessions. Nevertheless, the truly impressive implementation results of theSCALE-UP learning environment suggest that such beliefs are false (Beichner et al., 2000. In this study, we present a design of an active learning environment with positive effect on students. The design is based on the following elements: (1 helping students to learn from interactive lecture experiment; (2 guiding students to use justified explanation and prediction after observing and exploring a phenomenon; (3 developing a conceptual question sequencedesigned for use in an interactive lecture with students answering questions in worksheets by writing and drawing; (4 evaluating students’ conceptual change and gains by questions related to light reflection, refraction, and image formation in an exam held a week after the active learning session. Data were collected from 95 science freshmen with different secondary school backgrounds. They participated in geometrical optics classes organized for collecting research results during and after only one active learning session.The results have showed that around 60% of the students changed their initial alternative conceptions of vision and of image formation. It was also found that a large group of university students is likely to be engaged in active learning, shifting from a passive role they usually play during teacher’s lectures.

  8. Effects of Sharing Clickers in an Active Learning Environment

    Science.gov (United States)

    Daniel, Todd; Tivener, Kristin

    2016-01-01

    Scientific research into learning enhancement gained by the use of clickers in active classrooms has largely focused on the use of individual clickers. In this study, we compared the learning experiences of participants in active learning groups in which an entire small group shared a single clicker to groups in which each member of the group had…

  9. Learning Microbiology Through Cooperation: Designing Cooperative Learning Activities that Promote Interdependence, Interaction, and Accountability

    Directory of Open Access Journals (Sweden)

    Janine E. Trempy

    2009-12-01

    Full Text Available A microbiology course and its corresponding learning activities have been structured according to the Cooperative Learning Model. This course, The World According to Microbes, integrates science, math, engineering, and technology (SMET majors and non-SMET majors into teams of students charged with problem solving activities that are microbial in origin. In this study we describe development of learning activities that utilize key components of Cooperative Learning—positive interdependence, promotive interaction, individual accountability, teamwork skills, and group processing. Assessments and evaluations over an 8-year period demonstrate high retention of key concepts in microbiology and high student satisfaction with the course.

  10. Application of active learning modalities to achieve medical genetics competencies and their learning outcome assessments

    Directory of Open Access Journals (Sweden)

    Hagiwara N

    2017-12-01

    Full Text Available Nobuko Hagiwara Division of Cardiovascular Medicine, Department of Internal Medicine, School of Medicine, University of California, Davis, CA, USA Abstract: The steadily falling costs of genome sequencing, coupled with the growing number of genetic tests with proven clinical validity, have made the use of genetic testing more common in clinical practice. This development has necessitated nongeneticist physicians, especially primary care physicians, to become more responsible for assessing genetic risks for their patients. Providing undergraduate medical students a solid foundation in genomic medicine, therefore, has become all the more important to ensure the readiness of future physicians in applying genomic medicine to their patient care. In order to further enhance the effectiveness of instructing practical skills in medical genetics, the emphasis of active learning modules in genetics curriculum at medical schools has increased in recent years. This is because of the general acceptance of a better efficacy of active learner-centered pedagogy over passive lecturer-centered pedagogy. However, an objective standard to evaluate students’ skill levels in genomic medicine achieved by active learning is currently missing. Recently, entrustable professional activities (EPAs in genomic medicine have been proposed as a framework for developing physician competencies in genomic medicine. EPAs in genomic medicine provide a convenient guideline for not only developing genomic medicine curriculum but also assessing students’ competency levels in practicing genomic medicine. In this review, the efficacy of different types of active learning modules reported for medical genetics curricula is discussed using EPAs in genomic medicine as a common evaluation standard for modules’ learning outcomes. The utility of the EPAs in genomic medicine for designing active learning modules in undergraduate medical genetics curricula is also discussed. Keywords

  11. Lifeguard Final Exam—Encouraging the Use of Active Learning

    OpenAIRE

    Griswold, Elise N.; Klionsky, Daniel J.

    2015-01-01

    To anyone familiar with the extensive literature on teaching and learning, there is little question that active learning is more effective than passive learning. Thus, we are not directing this letter to that particular audience. Instead, we are attempting to address the question of the best way to convince instructors who have not tried to incorporate elements of active learning into their courses to make such an attempt. There are numerous examples where it becomes immediately clear that ac...

  12. Dental Students' Study Habits in Flipped/Blended Classrooms and Their Association with Active Learning Practices.

    Science.gov (United States)

    Gadbury-Amyot, Cynthia C; Redford, Gloria J; Bohaty, Brenda S

    2017-12-01

    In recognition of the importance for dental education programs to take a student-centered approach in which students are encouraged to take responsibility for their learning, a pediatric dentistry course redesign aimed at promoting greater active and self-directed learning was implemented at one U.S. dental school. The aim of this study was to examine the association between the students' self-reported study habits and active learning practices necessary for meaningful learning in the flipped/blended classroom. A convenience sample of two classes of second-year dental students in spring 2014 (SP14, n=106) and spring 2015 (SP15, n=106) was invited to participate in the study. Of the SP14 students, 84 participated, for a response rate of 79%; of the SP15 students, 94 participated, for a response rate of 87%. Students' self-reported responses to questions about study strategies with the prerecorded lecture materials and assigned reading materials were examined. Non-parametric analyses resulted in a cohort effect, so data are reported by class. In the SP15 class, 72% reported watching all/more than half of the prerecorded lectures versus 62% of the SP14 class, with a majority watching more than one lecture per week. In the SP15 cohort, 68% used active learning strategies when watching the lectures versus 58.3% of the SP14 cohort. The time of day preferred by the majority of both cohorts for interacting with course materials was 7-11 pm. Both SP14 and SP15 students reported being unlikely to read assigned materials prior to coming to class. Overall, the course redesign appeared to engage students in self-directed active learning. However, the degree to which active learning practices were taking place to achieve meaningful learning was questionable given students' self-reported study strategies. More work is needed to examine strategies for promoting study practices that will lead to meaningful learning.

  13. Postnatal TLR2 activation impairs learning and memory in adulthood.

    Science.gov (United States)

    Madar, Ravit; Rotter, Aviva; Waldman Ben-Asher, Hiba; Mughal, Mohamed R; Arumugam, Thiruma V; Wood, W H; Becker, K G; Mattson, Mark P; Okun, Eitan

    2015-08-01

    Neuroinflammation in the central nervous system is detrimental for learning and memory, as evident form epidemiological studies linking developmental defects and maternal exposure to harmful pathogens. Postnatal infections can also induce neuroinflammatory responses with long-term consequences. These inflammatory responses can lead to motor deficits and/or behavioral disabilities. Toll like receptors (TLRs) are a family of innate immune receptors best known as sensors of microbial-associated molecular patterns, and are the first responders to infection. TLR2 forms heterodimers with either TLR1 or TLR6, is activated in response to gram-positive bacterial infections, and is expressed in the brain during embryonic development. We hypothesized that early postnatal TLR2-mediated neuroinflammation would adversely affect cognitive behavior in the adult. Our data indicate that postnatal TLR2 activation affects learning and memory in adult mice in a heterodimer-dependent manner. TLR2/6 activation improved motor function and fear learning, while TLR2/1 activation impaired spatial learning and enhanced fear learning. Moreover, developmental TLR2 deficiency significantly impairs spatial learning and enhances fear learning, stressing the involvement of the TLR2 pathway in learning and memory. Analysis of the transcriptional effects of TLR2 activation reveals both common and unique transcriptional programs following heterodimer-specific TLR2 activation. These results imply that adult cognitive behavior could be influenced in part, by activation or alterations in the TLR2 pathway at birth. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. Active Learning to Improve Fifth Grade Mathematics Achievement in Banten

    Directory of Open Access Journals (Sweden)

    Andri Suherman

    2011-12-01

    Full Text Available Teaching for active learning is a pedagogical technique that has been actively promoted in Indonesian education through government reform efforts and international development assistance projects for decades. Recently, elementary schools in Banten province received training in active learning instructional strategies from the USAID-funded project, Decentralized Basic Education 2. Post-training evaluations conducted by lecturers from the University of Sultan Ageng Tirtayasa (UNTIRTA: Universitas Sultan Ageng Tirtayasa suggested that teachers were successfully employing active learning strategies in some subjects, but not mathematics. In order to understand the difficulties teachers were having in teaching for active learning in mathematics, and to assist them in using active learning strategies, a team of lecturers from UNTIRTA designed and carried out an action research project to train teachers in an elementary school in the city of Cilegon to use a technique called Magic Fingers in teaching Grade 5 multiplication. During the course of the project the research team discovered that teachers were having problems transferring knowledge gained from training in one context and subject to other school subjects and contexts. Key Words: Mathematics, Teaching for Active Learning, Indonesia, Banten

  15. Changes in prefrontal neuronal activity after learning to perform a spatial working memory task.

    Science.gov (United States)

    Qi, Xue-Lian; Meyer, Travis; Stanford, Terrence R; Constantinidis, Christos

    2011-12-01

    The prefrontal cortex is considered essential for learning to perform cognitive tasks though little is known about how the representation of stimulus properties is altered by learning. To address this issue, we recorded neuronal activity in monkeys before and after training on a task that required visual working memory. After the subjects learned to perform the task, we observed activation of more prefrontal neurons and increased activity during working memory maintenance. The working memory-related increase in firing rate was due mostly to regular-spiking putative pyramidal neurons. Unexpectedly, the selectivity of neurons for stimulus properties and the ability of neurons to discriminate between stimuli decreased as the information about stimulus properties was apparently present in neural firing prior to training and neuronal selectivity degraded after training in the task. The effect was robust and could not be accounted for by differences in sampling sites, selection of neurons, level of performance, or merely the elapse of time. The results indicate that, in contrast to the effects of perceptual learning, mastery of a cognitive task degrades the apparent stimulus selectivity as neurons represent more abstract information related to the task. This effect is countered by the recruitment of more neurons after training.

  16. Active Learning of Classification Models with Likert-Scale Feedback.

    Science.gov (United States)

    Xue, Yanbing; Hauskrecht, Milos

    2017-01-01

    Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone.

  17. Active Learning Increases Children's Physical Activity across Demographic Subgroups.

    Science.gov (United States)

    Bartholomew, John B; Jowers, Esbelle M; Roberts, Gregory; Fall, Anna-Mária; Errisuriz, Vanessa L; Vaughn, Sharon

    2018-01-01

    Given the need to find more opportunities for physical activity within the elementary school day, this study was designed to asses the impact of I-CAN!, active lessons on: 1) student physical activity (PA) outcomes via accelerometry; and 2) socioeconomic status (SES), race, sex, body mass index (BMI), or fitness as moderators of this impact. Participants were 2,493 fourth grade students (45.9% male, 45.8% white, 21.7% low SES) from 28 central Texas elementary schools randomly assigned to intervention (n=19) or control (n=9). Multilevel regression models evaluated the effect of I-CAN! on PA and effect sizes were calculated. The moderating effects of SES, race, sex, BMI, and fitness were examined in separate models. Students in treatment schools took significantly more steps than those in control schools (β = 125.267, SE = 41.327, p = .002, d = .44). I-CAN! had a significant effect on MVPA with treatment schools realizing 80% (β = 0.796, SE =0.251, p = .001; d = .38) more MVPA than the control schools. There were no significant school-level differences on sedentary behavior (β = -0.177, SE = 0.824, p = .83). SES, race, sex, BMI, and fitness level did not moderate the impact of active learning on step count and MVPA. Active learning increases PA within elementary students, and does so consistently across demographic sub-groups. This is important as these sub-groups represent harder to reach populations for PA interventions. While these lessons may not be enough to help children reach daily recommendations of PA, they can supplement other opportunities for PA. This speaks to the potential of schools to adopt policy change to require active learning.

  18. Learning Activity Package, Algebra.

    Science.gov (United States)

    Evans, Diane

    A set of ten teacher-prepared Learning Activity Packages (LAPs) in beginning algebra and nine in intermediate algebra, these units cover sets, properties of operations, number systems, open expressions, solution sets of equations and inequalities in one and two variables, exponents, factoring and polynomials, relations and functions, radicals,…

  19. An active-learning strategies primer for achieving ability-based educational outcomes.

    Science.gov (United States)

    Gleason, Brenda L; Peeters, Michael J; Resman-Targoff, Beth H; Karr, Samantha; McBane, Sarah; Kelley, Kristi; Thomas, Tyan; Denetclaw, Tina H

    2011-11-10

    Active learning is an important component of pharmacy education. By engaging students in the learning process, they are better able to apply the knowledge they gain. This paper describes evidence supporting the use of active-learning strategies in pharmacy education and also offers strategies for implementing active learning in pharmacy curricula in the classroom and during pharmacy practice experiences.

  20. The philosophical and pedagogical underpinnings of Active Learning in Engineering Education

    Science.gov (United States)

    Christie, Michael; de Graaff, Erik

    2017-01-01

    In this paper the authors draw on three sequential keynote addresses that they gave at Active Learning in Engineering Education (ALE) workshops in Copenhagen (2012), Caxias do Sol (2014) and San Sebastian (2015). Active Learning in Engineering Education is an informal international network of engineering educators dedicated to improving engineering education through active learning (http://www.ale-net.org/). The paper reiterates themes from those keynotes, namely, the philosophical and pedagogical underpinnings of Active Learning in Engineering Education, the scholarly questions that inspire engineering educators to go on improving their practice and exemplary models designed to activate the learning of engineering students. This paper aims to uncover the bedrock of established educational philosophies and theories that define and support active learning. The paper does not claim to present any new or innovative educational theory. There is already a surfeit of them. Rather, the aim is to assist Engineering Educators who wish to research how they can best activate the learning of their students by providing a readable, reasonable and solid underpinning for best practice in this field.

  1. The use of an active learning approach in a SCALE-UP learning space improves academic performance in undergraduate General Biology.

    Science.gov (United States)

    Hacisalihoglu, Gokhan; Stephens, Desmond; Johnson, Lewis; Edington, Maurice

    2018-01-01

    Active learning is a pedagogical approach that involves students engaging in collaborative learning, which enables them to take more responsibility for their learning and improve their critical thinking skills. While prior research examined student performance at majority universities, this study focuses on specifically Historically Black Colleges and Universities (HBCUs) for the first time. Here we present work that focuses on the impact of active learning interventions at Florida A&M University, where we measured the impact of active learning strategies coupled with a SCALE-UP (Student Centered Active Learning Environment with Upside-down Pedagogies) learning environment on student success in General Biology. In biology sections where active learning techniques were employed, students watched online videos and completed specific activities before class covering information previously presented in a traditional lecture format. In-class activities were then carefully planned to reinforce critical concepts and enhance critical thinking skills through active learning techniques such as the one-minute paper, think-pair-share, and the utilization of clickers. Students in the active learning and control groups covered the same topics, took the same summative examinations and completed identical homework sets. In addition, the same instructor taught all of the sections included in this study. Testing demonstrated that these interventions increased learning gains by as much as 16%, and students reported an increase in their positive perceptions of active learning and biology. Overall, our results suggest that active learning approaches coupled with the SCALE-UP environment may provide an added opportunity for student success when compared with the standard modes of instruction in General Biology.

  2. Development and Study the Usage of Blended Learning Environment Model Using Engineering Design Concept Learning Activities to Computer Programming Courses for Undergraduate Students of Rajabhat Universities

    Directory of Open Access Journals (Sweden)

    Kasame Tritrakan

    2017-06-01

    Full Text Available The objectives of this research were to study and Synthesise the components, to develop, and to study the usage of blended learning environment model using engineering design concept learning activities to computer programming courses for undergraduate students of Rajabhat universities. The research methodology was divided into 3 phases. Phase I: surveying presents, needs and problems in teaching computer programming of 52 lecturers by using in-depth interview from 5 experienced lecturers. The model’s elements were evaluated by 5 experts. The tools were questionnaire, interview form, and model’s elements assessment form. Phase II: developing the model of blended learning environment and learning activities based on engineering design processes and confirming model by 8 experts. The tools were the draft of learning environment, courseware, and assessment forms. Phase III evaluating the effects of using the implemented environment. The samples were students which formed into 2 groups, 25 people in the experiment group and 27 people in the control group by cluster random sampling. The tools were learning environment, courseware, and assessment tools. The statistics used in this research were means, standard deviation, t-test dependent, and one-way MANOVA. The results found that: 1 Lecturers quite agreed with the physical, mental, social, and information learning environment, learning processes, and assessments. There were all needs in high level. However there were physical environment problems in high level yet quite low in other aspects. 2 The developed learning environment had 4 components which were a 4 types of environments b the inputs included blended learning environment, learning motivation factors, and computer programming content c the processes were analysis of state objectives, design learning environment and activities, developing learning environment and testing materials, implement, ation evaluation and evaluate, 4 the outputs

  3. Dopamine, reward learning, and active inference.

    Science.gov (United States)

    FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl

    2015-01-01

    Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

  4. Quantum Speedup for Active Learning Agents

    Directory of Open Access Journals (Sweden)

    Giuseppe Davide Paparo

    2014-07-01

    Full Text Available Can quantum mechanics help us build intelligent learning agents? A defining signature of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in real-life situations is the size and complexity of the corresponding task environment. Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation. If the environment is impatient, allowing only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all. Here, we show that quantum physics can help and provide a quadratic speedup for active learning as a genuine problem of artificial intelligence. This result will be particularly relevant for applications involving complex task environments.

  5. Examining factors affecting beginning teachers' transfer of learning of ICT-enhanced learning activities in their teaching practice

    NARCIS (Netherlands)

    Agyei, D.D.; Voogt, J.

    2014-01-01

    This study examined 100 beginning teachers’ transfer of learning when utilising Information Communication Technology-enhanced activity-based learning activities. The beginning teachers had participated in a professional development program that was characterised by ‘learning technology by

  6. Resting alpha activity predicts learning ability in alpha neurofeedback

    Directory of Open Access Journals (Sweden)

    Wenya eNan

    2014-07-01

    Full Text Available Individuals differ in their ability to learn how to regulate the alpha activity by neurofeedback. This study aimed to investigate whether the resting alpha activity is related to the learning ability of alpha enhancement in neurofeedback and could be used as a predictor. A total of 25 subjects performed 20 sessions of individualized alpha neurofeedback in order to learn how to enhance activity in the alpha frequency band. The learning ability was assessed by three indices respectively: the training parameter changes between two periods, within a short period and across the whole training time. It was found that the resting alpha amplitude measured before training had significant positive correlations with all learning indices and could be used as a predictor for the learning ability prediction. This finding would help the researchers in not only predicting the training efficacy in individuals but also gaining further insight into the mechanisms of alpha neurofeedback.

  7. Mapping Learning Outcomes and Assignment Tasks for SPIDER Activities

    Directory of Open Access Journals (Sweden)

    Lyn Brodie

    2011-05-01

    Full Text Available Modern engineering programs have to address rapidly changing technical content and have to enable students to develop transferable skills such as critical evaluation, communication skills and lifelong learning. This paper introduces a combined learning and assessment activity that provides students with opportunities to develop and practice their soft skills, but also extends their theoretical knowledge base. Key tasks included self directed inquiry, oral and written communication as well as peer assessment. To facilitate the SPIDER activities (Select, Prepare and Investigate, Discuss, Evaluate, Reflect, a software tool has been implemented in the learning management system Moodle. Evidence shows increased student engagement and better learning outcomes for both transferable as well as technical skills. The study focuses on generalising the relationship between learning outcomes and assignment tasks as well as activities that drive these tasks. Trail results inform the approach. Staff evaluations and their views of assignments and intended learning outcomes also supported this analysis.

  8. Competency and an active learning program in undergraduate nursing education.

    Science.gov (United States)

    Shin, Hyunsook; Sok, Sohyune; Hyun, Kyung Sun; Kim, Mi Ja

    2015-03-01

    To evaluate the effect of an active learning program on competency of senior students. Active learning strategies have been used to help students achieve desired nursing competency, but their effectiveness has not been systematically examined. A descriptive, cross-sectional comparative design was used. Two cohort group comparisons using t-test were made: one in an active learning group and the other in a traditional learning group. A total of 147 senior nursing students near graduation participated in this study: 73 in 2010 and 74 in 2013. The active learning program incorporated high-fidelity simulation, situation-based case studies, standardized patients, audio-video playback, reflective activities and technology such as a SmartPad-based program. The overall scores of the nursing competency in the active group were significantly higher than those in the traditional group. Of five overall subdomains, the scores of the special and general clinical performance competency, critical thinking and human understanding were significantly higher in the active group than in the traditional group. Importance-performance analysis showed that all five subdomains of the active group clustered in the high importance and high performance quadrant, indicating significantly better achievements. In contrast, the students in the traditional group showed scattered patterns in three quadrants, excluding the low importance and low performance quadrants. This pattern indicates that the traditional learning method did not yield the high performance in most important areas. The findings of this study suggest that an active learning strategy is useful for helping undergraduate students to gain competency. © 2014 John Wiley & Sons Ltd.

  9. Active learning increases student performance in science, engineering, and mathematics.

    Science.gov (United States)

    Freeman, Scott; Eddy, Sarah L; McDonough, Miles; Smith, Michelle K; Okoroafor, Nnadozie; Jordt, Hannah; Wenderoth, Mary Pat

    2014-06-10

    To test the hypothesis that lecturing maximizes learning and course performance, we metaanalyzed 225 studies that reported data on examination scores or failure rates when comparing student performance in undergraduate science, technology, engineering, and mathematics (STEM) courses under traditional lecturing versus active learning. The effect sizes indicate that on average, student performance on examinations and concept inventories increased by 0.47 SDs under active learning (n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing (n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on concept inventories more than on course examinations, and that active learning appears effective across all class sizes--although the greatest effects are in small (n ≤ 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms.

  10. Metabolic Profiling and Classification of Propolis Samples from Southern Brazil: An NMR-Based Platform Coupled with Machine Learning.

    Science.gov (United States)

    Maraschin, Marcelo; Somensi-Zeggio, Amélia; Oliveira, Simone K; Kuhnen, Shirley; Tomazzoli, Maíra M; Raguzzoni, Josiane C; Zeri, Ana C M; Carreira, Rafael; Correia, Sara; Costa, Christopher; Rocha, Miguel

    2016-01-22

    The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching ∼90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

  11. Learning models of activities involving interacting objects

    DEFF Research Database (Denmark)

    Manfredotti, Cristina; Pedersen, Kim Steenstrup; Hamilton, Howard J.

    2013-01-01

    We propose the LEMAIO multi-layer framework, which makes use of hierarchical abstraction to learn models for activities involving multiple interacting objects from time sequences of data concerning the individual objects. Experiments in the sea navigation domain yielded learned models that were t...

  12. Sample collection and sample analysis plan in support of the 105-C/190-C concrete and soil sampling activities

    International Nuclear Information System (INIS)

    Marske, S.G.

    1996-07-01

    This sampling and analysis plan describes the sample collection and sample analysis in support of the 105-C water tunnels and 190-C main pumphouse concrete and soil sampling activities. These analytical data will be used to identify the radiological contamination and presence of hazardous materials to support the decontamination and disposal activities

  13. Constructivist Learning Environment During Virtual and Real Laboratory Activities

    Directory of Open Access Journals (Sweden)

    Ari Widodo

    2017-04-01

    Full Text Available Laboratory activities and constructivism are two notions that have been playing significant roles in science education. Despite common beliefs about the importance of laboratory activities, reviews reported inconsistent results about the effectiveness of laboratory activities. Since laboratory activities can be expensive and take more time, there is an effort to introduce virtual laboratory activities. This study aims at exploring the learning environment created by a virtual laboratory and a real laboratory. A quasi experimental study was conducted at two grade ten classes at a state high school in Bandung, Indonesia. Data were collected using a questionnaire called Constructivist Learning Environment Survey (CLES before and after the laboratory activities. The results show that both types of laboratories can create constructivist learning environments. Each type of laboratory activity, however, may be stronger in improving certain aspects compared to the other. While a virtual laboratory is stronger in improving critical voice and personal relevance, real laboratory activities promote aspects of personal relevance, uncertainty and student negotiation. This study suggests that instead of setting one type of laboratory against the other, lessons and follow up studies should focus on how to combine both types of laboratories to support better learning.

  14. Student Buy-In to Active Learning in a College Science Course.

    Science.gov (United States)

    Cavanagh, Andrew J; Aragón, Oriana R; Chen, Xinnian; Couch, Brian; Durham, Mary; Bobrownicki, Aiyana; Hanauer, David I; Graham, Mark J

    2016-01-01

    The benefits of introducing active learning in college science courses are well established, yet more needs to be understood about student buy-in to active learning and how that process of buy-in might relate to student outcomes. We test the exposure-persuasion-identification-commitment (EPIC) process model of buy-in, here applied to student (n = 245) engagement in an undergraduate science course featuring active learning. Student buy-in to active learning was positively associated with engagement in self-regulated learning and students' course performance. The positive associations among buy-in, self-regulated learning, and course performance suggest buy-in as a potentially important factor leading to student engagement and other student outcomes. These findings are particularly salient in course contexts featuring active learning, which encourage active student participation in the learning process. © 2016 A. J. Cavanagh et al. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  15. Combining traditional anatomy lectures with e-learning activities: how do students perceive their learning experience?

    Science.gov (United States)

    Lochner, Lukas; Wieser, Heike; Waldboth, Simone; Mischo-Kelling, Maria

    2016-02-21

    The purpose of this study was to investigate how students perceived their learning experience when combining traditional anatomy lectures with preparatory e-learning activities that consisted of fill-in-the-blank assignments, videos, and multiple-choice quizzes. A qualitative study was conducted to explore changes in study behaviour and perception of learning. Three group interviews with students were conducted and thematically analysed. Data was categorized into four themes: 1. Approaching the course material, 2. Understanding the material, 3. Consolidating the material, and 4. Perceived learning outcome. Students appreciated the clear structure of the course, and reported that online activities encouraged them towards a first engagement with the material. They felt that they were more active during in-class sessions, described self-study before the end-of-term exam as easier, and believed that contents would remain in their memories for a longer time. By adjusting already existing resources, lectures can be combined fairly easily and cost-effectively with preparatory e-learning activities. The creation of online components promote well-structured courses, can help minimize 'student passivity' as a characteristic element of lectures, and can support students in distributing their studies throughout the term, thus suggesting enhanced learning. Further research work should be designed to confirm the afore-mentioned findings through objective measurements of student learning outcomes.

  16. Combining traditional anatomy lectures with e-learning activities: how do students perceive their learning experience?

    Science.gov (United States)

    Wieser, Heike; Waldboth, Simone; Mischo-Kelling, Maria

    2016-01-01

    Objectives The purpose of this study was to investigate how students perceived their learning experience when combining traditional anatomy lectures with preparatory e-learning activities that consisted of fill-in-the-blank assignments, videos, and multiple-choice quizzes. Methods A qualitative study was conducted to explore changes in study behaviour and perception of learning. Three group interviews with students were conducted and thematically analysed. Results Data was categorized into four themes: 1. Approaching the course material, 2. Understanding the material, 3. Consolidating the material, and 4. Perceived learning outcome.  Students appreciated the clear structure of the course, and reported that online activities encouraged them towards a first engagement with the material. They felt that they were more active during in-class sessions, described self-study before the end-of-term exam as easier, and believed that contents would remain in their memories for a longer time. Conclusions By adjusting already existing resources, lectures can be combined fairly easily and cost-effectively with preparatory e-learning activities. The creation of online components promote well-structured courses, can help minimize ‘student passivity’ as a characteristic element of lectures, and can support students in distributing their studies throughout the term, thus suggesting enhanced learning. Further research work should be designed to confirm the afore-mentioned findings through objective measurements of student learning outcomes. PMID:26897012

  17. Active Learning Promoting Student Teachers' Professional Competences in Finland and Turkey

    Science.gov (United States)

    Niemi, Hannele; Nevgi, Anne; Aksit, Fisun

    2016-01-01

    This study investigates student teachers' active learning experiences in teacher education (TE) in Finnish and Turkish contexts and attempts to determine how active learning methods' impact student teachers' professional competences. Student teachers (N = 728) assessed their active learning experiences and the professional competences they…

  18. Dopamine, reward learning, and active inference

    Directory of Open Access Journals (Sweden)

    Thomas eFitzgerald

    2015-11-01

    Full Text Available Temporal difference learning models propose phasic dopamine signalling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behaviour. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

  19. Grooming. Learning Activity Package.

    Science.gov (United States)

    Stark, Pamela

    This learning activity package on grooming for health workers is one of a series of 12 titles developed for use in health occupations education programs. Materials in the package include objectives, a list of materials needed, information sheets, reviews (self evaluations) of portions of the content, and answers to reviews. These topics are…

  20. Active Learning: Qualitative Inquiries into Vocabulary Instruction in Chinese L2 Classrooms

    Science.gov (United States)

    Shen, Helen H.; Xu, Wenjing

    2015-01-01

    Active learning emerged as a new approach to learning in the 1980s. The core concept of active learning involves engaging students not only in actively exploring knowledge but also in reflecting on their own learning process in order to become more effective learners. Because the nonalphabetic nature of the Chinese writing system makes learning to…

  1. Measurement of neutron activation in concrete samples

    International Nuclear Information System (INIS)

    Zagar, T.; Ravnik, M.

    2000-01-01

    The results of activation studies of ordinary and barytes concrete samples relevant for research reactor decommissioning are given. Five important long-lived radioactive isotopes ( 54 Mn, 60 Co, 65 Zn, 133 Ba, and 152 Eu) were identified from the gamma-ray spectra measured in the irradiated concrete samples. Activation of these samples was also calculated using ORIGEN2 code. Comparison of calculated and measured results is given. (author)

  2. Positivity effect in healthy aging in observational but not active feedback-learning.

    Science.gov (United States)

    Bellebaum, Christian; Rustemeier, Martina; Daum, Irene

    2012-01-01

    The present study investigated the impact of healthy aging on the bias to learn from positive or negative performance feedback in observational and active feedback learning. In active learning, a previous study had already shown a negative learning bias in healthy seniors older than 75 years, while no bias was found for younger seniors. However, healthy aging is accompanied by a 'positivity effect', a tendency to primarily attend to stimuli with positive valence. Based on recent findings of dissociable neural mechanisms in active and observational feedback learning, the positivity effect was hypothesized to influence older participants' observational feedback learning in particular. In two separate experiments, groups of young (mean age 27) and older participants (mean age 60 years) completed an observational or active learning task designed to differentially assess positive and negative learning. Older but not younger observational learners showed a significant bias to learn better from positive than negative feedback. In accordance with previous findings, no bias was found for active learning. This pattern of results is discussed in terms of differences in the neural underpinnings of active and observational learning from performance feedback.

  3. INTEGRATION OF GAMIFICATION AND ACTIVE LEARNING IN THE CLASSROOM

    Directory of Open Access Journals (Sweden)

    Sergio Zepeda-Hernández

    2016-07-01

    Full Text Available Teachers who currently use the traditional method teacher-centered learning, are having various difficulties with the new generations of students. New learning methods are required to allow students to focus more positive attitudes towards their learning. In this paper, we show how the evaluation and activities based on Active Learning and Gamification, can be an alternative to generate a more positive attitude of students and create a more friendly environment in the classroom. This research was conducted using the qualitative research and ethnographic method as technique.

  4. Characterizing Engineering Learners' Preferences for Active and Passive Learning Methods

    Science.gov (United States)

    Magana, Alejandra J.; Vieira, Camilo; Boutin, Mireille

    2018-01-01

    This paper studies electrical engineering learners' preferences for learning methods with various degrees of activity. Less active learning methods such as homework and peer reviews are investigated, as well as a newly introduced very active (constructive) learning method called "slectures," and some others. The results suggest that…

  5. Orchestration Framework for Learning Activities in Augmented Reality Environments

    OpenAIRE

    Ibáñez, María Blanca; Delgado Kloos, Carlos; Di Serio, Angela

    2011-01-01

    Proceedings of: Across Spaces11 Workshop in conjunction with the EC-TEL2011, Palermo, Italy, September 21, 2011 In this paper we show how Augmented Reality (AR) technology restricted to the use of mobiles or PCs, can be used to develop learning activities with the minimun level of orchestation required by meaningful learning sequences. We use Popcode as programming language to deploy orchestrated learning activities specified with an AR framework. Publicado

  6. Teacher Knowledge for Active-Learning Instruction: Expert-Novice Comparison Reveals Differences.

    Science.gov (United States)

    Auerbach, A J; Higgins, M; Brickman, P; Andrews, T C

    2018-01-01

    Active-learning strategies can improve science, technology, engineering, and mathematics (STEM) undergraduates' abilities to learn fundamental concepts and skills. However, the results instructors achieve vary substantially. One explanation for this is that instructors commonly implement active learning differently than intended. An important factor affecting how instructors implement active learning is knowledge of teaching and learning. We aimed to discover knowledge that is important to effective active learning in large undergraduate courses. We developed a lesson-analysis instrument to elicit teacher knowledge, drawing on the theoretical construct of teacher noticing. We compared the knowledge used by expert ( n = 14) and novice ( n = 29) active-learning instructors as they analyzed lessons. Experts and novices differed in what they noticed, with experts more commonly considering how instructors hold students accountable, topic-specific student difficulties, whether the instructor elicited and responded to student thinking, and opportunities students had to generate their own ideas and work. Experts were also better able to support their lesson analyses with reasoning. This work provides foundational knowledge for the future design of preparation and support for instructors adopting active learning. Improving teacher knowledge will improve the implementation of active learning, which will be necessary to widely realize the potential benefits of active learning in undergraduate STEM. © 2018 A. J. Auerbach et al. CBE—Life Sciences Education © 2018 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  7. A Conceptual Framework for Organizing Active Learning Experiences in Biology Instruction

    Science.gov (United States)

    Gardner, Joel; Belland, Brian R.

    2012-01-01

    Introductory biology courses form a cornerstone of undergraduate instruction. However, the predominantly used lecture approach fails to produce higher-order biology learning. Research shows that active learning strategies can increase student learning, yet few biology instructors use all identified active learning strategies. In this paper, we…

  8. Prioritizing Active Learning: An Exploration of Gateway Courses in Political Science

    Science.gov (United States)

    Archer, Candace C.; Miller, Melissa K.

    2011-01-01

    Prior research in political science and other disciplines demonstrates the pedagogical and practical benefits of active learning. Less is known, however, about the extent to which active learning is used in political science classrooms. This study assesses the prioritization of active learning in "gateway" political science courses, paying…

  9. The Managers’ Experiential Learning of Program Planning in Active Ageing Learning Centers

    Directory of Open Access Journals (Sweden)

    Chun-Ting Yeh

    2016-12-01

    Full Text Available Planning older adult learning programs is really a complex work. Program planners go through different learning stages and accumulate experiences to be able to undertake the task alone. This study aimed to explore the experiential learning process of older adult learning program planners who work in the Active Ageing Learning Centers (AALCs. Semi-structure interviews were conducted with seven program planners. The findings of this study were identified as follows. 1 Before being a program planner, the participants’ knowledge results from grasping and transforming experience gained from their family, their daily lives and past learning experiences; 2 after being a program planner, the participants’ experiential learning focused on leadership, training in the institute, professional development, as well as involvement in organizations for elderly people; and 3 the participants’ experiential learning outcomes in the older adult learning program planning include: their ability to reflect on the appropriateness and fulfillment of program planning, to apply theoretical knowledge and professional background in the field, and to make plans for future learning and business strategies.

  10. The CanMars Analogue Mission: Lessons Learned for Mars Sample Return

    Science.gov (United States)

    Osinski, G. R.; Beaty, D.; Battler, M.; Caudill, C.; Francis, R.; Haltigin, T.; Hipkin, V.; Pilles, E.

    2018-04-01

    We present an overview and lessons learned for Mars Sample Return from CanMars — an analogue mission that simulated a Mars 2020-like cache mission. Data from 39 sols of operations conducted in the Utah desert in 2015 and 2016 are presented.

  11. Active learning in the presence of unlabelable examples

    Science.gov (United States)

    Mazzoni, Dominic; Wagstaff, Kiri

    2004-01-01

    We propose a new active learning framework where the expert labeler is allowed to decline to label any example. This may be necessary because the true label is unknown or because the example belongs to a class that is not part of the real training problem. We show that within this framework, popular active learning algorithms (such as Simple) may perform worse than random selection because they make so many queries to the unlabelable class. We present a method by which any active learning algorithm can be modified to avoid unlabelable examples by training a second classifier to distinguish between the labelable and unlabelable classes. We also demonstrate the effectiveness of the method on two benchmark data sets and a real-world problem.

  12. Google classroom as a tool for active learning

    Science.gov (United States)

    Shaharanee, Izwan Nizal Mohd; Jamil, Jastini Mohd; Rodzi, Sarah Syamimi Mohamad

    2016-08-01

    As the world is being developed with the new technologies, discovering and manipulating new ideas and concepts of online education are changing rapidly. In response to these changes, many states, institutions, and organizations have been working on strategic plans to implement online education. At the same time, misconceptions and myths related to the difficulty of teaching and learning online, technologies available to support online instruction, the support and compensation needed for high-quality instructors, and the needs of online students create challenges for such vision statements and planning documents. This paper provides analysis and evaluation of the effectiveness of Google Classroom's active learning activities for data mining subject under the Decision Sciences program. Technology Acceptance Model (TAM) has been employed to measure the effectiveness of the learning activities. A total of 100 valid unduplicated responses from students who enrolled data mining subject were used in this study. The results indicated that majority of the students satisfy with the Google Classroom's tool that were introduced in the class. Results of data analyzed showed that all ratios are above averages. In particular, comparative performance is good in the areas of ease of access, perceived usefulness, communication and interaction, instruction delivery and students' satisfaction towards the Google Classroom's active learning activities.

  13. Study on a pattern classification method of soil quality based on simplified learning sample dataset

    Science.gov (United States)

    Zhang, Jiahua; Liu, S.; Hu, Y.; Tian, Y.

    2011-01-01

    Based on the massive soil information in current soil quality grade evaluation, this paper constructed an intelligent classification approach of soil quality grade depending on classical sampling techniques and disordered multiclassification Logistic regression model. As a case study to determine the learning sample capacity under certain confidence level and estimation accuracy, and use c-means algorithm to automatically extract the simplified learning sample dataset from the cultivated soil quality grade evaluation database for the study area, Long chuan county in Guangdong province, a disordered Logistic classifier model was then built and the calculation analysis steps of soil quality grade intelligent classification were given. The result indicated that the soil quality grade can be effectively learned and predicted by the extracted simplified dataset through this method, which changed the traditional method for soil quality grade evaluation. ?? 2011 IEEE.

  14. Learning, Learning Analytics, Activity Visualisation and Open learner Model

    DEFF Research Database (Denmark)

    Bull, Susan; Kickmeier-Rust, Michael; Vatrapu, Ravi

    2013-01-01

    This paper draws on visualisation approaches in learning analytics, considering how classroom visualisations can come together in practice. We suggest an open learner model in situations where many tools and activity visualisations produce more visual information than can be readily interpreted....

  15. Identification of critical timeconsuming student support activities in e-learning

    Directory of Open Access Journals (Sweden)

    Fred J. de Vries

    2005-12-01

    Full Text Available Higher education staff involved in e-learning often struggle with organising their student support activities. To a large extent this is due to the high workload involved with such activities. We distinguish support related to learning content, learning processes and student products. At two different educational institutions, surveys were conducted to identify the most critical support activities, using the Nominal Group Method. The results are discussed and brought to bear on the distinction between content-related, process-related and product-related support activities.

  16. Effectiveness of Student's Note-Taking Activities and Characteristics of Their Learning Performance in Two Types of Online Learning

    Science.gov (United States)

    Nakayama, Minoru; Mutsuura, Kouichi; Yamamoto, Hiroh

    2017-01-01

    Aspects of learning behavior during two types of university courses, a blended learning course and a fully online course, were examined using note-taking activity. The contribution of students' characteristics and styles of learning to note-taking activity and learning performance were analyzed, and the relationships between the two types of…

  17. SNS Sample Activation Calculator Flux Recommendations and Validation

    Energy Technology Data Exchange (ETDEWEB)

    McClanahan, Tucker C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Spallation Neutron Source (SNS); Gallmeier, Franz X. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Spallation Neutron Source (SNS); Iverson, Erik B. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Spallation Neutron Source (SNS); Lu, Wei [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Spallation Neutron Source (SNS)

    2015-02-01

    The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) uses the Sample Activation Calculator (SAC) to calculate the activation of a sample after the sample has been exposed to the neutron beam in one of the SNS beamlines. The SAC webpage takes user inputs (choice of beamline, the mass, composition and area of the sample, irradiation time, decay time, etc.) and calculates the activation for the sample. In recent years, the SAC has been incorporated into the user proposal and sample handling process, and instrument teams and users have noticed discrepancies in the predicted activation of their samples. The Neutronics Analysis Team validated SAC by performing measurements on select beamlines and confirmed the discrepancies seen by the instrument teams and users. The conclusions were that the discrepancies were a result of a combination of faulty neutron flux spectra for the instruments, improper inputs supplied by SAC (1.12), and a mishandling of cross section data in the Sample Activation Program for Easy Use (SAPEU) (1.1.2). This report focuses on the conclusion that the SAPEU (1.1.2) beamline neutron flux spectra have errors and are a significant contributor to the activation discrepancies. The results of the analysis of the SAPEU (1.1.2) flux spectra for all beamlines will be discussed in detail. The recommendations for the implementation of improved neutron flux spectra in SAPEU (1.1.3) are also discussed.

  18. Enhancing students' learning in problem based learning: validation of a self-assessment scale for active learning and critical thinking

    NARCIS (Netherlands)

    Khoiriyah, U.; Roberts, C.; Jorm, C.; Vleuten, C.P. van der

    2015-01-01

    BACKGROUND: Problem based learning (PBL) is a powerful learning activity but fidelity to intended models may slip and student engagement wane, negatively impacting learning processes, and outcomes. One potential solution to solve this degradation is by encouraging self-assessment in the PBL

  19. Building Maintenance. Math Learning Activity Packet.

    Science.gov (United States)

    Grant, Shelia I.

    This collection of learning activities is intended for use in reinforcing mathematics instruction as it relates to building maintenance. Fifty activity sheets are provided. These are organized into units on the following topics: numeration, adding whole numbers, subtracting whole numbers, multiplying whole numbers, dividing whole numbers,…

  20. A Preliminary Investigation of Self-Directed Learning Activities in a Non-Formal Blended Learning Environment

    Science.gov (United States)

    Schwier, Richard A.; Morrison, Dirk; Daniel, Ben K.

    2009-01-01

    This research considers how professional participants in a non-formal self-directed learning environment (NFSDL) made use of self-directed learning activities in a blended face-to-face and on line learning professional development course. The learning environment for the study was a professional development seminar on teaching in higher education…

  1. Dissociation between active and observational learning from positive and negative feedback in Parkinsonism.

    Science.gov (United States)

    Kobza, Stefan; Ferrea, Stefano; Schnitzler, Alfons; Pollok, Bettina; Südmeyer, Martin; Bellebaum, Christian

    2012-01-01

    Feedback to both actively performed and observed behaviour allows adaptation of future actions. Positive feedback leads to increased activity of dopamine neurons in the substantia nigra, whereas dopamine neuron activity is decreased following negative feedback. Dopamine level reduction in unmedicated Parkinson's Disease patients has been shown to lead to a negative learning bias, i.e. enhanced learning from negative feedback. Recent findings suggest that the neural mechanisms of active and observational learning from feedback might differ, with the striatum playing a less prominent role in observational learning. Therefore, it was hypothesized that unmedicated Parkinson's Disease patients would show a negative learning bias only in active but not in observational learning. In a between-group design, 19 Parkinson's Disease patients and 40 healthy controls engaged in either an active or an observational probabilistic feedback-learning task. For both tasks, transfer phases aimed to assess the bias to learn better from positive or negative feedback. As expected, actively learning patients showed a negative learning bias, whereas controls learned better from positive feedback. In contrast, no difference between patients and controls emerged for observational learning, with both groups showing better learning from positive feedback. These findings add to neural models of reinforcement-learning by suggesting that dopamine-modulated input to the striatum plays a minor role in observational learning from feedback. Future research will have to elucidate the specific neural underpinnings of observational learning.

  2. Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

    Science.gov (United States)

    Han, Wenjing; Coutinho, Eduardo; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

    2016-01-01

    Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances. PMID:27627768

  3. Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

    Science.gov (United States)

    Han, Wenjing; Coutinho, Eduardo; Ruan, Huabin; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

    2016-01-01

    Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances.

  4. An Integrative Review of In-Class Activities That Enable Active Learning in College Science Classroom Settings

    Science.gov (United States)

    Arthurs, Leilani A.; Kreager, Bailey Zo

    2017-01-01

    Engaging students in active learning is linked to positive learning outcomes. This study aims to synthesise the peer-reviewed literature about "active learning" in college science classroom settings. Using the methodology of an integrative literature review, 337 articles archived in the Educational Resources Information Center (ERIC) are…

  5. Using Active Learning in a Studio Classroom to Teach Molecular Biology

    Science.gov (United States)

    Nogaj, Luiza A.

    2013-01-01

    This article describes the conversion of a lecture-based molecular biology course into an active learning environment in a studio classroom. Specific assignments and activities are provided as examples. The goal of these activities is to involve students in collaborative learning, teach them how to participate in the learning process, and give…

  6. Active Learning in Introductory Climatology.

    Science.gov (United States)

    Dewey, Kenneth F.; Meyer, Steven J.

    2000-01-01

    Introduces a software package available for the climatology curriculum that determines possible climatic events according to a long-term climate history. Describes the integration of the software into the curriculum and presents examples of active learning. (Contains 19 references.) (YDS)

  7. Minimax bounds for active learning

    NARCIS (Netherlands)

    Castro, R.M.; Nowak, R.; Bshouty, N.H.; Gentile, C.

    2007-01-01

    This paper aims to shed light on achievable limits in active learning. Using minimax analysis techniques, we study the achievable rates of classification error convergence for broad classes of distributions characterized by decision boundary regularity and noise conditions. The results clearly

  8. Learning by Doing: Twenty Successful Active Learning Exercises for Information Systems Courses

    Science.gov (United States)

    Mitchell, Alanah; Petter, Stacie; Harris, Albert L.

    2017-01-01

    Aim/Purpose: This paper provides a review of previously published work related to active learning in information systems (IS) courses. Background: There are a rising number of strategies in higher education that offer promise in regards to getting students' attention and helping them learn, such as flipped classrooms and offering courses online.…

  9. Discussing Active Learning from the Practitioner's Perspective

    Science.gov (United States)

    Bamba, Priscilla

    2015-01-01

    The purpose of this paper is to present an overview of how active learning took place in a class containing specific readings,cooperative and collaborative group work, and a writing assignment for college students at a Northern Virginia Community College campus (NVCC). Requisite knowledge, skills, learner characteristics, brain-based learning, and…

  10. Active learning approach in Moodle for the organization of student’s self-study practice-based learning activities

    Directory of Open Access Journals (Sweden)

    Ivanova Veronica

    2016-01-01

    Full Text Available Nowadays e-learning tools and delivery methods have been constantly expanding. Employs use e-learning to train their employees more often and often. New and experienced employees have the opportunity to improve upon their knowledge base and expand their skill sets. At home, individuals are granted the access to the programs that provided them with the ability to earn online degrees and enrich their lives through the expanded knowledge. The paper focuses on the analysis of the advantages and disadvantages of e- learning. The ways of applying on-line training used by employers are demonstrated. The experience of implementing active methods of e-learning is described as well as the conclusion about the possibility of their application is made. The paper also presents the results of the survey conducted among TPU teacher and students concerning the advisability of e-learning usage.

  11. Understanding Fatty Acid Metabolism through an Active Learning Approach

    Science.gov (United States)

    Fardilha, M.; Schrader, M.; da Cruz e Silva, O. A. B.; da Cruz e Silva, E. F.

    2010-01-01

    A multi-method active learning approach (MALA) was implemented in the Medical Biochemistry teaching unit of the Biomedical Sciences degree at the University of Aveiro, using problem-based learning as the main learning approach. In this type of learning strategy, students are involved beyond the mere exercise of being taught by listening. Less…

  12. Active Learning of Markov Decision Processes for System Verification

    DEFF Research Database (Denmark)

    Chen, Yingke; Nielsen, Thomas Dyhre

    2012-01-01

    deterministic Markov decision processes from data by actively guiding the selection of input actions. The algorithm is empirically analyzed by learning system models of slot machines, and it is demonstrated that the proposed active learning procedure can significantly reduce the amount of data required...... demanding process, and this shortcoming has motivated the development of algorithms for automatically learning system models from observed system behaviors. Recently, algorithms have been proposed for learning Markov decision process representations of reactive systems based on alternating sequences...... of input/output observations. While alleviating the problem of manually constructing a system model, the collection/generation of observed system behaviors can also prove demanding. Consequently we seek to minimize the amount of data required. In this paper we propose an algorithm for learning...

  13. Teaching for Engagement: Part 3: Designing for Active Learning

    Science.gov (United States)

    Hunter, William J.

    2015-01-01

    In the first two parts of this series, ("Teaching for Engagement: Part 1: Constructivist Principles, Case-Based Teaching, and Active Learning") and ("Teaching for Engagement: Part 2: Technology in the Service of Active Learning"), William J. Hunter sought to outline the theoretical rationale and research basis for such active…

  14. An investigation of the impact of selected prereading activities on student content learning through laboratory activities

    Science.gov (United States)

    Kass, Jesse (Shaya)

    This study investigated whether two prereading activities impacted student learning from hands-on science activities. The study was based on constructivist learning theory. Based on the work of Piaget, it was hypothesized that students who activated prior knowledge would learn more from the activities. Based on the work of Vygotsky it was hypothesized that students who talk more and write more would learn more from the activity. The K-W-L chart and anticipation guide strategies were used with eighth grade students at Graves Middle School in Whittier, California before learning about levers and convection currents. D. M. Ogle (1986) created the three-column K-W-L chart to have students activate prior knowledge. In the first column, the students write what they already know about a subject, in the second column, the students write what they want to know about the subject, and the students complete the third column after learning about a subject by writing answers to the questions that they asked in the second column. Duffelmeyer (1994) created the anticipation guide based on Herber's (1978) reasoning guide. In the anticipation guide, the teacher creates three or four sentences that convey the major ideas of the topic and the students either agree or disagree with the statements. After learning about the topic, students revisit their answers and decide if they were correct or incorrect and they must defend their choices. This research used the Solomon (1947) four-square design and compared both the experimental groups to a control group that simply discussed the concepts before completing the activity. The research showed no significant difference between the control group and either of the treatment groups. The reasons for the lack of significant differences are considered. It was hypothesized that since the students were unfamiliar with the prereading activities and did not have much experience with using either writing-to-learn or talking-to-learn strategies, the

  15. A Randomized Crossover Design to Assess Learning Impact and Student Preference for Active and Passive Online Learning Modules.

    Science.gov (United States)

    Prunuske, Amy J; Henn, Lisa; Brearley, Ann M; Prunuske, Jacob

    Medical education increasingly involves online learning experiences to facilitate the standardization of curriculum across time and space. In class, delivering material by lecture is less effective at promoting student learning than engaging students in active learning experience and it is unclear whether this difference also exists online. We sought to evaluate medical student preferences for online lecture or online active learning formats and the impact of format on short- and long-term learning gains. Students participated online in either lecture or constructivist learning activities in a first year neurologic sciences course at a US medical school. In 2012, students selected which format to complete and in 2013, students were randomly assigned in a crossover fashion to the modules. In the first iteration, students strongly preferred the lecture modules and valued being told "what they need to know" rather than figuring it out independently. In the crossover iteration, learning gains and knowledge retention were found to be equivalent regardless of format, and students uniformly demonstrated a strong preference for the lecture format, which also on average took less time to complete. When given a choice for online modules, students prefer passive lecture rather than completing constructivist activities, and in the time-limited environment of medical school, this choice results in similar performance on multiple-choice examinations with less time invested. Instructors need to look more carefully at whether assessments and learning strategies are helping students to obtain self-directed learning skills and to consider strategies to help students learn to value active learning in an online environment.

  16. Adaptive local learning in sampling based motion planning for protein folding.

    Science.gov (United States)

    Ekenna, Chinwe; Thomas, Shawna; Amato, Nancy M

    2016-08-01

    Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods and variants contain several phases (i.e., sampling, connection, and path extraction). Most of the time is spent in the connection phase and selecting which variant to employ is a difficult task. Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes. We develop a local learning algorithm that exploits the past performance of methods within the neighborhood of the current connection attempts as a basis for learning. It is sensitive not only to different types of landscapes but also to differing regions in the landscape itself, removing the need to explicitly partition the landscape. We perform experiments on 23 proteins of varying secondary structure makeup with 52-114 residues. We compare the success rate when using our methods and other methods. We demonstrate a clear need for learning (i.e., only learning methods were able to validate against all available experimental data) and show that local learning is superior to global learning producing, in many cases, significantly higher quality results than the other methods. We present an algorithm that uses local learning to select appropriate connection methods in the context of roadmap construction for protein folding. Our method removes the burden of deciding which method to use, leverages the strengths of the individual input methods, and it is extendable to include other future connection methods.

  17. Teacher Knowledge for Active-Learning Instruction: Expert-Novice Comparison Reveals Differences

    Science.gov (United States)

    Auerbach, A. J.; Higgins, M.; Brickman, P.; Andrews, T. C.

    2018-01-01

    Active-learning strategies "can" improve science, technology, engineering, and mathematics (STEM) undergraduates' abilities to learn fundamental concepts and skills. However, the results instructors achieve vary substantially. One explanation for this is that instructors commonly implement active learning differently than intended. An…

  18. Observing and Understanding an On-Line Learning Activity: A Model-Based Approach for Activity Indicator Engineering

    Science.gov (United States)

    Djouad, Tarek; Mille, Alain

    2018-01-01

    Although learning indicators are now properly studied and published, it is still very difficult to manage them freely within most distance learning platforms. As all activity indicators need to collect and analyze properly traces of the learning activity, we propose to use these traces as a starting point for a platform independent Trace…

  19. The planning illusion: Does active planning of a learning route support learning as well as learners think it does?

    NARCIS (Netherlands)

    Bonestroo, W.J.; de Jong, Anthonius J.M.

    2012-01-01

    Is actively planning one’s learning route through a learning domain beneficial for learning? Moreover, can learners accurately judge the extent to which planning has been beneficial for them? This study examined the effects of active planning on learning. Participants received a tool in which they

  20. Active Learning Strategies for Phenotypic Profiling of High-Content Screens.

    Science.gov (United States)

    Smith, Kevin; Horvath, Peter

    2014-06-01

    High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied. © 2014 Society for Laboratory Automation and Screening.

  1. Is engagement with a purpose the essence of active learning?

    OpenAIRE

    Álvarez Mesa, Mauricio

    2009-01-01

    In the 2009 edition of the conference on “Active Learning in Engineering Education”, there were several and fruitful discussions within a small workgroup about the essence of active learning. At the end we came with an attempt to sum up our whole discussion with one question. Our question is the same as the title of this essay. Taking this question as a starting point this article propose a specific purpose from which active learning can be based. Peer Reviewed

  2. People with Learning Disabilities and "Active Ageing"

    Science.gov (United States)

    Foster, Liam; Boxall, Kathy

    2015-01-01

    Background: People (with and without learning disabilities) are living longer. Demographic ageing creates challenges and the leading policy response to these challenges is "active ageing". "Active" does not just refer to the ability to be physically and economically active, but also includes ongoing social and civic engagement…

  3. Student Motivation from and Resistance to Active Learning Rooted in Essential Science Practices

    Science.gov (United States)

    Owens, David C.; Sadler, Troy D.; Barlow, Angela T.; Smith-Walters, Cindi

    2017-12-01

    Several studies have found active learning to enhance students' motivation and attitudes. Yet, faculty indicate that students resist active learning and censure them on evaluations after incorporating active learning into their instruction, resulting in an apparent paradox. We argue that the disparity in findings across previous studies is the result of variation in the active learning instruction that was implemented. The purpose of this study was to illuminate sources of motivation from and resistance to active learning that resulted from a novel, exemplary active-learning approach rooted in essential science practices and supported by science education literature. This approach was enacted over the course of 4 weeks in eight sections of an introductory undergraduate biology laboratory course. A plant concept inventory, administered to students as a pre-, post-, and delayed-posttest indicated significant proximal and distal learning gains. Qualitative analysis of open-response questionnaires and interviews elucidated sources of motivation and resistance that resulted from this active-learning approach. Several participants indicated this approach enhanced interest, creativity, and motivation to prepare, and resulted in a challenging learning environment that facilitated the sharing of diverse perspectives and the development of a community of learners. Sources of resistance to active learning included participants' unfamiliarity with essential science practices, having to struggle with uncertainty in the absence of authoritative information, and the extra effort required to actively construct knowledge as compared to learning via traditional, teacher-centered instruction. Implications for implementation, including tips for reducing student resistance to active learning, are discussed.

  4. Active controllers and the time duration to learn a task

    Science.gov (United States)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  5. Grooming. Instructor's Packet. Learning Activity Package.

    Science.gov (United States)

    Stark, Pamela

    This instructor's packet accompanies the learning activity package (LAP) on grooming. Contents included in the packet are a time sheet, suggested uses for the LAP, an instruction sheet, final LAP reviews, a final LAP review answer key, suggested activities, an additional resources list, and student completion cards to issue to students as an…

  6. A Bridge to Active Learning: A Summer Bridge Program Helps Students Maximize Their Active-Learning Experiences and the Active-Learning Experiences of Others

    Science.gov (United States)

    Cooper, Katelyn M.; Ashley, Michael; Brownell, Sara E.

    2017-01-01

    National calls to improve student academic success in college have sparked the development of bridge programs designed to help students transition from high school to college. We designed a 2-week Summer Bridge program that taught introductory biology content in an active-learning way. Through a set of exploratory interviews, we unexpectedly…

  7. Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network.

    Directory of Open Access Journals (Sweden)

    Christoph Hartmann

    2015-12-01

    Full Text Available Even in the absence of sensory stimulation the brain is spontaneously active. This background "noise" seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN, which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network's spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network's behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural

  8. Performance in physiology evaluation: possible improvement by active learning strategies.

    Science.gov (United States)

    Montrezor, Luís H

    2016-12-01

    The evaluation process is complex and extremely important in the teaching/learning process. Evaluations are constantly employed in the classroom to assist students in the learning process and to help teachers improve the teaching process. The use of active methodologies encourages students to participate in the learning process, encourages interaction with their peers, and stimulates thinking about physiological mechanisms. This study examined the performance of medical students on physiology over four semesters with and without active engagement methodologies. Four activities were used: a puzzle, a board game, a debate, and a video. The results show that engaging in activities with active methodologies before a physiology cognitive monitoring test significantly improved student performance compared with not performing the activities. We integrate the use of these methodologies with classic lectures, and this integration appears to improve the teaching/learning process in the discipline of physiology and improves the integration of physiology with cardiology and neurology. In addition, students enjoy the activities and perform better on their evaluations when they use them. Copyright © 2016 The American Physiological Society.

  9. High-frequency TRNS reduces BOLD activity during visuomotor learning.

    Directory of Open Access Journals (Sweden)

    Catarina Saiote

    Full Text Available Transcranial direct current stimulation (tDCS and transcranial random noise stimulation (tRNS consist in the application of electrical current of small intensity through the scalp, able to modulate perceptual and motor learning, probably by changing brain excitability. We investigated the effects of these transcranial electrical stimulation techniques in the early and later stages of visuomotor learning, as well as associated brain activity changes using functional magnetic resonance imaging (fMRI. We applied anodal and cathodal tDCS, low-frequency and high-frequency tRNS (lf-tRNS, 0.1-100 Hz; hf-tRNS 101-640 Hz, respectively and sham stimulation over the primary motor cortex (M1 during the first 10 minutes of a visuomotor learning paradigm and measured performance changes for 20 minutes after stimulation ceased. Functional imaging scans were acquired throughout the whole experiment. Cathodal tDCS and hf-tRNS showed a tendency to improve and lf-tRNS to hinder early learning during stimulation, an effect that remained for 20 minutes after cessation of stimulation in the late learning phase. Motor learning-related activity decreased in several regions as reported previously, however, there was no significant modulation of brain activity by tDCS. In opposition to this, hf-tRNS was associated with reduced motor task-related-activity bilaterally in the frontal cortex and precuneous, probably due to interaction with ongoing neuronal oscillations. This result highlights the potential of lf-tRNS and hf-tRNS to differentially modulate visuomotor learning and advances our knowledge on neuroplasticity induction approaches combined with functional imaging methods.

  10. Active learning in optics for girls

    Science.gov (United States)

    Ali, R.; Ashraf, I.

    2017-08-01

    Active learning in Optics (ALO) is a self-funded program under the umbrella of the Abdus Salam International Centre for Theoretical Physics (ICTP) and Quaid-i-Azam University (QAU) to bring physical sciences to traditionally underserved Girls high schools and colleges in Pakistan. There is a significant gender disparity in physical Sciences in Pakistan. In Department of Physics at QAU, approximately 10 to 20% of total students were used to be females from past many decades, but now this percentage is increasing. To keep it up at same pace, we started ALO in January 2016 as a way to provide girls an enriching science experiences, in a very friendly atmosphere. We have organized many one-day activities, to support and encourage girls' students of government high schools and colleges to pursue careers in sciences. In this presentation we will describe our experience and lesson learned in these activities.

  11. Technology transfer and technological learning through CERN's procurement activity

    CERN Document Server

    Autio, Erkko; Hameri, Ari-Pekka; CERN. Geneva

    2003-01-01

    This report analyses the technological learning and innovation benefits derived from CERN's procurement activity during the period 1997-2001. The base population of our study, the technology-intensive suppliers to CERN, consisted of 629 companies out of 6806 companies during the same period, representing 1197 MCHF in procurement. The main findings from the study can be summarized as follows: the various learning and innovation benefits (e.g., technological learning, organizational capability development, market learning) tend to occur together. Learning and innovation benefits appear to be regulated by the quality of the supplier's relationship with CERN: the greater the amount of social capital built into the relationship, the greater the learning and innovation benefits. Regardless of relationship quality, virtually all suppliers derived significant marketing reference benefits from CERN. Many corollary benefits are associated with procurement activity. As an example, as many as 38% of the respondents devel...

  12. Sampling frequency affects ActiGraph activity counts

    DEFF Research Database (Denmark)

    Brønd, Jan Christian; Arvidsson, Daniel

    that is normally performed at frequencies higher than 2.5 Hz. With the ActiGraph model GT3X one has the option to select sample frequency from 30 to 100 Hz. This study investigated the effect of the sampling frequency on the ouput of the bandpass filter.Methods: A synthetic frequency sweep of 0-15 Hz was generated...... in Matlab and sampled at frequencies of 30-100 Hz. Also, acceleration signals during indoor walking and running were sampled at 30 Hz using the ActiGraph GT3X and resampled in Matlab to frequencies of 40-100 Hz. All data was processed with the ActiLife software.Results: Acceleration frequencies between 5......-15 Hz escaped the bandpass filter when sampled at 40, 50, 70, 80 and 100 Hz, while this was not the case when sampled at 30, 60 and 90 Hz. During the ambulatory activities this artifact resultet in different activity count output from the ActiLife software with different sampling frequency...

  13. Active Reading Behaviors in Tablet-Based Learning

    Science.gov (United States)

    Palilonis, Jennifer; Bolchini, Davide

    2015-01-01

    Active reading is fundamental to learning. However, there is little understanding about whether traditional active reading frameworks sufficiently characterize how learners study multimedia tablet textbooks. This paper explores the nature of active reading in the tablet environment through a qualitative study that engaged 30 students in an active…

  14. Generalized query-based active learning to identify differentially methylated regions in DNA.

    Science.gov (United States)

    Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

    2013-01-01

    Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

  15. Quantum Ensemble Classification: A Sampling-Based Learning Control Approach.

    Science.gov (United States)

    Chen, Chunlin; Dong, Daoyi; Qi, Bo; Petersen, Ian R; Rabitz, Herschel

    2017-06-01

    Quantum ensemble classification (QEC) has significant applications in discrimination of atoms (or molecules), separation of isotopes, and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging, since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast QEC as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). First, a new discrimination method is proposed for two similar quantum systems. Then, an SLC method is presented for QEC. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.

  16. Active Learning and Self-Regulation Enhance Student Teachers' Professional Competences

    Science.gov (United States)

    Virtanen, Päivi; Niemi, Hannele M.; Nevgi, Anne

    2017-01-01

    The study identifies the relationships between active learning, student teachers' self-regulated learning and professional competences. Further, the aim is to investigate how active learning promotes professional competences of student teachers with different self-regulation profiles. Responses from 422 student teachers to an electronic survey…

  17. Collaborative learning in higher education : design, implementation and evaluation of group learning activities

    NARCIS (Netherlands)

    Hei, de M.S.A.

    2016-01-01

    In higher education, group learning activities (GLAs) are frequently implemented in online, blended or face-to-face educational contexts. A major problem for the design and implementation of good quality GLAs that lead to the desired learning outcomes is that many approaches to GLAs have been

  18. Does the Room Matter? Active Learning in Traditional and Enhanced Lecture Spaces

    Science.gov (United States)

    Stoltzfus, Jon R.; Libarkin, Julie

    2016-01-01

    SCALE-UP-type classrooms, originating with the Student-Centered Active Learning Environment with Upside-down Pedagogies project, are designed to facilitate active learning by maximizing opportunities for interactions between students and embedding technology in the classroom. Positive impacts when active learning replaces lecture are well…

  19. Point-of-Purchase Advertising. Learning Activity.

    Science.gov (United States)

    Shackelford, Ray

    1998-01-01

    In this technology education activity, students learn the importance of advertising, conduct a day-long survey of advertising strategies, and design and produce a tabletop point-of-purchase advertisement. (JOW)

  20. Neutron activation analysis for antimetabolites. [in food samples

    Science.gov (United States)

    1973-01-01

    Determination of metal ion contaminants in food samples is studied. A weighed quantity of each sample was digested in a concentrated mixture of nitric, hydrochloric and perchloric acids to affect complete solution of the food products. The samples were diluted with water and the pH adjusted according to the specific analysis performed. The samples were analyzed by neutron activation analysis, polarography, and atomic absorption spectrophotometry. The solid food samples were also analyzed by neutron activation analysis for increased sensitivity and lower levels of detectability. The results are presented in tabular form.

  1. The Use of "Socrative" in ESL Classrooms: Towards Active Learning

    Science.gov (United States)

    El Shaban, Abir

    2017-01-01

    The online student response system (SRS) is a technological tool that can be effectively implemented in English language classroom contexts and be used to promote students' active learning. In this qualitative study, "Socrative", a Web 2.0 software, was integrated with active learning activities and used as an SRS to explore English…

  2. Is Active Learning Like Broccoli? Student Perceptions of Active Learning in Large Lecture Classes

    Science.gov (United States)

    Smith, C. Veronica; Cardaciotto, LeeAnn

    2011-01-01

    Although research suggests that active learning is associated with positive outcomes (e.g., memory, test performance), use of such techniques can be difficult to implement in large lecture-based classes. In the current study, 1,091 students completed out-of-class group exercises to complement course material in an Introductory Psychology class.…

  3. Application of active learning modalities to achieve medical genetics competencies and their learning outcome assessments.

    Science.gov (United States)

    Hagiwara, Nobuko

    2017-01-01

    The steadily falling costs of genome sequencing, coupled with the growing number of genetic tests with proven clinical validity, have made the use of genetic testing more common in clinical practice. This development has necessitated nongeneticist physicians, especially primary care physicians, to become more responsible for assessing genetic risks for their patients. Providing undergraduate medical students a solid foundation in genomic medicine, therefore, has become all the more important to ensure the readiness of future physicians in applying genomic medicine to their patient care. In order to further enhance the effectiveness of instructing practical skills in medical genetics, the emphasis of active learning modules in genetics curriculum at medical schools has increased in recent years. This is because of the general acceptance of a better efficacy of active learner-centered pedagogy over passive lecturer-centered pedagogy. However, an objective standard to evaluate students' skill levels in genomic medicine achieved by active learning is currently missing. Recently, entrustable professional activities (EPAs) in genomic medicine have been proposed as a framework for developing physician competencies in genomic medicine. EPAs in genomic medicine provide a convenient guideline for not only developing genomic medicine curriculum but also assessing students' competency levels in practicing genomic medicine. In this review, the efficacy of different types of active learning modules reported for medical genetics curricula is discussed using EPAs in genomic medicine as a common evaluation standard for modules' learning outcomes. The utility of the EPAs in genomic medicine for designing active learning modules in undergraduate medical genetics curricula is also discussed.

  4. Predicting Solar Activity Using Machine-Learning Methods

    Science.gov (United States)

    Bobra, M.

    2017-12-01

    Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

  5. Tying knots: an activity theory analysis of student learning goals in clinical education.

    Science.gov (United States)

    Larsen, Douglas P; Wesevich, Austin; Lichtenfeld, Jana; Artino, Antony R; Brydges, Ryan; Varpio, Lara

    2017-07-01

    Learning goal programmes are often created to help students develop self-regulated learning skills; however, these programmes do not necessarily consider the social contexts surrounding learning goals or how they fit into daily educational practice. We investigated a high-frequency learning goal programme in which students generated and shared weekly learning goals with their clinical teams in core Year 3 clerkships. Our study explores: (i) how learning goals were incorporated into the clinical work, and (ii) the factors that influenced the use of students' learning goals in work-based learning. We conducted semi-structured interviews with 14 students and 14 supervisors (attending physicians and residents) sampled from all participating core clerkships. Interviews were coded for emerging themes. Using cultural historical activity theory and knotworking as theoretical lenses, we developed a model of the factors that influenced students' learning goal usage in a work-based learning context. Students and supervisors often faced the challenge of reconciling contradictions that arose when the desired outcomes of student skill development, grading and patient care were not aligned. Learning goals could function as tools for developing new ways of acting that overcame those contradictions by facilitating collaborative effort between students and their supervisors. However, for new collaborations to take place, both students and supervisors had to engage with the goals, and the necessary patients needed to be present. When any one part of the system did not converge around the learning goals, the impact of the learning goals programme was limited. Learning goals are potentially powerful tools to mediate interactions between students, supervisors and patients, and to reconcile contradictions in work-based learning environments. Learning goals provide a means to develop not only learners, but also learning systems. © 2017 John Wiley & Sons Ltd and The Association for the

  6. Signs of learning in kinaesthetic science activities

    DEFF Research Database (Denmark)

    Bruun, Jesper; Johannsen, Bjørn Friis

    that students use bodily explorations to construct meaning and understanding from kinaesthetic learning that is relevant to school physics? To answer the question, we employ a semiotics perspective to analyse data from a 1-hour lesson for 8-9th graders which introduced students to kinaesthetic activities, where......?”). The analysis is conducted by searching the data to find episodes that illustrate student activity which can serve as a sign of the object that the ‘experiential gestalt of causation’ is employed in the construction of the intended learning outcome. In essence, we study a chaotic but authentic teaching...

  7. Active learning techniques for librarians practical examples

    CERN Document Server

    Walsh, Andrew

    2010-01-01

    A practical work outlining the theory and practice of using active learning techniques in library settings. It explains the theory of active learning and argues for its importance in our teaching and is illustrated using a large number of examples of techniques that can be easily transferred and used in teaching library and information skills to a range of learners within all library sectors. These practical examples recognise that for most of us involved in teaching library and information skills the one off session is the norm, so we need techniques that allow us to quickly grab and hold our

  8. A Qualitative Research on Active Learning Practices in Pre-School Education

    Science.gov (United States)

    Pekdogan, Serpil; Kanak, Mehmet

    2016-01-01

    In educational environments prepared based on the active learning method, children learn with interest and pleasure, doing and experiencing, and directly through their own experiences. Considering the contributions of the active learning method and the educational environments designed based on it to children's development, it can be said that…

  9. Perceptions of Active Learning between Faculty and Undergraduates: Differing Views among Departments

    Science.gov (United States)

    Patrick, Lorelei E.; Howell, Leigh Anne; Wischusen, William

    2016-01-01

    There have been numerous calls recently to increase the use of active learning in university science, technology, engineering, and math (STEM) classrooms to more actively engage students and enhance student learning. However, few studies have investigated faculty and student perceptions regarding the effectiveness of active learning or the…

  10. Active explorers show low learning performance in a social insect

    Institute of Scientific and Technical Information of China (English)

    Eve UDINO; Margot PEREZ; Claudio CARERE; Patrizia d'ETTORRE

    2017-01-01

    An intriguing question in behavioral biology is whether consistent individual differences (called animal personalities) relate to variation in cognitive performance because commonly measured personality traits may be associated with risk-reward trade-offs.Social insects,whose learning abilities have been extensively characterized,show consistent behavioral variability,both at colony and at individual level.We investigated the possible link between personality traits and learning performance in the carpenter ant Camponotus aethiops.Exploratory activity,sociability,and aggression were assessed twice in ant foragers.Behaviors differed among individuals,they were partly repeatable across time and exploratory activity correlated positively with aggression.Learning abilities were quantified by differential conditioning of the maxilla-labium extension response,a task that requires cue perception and information storage.We found that exploratory activity of individual ants significantly predicted learning performance:"active-explorers" were slower in learning the task than "inactive-explorers".The results suggest for the first time a link between a personality trait and cognitive performance in eusocial insects,and that the underlying individual variability could affect colony performance and success.

  11. Lessons learned from surface wipe sampling for lead in three workplaces.

    Science.gov (United States)

    Beaucham, Catherine; Ceballos, Diana; King, Bradley

    2017-08-01

    Surface wipe sampling in the occupational environment is a technique widely used by industrial hygienists. Although several organizations have promulgated standards for sampling lead and other metals, uncertainty still exists when trying to determine an appropriate wipe sampling strategy and how to interpret sampling results. Investigators from the National Institute for Occupational Safety and Health (NIOSH) Health Hazard Evaluation Program have used surface wipe sampling as part of their exposure assessment sampling strategies in a wide range of workplaces. This article discusses wipe sampling for measuring lead on surfaces in three facilities: (1) a battery recycling facility; (2) a firing range and gun store; and (3) an electronic scrap recycling facility. We summarize our findings from the facilities and what we learned by integrating wipe sampling into our sampling plan. Wiping sampling demonstrated lead in non-production surfaces in all three workplaces and that the potential that employees were taking lead home to their families existed. We also found that the presence of metals such as tin can interfere with the colorimetric results. We also discuss the advantages and disadvantages of colorimetric analysis of surface wipe samples and the challenges we faced when interpreting wipe sampling results.

  12. Engaging colleagues in active learning pedagogies through mentoring and co-design

    Science.gov (United States)

    Adams, Rhys; Lenton, Kevin

    2017-08-01

    When implemented correctly, active learning pedagogies increase student engagement with discipline content. In addition, there is accumulating evidence that they also positively impact the learning of this content. This is particularly relevant for teaching science disciplines because many students perceive science as being difficult to fully understand. However, an ongoing problem is that instructors have difficulty implementing active learning pedagogies effectively and therefore see no benefit to it. Without persistence or guidance, instructors can become discouraged and return to a more traditional style of teaching. We report on how the Faculty of Science at Vanier College is getting more instructors to engage in active learning pedagogies through mentoring and activity co-design.

  13. Applying Active Learning at the Graduate Level: Merger Issues at Newco.

    Science.gov (United States)

    Berger, Bruce K.

    2002-01-01

    Suggests that active learning can benefit students in public relations and integrated communication courses at the graduate level. Describes how three active learning approaches--research and field work, student accountabilities for learning, and student reflection and reflexive exercises--were used in a graduate class project to help a Fortune 50…

  14. The Impact of Peer Review on Creative Self-Efficacy and Learning Performance in Web 2.0 Learning Activities

    Science.gov (United States)

    Liu, Chen-Chung; Lu, Kuan-Hsien; Wu, Leon Yufeng; Tsai, Chin-Chung

    2016-01-01

    Many studies have pointed out the significant contrast between the creative nature of Web 2.0 learning activities and the structured learning in school. This study proposes an approach to leveraging Web 2.0 learning activities and classroom teaching to help students develop both specific knowledge and creativity based on Csikzentmihalyi's system…

  15. "Heart Shots": a classroom activity to instigate active learning.

    Science.gov (United States)

    Abraham, Reem Rachel; Vashe, Asha; Torke, Sharmila

    2015-09-01

    The present study aimed to provide undergraduate medical students at Melaka Manipal Medical College (Manipal Campus), Manipal University, in Karnataka, India, an opportunity to apply their knowledge in cardiovascular concepts to real-life situations. A group activity named "Heart Shots" was implemented for a batch of first-year undergraduate students (n = 105) at the end of a block (teaching unit). Students were divided into 10 groups each having 10-11 students. They were requested to make a video/PowerPoint presentation about the application of cardiovascular principles to real-life situations. The presentation was required to be of only pictures/photos and no text material, with a maximum duration of 7 min. More than 95% of students considered that the activity helped them to apply their knowledge in cardiovascular concepts to real-life situations and understand the relevance of physiology in medicine and to revise the topic. More than 90% of students agreed that the activity helped them to apply their creativity in improving their knowledge and to establish a link between concepts rather than learning them as isolated facts. Based on the feedback, we conclude that the activity was student centered and that it facilitated learning. Copyright © 2015 The American Physiological Society.

  16. Measurements of {sup 55}Fe activity in activated steel samples with GEMPix

    Energy Technology Data Exchange (ETDEWEB)

    Curioni, A. [CERN, 1211 Geneva 23 (Switzerland); Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano (Italy); Dinar, N. [CERN, 1211 Geneva 23 (Switzerland); Université de Paris VII, 5 rue Thomas-Mann, 75013 Paris (France); La Torre, F.P. [CERN, 1211 Geneva 23 (Switzerland); Leidner, J., E-mail: johannes.leidner@cern.ch [CERN, 1211 Geneva 23 (Switzerland); RWTH Aachen, Templergraben 55, 52056 Aachen (Germany); Murtas, F. [CERN, 1211 Geneva 23 (Switzerland); INFN-LNF, Via E. Fermi 40, 00044 Frascati (Italy); Puddu, S.; Silari, M. [CERN, 1211 Geneva 23 (Switzerland)

    2017-03-21

    In this paper we present a novel method, based on the recently developed GEMPix detector, to measure the {sup 55}Fe content in samples of metallic material activated during operation of CERN accelerators and experimental facilities. The GEMPix, a gas detector with highly pixelated read-out, has been obtained by coupling a triple Gas Electron Multiplier (GEM) to a quad Timepix ASIC. Sample preparation, measurements performed on 45 samples and data analysis are described. The calibration factor (counts per second per unit specific activity) has been obtained via measurements of the {sup 55}Fe activity determined by radiochemical analysis of the same samples. Detection limit and sensitivity to the current Swiss exemption limit are calculated. Comparison with radiochemical analysis shows inconsistency for the sensitivity for only two samples, most likely due to underestimated uncertainties of the GEMPix analysis. An operative test phase of this technique is already planned at CERN.

  17. Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning

    DEFF Research Database (Denmark)

    Franz, Thomas; Zimmermann, Ralf; Goertz, Stefan

    2017-01-01

    We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approxi...... to detect and fill up gaps in the sampling in the embedding space. The performance of the proposed manifold filling method will be illustrated by numerical experiments, where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime.......We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space...

  18. Learning Activities in a Sociable Smart City

    Directory of Open Access Journals (Sweden)

    Dimitrios Ringas

    2013-08-01

    Full Text Available We present our approach on how smart city technologies may enhance the learning process. We have developed the CLIO urban computing system, which invites people to share personal memories and interact the collective city memory. Various educational scenarios and activities were performed exploiting CLIO; in this paper we present the methodology we followed and the experience we gained. Learning has always been the cognitive process of acquiring skills or knowledge, while teachers are often eager to experiment with novel technological means and methods; our aim was to explore the effect that urban computing could have to the learning process. We applied our methodology in the city of Corfu inviting schools to engage their students in learning through the collective city memory while exploiting urban computing. Results from our experience demonstrate the potential of exploiting urban computing in the learning process and the benefits of learning out of the classroom.

  19. User-driven sampling strategies in image exploitation

    Science.gov (United States)

    Harvey, Neal; Porter, Reid

    2013-12-01

    Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. In preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

  20. Learning Environments’ Activity Potential for Preschoolers (LEAPP): Study Rationale and Design

    OpenAIRE

    Tucker, Patricia; Vanderloo, Leigh M.; Newnham-Kanas, Courtney; Burke, Shauna M.; Irwin, Jennifer D.; Johnson, Andrew M.; van Zandvoort, Melissa M.

    2013-01-01

    Background The purpose of this paper is to provide an overview of the study protocol for the Learning Environments’ Activity Potential for Preschoolers (LEAPP) study, the goal of which is to describe the activity levels of preschoolers attending various early learning venues and explore which attributes of these facilities (e.g. curriculum, policies, equipment, etc.) support activity participation. Design and methods This cross-sectional study aimed to recruit approximately 30 early learning ...

  1. Active learning approach for detection of hard exudates, cotton wool spots, and drusen in retinal images

    Science.gov (United States)

    Sánchez, Clara I.; Niemeijer, Meindert; Kockelkorn, Thessa; Abràmoff, Michael D.; van Ginneken, Bram

    2009-02-01

    Computer-aided Diagnosis (CAD) systems for the automatic identification of abnormalities in retinal images are gaining importance in diabetic retinopathy screening programs. A huge amount of retinal images are collected during these programs and they provide a starting point for the design of machine learning algorithms. However, manual annotations of retinal images are scarce and expensive to obtain. This paper proposes a dynamic CAD system based on active learning for the automatic identification of hard exudates, cotton wool spots and drusen in retinal images. An uncertainty sampling method is applied to select samples that need to be labeled by an expert from an unlabeled set of 4000 retinal images. It reduces the number of training samples needed to obtain an optimum accuracy by dynamically selecting the most informative samples. Results show that the proposed method increases the classification accuracy compared to alternative techniques, achieving an area under the ROC curve of 0.87, 0.82 and 0.78 for the detection of hard exudates, cotton wool spots and drusen, respectively.

  2. Machine learning-enabled discovery and design of membrane-active peptides.

    Science.gov (United States)

    Lee, Ernest Y; Wong, Gerard C L; Ferguson, Andrew L

    2017-07-08

    Antimicrobial peptides are a class of membrane-active peptides that form a critical component of innate host immunity and possess a diversity of sequence and structure. Machine learning approaches have been profitably employed to efficiently screen sequence space and guide experiment towards promising candidates with high putative activity. In this mini-review, we provide an introduction to antimicrobial peptides and summarize recent advances in machine learning-enabled antimicrobial peptide discovery and design with a focus on a recent work Lee et al. Proc. Natl. Acad. Sci. USA 2016;113(48):13588-13593. This study reports the development of a support vector machine classifier to aid in the design of membrane active peptides. We use this model to discover membrane activity as a multiplexed function in diverse peptide families and provide interpretable understanding of the physicochemical properties and mechanisms governing membrane activity. Experimental validation of the classifier reveals it to have learned membrane activity as a unifying signature of antimicrobial peptides with diverse modes of action. Some of the discriminating rules by which it performs classification are in line with existing "human learned" understanding, but it also unveils new previously unknown determinants and multidimensional couplings governing membrane activity. Integrating machine learning with targeted experimentation can guide both antimicrobial peptide discovery and design and new understanding of the properties and mechanisms underpinning their modes of action. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Inside Out: Active learning in fluid dynamics in and out of the classroom

    Science.gov (United States)

    Kaye, Nigel; Benson, Lisa; Sill, Ben

    2014-11-01

    Active learning can be broadly defined as any activity that engages students beyond just listening. But is it worth the effort, when we can just lecture and tell students all they need to know? Learning theories posit that students remember far more of what they say and do than of what they hear and see. The benefits of active learning include increased attendance (because class is now something different and attending is more worthwhile) and deeper understanding of concepts (because students get to practice answering and generating questions). A recent meta-analysis of research on active learning has summarized evidence of real outcomes of active learning. Research is showing that students' performance on exams are higher and that they fail at lower rates in classes that involve active learning compared to traditional lecturing. Other studies have shown evidence of improved performance in follow-on classes, showing that the improved learning lasts. There are some topics and concepts that are best taught (or at least introduced) through lecturing, but even lecturing can be broken up by short activities that engage students so they learn more effectively. In this presentation, we will review the findings of the meta study and provide examples of active learning both inside and outside the classroom that demonstrate simple ways of introducing this approach in fluid dynamics classes.

  4. From feedback- to response-based performance monitoring in active and observational learning.

    Science.gov (United States)

    Bellebaum, Christian; Colosio, Marco

    2014-09-01

    Humans can adapt their behavior by learning from the consequences of their own actions or by observing others. Gradual active learning of action-outcome contingencies is accompanied by a shift from feedback- to response-based performance monitoring. This shift is reflected by complementary learning-related changes of two ACC-driven ERP components, the feedback-related negativity (FRN) and the error-related negativity (ERN), which have both been suggested to signal events "worse than expected," that is, a negative prediction error. Although recent research has identified comparable components for observed behavior and outcomes (observational ERN and FRN), it is as yet unknown, whether these components are similarly modulated by prediction errors and thus also reflect behavioral adaptation. In this study, two groups of 15 participants learned action-outcome contingencies either actively or by observation. In active learners, FRN amplitude for negative feedback decreased and ERN amplitude in response to erroneous actions increased with learning, whereas observational ERN and FRN in observational learners did not exhibit learning-related changes. Learning performance, assessed in test trials without feedback, was comparable between groups, as was the ERN following actively performed errors during test trials. In summary, the results show that action-outcome associations can be learned similarly well actively and by observation. The mechanisms involved appear to differ, with the FRN in active learning reflecting the integration of information about own actions and the accompanying outcomes.

  5. The role of picture of process (pp) on senior high school students’ collision concept learning activities and multirepresentation ability

    Science.gov (United States)

    Sutarto; Indrawati; Wicaksono, I.

    2018-04-01

    The objectives of the study are to describe the effect of PP collision concepts to high school students’ learning activities and multirepresentation abilities. This study was a quasi experimental with non- equivalent post-test only control group design. The population of this study were students who will learn the concept of collision in three state Senior High Schools in Indonesia, with a sample of each school 70 students, 35 students as an experimental group and 35 students as a control group. Technique of data collection were observation and test. The data were analized by descriptive and inferensial statistic. Student learning activities were: group discussions, describing vectors of collision events, and formulating problem-related issues of impact. Multirepresentation capabilities were student ability on image representation, verbal, mathematics, and graph. The results showed that the learning activities in the three aspects for the three high school average categorized good. The impact of using PP on students’ ability on image and graph representation were a significant impact, but for verbal and mathematical skills there are differences but not significant.

  6. Research and Teaching: Instructor Use of Group Active Learning in an Introductory Biology Sequence

    Science.gov (United States)

    Auerbach, Anna Jo; Schussler, Elisabeth E.

    2016-01-01

    Active learning (or learner-centered) pedagogies have been shown to enhance student learning in introductory biology courses. Student collaboration has also been shown to enhance student learning and may be a critical part of effective active learning practices. This study focused on documenting the use of individual active learning and group…

  7. Active Learning Classrooms and Educational Alliances: Changing Relationships to Improve Learning

    Science.gov (United States)

    Baepler, Paul; Walker, J. D.

    2014-01-01

    This chapter explores the "educational alliance" among students and between students and instructors. We contend that this is a framework that can help us understand how active learning classrooms facilitate positive educational outcomes.

  8. ASPECT: A Survey to Assess Student Perspective of Engagement in an Active-Learning Classroom

    Science.gov (United States)

    Wiggins, Benjamin L.; Eddy, Sarah L.; Wener-Fligner, Leah; Freisem, Karen; Grunspan, Daniel Z.; Theobald, Elli J.; Timbrook, Jerry; Crowe, Alison J.

    2017-01-01

    The primary measure used to determine relative effectiveness of in-class activities has been student performance on pre/posttests. However, in today's active-learning classrooms, learning is a social activity, requiring students to interact and learn from their peers. To develop effective active-learning exercises that engage students, it is…

  9. Active Learning in ASTR 101 Lectures

    Science.gov (United States)

    Deming, Grace L.

    1998-12-01

    The lecture is the most common teaching method used at colleges and universities, but does this format facilitate student learning? Lectures can be brilliantly delivered, but they are received by a passive audience. As time passes during a lecture, student attention and effective notetaking diminish. Many students become more interested in a subject and retain information longer in courses that rely on active rather than passive teaching methods. Interactive teaching strategies such as the think-pair-share-(write), the 3-minute paper, and the misconception confrontation can be used to actively engage students during lecture. As a cooperative learning strategy, the think-pair-share-(write) technique requires active discussion by everyone in the class. The "write" component structures individual accountability into the activity. The 3-minute paper is an expansion of the standard 1-minute paper feedback technique, but is required of all students rather than voluntary or anonymous. The misconception confrontation technique allows students to focus on how their pre- conceived notions differ from the scientific explanation. These techniques can be easily adopted by anyone currently using a standard lecture format for introductory astronomy. The necessary components are a commitment by the instructor to require active participation by all students and a willingness to try new teaching methods.

  10. Research-based active-learning instruction in physics

    Science.gov (United States)

    Meltzer, David E.; Thornton, Ronald K.

    2013-04-01

    The development of research-based active-learning instructional methods in physics has significantly altered the landscape of U.S. physics education during the past 20 years. Based on a recent review [D.E. Meltzer and R.K. Thornton, Am. J. Phys. 80, 478 (2012)], we define these methods as those (1) explicitly based on research in the learning and teaching of physics, (2) that incorporate classroom and/or laboratory activities that require students to express their thinking through speaking, writing, or other actions that go beyond listening and the copying of notes, or execution of prescribed procedures, and (3) that have been tested repeatedly in actual classroom settings and have yielded objective evidence of improved student learning. We describe some key features common to methods in current use. These features focus on (a) recognizing and addressing students' physics ideas, and (b) guiding students to solve problems in realistic physical settings, in novel and diverse contexts, and to justify or explain the reasoning they have used.

  11. An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

    Science.gov (United States)

    Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

    2015-01-01

    Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

  12. Supporting intra-group social metacognitive activities with technology: A grammar learning game

    NARCIS (Netherlands)

    Molenaar, I.; Horvers, A.; Desain, P.W.M.

    2017-01-01

    This study investigates the effects of a technology enhanced collaborative grammar learning activity on students sentence parsing and formulation. These types of collaborative learning activities for grammar education are expected to support more effective learning. Yet, effective intra-group social

  13. A Nap But Not Rest or Activity Consolidates Language Learning

    Directory of Open Access Journals (Sweden)

    Stefan Heim

    2017-05-01

    Full Text Available Recent evidence suggests that a period of sleep after a motor learning task is a relevant factor for memory consolidation. However, it is yet open whether this also holds true for language-related learning. Therefore, the present study compared the short- and long-term effects of a daytime nap, rest, or an activity task after vocabulary learning on learning outcome. Thirty healthy subjects were divided into three treatment groups. Each group received a pseudo-word learning task in which pictures of monsters were associated with unique pseudo-word names. At the end of the learning block a first test was administered. Then, one group went for a 90-min nap, one for a waking rest period, and one for a resting session with interfering activity at the end during which a new set of monster names was to be learned. After this block, all groups performed a first re-test of the names that they initially learned. On the morning of the following day, a second re-test was administered to all groups. The nap group showed significant improvement from test to re-test and a stable performance onto the second re-test. In contrast, the rest and the interference groups showed decline in performance from test to re-test, with persistently low performance at re-test 2. The 3 (GROUP × 3 (TIME ANOVA revealed a significant interaction, indicating that the type of activity (nap/rest/interfering action after initial learning actually had an influence on the memory outcome. These data are discussed with respect to translation to clinical settings with suggestions for improvement of intervention outcome after speech-language therapy if it is followed by a nap rather than interfering activity.

  14. Iranian Clinical Nurses' Activities for Self-Directed Learning: A Qualitative Study.

    Science.gov (United States)

    Ghiyasvandian, Shahrzad; Malekian, Morteza; Cheraghi, Mohammad Ali

    2015-09-01

    Clinical nurses need lifelong learning skills for responding to the rapid changes of clinical settings. One of the best strategies for lifelong learning is self-directed learning. The aim of this study was to explore Iranian clinical nurses' activities for self-directed learning. In this qualitative study, 23 semi-structured personal interviews were conducted with nineteen clinical nurses working in all four hospitals affiliated to Isfahan Social Security Organization, Isfahan, Iran. Study data were analyzed by using the content analysis approach. The study was conducted from June 2013 to October 2014. Study participants' activities for self-directed learning fell into two main categories of striving for knowledge acquisition and striving for skill development. The main theme of the study was 'Revising personal performance based on intellectual-experiential activities'. Study findings suggest that Iranian clinical nurses continually revise their personal performance by performing self-directed intellectual and experiential activities to acquire expertise. The process of acquiring expertise is a linear process which includes two key steps of knowledge acquisition and knowledge development. In order to acquire and advance their knowledge, nurses perform mental learning activities such as sensory perception, self-evaluation, and suspended judgment step-by-step. Moreover, they develop their skills through doing activities like apprenticeship, masterly performance, and self-regulation. The absolute prerequisite to expertise acquisition is that a nurse needs to follow these two steps in a sequential manner.

  15. Embedding responses in spontaneous neural activity shaped through sequential learning.

    Directory of Open Access Journals (Sweden)

    Tomoki Kurikawa

    Full Text Available Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in

  16. Learning with peers, active citizenship and student engagement in Enabling Education

    Directory of Open Access Journals (Sweden)

    Nick Zepke

    2018-02-01

    Full Text Available This paper examines one specific question:  What support do students in Enabling Education need to learn the behaviours, knowledge and attitudes required to succeed in tertiary education, employment and life? Success appears in many guises. It can mean achieving officially desired outcomes such as retention, completion and employment. It can also mean achieving less measurable outcomes such as deep learning, wellbeing and active citizenship. The paper first introduces an overarching success framework before exploring how the widely used student engagement pedagogy can support learners to achieve both official and personal success outcomes. It then develops two specific constructs applicable to Enabling Education as found in student engagement: facilitated peer learning and active citizenship. Peer learning is here connected to tutor supported but peer facilitated mentoring; active citizenship to educational experiences in classrooms, institutions and workplaces that support flexibility, resilience, openness to change and diversity. The paper includes examples of how facilitated peer learning and active citizenship can build success in practice.

  17. Developing design-based STEM education learning activities to enhance students' creative thinking

    Science.gov (United States)

    Pinasa, Siwa; Siripun, Kulpatsorn; Yuenyong, Chokchai

    2018-01-01

    Creative thinking on applying science and mathematics knowledge is required by the future STEM career. The STEM education should be provided for the required skills of future STEM career. This paper aimed to clarify the developing STEM education learning activities to enhance students' creative thinking. The learning activities were developed for Grade 10 students who will study in the subject of independent study (IS) of Khon Kaen Wittayayon School, Khon Kaen, Thailand. The developing STEM education learning activities for enhancing students' creative thinking was developed regarding on 6 steps including (1) providing of understanding of fundamental STEM education concept, (2) generating creative thinking from prototype, (4) revised ideas, (5) engineering ability, and (6) presentation and discussion. The paper will clarify the 18 weeks activities that will be provided based these 6 steps of developing learning activities. Then, these STEM learning activities will be discussed to provide the chance of enhancing students' creative thinking. The paper may have implication for STEM education in school setting.

  18. Oral Hygiene. Learning Activity Package.

    Science.gov (United States)

    Hime, Kirsten

    This learning activity package on oral hygiene is one of a series of 12 titles developed for use in health occupations education programs. Materials in the package include objectives, a list of materials needed, a list of definitions, information sheets, reviews (self evaluations) of portions of the content, and answers to reviews. These topics…

  19. Is it better to select or to receive? Learning via active and passive hypothesis testing.

    Science.gov (United States)

    Markant, Douglas B; Gureckis, Todd M

    2014-02-01

    People can test hypotheses through either selection or reception. In a selection task, the learner actively chooses observations to test his or her beliefs, whereas in reception tasks data are passively encountered. People routinely use both forms of testing in everyday life, but the critical psychological differences between selection and reception learning remain poorly understood. One hypothesis is that selection learning improves learning performance by enhancing generic cognitive processes related to motivation, attention, and engagement. Alternatively, we suggest that differences between these 2 learning modes derives from a hypothesis-dependent sampling bias that is introduced when a person collects data to test his or her own individual hypothesis. Drawing on influential models of sequential hypothesis-testing behavior, we show that such a bias (a) can lead to the collection of data that facilitates learning compared with reception learning and (b) can be more effective than observing the selections of another person. We then report a novel experiment based on a popular category learning paradigm that compares reception and selection learning. We additionally compare selection learners to a set of "yoked" participants who viewed the exact same sequence of observations under reception conditions. The results revealed systematic differences in performance that depended on the learner's role in collecting information and the abstract structure of the problem.

  20. Mobile Collaborative Informal Learning Design: Study of collaborative effectiveness using Activity Theory

    Directory of Open Access Journals (Sweden)

    Hasnain Zafar Baloch

    2012-07-01

    Full Text Available Smart Mobile Devices (SMD are there for many years but using them as learning tools started to emerge as new research area. The trend to merge collaborative learning methodology by using mobile devices in informal context is important for implementation of Learner Centric Learning (LCL. Survey and numerous studies show that more than 95% of students in colleges are users of these smart mobile devices in developed world. Developing counties are also catching up and we can see this percentage is almost same in university level in these countries. Students are using SMDs for learning in some form. Higher education Institutions also try to embark their E-learning to Mobile learning (ML. The aim of this paper is to do propose operational framework for designing Mobile Collaborative Informal learning activities using SMDs. Show results of experimental and case study done to study the Mobile Collaborative Informal learning using Activity Theory (AT. Core Components of framework are Mobile Learning Activities/Objects, Wireless/Mobile Smart devices, Collaborative knowledge and Collaborative learning. The research mention here is its infancy stage.

  1. When I grow up: the relationship of science learning activation to STEM career preferences

    Science.gov (United States)

    Dorph, Rena; Bathgate, Meghan E.; Schunn, Christian D.; Cannady, Matthew A.

    2018-06-01

    This paper proposes three new measures of components STEM career preferences (affinity, certainty, and goal), and then explores which dimensions of science learning activation (fascination, values, competency belief, and scientific sensemaking) are predictive of STEM career preferences. Drawn from the ALES14 dataset, a sample of 2938 sixth and eighth grade middle-school students from 11 schools in two purposefully selected diverse areas (Western Pennsylvania & the Bay Area of California) was used for the analyses presented in this paper. These schools were chosen to represent socio-economic and ethnic diversity. Findings indicate that, overall, youth who are activated towards science learning are more likely to have affinity towards STEM careers, certainty about their future career goals, and have identified a specific STEM career goal. However, different dimensions of science learning activation are more strongly correlated with different aspects career preference across different STEM career foci (e.g. science, engineering, technology, health, etc.). Gender, age, minority status, and home resources also have explanatory power. While many results are consistent with prior research, there are also novel results that offer important fodder for future research. Critically, our strategy of measuring affinity towards the specific disciplines that make up STEM, measuring STEM and health career goals separately, and looking at career affinity and career goals separately, offers interesting results and underscores the value of disentangling the conceptual melting pot of what has previously been known as 'career interest.' Study findings also have implications for design of science learning opportunities for youth.

  2. Oral Hygiene. Instructor's Packet. Learning Activity Package.

    Science.gov (United States)

    Hime, Kirsten

    This instructor's packet accompanies the learning activity package (LAP) on oral hygiene. Contents included in the packet are a time sheet, suggested uses for the LAP, an instruction sheet, final LAP reviews, a final LAP review answer key, suggested activities, additional resources (student handouts), student performance checklists for both…

  3. Engaging Students in Large Health Classes with Active Learning Strategies

    Science.gov (United States)

    Elliott, Steven; Combs, Sue; Huelskamp, Amelia; Hritz, Nancy

    2017-01-01

    Creative K-12 health teachers can engage students in large classes by utilizing active learning strategies. Active learning involves engaging students in higher-order tasks, such as analysis and synthesis, which is a crucial element of the movement toward what is commonly called "learner-centered" teaching. Health education teachers who…

  4. Implementation of Active Learning Method in Unit Operations II Subject

    OpenAIRE

    Ma'mun, Sholeh

    2018-01-01

    ABSTRACT: Active Learning Method which requires students to take an active role in the process of learning in the classroom has been applied in Department of Chemical Engineering, Faculty of Industrial Technology, Islamic University of Indonesia for Unit Operations II subject in the Even Semester of Academic Year 2015/2016. The purpose of implementation of the learning method is to assist students in achieving competencies associated with the Unit Operations II subject and to help in creating...

  5. Improving the interpersonal competences of head nurses through Peplau's theoretical active learning approach.

    Science.gov (United States)

    Suhariyanto; Hariyati, Rr Tutik Sri; Ungsianik, Titin

    2018-02-01

    Effective interpersonal skills are essential for head nurses in governing and managing their work units. Therefore, an active learning strategy could be the key to enhance the interpersonal competences of head nurses. This study aimed to investigate the effects of Peplau's theoretical approach of active learning on the improvement of head nurses' interpersonal skills. This study used a pre-experimental design with one group having pretests and posttests, without control group. A total sample of 25 head nurses from inpatient units of a wellknown private hospital in Jakarta was involved in the study. Data were analyzed using the paired t-test. The results showed a significant increase in head nurses' knowledge following the training to strengthen their interpersonal roles (P=.003). The results also revealed significant increases in the head nurses' skills in playing the roles of leader (P=.006), guardian (P=.014), and teacher/speaker (P=.015). Nonetheless, the results showed no significant increases in the head nurses' skills in playing the roles of counselor (P=.092) and stranger (P=.182). Training in strengthening the interpersonal roles of head nurses significantly increased the head nurses' knowledge and skills. The results of the study suggested the continuation of active learning strategies to improve the interpersonal abilities of head nurses. Furthermore, these strategies could be used to build the abilities of head nurses in other managerial fields. Copyright © 2018 Elsevier España, S.L.U. All rights reserved.

  6. Investigating the Relationship between Instructors' Use of Active-Learning Strategies and Students' Conceptual Understanding and Affective Changes in Introductory Biology: A Comparison of Two Active-Learning Environments.

    Science.gov (United States)

    Cleveland, Lacy M; Olimpo, Jeffrey T; DeChenne-Peters, Sue Ellen

    2017-01-01

    In response to calls for reform in undergraduate biology education, we conducted research examining how varying active-learning strategies impacted students' conceptual understanding, attitudes, and motivation in two sections of a large-lecture introductory cell and molecular biology course. Using a quasi-experimental design, we collected quantitative data to compare participants' conceptual understanding, attitudes, and motivation in the biological sciences across two contexts that employed different active-learning strategies and that were facilitated by unique instructors. Students participated in either graphic organizer/worksheet activities or clicker-based case studies. After controlling for demographic and presemester affective differences, we found that students in both active-learning environments displayed similar and significant learning gains. In terms of attitudinal and motivational data, significant differences were observed for two attitudinal measures. Specifically, those students who had participated in graphic organizer/worksheet activities demonstrated more expert-like attitudes related to their enjoyment of biology and ability to make real-world connections. However, all motivational and most attitudinal data were not significantly different between the students in the two learning environments. These data reinforce the notion that active learning is associated with conceptual change and suggests that more research is needed to examine the differential effects of varying active-learning strategies on students' attitudes and motivation in the domain. © 2017 L. M. Cleveland et al. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  7. Collaborative activities for improving the quality of science teaching and learning and learning to teach science

    Science.gov (United States)

    Tobin, Kenneth

    2012-03-01

    I have been involved in research on collaborative activities for improving the quality of teaching and learning high school science. Initially the collaborative activities we researched involved the uses of coteaching and cogenerative dialogue in urban middle and high schools in Philadelphia and New York (currently I have active research sites in New York and Brisbane, Australia). The research not only transformed practices but also produced theories that informed the development of additional collaborative activities and served as interventions for research and creation of heuristics for professional development programs and teacher certification courses. The presentation describes a collage of collaborative approaches to teaching and learning science, including coteaching, cogenerative dialogue, radical listening, critical reflection, and mindful action. For each activity in the collage I provide theoretical frameworks and empirical support, ongoing research, and priorities for the road ahead. I also address methodologies used in the research, illustrating how teachers and students collaborated as researchers in multilevel investigations of teaching and learning and learning to teach that included ethnography, video analysis, and sophisticated analyses of the voice, facial expression of emotion, eye gaze, and movement of the body during classroom interactions. I trace the evolution of studies of face-to-face interactions in science classes to the current focus on emotions and physiological aspects of teaching and learning (e.g., pulse rate, pulse strength, breathing patterns) that relate to science participation and achievement.

  8. Human medial frontal cortex activity predicts learning from errors.

    Science.gov (United States)

    Hester, Robert; Barre, Natalie; Murphy, Kevin; Silk, Tim J; Mattingley, Jason B

    2008-08-01

    Learning from errors is a critical feature of human cognition. It underlies our ability to adapt to changing environmental demands and to tune behavior for optimal performance. The posterior medial frontal cortex (pMFC) has been implicated in the evaluation of errors to control behavior, although it has not previously been shown that activity in this region predicts learning from errors. Using functional magnetic resonance imaging, we examined activity in the pMFC during an associative learning task in which participants had to recall the spatial locations of 2-digit targets and were provided with immediate feedback regarding accuracy. Activity within the pMFC was significantly greater for errors that were subsequently corrected than for errors that were repeated. Moreover, pMFC activity during recall errors predicted future responses (correct vs. incorrect), despite a sizeable interval (on average 70 s) between an error and the next presentation of the same recall probe. Activity within the hippocampus also predicted future performance and correlated with error-feedback-related pMFC activity. A relationship between performance expectations and pMFC activity, in the absence of differing reinforcement value for errors, is consistent with the idea that error-related pMFC activity reflects the extent to which an outcome is "worse than expected."

  9. Developing a Mobile Learning Management System for Outdoors Nature Science Activities Based on 5E Learning Cycle

    Science.gov (United States)

    Lai, Ah-Fur; Lai, Horng-Yih; Chuang, Wei-Hsiang; Wu, Zih-Heng

    2015-01-01

    Traditional outdoor learning activities such as inquiry-based learning in nature science encounter many dilemmas. Due to prompt development of mobile computing and widespread of mobile devices, mobile learning becomes a big trend on education. The main purpose of this study is to develop a mobile-learning management system for overcoming the…

  10. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

  11. Experienced teachers' informal workplace learning and perceptions of workplace conditions

    NARCIS (Netherlands)

    Hoekstra, A.; Korthagen, F.; Brekelmans, M.; Beijaard, D.; Imants, J.

    2009-01-01

    Purpose: The purpose of this paper is to explore in detail how teachers' perceptions of workplace conditions for learning are related to their informal workplace learning activities and learning outcomes. Design/methodology/approach: From a sample of 32 teachers, a purposeful sampling technique of

  12. Variation in behavioral engagement during an active learning activity leads to differential knowledge gains in college students.

    Science.gov (United States)

    LaDage, Lara D; Tornello, Samantha L; Vallejera, Jennilyn M; Baker, Emily E; Yan, Yue; Chowdhury, Anik

    2018-03-01

    There are many pedagogical techniques used by educators in higher education; however, some techniques and activities have been shown to be more beneficial to student learning than others. Research has demonstrated that active learning and learning in which students cognitively engage with the material in a multitude of ways result in better understanding and retention. The aim of the present study was to determine which of three pedagogical techniques led to improvement in learning and retention in undergraduate college students. Subjects partook in one of three different types of pedagogical engagement: hands-on learning with a model, observing someone else manipulate the model, and traditional lecture-based presentation. Students were then asked to take an online quiz that tested their knowledge of the new material, both immediately after learning the material and 2 wk later. Students who engaged in direct manipulation of the model scored higher on the assessment immediately after learning the material compared with the other two groups. However, there were no differences among the three groups when assessed after a 2-wk retention interval. Thus active learning techniques that involve direct interaction with the material can lead to learning benefits; however, how these techniques benefit long-term retention of the information is equivocal.

  13. Improving the Students' Activity and Learning Outcomes on Social Sciences Subject Using Round Table and Rally Coach of Cooperative Learning Model

    Science.gov (United States)

    Ningsih; Soetjipto, Budi Eko; Sumarmi

    2017-01-01

    The purpose of this study was: (1) to analyze increasing students' learning activity and learning outcomes. Student activities which were observed include the visual, verbal, listening, writing and mental visual activity; (2) to analyze the improvement of student learning outcomes using "Round Table" and "Rally Coach" Model of…

  14. Using active learning strategies to investigate student learning and attitudes in a large enrollment, introductory geology course

    Science.gov (United States)

    Berry, Stacy Jane

    There has been an increased emphasis for college instruction to incorporate more active and collaborative involvement of students in the learning process. These views have been asserted by The Association of American Colleges (AAC), the National Science Foundation (NSF), and The National Research Counsel (NRC), which are advocating for the modification of traditional instructional techniques to allow students the opportunity to be more cooperative (Task Group on General Education, 1988). This has guided educators and facilitators into shifting teaching paradigms from a teacher centered to a more student-centered curriculum. The present study investigated achievement outcomes and attitudes of learners in a large enrollment (n ~ 200), introductory geology course using a student centered learning cycle format of instruction versus another similar section that used a traditional lecture format. Although the course is a recruiting class for majors, over 95% of the students that enroll are non-majors. Measurements of academic evaluation were through four unit exams, classroom communication systems, weekly web-based homework, in-class activities, and a thematic collaborative poster/paper project and presentation. The qualitative methods to investigate the effectiveness of the teaching design included: direct observation, self-reporting about learning, and open-ended interviews. By disaggregating emerging data, we tried to concentrate on patterns and causal relationships between achievement performance and attitudes regarding learning geology. Statistical analyses revealed positive relationships between student engagement in supplemental activities and achievement mean scores within and between the two sections. Completing weekly online homework had the most robust relationship with overall achievement performance. Contrary to expectations, a thematic group project only led to modest gains in achievement performance, although the social and professional gains could be

  15. One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM.

    Science.gov (United States)

    Rodriguez, Mario; Orrite, Carlos; Medrano, Carlos; Makris, Dimitrios

    2016-05-10

    This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.

  16. Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

    Science.gov (United States)

    Zeng, Xueqiang; Luo, Gang

    2017-12-01

    Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

  17. Cognitive Neurostimulation: Learning to Volitionally Sustain Ventral Tegmental Area Activation.

    Science.gov (United States)

    MacInnes, Jeff J; Dickerson, Kathryn C; Chen, Nan-Kuei; Adcock, R Alison

    2016-03-16

    Activation of the ventral tegmental area (VTA) and mesolimbic networks is essential to motivation, performance, and learning. Humans routinely attempt to motivate themselves, with unclear efficacy or impact on VTA networks. Using fMRI, we found untrained participants' motivational strategies failed to consistently activate VTA. After real-time VTA neurofeedback training, however, participants volitionally induced VTA activation without external aids, relative to baseline, Pre-test, and control groups. VTA self-activation was accompanied by increased mesolimbic network connectivity. Among two comparison groups (no neurofeedback, false neurofeedback) and an alternate neurofeedback group (nucleus accumbens), none sustained activation in target regions of interest nor increased VTA functional connectivity. The results comprise two novel demonstrations: learning and generalization after VTA neurofeedback training and the ability to sustain VTA activation without external reward or reward cues. These findings suggest theoretical alignment of ideas about motivation and midbrain physiology and the potential for generalizable interventions to improve performance and learning. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Trends in Research on Writing as a Learning Activity

    Directory of Open Access Journals (Sweden)

    Perry D. Klein

    2016-02-01

    Full Text Available This article discusses five trends in research on writing as a learning activity. Firstly, earlier decades were marked by conflicting views about the effects of writing on learning; in the past decade, the use of meta-analysis has shown that the effects of writing on learning are reliable, and that several variables mediate and moderate these effects. Secondly, in earlier decades, it was thought that text as a medium inherently elicited thinking and learning. Research during the past decade has indicated that writing to learn is a self-regulated activity, dependent on the goals and strategies of the writer. Thirdly, the Writing Across the Curriculum (WAC movement emphasized domain-general approaches to WTL. Much recent research is consistent with the Writing in the Disciplines (WID movement, incorporating genres that embody forms of reasoning specific to a given discipline. Fourthly, WTL as a classroom practice was always partially social, but the theoretical conceptualization of it was largely individual. During the past two decades, WTL has broadened to include theories and research that integrate social and psychological processes. Fifthly, WTL research has traditionally focused on epistemic learning in schools; more recently, it has been extended to include reflective learning in the professions and additional kinds of outcomes.

  19. Student's Reflections on Their Learning and Note-Taking Activities in a Blended Learning Course

    Science.gov (United States)

    Nakayama, Minoru; Mutsuura, Kouichi; Yamamoto, Hiroh

    2016-01-01

    Student's emotional aspects are often discussed in order to promote better learning activity in blended learning courses. To observe these factors, course participant's self-efficacy and reflections upon their studies were surveyed, in addition to the surveying of the metrics of student's characteristics during a Bachelor level credit course.…

  20. The Effectiveness of WhatsApp Mobile Learning Activities Guided by Activity Theory on Students' Knowledge Management

    Science.gov (United States)

    Barhoumi, Chokri

    2015-01-01

    This research paper explores the effectiveness of using mobile technologies to support a blended learning course titled Scientific Research Methods in Information Science. Specifically, it discusses the effects of WhatsApp mobile learning activities guided by activity theory on students' knowledge Management (KM). During the 2014 academic year,…

  1. Learning activism, acting with phronesis

    Science.gov (United States)

    Lee, Yew-Jin

    2015-12-01

    The article "Socio-political development of private school children mobilising for disadvantaged others" by Darren Hoeg, Natalie Lemelin, and Lawrence Bencze described a language-learning curriculum that drew on elements of Socioscientific issues and Science, Technology, Society and Environment. Results showed that with a number of enabling factors acting in concert, learning about and engagement in practical action for social justice and equity are possible. An alternative but highly compatible framework is now introduced—phronetic social research—as an action-oriented, wisdom-seeking research stance for the social sciences. By so doing, it is hoped that forms of phronetic social research can gain wider currency among those that promote activism as one of many valued outcomes of an education in science.

  2. Use of Online Learning Resources in the Development of Learning Environments at the Intersection of Formal and Informal Learning: The Student as Autonomous Designer

    Directory of Open Access Journals (Sweden)

    Maja Lebeničnik

    2015-06-01

    Full Text Available Learning resources that are used in the education of university students are often available online. The nature of new technologies causes an interweaving of formal and informal learning, with the result that a more active role is expected from students with regard to the use of ICT for their learning. The variety of online learning resources (learning content and learning tools facilitates informed use and enables students to create the learning environment that is most appropriate for their personal learning needs and preferences. In contemporary society, the creation of an inclusive learning environment supported by ICT is pervasive. The model of Universal Design for Learning is becoming increasingly significant in responding to the need for inclusive learning environments. In this article, we categorize different online learning activities into the principles of Universal Design for Learning. This study examines ICT use among university students (N = 138, comparing student teachers with students in other study programs. The findings indicate that among all students, activities with lower demands for engagement are most common. Some differences were observed between student teachers and students from other programs. Student teachers were more likely than their peers to perform certain activities aimed at meeting diverse learner needs, but the percentage of students performing more advanced activities was higher for students in other study programs than for student teachers. The categorization of activities revealed that student teachers are less likely to undertake activities that involve interaction with others. Among the sample of student teachers, we found that personal innovativeness is correlated with diversity of activities in only one category. The results show that student teachers should be encouraged to perform more advanced activities, especially activities involving interaction with others, collaborative learning and use of ICT to

  3. Toward accelerating landslide mapping with interactive machine learning techniques

    Science.gov (United States)

    Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

    2013-04-01

    Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also

  4. The effectiveness of integration of virtual patients in a collaborative learning activity.

    Science.gov (United States)

    Marei, Hesham F; Donkers, Jeroen; Van Merrienboer, Jeroen J G

    2018-05-07

    Virtual patients (VPs) have been recently integrated within different learning activities. To compare between the effect of using VPs in a collaborative learning activity and using VPs in an independent learning activity on students' knowledge acquisition, retention and transfer. For two different topics, respectively 82 and 76 dental students participated in teaching, learning and assessment sessions with VPs. Students from a female campus and from a male campus have been randomly assigned to condition (collaborative and independent), yielding four experimental groups. Each group received a lecture followed by a learning session using two VPs per topic. Students were administrated immediate and delayed written tests as well as transfer tests using two VPs to assess their knowledge in diagnosis and treatment. For the treatment items of the immediate and delayed written tests, females outperformed males in the collaborative VP group but not in the independent VP group. On the female campus, the use of VPs in a collaborative learning activity is more effective than its use as an independent learning activity in enhancing students' knowledge acquisition and retention. However, the collaborative use of VPs by itself is not enough to produce consistent results across different groups of students and attention should be given to all the factors that would affect students' interaction.

  5. On the asymptotic improvement of supervised learning by utilizing additional unlabeled samples - Normal mixture density case

    Science.gov (United States)

    Shahshahani, Behzad M.; Landgrebe, David A.

    1992-01-01

    The effect of additional unlabeled samples in improving the supervised learning process is studied in this paper. Three learning processes. supervised, unsupervised, and combined supervised-unsupervised, are compared by studying the asymptotic behavior of the estimates obtained under each process. Upper and lower bounds on the asymptotic covariance matrices are derived. It is shown that under a normal mixture density assumption for the probability density function of the feature space, the combined supervised-unsupervised learning is always superior to the supervised learning in achieving better estimates. Experimental results are provided to verify the theoretical concepts.

  6. Lifelong learning for active ageing in nordic museums; archives and street art

    DEFF Research Database (Denmark)

    Fristrup, Tine; Grut, Sara

    2016-01-01

    to lifelong learning as a way to conceptualise activities for older adults’ in museums, as we emphasise an approach to adult education for active ageing articulated as ‘lifelong learning for active ageing’. To illustrate this framing, we outline a number of activities taken from publications, cultural sites...... and conferences in which we have been involved over the last decade in the context of the Nordic Centre of Heritage Learning and Creativity in Östersund, Sweden. We argue that lifelong learning for active ageing in cultural heritage institutions can contribute to the development of older adults’ civic......In this article, we develop a framework that demonstrates how older adults need to develop diverse capabilities in relation to their educational life course through engagements in Nordic museums, archives and street art activities. We discuss how European museums have taken up UNESCO’s approach...

  7. Active Affordance Learning in Continuous State and Action Spaces

    NARCIS (Netherlands)

    Wang, C.; Hindriks, K.V.; Babuska, R.

    2014-01-01

    Learning object affordances and manipulation skills is essential for developing cognitive service robots. We propose an active affordance learning approach in continuous state and action spaces without manual discretization of states or exploratory motor primitives. During exploration in the action

  8. Ask-the-expert: Active Learning Based Knowledge Discovery Using the Expert

    Science.gov (United States)

    Das, Kamalika; Avrekh, Ilya; Matthews, Bryan; Sharma, Manali; Oza, Nikunj

    2017-01-01

    Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the backend. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

  9. Linking Motivation and Commitment through Learning Activities in the Volunteer Sector.

    Science.gov (United States)

    Serafino, Allan

    2001-01-01

    Volunteer motivation and commitment are linked through learning about the organization, the job, and oneself. Volunteer managers should (1) identity volunteer motivations and establish conditions to support them; (2) identify learning activities appropriate for motivations and learning styles; (3) ensure congruence between volunteer learning and…

  10. Teacher learning through reciprocal peer coaching :an analysis of activity sequences

    NARCIS (Netherlands)

    Zwart, R.C.; Wubbels, Th.; Bolhuis, S.M; Bergen, T.C.M.

    2008-01-01

    Just what and how eight experienced teachers in four coaching dyads learned during a 1-year reciprocal peer coaching trajectory was examined in the present study. The learning processes were mapped by providing a detailed description of reported learning activities, reported learning outcomes, and

  11. The Effect of Active Learning Approach on Attitudes of 7th Grade Students

    Science.gov (United States)

    Demirci, Cavide

    2017-01-01

    Active learning is a student's active impact on learning and a student's involvement in the learning process which allows students to focus on creating knowledge with an emphasis on skills such as analytical thinking, problem-solving and meta-cognitive activities that develop students' thinking. The main purpose of this study is to determine…

  12. Effects of team-based learning on self-regulated online learning.

    Science.gov (United States)

    Whittaker, Alice A

    2015-04-10

    Online learning requires higher levels of self-regulation in order to achieve optimal learning outcomes. As nursing education moves further into the blended and online learning venue, new teaching/learning strategies will be required to develop and enhance self-regulated learning skills in nursing students. The purpose of this study was to compare the effectiveness of team-based learning (TBL) with traditional instructor-led (IL) learning, on self-regulated online learning outcomes, in a blended undergraduate research and evidence-based practice course. The nonrandomized sample consisted of 98 students enrolled in the IL control group and 86 students enrolled in the TBL intervention group. The percentage of total possible online viewing time was used as the measure of self-regulated online learning activity. The TBL group demonstrated a significantly higher percentage (p learning activities than the IL control group. The TBL group scored significantly higher on the course examinations (p = 0.003). The findings indicate that TBL is an effective instructional strategy that can be used to achieve the essential outcomes of baccalaureate nursing education by increasing self-regulated learning capabilities in nursing students.

  13. Engaging Students' Learning Through Active Learning

    Directory of Open Access Journals (Sweden)

    Margaret Fitzsimons

    2014-06-01

    Full Text Available This paper discusses a project carried out with thirty six final year undergraduate students, studying the Bachelor of Science in Business and Management and taking the module Small Business Management during the academic year 2012 and 2013 in Dublin Institute of Technology. The research had two separate objectives, 1 to engage in active learning by having students work on a consulting project in groups for a real life business and 2 to improve student learning. The Small Business Management previously had a group assignment that was to choose an article related to entrepreneurship and critic it and present it to the class. Anecdotally, from student feedback, it was felt that this process did not engage students and also did not contribute to the key competencies necessary in order to be an entrepreneur. The desire was for students on successful completion of this module to have better understood how business is conducted and equip them with core skills such as innovation, critical thinking, problem solving and decision making .Student buy in was achieved by getting the students to select their own groups and also work out between each group from a one page brief provided by the businesses which business they would like to work with. It was important for the businesses to also feel their time spent with students was worthwhile so they were presented with a report from the students at the end of the twelve weeks and invited into the College to hear the presentations from students. Students were asked to provide a reflection on their three key learning points from the assignment and to answer specific questions designed to understand what they learnt and how and their strengths and weaknesses. A survey was sent to the businesses that took part to understand their experiences. The results were positive with student engagement and learning rating very highly and feedback from the businesses demonstrated an appreciation of having a different

  14. Active Learning Strategies: An illustrative approach to bring out better learning outcomes from Science, Technology, Engineering and Mathematics (STEM students

    Directory of Open Access Journals (Sweden)

    Adusumilli Srinath

    2014-10-01

    Full Text Available Teaching in a Teacher centric manner has been the mainframe teaching style in engineering education, however students feel it as a single sided approach and feel they are only passive listeners thus this style has now paved way to a Learner centric style of teaching-learning which is ACTIVE LEARNING, wherein every student is actively involved in one or the other form of learning and thus gets a chance to develop the key aspects of the course either on their own or by being a member of an active-learning group. They thus not only learn and practice the course contents but also learn managerial and team skills which are of much importance in present scenario in regard to Industries and companies where these students will be ultimately hired as employees. Professional education is making one’s students ready for the profession which includes team work, management and technical skills, thus Active learning has emerged as a mainframe tool for cherishing this aim of professional education, especially Science, Technology, Engineering and Management (STEM education. This paper aims to focus on a few facets of this active learning process and give an overview to the teaching faculty as well as students on what their individual roles must be like in this process for getting the most out of this process.

  15. Understanding Insurance. A Guide for Industrial Cooperative Training Programs. Learning Activity Package No. 15.

    Science.gov (United States)

    Duenk, Lester G.; Tuel, Charles

    This learning activity package (LAP) on the insurance industry and the methods used to give protection to the insured is designed for student self-study. Following a list of learning objectives, the LAP contains a pretest (answer key provided at the back). Six learning activities follow. The learning activities cover the following material: terms…

  16. Performance in Physiology Evaluation: Possible Improvement by Active Learning Strategies

    Science.gov (United States)

    Montrezor, Luís H.

    2016-01-01

    The evaluation process is complex and extremely important in the teaching/learning process. Evaluations are constantly employed in the classroom to assist students in the learning process and to help teachers improve the teaching process. The use of active methodologies encourages students to participate in the learning process, encourages…

  17. Promoting Female Students' Learning Motivation towards Science by Exercising Hands-On Activities

    Science.gov (United States)

    Wen-jin, Kuo; Chia-ju, Liu; Shi-an, Leou

    2012-01-01

    The purpose of this study is to design different hands-on science activities and investigate which activities could better promote female students' learning motivation towards science. This study conducted three types of science activities which contains nine hands-on activities, an experience scale and a learning motivation scale for data…

  18. Problem-based Learning (PBL in Sociolinguistics as a Way of Encouraging Active Learning

    Directory of Open Access Journals (Sweden)

    Engku Ibrahim Engku Haliza

    2018-01-01

    Full Text Available The major concern of this paper is to advocate the integration of PBL strategies in classroom instruction as a way of promoting active learning. It is undoubted that the benefits of problem-based learning (PBL are numerous. In the sciences, PBL has been well integrated in the curriculum. This research reports of an experience of integrating problem-based learning in an introductory Sociolinguistics course for 60 undergraduates of a Bachelors of English programme through a semester that ran for 14 weeks. A focused group interview and questionnaire were used to find out the perceptions of the students undergoing the hybrid PBL course. The findings of this study reveal that students generally enjoyed the PBL approach and found that they had little choice but to become active learners. Some challenges faced by the learners were also highlighted. These findings have implications for the integration of PBL in the field of social sciences.

  19. A New Approach to Teaching Biomechanics Through Active, Adaptive, and Experiential Learning.

    Science.gov (United States)

    Singh, Anita

    2017-07-01

    Demand of biomedical engineers continues to rise to meet the needs of healthcare industry. Current training of bioengineers follows the traditional and dominant model of theory-focused curricula. However, the unmet needs of the healthcare industry warrant newer skill sets in these engineers. Translational training strategies such as solving real world problems through active, adaptive, and experiential learning hold promise. In this paper, we report our findings of adding a real-world 4-week problem-based learning unit into a biomechanics capstone course for engineering students. Surveys assessed student perceptions of the activity and learning experience. While students, across three cohorts, felt challenged to solve a real-world problem identified during the simulation lab visit, they felt more confident in utilizing knowledge learned in the biomechanics course and self-directed research. Instructor evaluations indicated that the active and experiential learning approach fostered their technical knowledge and life-long learning skills while exposing them to the components of adaptive learning and innovation.

  20. Context matters when striving to promote active and lifelong learning in medical education.

    Science.gov (United States)

    Berkhout, Joris J; Helmich, Esther; Teunissen, Pim W; van der Vleuten, Cees P M; Jaarsma, A Debbie C

    2018-01-01

    WHERE DO WE STAND NOW?: In the 30 years that have passed since The Edinburgh Declaration on Medical Education, we have made tremendous progress in research on fostering 'self-directed and independent study' as propagated in this declaration, of which one prime example is research carried out on problem-based learning. However, a large portion of medical education happens outside of classrooms, in authentic clinical contexts. Therefore, this article discusses recent developments in research regarding fostering active learning in clinical contexts. Clinical contexts are much more complex and flexible than classrooms, and therefore require a modified approach when fostering active learning. Recent efforts have been increasingly focused on understanding the more complex subject of supporting active learning in clinical contexts. One way of doing this is by using theory regarding self-regulated learning (SRL), as well as situated learning, workplace affordances, self-determination theory and achievement goal theory. Combining these different perspectives provides a holistic view of active learning in clinical contexts. ENTRY TO PRACTICE, VOCATIONAL TRAINING AND CONTINUING PROFESSIONAL DEVELOPMENT: Research on SRL in clinical contexts has mostly focused on the undergraduate setting, showing that active learning in clinical contexts requires not only proficiency in metacognition and SRL, but also in reactive, opportunistic learning. These studies have also made us aware of the large influence one's social environment has on SRL, the importance of professional relationships for learners, and the role of identity development in learning in clinical contexts. Additionally, research regarding postgraduate lifelong learning also highlights the importance of learners interacting about learning in clinical contexts, as well as the difficulties that clinical contexts may pose for lifelong learning. However, stimulating self-regulated learning in undergraduate medical education

  1. Penerapan Model Active Learning untuk Meremediasi Miskonsepsi Siswa pada Materi Gerak Lurus di SMP

    OpenAIRE

    Yulindar, Arvitri; Djudin, Tomo; Hamdani

    2017-01-01

    This study aims to determine effectiveness of remediation application of active learning models that have misconceptions on rectilinear motion in class VIII SMP Negeri 2 Pontianak. This research is the form of pre-experiment using a one group pretest-postest. The study sample consisted of 38 students of class VIII B SMP Negeri 2 Pontianak. Data collection technique used in the form of a measurement technique using multiple choice diagnostic tests with reason that have total 10 questions. The ...

  2. Neutron activation analysis of certified samples by the absolute method

    Science.gov (United States)

    Kadem, F.; Belouadah, N.; Idiri, Z.

    2015-07-01

    The nuclear reactions analysis technique is mainly based on the relative method or the use of activation cross sections. In order to validate nuclear data for the calculated cross section evaluated from systematic studies, we used the neutron activation analysis technique (NAA) to determine the various constituent concentrations of certified samples for animal blood, milk and hay. In this analysis, the absolute method is used. The neutron activation technique involves irradiating the sample and subsequently performing a measurement of the activity of the sample. The fundamental equation of the activation connects several physical parameters including the cross section that is essential for the quantitative determination of the different elements composing the sample without resorting to the use of standard sample. Called the absolute method, it allows a measurement as accurate as the relative method. The results obtained by the absolute method showed that the values are as precise as the relative method requiring the use of standard sample for each element to be quantified.

  3. The search for active learning: Lessons from a happy accident

    OpenAIRE

    Bashforth, Hedley; Parmar, Nitin R

    2010-01-01

    This article suggests that the concept of ‘active learning’ has different meanings. These meanings are created in the dynamic and variable relationships between the uses of learning technologies and approaches to pedagogy. Institutions play a key role in mediating these relationships, privileging some meanings of ‘active learning’ over others. More dialogical forms of active learning call for changes in the mediating role of the institution. This article draws on a case study of the use of El...

  4. Activating teaching methods, studying responses and learning

    OpenAIRE

    Christensen, Hans Peter; Vigild, Martin E.; Thomsen, Erik; Szabo, Peter; Horsewell, Andy

    2009-01-01

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed. Peer Reviewed

  5. Beyond Lecture and Non-Lecture Classrooms: LA-student interactions in Active Learning Classrooms

    Science.gov (United States)

    Gonzalez, Dayana; Kornreich, Hagit; Rodriguez, Idaykis; Monslave, Camila; Pena-Flores, Norma

    Our expanded multi-site study on active learning classrooms supported by Learning Assistants (LAs) aims to understand the connections between three classroom elements: the activity, student learning, and how LAs support the learning process in the classroom. At FIU, LAs are used in a variety of active learning settings, from large auditorium settings to studio classroom with movable tables. Our study uses the COPUS observation protocol as a way to characterize LAs behaviors in these classrooms. With a focus on LA-student interactions, our analysis of how LAs interact with students during a 'learning session' generated new observational codes for specific new categories of LA roles. Preliminary results show that LAs spend more time interacting with students in some classes, regardless of the classroom setting, while in other classrooms, LA-student interactions are mostly brief. We discuss how LA-student interactions contribute to the dynamics and mechanism of the socially shared learning activity.

  6. Active Learning: 101 Strategies To Teach Any Subject.

    Science.gov (United States)

    Silberman, Mel

    This book contains specific, practical strategies that can be used for almost any subject matters to promote active learning. It brings together in one source a comprehensive collection of instructional strategies, with ways to get students to be active from the beginning through activities that build teamwork and get students thinking about the…

  7. Spontaneous brain activity predicts learning ability of foreign sounds.

    Science.gov (United States)

    Ventura-Campos, Noelia; Sanjuán, Ana; González, Julio; Palomar-García, María-Ángeles; Rodríguez-Pujadas, Aina; Sebastián-Gallés, Núria; Deco, Gustavo; Ávila, César

    2013-05-29

    Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.

  8. Employability and work-related learning activities in higher education

    DEFF Research Database (Denmark)

    Magnell, Marie; Kolmos, Anette

    2017-01-01

    The focus of this paper is on how academic staff perceive their roles and responsibilities regarding work-related learning, and how they approach and implement work-related learning activities in curricula across academic environments in higher education. The study is based on case studies...

  9. How an Active Learning Classroom Transformed IT Executive Management

    Science.gov (United States)

    Connolly, Amy; Lampe, Michael

    2016-01-01

    This article describes how our university built a unique classroom environment specifically for active learning. This classroom changed students' experience in the undergraduate executive information technology (IT) management class. Every college graduate should learn to think critically, solve problems, and communicate solutions, but 90% of…

  10. Using Expectancy Value Theory as a Framework to Reduce Student Resistance to Active Learning: A Proof of Concept.

    Science.gov (United States)

    Cooper, Katelyn M; Ashley, Michael; Brownell, Sara E

    2017-01-01

    There has been a national movement to transition college science courses from passive lectures to active learning environments. Active learning has been shown to be a more effective way for students to learn, yet there is concern that some students are resistant to active learning approaches. Although there is much discussion about student resistance to active learning, few studies have explored this topic. Furthermore, a limited number of studies have applied theoretical frameworks to student engagement in active learning. We propose using a theoretical lens of expectancy value theory to understand student resistance to active learning. In this study, we examined student perceptions of active learning after participating in 40 hours of active learning. We used the principal components of expectancy value theory to probe student experience in active learning: student perceived self-efficacy in active learning, value of active learning, and potential cost of participating in active learning. We found that students showed positive changes in the components of expectancy value theory and reported high levels of engagement in active learning, which provide proof of concept that expectancy value theory can be used to boost student perceptions of active learning and their engagement in active learning classrooms. From these findings, we have built a theoretical framework of expectancy value theory applied to active learning.

  11. Working Together: Librarian and Student Collaboration for Active Learning in a Library Eclassroom

    Directory of Open Access Journals (Sweden)

    Marcie Lynne Jacklin

    2010-06-01

    Full Text Available Active learning strategies based on several learning theories were incorporated during instruction sessions for second year Biological Sciences students. The instructional strategies described in this paper are based primarily on sociocultural and collaborative learning theory, with the goal being to expand the relatively small body of literature currently available that discusses the application of these learning theories to library instruction. The learning strategies employed successfully involved students in the learning process ensuring that the experiences were appropriate and effective. The researchers found that, as a result of these strategies (e.g. teaching moments based on the emerging needs of students students’ interest in learning information literacy was increased and students interacted with information given to them as well as with their peers. Collaboration between the Librarians, Co-op Student and Senior Lab Instructor helped to enhance the learning experience for students and also revealed new aspects of the active learning experiences. The primary learning objective, which was to increase the students’ information skills in the Biological Sciences, was realized. The advantages of active learning were realized by both instructors and students. Advantages for students attained during these sessions include having their diverse learning styles addressed; increased interaction with and retention of information; increased responsibility for their own learning; the opportunity to value not only the instructors, but also themselves and their peers as sources of authority and knowledge; improved problem solving abilities; increased interest and opportunities for critical thinking, as a result of the actively exchanging information in a group. The primary advantage enjoyed by the instructors was the opportunity to collaborate with colleagues to reduce the preparation required to create effective library instruction sessions

  12. Learning from instructional explanations: effects of prompts based on the active-constructive-interactive framework.

    Science.gov (United States)

    Roelle, Julian; Müller, Claudia; Roelle, Detlev; Berthold, Kirsten

    2015-01-01

    Although instructional explanations are commonly provided when learners are introduced to new content, they often fail because they are not integrated into effective learning activities. The recently introduced active-constructive-interactive framework posits an effectiveness hierarchy in which interactive learning activities are at the top; these are then followed by constructive and active learning activities, respectively. Against this background, we combined instructional explanations with different types of prompts that were designed to elicit these learning activities and tested the central predictions of the active-constructive-interactive framework. In Experiment 1, N = 83 students were randomly assigned to one of four combinations of instructional explanations and prompts. To test the active learning hypothesis, the learners received either (1) complete explanations and engaging prompts designed to elicit active activities or (2) explanations that were reduced by inferences and inference prompts designed to engage learners in constructing the withheld information. Furthermore, in order to explore how interactive learning activities can be elicited, we gave the learners who had difficulties in constructing the prompted inferences adapted remedial explanations with either (3) unspecific engaging prompts or (4) revision prompts. In support of the active learning hypothesis, we found that the learners who received reduced explanations and inference prompts outperformed the learners who received complete explanations and engaging prompts. Moreover, revision prompts were more effective in eliciting interactive learning activities than engaging prompts. In Experiment 2, N = 40 students were randomly assigned to either (1) a reduced explanations and inference prompts or (2) a reduced explanations and inference prompts plus adapted remedial explanations and revision prompts condition. In support of the constructive learning hypothesis, the learners who received

  13. Does using active learning in thermodynamics lectures improve students’ conceptual understanding and learning experiences?

    International Nuclear Information System (INIS)

    Georgiou, H; Sharma, M D

    2015-01-01

    Encouraging ‘active learning’ in the large lecture theatre emerges as a credible recommendation for improving university courses, with reports often showing significant improvements in learning outcomes. However, the recommendations are based predominantly on studies undertaken in mechanics. We set out to examine those claims in the thermodynamics module of a large first year physics course with an established technique, called interactive lecture demonstrations (ILDs). The study took place at The University of Sydney, where four parallel streams of the thermodynamics module were divided into two streams that experienced the ILDs and two streams that did not. The programme was first implemented in 2011 to gain experience and refine logistical matters and repeated in 2012 with approximately 500 students. A validated survey, the thermal concepts survey, was used as pre-test and post-test to measure learning gains while surveys and interviews provided insights into what the ‘active learning’ meant from student experiences. We analysed lecture recordings to capture the time devoted to different activities in a lecture, including interactivity. The learning gains were in the ‘high gain’ range for the ILD streams and ‘medium gain’ for the other streams. The analysis of the lecture recordings showed that the ILD streams devoted significantly more time to interactivity while surveys and interviews showed that students in the ILD streams were thinking in deep ways. Our study shows that ILDs can make a difference in students’ conceptual understanding as well as their experiences, demonstrating the potential value-add that can be provided by investing in active learning to enhance lectures. (paper)

  14. An Activity-based Approach to the Learning and Teaching of Research Methods: Measuring Student Engagement and Learning

    Directory of Open Access Journals (Sweden)

    Eimear Fallon

    2013-05-01

    Full Text Available This paper discusses a research project carried out with 82 final and third year undergraduate students, learning Research Methods prior to undertaking an undergraduate thesis during the academic years 2010 and 2011. The research had two separate, linked objectives, (a to develop a Research Methods module that embraces an activity-based approach to learning in a group environment, (b to improve engagement by all students. The Research Methods module was previously taught through a traditional lecture-based format. Anecdotally, it was felt that student engagement was poor and learning was limited. It was believed that successful completion of the development of this Module would equip students with a deeply-learned battery of research skills to take into their further academic and professional careers. Student learning was achieved through completion of a series of activities based on different research methods. In order to encourage student engagement, a wide variety of activities were used. These activities included workshops, brainstorming, mind-mapping, presentations, written submissions, peer critiquing, lecture/seminar, and ‘speed dating’ with more senior students and self reflection. Student engagement was measured through a survey based on a U.S. National Survey of Student Engagement (2000. A questionnaire was devised to establish whether, and to what degree, students were engaged in the material that they were learning, while they were learning it. The results of the questionnaire were very encouraging with between 63% and 96% of students answering positively to a range of questions concerning engagement. In terms of the two objectives set, these were satisfactorily met. The module was successfully developed and continues to be delivered, based upon this new and significant level of student engagement.

  15. Teacher Feedback during Active Learning: Current Practices in Primary Schools

    Science.gov (United States)

    van den Bergh, Linda; Ros, Anje; Beijaard, Douwe

    2013-01-01

    Background: Feedback is one of the most powerful tools, which teachers can use to enhance student learning. It appears dif?cult for teachers to give qualitatively good feedback, especially during active learning. In this context, teachers should provide facilitative feedback that is focused on the development of meta-cognition and social learning.…

  16. Active semi-supervised learning method with hybrid deep belief networks.

    Science.gov (United States)

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

  17. The control of tonic pain by active relief learning

    Science.gov (United States)

    Mano, Hiroaki; Lee, Michael; Yoshida, Wako; Kawato, Mitsuo; Robbins, Trevor W

    2018-01-01

    Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty (‘associability’) signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief. PMID:29482716

  18. The control of tonic pain by active relief learning.

    Science.gov (United States)

    Zhang, Suyi; Mano, Hiroaki; Lee, Michael; Yoshida, Wako; Kawato, Mitsuo; Robbins, Trevor W; Seymour, Ben

    2018-02-27

    Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty ('associability') signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief. © 2018, Zhang et al.

  19. Using Active Learning to Teach Concepts and Methods in Quantitative Biology.

    Science.gov (United States)

    Waldrop, Lindsay D; Adolph, Stephen C; Diniz Behn, Cecilia G; Braley, Emily; Drew, Joshua A; Full, Robert J; Gross, Louis J; Jungck, John A; Kohler, Brynja; Prairie, Jennifer C; Shtylla, Blerta; Miller, Laura A

    2015-11-01

    This article provides a summary of the ideas discussed at the 2015 Annual Meeting of the Society for Integrative and Comparative Biology society-wide symposium on Leading Students and Faculty to Quantitative Biology through Active Learning. It also includes a brief review of the recent advancements in incorporating active learning approaches into quantitative biology classrooms. We begin with an overview of recent literature that shows that active learning can improve students' outcomes in Science, Technology, Engineering and Math Education disciplines. We then discuss how this approach can be particularly useful when teaching topics in quantitative biology. Next, we describe some of the recent initiatives to develop hands-on activities in quantitative biology at both the graduate and the undergraduate levels. Throughout the article we provide resources for educators who wish to integrate active learning and technology into their classrooms. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

  20. Promoting readiness to practice: which learning activities promote competence and professional identity for student social workers during practice learning?

    OpenAIRE

    Roulston, Audrey; Cleak, Helen; Vreugdenhil, Anthea

    2016-01-01

    Practice learning is integral to the curriculum for qualifying social work students. Accreditation standards require regular student supervision and exposure to specific learning activities. Most agencies offer high quality placements but organisational cutbacks may affect supervision and restrict the development of competence and professional identity. Undergraduate social work students in Northern Ireland universities (n = 396) were surveyed about the usefulness of the learning activities t...

  1. Monitoring Implementation of Active Learning Classrooms at Lethbridge College, 2014-2015

    Science.gov (United States)

    Benoit, Andy

    2017-01-01

    Having experienced preliminary success in designing two active learning classrooms, Lethbridge College developed an additional eight active learning classrooms as part of a three-year initiative spanning 2014-2017. Year one of the initiative entailed purchasing new audio-visual equipment and classroom furniture followed by installation. This…

  2. Development of inquiry-based learning activities integrated with the local learning resource to promote learning achievement and analytical thinking ability of Mathayomsuksa 3 student

    Science.gov (United States)

    Sukji, Paweena; Wichaidit, Pacharee Rompayom; Wichaidit, Sittichai

    2018-01-01

    The objectives of this study were to: 1) compare learning achievement and analytical thinking ability of Mathayomsuksa 3 students before and after learning through inquiry-based learning activities integrated with the local learning resource, and 2) compare average post-test score of learning achievement and analytical thinking ability to its cutting score. The target of this study was 23 Mathayomsuksa 3 students who were studying in the second semester of 2016 academic year from Banchatfang School, Chainat Province. Research instruments composed of: 1) 6 lesson plans of Environment and Natural Resources, 2) the learning achievement test, and 3) analytical thinking ability test. The results showed that 1) student' learning achievement and analytical thinking ability after learning were higher than that of before at the level of .05 statistical significance, and 2) average posttest score of student' learning achievement and analytical thinking ability were higher than its cutting score at the level of .05 statistical significance. The implication of this research is for science teachers and curriculum developers to design inquiry activities that relate to student's context.

  3. Does the Room Matter? Active Learning in Traditional and Enhanced Lecture Spaces

    Science.gov (United States)

    Stoltzfus, Jon R.; Libarkin, Julie

    2016-01-01

    SCALE-UP–type classrooms, originating with the Student-Centered Active Learning Environment with Upside-down Pedagogies project, are designed to facilitate active learning by maximizing opportunities for interactions between students and embedding technology in the classroom. Positive impacts when active learning replaces lecture are well documented, both in traditional lecture halls and SCALE-UP–type classrooms. However, few studies have carefully analyzed student outcomes when comparable active learning–based instruction takes place in a traditional lecture hall and a SCALE-UP–type classroom. Using a quasi-experimental design, we compared student perceptions and performance between sections of a nonmajors biology course, one taught in a traditional lecture hall and one taught in a SCALE-UP–type classroom. Instruction in both sections followed a flipped model that relied heavily on cooperative learning and was as identical as possible given the infrastructure differences between classrooms. Results showed that students in both sections thought that SCALE-UP infrastructure would enhance performance. However, measures of actual student performance showed no difference between the two sections. We conclude that, while SCALE-UP–type classrooms may facilitate implementation of active learning, it is the active learning and not the SCALE-UP infrastructure that enhances student performance. As a consequence, we suggest that institutions can modify existing classrooms to enhance student engagement without incorporating expensive technology. PMID:27909018

  4. Enhanced Memory as a Common Effect of Active Learning

    Science.gov (United States)

    Markant, Douglas B.; Ruggeri, Azzurra; Gureckis, Todd M.; Xu, Fei

    2016-01-01

    Despite widespread consensus among educators that "active learning" leads to better outcomes than comparatively passive forms of instruction, it is often unclear why these benefits arise. In this article, we review research showing that the opportunity to control the information experienced while learning leads to improved memory…

  5. The Internet: A Learning Environment.

    Science.gov (United States)

    McGreal, Rory

    1997-01-01

    The Internet environment is suitable for many types of learning activities and teaching and learning styles. Every World Wide Web-based course should provide: home page; introduction; course overview; course requirements, vital information; roles and responsibilities; assignments; schedule; resources; sample tests; teacher biography; course…

  6. Autonomous Motion Learning for Intra-Vehicular Activity Space Robot

    Science.gov (United States)

    Watanabe, Yutaka; Yairi, Takehisa; Machida, Kazuo

    Space robots will be needed in the future space missions. So far, many types of space robots have been developed, but in particular, Intra-Vehicular Activity (IVA) space robots that support human activities should be developed to reduce human-risks in space. In this paper, we study the motion learning method of an IVA space robot with the multi-link mechanism. The advantage point is that this space robot moves using reaction force of the multi-link mechanism and contact forces from the wall as space walking of an astronaut, not to use a propulsion. The control approach is determined based on a reinforcement learning with the actor-critic algorithm. We demonstrate to clear effectiveness of this approach using a 5-link space robot model by simulation. First, we simulate that a space robot learn the motion control including contact phase in two dimensional case. Next, we simulate that a space robot learn the motion control changing base attitude in three dimensional case.

  7. Learning spectrum's selection in OLAM network for analysis cement samples

    International Nuclear Information System (INIS)

    Huang Ning; Wang Peng; Tang Daiquan; Hu Renlan

    2010-01-01

    It uses OLAM artificial neural network to analyze the samples of cement raw material. Two kinds of spectrums are used for network learning: pure-element spectrum and mix-element spectrum. The output of pure-element method can be used to construct a simulate spectrum, which can be compared with the original spectrum and judge the shift of spectrum; the mix-element method can store more message and correct the matrix effect, but the multicollinearity among spectrums can cause some side effect to the results. (authors)

  8. From Swimming Pool to Collaborative Learning Studio: Pedagogy, Space, and Technology in a Large Active Learning Classroom

    Science.gov (United States)

    Lee, Dabae; Morrone, Anastasia S.; Siering, Greg

    2018-01-01

    To promote student learning and bolster student success, higher education institutions are increasingly creating large active learning classrooms to replace traditional lecture halls. Although there have been many efforts to examine the effects of those classrooms on learning outcomes, there is paucity of research that can inform the design and…

  9. The Philosophical and Pedagogical Underpinnings of Active Learning in Engineering Education

    Science.gov (United States)

    Christie, Michael; de Graaff, Erik

    2017-01-01

    In this paper the authors draw on three sequential keynote addresses that they gave at Active Learning in Engineering Education (ALE) workshops in Copenhagen (2012), Caxias do Sol (2014) and San Sebastian (2015). Active Learning in Engineering Education is an informal international network of engineering educators dedicated to improving…

  10. Teaching Diversity and Aging through Active Learning Strategies: An Annotated Bibliography.

    Science.gov (United States)

    Fried, Stephen B.; Mehrotra, Chandra M.

    Covering 10 topical areas, this annotated bibliography offers a guide to journal articles, book chapters, monographs, and books useful for teaching diversity and aging through active learning. Active learning experiences may help expand students' awareness of elements of their own diversity, broaden their world view, and enhance their culturally…

  11. Designing Online Teaching and Learning Activities for Higher Education in Hong Kong

    Directory of Open Access Journals (Sweden)

    Kevin Downing

    2008-06-01

    Full Text Available Instruction using the Web as a vehicle for content dissemination has increasingly dominated debates related to online learning (Nash, 2004 and there is little doubt that the exponential growth in the use of the internet and web-based instruction continues to present educators with considerable opportunities and challenges (Boettcher, 1999; McNaught & Lam, 2005. Many teachers and researchers (Wood, 1997; Littlejohn et al., 1999 point out that the organization and reflection necessary to effectively teach online often improves an instructor’s traditional teaching. This is a theme continued by Downing (2001 who identifies the eventual success or failure of online teaching as largely due to the same factors that have always been central to the provision of a quality learning experience. These factors include the energy, commitment and imagination of those responsible for providing the teaching and learning environment, whether it is virtual or actual. It is within this context that the authors of this paper set themselves the task of designing innovative online teaching and learning activities which add value to the student experience and genuinely assist learning traditionally difficult and dynamic concepts. The increasing adoption of outcomes based teaching and learning environments in universities around the world has provided wide-ranging opportunities to reflect on current learning and teaching practice. Whilst outcomes based teaching and learning is not a new idea (Biggs, 1999, many academic colleagues are actively seeking ways to leverage information technology solutions to design constructively aligned online teaching and learning activities which add value to the student learning experience and significantly assist in the understanding of difficult concepts and processes. This paper will describe and demonstrate the innovative development of online teaching and learning activities which adhere to the principles of both outcomes based

  12. Three Dimensions of Learning: Experiential Activity for Engineering Innovation Education and Research

    Science.gov (United States)

    Killen, Catherine P.

    2015-01-01

    This paper outlines a novel approach to engineering education research that provides three dimensions of learning through an experiential class activity. A simulated decision activity brought current research into the classroom, explored the effect of experiential activity on learning outcomes and contributed to the research on innovation decision…

  13. Using Active Learning for Speeding up Calibration in Simulation Models.

    Science.gov (United States)

    Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

    2016-07-01

    Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

  14. Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction

    OpenAIRE

    Lopes, Manuel; Montesano, Luis

    2014-01-01

    In this survey we present different approaches that allow an intelligent agent to explore autonomous its environment to gather information and learn multiple tasks. Different communities proposed different solutions, that are in many cases, similar and/or complementary. These solutions include active learning, exploration/exploitation, online-learning and social learning. The common aspect of all these approaches is that it is the agent to selects and decides what information to gather next. ...

  15. Facilitating Adoption of Web Tools for Problem and Project Based Learning Activities

    DEFF Research Database (Denmark)

    Khalid, Md. Saifuddin; Rongbutsri, Nikorn; Buus, Lillian

    2012-01-01

    and project based learning. In the area of problem and project based learning, facilitation is the core term and the teacher often has the role as facilitator or moderator instead of a teacher teaching. Technology adoption for learning activities needs facilitation, which is mostly absent. Sustainable......This paper builds on research directions from ‘activity theory’ and ‘learning design’ to provide ‘facilitation’ for students standing within decision making related to selection of web 2.0 tools and university provided web-based applications for supporting students activities within problem...... adoption might be facilitated based on tool appropriation with activities associated with courses and projects. Our mapping of different tools in a framework is reported based on interviews, observations, narratives and survey. A direction towards facilitation process for adoption is discussed as part...

  16. Physical Activity Is Associated with Reduced Implicit Learning but Enhanced Relational Memory and Executive Functioning in Young Adults.

    Directory of Open Access Journals (Sweden)

    Chelsea M Stillman

    Full Text Available Accumulating evidence suggests that physical activity improves explicit memory and executive cognitive functioning at the extreme ends of the lifespan (i.e., in older adults and children. However, it is unknown whether these associations hold for younger adults who are considered to be in their cognitive prime, or for implicit cognitive functions that do not depend on motor sequencing. Here we report the results of a study in which we examine the relationship between objectively measured physical activity and (1 explicit relational memory, (2 executive control, and (3 implicit probabilistic sequence learning in a sample of healthy, college-aged adults. The main finding was that physical activity was positively associated with explicit relational memory and executive control (replicating previous research, but negatively associated with implicit learning, particularly in females. These results raise the intriguing possibility that physical activity upregulates some cognitive processes, but downregulates others. Possible implications of this pattern of results for physical health and health habits are discussed.

  17. Investigating Language Learning Activity Using a CALL Task in the Self-access Centre

    Directory of Open Access Journals (Sweden)

    Carlos Montoro

    2011-09-01

    Full Text Available This article describes a small study of the language learning activity of individual learners using a CALL task in a self-access environment. The research focuses on the nature of the language learning activity, the most salient elements that make up its structure and major disturbances observed between and within some of those elements. It is set in the context of computer-assisted language learning (CALL and activity theory. A CALL task designed by the authors was made available online to be used as a research and learning tool. Empirical data was collected from two participants using ethnographic tools, such as participant observation and stimulated recall sessions. The analysis focuses on disturbances mainly involving the subject (i.e., the learner, mediating artefacts (e.g., the CALL task, the community (e.g., management and other self-access centre users and the object of the activity (i.e., learning English. It is recommended that future studies should look deeper into contradictions in the learning activity from a cultural-historical perspective.

  18. Learning Active Citizenship: Conflicts between Students' Conceptualisations of Citizenship and Classroom Learning Experiences in Lebanon

    Science.gov (United States)

    Akar, Bassel

    2016-01-01

    Education for active citizenship continues to be a critical response for social cohesion and reconstruction in conflict-affected areas. Oftentimes, approaches to learning and teaching in such contexts can do as much harm as good. This study qualitatively examines 435 students' reflections of their civics classroom learning experiences and their…

  19. Invention activities as preparation for learning laboratory data handling skills

    Science.gov (United States)

    Day, James

    2012-10-01

    Undergraduate physics laboratories are often driven by a mix of goals, and usually enough of them to cause cognitive overload for the student. Our recent findings align well with studies indicating that students often exit a physics lab without having properly learned how to handle real data. The value of having students explore the underlying structure of a problem before being able to solve it has been shown as an effective way to ready students for learning. Borrowing on findings from the fields of education and cognitive psychology, we use ``invention activities'' to precede direct instruction and bolster learning. In this talk I will show some of what we have learned about students' data handling skills, explain how an invention activity works, and share some observations of successful transfer.

  20. Active learning and decision making: an introduction to the collection.

    Science.gov (United States)

    Gottlieb, Jacqueline; Lopes, Manuel; Oudeyer, Pierre-Yves

    2014-01-01

    The importance of exploratory behaviors by which agents actively sample information has been long appreciated in a wide range of disciplines ranging from machine and robot learning to neuroscience and psychology.  Given the complexity of these behaviors, progress in understanding them will require a confluence of ideas from these multiple fields.  This collection of articles in F1000Research aims to provide a home for a broad range of studies addressing this topic, including full length research articles, brief communications, single figure studies, and review/opinion articles, and studies using computational, behavioral or neural approaches.  Here, we provide an introduction to the collection which we hope will grow and become a valuable resource for the researchers exploring this topic.

  1. [Flipped classroom as a strategy to enhance active learning].

    Science.gov (United States)

    Wakabayashi, Noriyuki

    2015-03-01

    This paper reviews the introduction of a flipped class for fourth grade dentistry students, and analyzes the characteristics of the learning method. In fiscal 2013 and 2014, a series of ten three-hour units for removable partial prosthodontics were completed with the flipped class method; a lecture video of approximately 60 minutes was made by the teacher (author) and uploaded to the university's e-learning website one week before each class. Students were instructed to prepare for the class by watching the streaming video on their PC, tablet, or smartphone. In the flipped class, students were not given a lecture, but were asked to solve short questions displayed on screen, to make a short presentation about a part of the video lecture, and to discuss a critical question related to the main subject of the day. An additional team-based learning (TBL) session with individual and group answers was implemented. The average individual scores were considerably higher in the last two years, when the flipped method was implemented, than in the three previous years when conventional lectures were used. The following learning concepts were discussed: the role of the flipped method as an active learning strategy, the efficacy of lecture videos and short questions, students' participation in the class discussion, present-day value of the method, cooperation with TBL, the significance of active learning in relation with the students' learning ability, and the potential increase in the preparation time and workload for students.

  2. Chemotherapy disrupts learning, neurogenesis and theta activity in the adult brain.

    Science.gov (United States)

    Nokia, Miriam S; Anderson, Megan L; Shors, Tracey J

    2012-12-01

    Chemotherapy, especially if prolonged, disrupts attention, working memory and speed of processing in humans. Most cancer drugs that cross the blood-brain barrier also decrease adult neurogenesis. Because new neurons are generated in the hippocampus, this decrease may contribute to the deficits in working memory and related thought processes. The neurophysiological mechanisms that underlie these deficits are generally unknown. A possible mediator is hippocampal oscillatory activity within the theta range (3-12 Hz). Theta activity predicts and promotes efficient learning in healthy animals and humans. Here, we hypothesised that chemotherapy disrupts learning via decreases in hippocampal adult neurogenesis and theta activity. Temozolomide was administered to adult male Sprague-Dawley rats in a cyclic manner for several weeks. Treatment was followed by training with different types of eyeblink classical conditioning, a form of associative learning. Chemotherapy reduced both neurogenesis and endogenous theta activity, as well as disrupted learning and related theta-band responses to the conditioned stimulus. The detrimental effects of temozolomide only occurred after several weeks of treatment, and only on a task that requires the association of events across a temporal gap and not during training with temporally overlapping stimuli. Chemotherapy did not disrupt the memory for previously learned associations, a memory independent of (new neurons in) the hippocampus. In conclusion, prolonged systemic chemotherapy is associated with a decrease in hippocampal adult neurogenesis and theta activity that may explain the selective deficits in processes of learning that describe the 'chemobrain'. © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  3. Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images

    Science.gov (United States)

    Rougier, Simon; Puissant, Anne; Stumpf, André; Lachiche, Nicolas

    2016-09-01

    Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed.

  4. Radiochemical neutron activation analysis of gold in geochemical samples

    International Nuclear Information System (INIS)

    Zilliacus, R.

    1983-01-01

    A fast method for the radiochemical neutron activation analysis of gold in geochemical samples is described. The method is intended for samples having background concentrations of gold. The method is based on the dissolution of samples with hydrofluoric acid and aqua regia followed by the dissolution of the fluorides with boric acid and hydrochloric acid. Gold is then adsorbed on activated carbon by filtrating the solution through a thin carbon layer. The activity measurements are carried out using a Ge(Li)-detector and a multichannel analyzer. The chemical yields of the separation determined by reirradiation vary between 60 and 90%. The detection limit of the method is 0.2 ng/g gold in rock samples. USGS standard rocks and exploration reference materials are analyzed and the results are presented and compared with literature data. (author)

  5. PENGARUH MODEL PEMBELAJARAN ASSURANCE, RELEVANCE, INTEREST, ASSESSMENT, SATISFACTION DENGAN STRATEGI ACTIVE LEARNING TIPE INDEX CARD MATCH TERHADAP KEMAMPUAN PEMECAHAN MASALAH MATEMATIK SISWA SMA

    Directory of Open Access Journals (Sweden)

    Frasticha Frasticha

    2016-08-01

    Full Text Available Pemecahan masalah merupakan kegiatan matematika yang sulit baik dalam mempelajari maupun mengajarkannya, sehingga diperlukan adanya suatu model pembelajaran yang dapat memberikan pengaruh positif terhadap kemampuan pemecahan masalah siswa. Salah satu model pembelajaran yang dapat digunakan yaitu model pembelajaran ARIAS dengan strategi active learning tipe ICM. Penelitian ini bertujuan untuk mengetahui: (1 model pembelajaran ARIAS dengan strategi active learning tipe ICM berpengaruh terhadap kemampuan pemecahan masalah matematik siswa SMA; (2 Sikap siswa terhadap pembelajaran matematika menggunakan model pembelajaran ARIAS dengan strategi active learning tipe ICM. Subjek penelitian ini adalah siswa kelas XI IPA 1 dengan jumlah 38 siswa sebagai kelas kontrol dan XI IPA 2 dengan jumlah 39 siswa sebagai kelas eksperimen di SMAN 19 Kabupaten Tangerang pada tahun ajaran 2015-2016. Metode penelitian yang digunakan adalah metode penelitian eksperimen dengan adalah desain kuasi eksperimen dengan bentuk Nonequivalent Control Group serta Cluster Sampling sebagai teknik pengambilan sampel. Analisis data dalam penelitian ini menggunakan SPSS Statistics Version 22. Hasil penelitian :(1 model pembelajaran ARIAS dengan strategi active learning tipe ICM berpengaruh terhadap kemampuan pemecahan masalah matematik siswa SMA dan memberikan pengaruh yang positif; (2 sikap siswa positif terhadap model pembelajaran ARIAS dengan strategi active learning tipe ICM. Kata Kunci: Assurance Relevance Interest Assessment Satisfaction, Index Card Match, Kemampuan Pemecahan Masalah

  6. Involving postgraduate's students in undergraduate small group teaching promotes active learning in both

    Science.gov (United States)

    Kalra, Ruchi; Modi, Jyoti Nath; Vyas, Rashmi

    2015-01-01

    Background: Lecture is a common traditional method for teaching, but it may not stimulate higher order thinking and students may also be hesitant to express and interact. The postgraduate (PG) students are less involved with undergraduate (UG) teaching. Team based small group active learning method can contribute to better learning experience. Aim: To-promote active learning skills among the UG students using small group teaching methods involving PG students as facilitators to impart hands-on supervised training in teaching and managerial skills. Methodology: After Institutional approval under faculty supervision 92 UGs and 8 PGs participated in 6 small group sessions utilizing the jigsaw technique. Feedback was collected from both. Observations: Undergraduate Feedback (Percentage of Students Agreed): Learning in small groups was a good experience as it helped in better understanding of the subject (72%), students explored multiple reading resources (79%), they were actively involved in self-learning (88%), students reported initial apprehension of performance (71%), identified their learning gaps (86%), team enhanced their learning process (71%), informal learning in place of lecture was a welcome change (86%), it improved their communication skills (82%), small group learning can be useful for future self-learning (75%). Postgraduate Feedback: Majority performed facilitation for first time, perceived their performance as good (75%), it was helpful in self-learning (100%), felt confident of managing students in small groups (100%), as facilitator they improved their teaching skills, found it more useful and better identified own learning gaps (87.5%). Conclusions: Learning in small groups adopting team based approach involving both UGs and PGs promoted active learning in both and enhanced the teaching skills of the PGs. PMID:26380201

  7. An active learning curriculum improves fellows' knowledge and faculty teaching skills.

    Science.gov (United States)

    Inra, Jennifer A; Pelletier, Stephen; Kumar, Navin L; Barnes, Edward L; Shields, Helen M

    2017-01-01

    Traditional didactic lectures are the mainstay of teaching for graduate medical education, although this method may not be the most effective way to transmit information. We created an active learning curriculum for Brigham and Women's Hospital (BWH) gastroenterology fellows to maximize learning. We evaluated whether this new curriculum improved perceived knowledge acquisition and knowledge base. In addition, our study assessed whether coaching faculty members in specific methods to enhance active learning improved their perceived teaching and presentation skills. We compared the Gastroenterology Training Exam (GTE) scores before and after the implementation of this curriculum to assess whether an improved knowledge base was documented. In addition, fellows and faculty members were asked to complete anonymous evaluations regarding their learning and teaching experiences. Fifteen fellows were invited to 12 lectures over a 2-year period. GTE scores improved in the areas of stomach ( p active learning curriculum. Scores in hepatology, as well as biliary and pancreatic study, showed a trend toward improvement ( p >0.05). All fellows believed the lectures were helpful, felt more prepared to take the GTE, and preferred the interactive format to traditional didactic lectures. All lecturers agreed that they acquired new teaching skills, improved teaching and presentation skills, and learned new tools that could help them teach better in the future. An active learning curriculum is preferred by GI fellows and may be helpful for improving transmission of information in any specialty in medical education. Individualized faculty coaching sessions demonstrating new ways to transmit information may be important for an individual faculty member's teaching excellence.

  8. An active role for machine learning in drug development

    Science.gov (United States)

    Murphy, Robert F.

    2014-01-01

    Due to the complexity of biological systems, cutting-edge machine-learning methods will be critical for future drug development. In particular, machine-vision methods to extract detailed information from imaging assays and active-learning methods to guide experimentation will be required to overcome the dimensionality problem in drug development. PMID:21587249

  9. Hilar GABAergic interneuron activity controls spatial learning and memory retrieval.

    Directory of Open Access Journals (Sweden)

    Yaisa Andrews-Zwilling

    Full Text Available Although extensive research has demonstrated the importance of excitatory granule neurons in the dentate gyrus of the hippocampus in normal learning and memory and in the pathogenesis of amnesia in Alzheimer's disease (AD, the role of hilar GABAergic inhibitory interneurons, which control the granule neuron activity, remains unclear.We explored the function of hilar GABAergic interneurons in spatial learning and memory by inhibiting their activity through Cre-dependent viral expression of enhanced halorhodopsin (eNpHR3.0--a light-driven chloride pump. Hilar GABAergic interneuron-specific expression of eNpHR3.0 was achieved by bilaterally injecting adeno-associated virus containing a double-floxed inverted open-reading frame encoding eNpHR3.0 into the hilus of the dentate gyrus of mice expressing Cre recombinase under the control of an enhancer specific for GABAergic interneurons. In vitro and in vivo illumination with a yellow laser elicited inhibition of hilar GABAergic interneurons and consequent activation of dentate granule neurons, without affecting pyramidal neurons in the CA3 and CA1 regions of the hippocampus. We found that optogenetic inhibition of hilar GABAergic interneuron activity impaired spatial learning and memory retrieval, without affecting memory retention, as determined in the Morris water maze test. Importantly, optogenetic inhibition of hilar GABAergic interneuron activity did not alter short-term working memory, motor coordination, or exploratory activity.Our findings establish a critical role for hilar GABAergic interneuron activity in controlling spatial learning and memory retrieval and provide evidence for the potential contribution of GABAergic interneuron impairment to the pathogenesis of amnesia in AD.

  10. Effectiveness and student perceptions of an active learning activity using a headline news story to enhance in-class learning of cell cycle regulation.

    Science.gov (United States)

    Dirks-Naylor, Amie J

    2016-06-01

    An active learning activity was used to engage students and enhance in-class learning of cell cycle regulation in a PharmD level integrated biological sciences course. The aim of the present study was to determine the effectiveness and perception of the in-class activity. After completion of a lecture on the topic of cell cycle regulation, students completed a 10-question multiple-choice quiz before and after engaging in the activity. The activity involved reading of a headline news article published by ScienceDaily.com entitled "One Gene Lost Equals One limb Regained." The name of the gene was deleted from the article and, thus, the end goal of the activity was to determine the gene of interest by the description in the story. The activity included compiling a list of all potential gene candidates before sufficient information was given to identify the gene of interest (p21). A survey was completed to determine student perceptions of the activity. Quiz scores improved by an average of 20% after the activity (40.1 ± 1.95 vs. 59.9 ± 2.14,Pactivity, found the news article interesting, and believed that the activity improved their understanding of cell cycle regulation. The majority of students agreed that the in-class activity piqued their interest for learning the subject matter and also agreed that if they understand a concept during class, they are more likely to want to study that concept outside of class. In conclusion, the activity improved in-class understanding and enhanced interest in cell cycle regulation. Copyright © 2016 The American Physiological Society.

  11. Faculty and second-year medical student perceptions of active learning in an integrated curriculum.

    Science.gov (United States)

    Tsang, Alexander; Harris, David M

    2016-12-01

    Patients expect physicians to be lifelong learners who are able to interpret and evaluate diagnostic tests, and most medical schools list the development of lifelong learning in their program objectives. However, lecture is the most often utilized form of teaching in the first two years and is considered passive learning. The current generation of medical students has many characteristics that should support active learning pedagogies. The purpose of this study was to analyze student and faculty perceptions of active learning in an integrated medical curriculum at the second-year mark, where students have been exposed to multiple educational pedagogies. The first hypothesis of the study was that faculty would favor active learning methods. The second hypothesis was that Millennial medical students would favor active learning due to their characteristics. Primary faculty for years 1 and 2 and second-year medical students were recruited for an e-mail survey consisting of 12 questions about active learning and lecture. Students perceived that lecture and passive pedagogies were more effective for learning, whereas faculty felt active and collaborative learning was more effective. Students believed that more content should be covered by lecture than faculty. There were also significant differences in perceptions of what makes a good teacher. Students and faculty both felt that lack of time in the curriculum and preparation time were barriers for faculty. The data suggest that students are not familiar with the process of learning and that more time may be needed to help students develop lifelong learning skills. Copyright © 2016 The American Physiological Society.

  12. Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning

    OpenAIRE

    Pallone, Stephen N.; Frazier, Peter I.; Henderson, Shane G.

    2017-01-01

    We analyze the problem of learning a single user's preferences in an active learning setting, sequentially and adaptively querying the user over a finite time horizon. Learning is conducted via choice-based queries, where the user selects her preferred option among a small subset of offered alternatives. These queries have been shown to be a robust and efficient way to learn an individual's preferences. We take a parametric approach and model the user's preferences through a linear classifier...

  13. TECHNOLOGIES OF INITIATING STUDENTS INTO INDEPENDENT (SELF-GUIDED ACTIVITY IN SUPPLEMENTARY DISTANCE LEARNING

    Directory of Open Access Journals (Sweden)

    Irina V. Abakumova

    2016-12-01

    Full Text Available The research in question investigates the technologies of initiating independent activity within the framework of distance learning and their psychological aspects. The authors’ classification of educational technologies of initiating students into independent cognitive activity is presented. Such technologies utilize various psychological mechanisms of exciting students’ cognitive interest, intensifying cognitive processes, developing independent activity skills, and, as a result, increase motivation for independent activity and learning on the whole. These include such types of technologies as developmental technologies, interactive technologies, technologies of information transfer, technologies of meaning-making initiation. The research of the attitude of distance learning educators to independent activity of students and the content of the academic courses were done at Moodle-based education programs. The findings show the differences in retention rate among distance learning educators whose competence in terms of initiating students into independent (self-guided activity varies. It’s emphasized that interactive lectures, videoconferences, audio-visual aids, interactive seminars, glossaries, interactive tests are considered the most efficient technologies in initiating students into independent (self-guided activity. The obtained results have made it possible to stress the developmental effect of distance learning technologies and the technologies of initiating students into independent (self-guided activity in various psychic spheres of students: cognitive, individual, emotional. We mention the changes in motivational sphere of students and their meaning-making activity. In the course of correct development of distance learning we notice the development of voluntary and nonvoluntary cognitive activity. A student starts actively participating in educational process, he becomes the creator of his own world.

  14. Self-Observation Model Employing an Instinctive Interface for Classroom Active Learning

    Science.gov (United States)

    Chen, Gwo-Dong; Nurkhamid; Wang, Chin-Yeh; Yang, Shu-Han; Chao, Po-Yao

    2014-01-01

    In a classroom, obtaining active, whole-focused, and engaging learning results from a design is often difficult. In this study, we propose a self-observation model that employs an instinctive interface for classroom active learning. Students can communicate with virtual avatars in the vertical screen and can react naturally according to the…

  15. Incorporating Active Learning and Student Inquiry into an Introductory Merchandising Class

    Science.gov (United States)

    Lee, Hyun-Hwa; Hines, Jean D.

    2012-01-01

    Many educators believe that student learning is enhanced when they are actively involved in classroom activities that require student inquiry. The purpose of this paper is to report on three student inquiry projects that were incorporated into a merchandising class with the focus on making students responsible for their learning, rather than the…

  16. The Effect of Mastery Learning Model with Reflective Thinking Activities on Medical Students' Academic Achievement: An Experimental Study

    Science.gov (United States)

    Elaldi, Senel

    2016-01-01

    This study aimed to determine the effect of mastery learning model supported with reflective thinking activities on the fifth grade medical students' academic achievement. Mixed methods approach was applied in two samples (n = 64 and n = 6). Quantitative part of the study was based on a pre-test-post-test control group design with an experiment…

  17. Developing capability through peer-assisted learning activities ...

    African Journals Online (AJOL)

    L-CAS) is an activity by means of which each student is exposed to primary healthcare learning and practice in communities. Capability has been described as 'an integration of knowledge, skills, personal qualities and understanding used ...

  18. The analysis of student’s critical thinking ability on discovery learning by using hand on activity based on the curiosity

    Science.gov (United States)

    Sulistiani, E.; Waluya, S. B.; Masrukan

    2018-03-01

    This study aims to determine (1) the effectiveness of Discovery Learning model by using Hand on Activity toward critical thinking abilities, and (2) to describe students’ critical thinking abilities in Discovery Learning by Hand on Activity based on curiosity. This study is mixed method research with concurrent embedded design. Sample of this study are students of VII A and VII B of SMP Daarul Qur’an Ungaran. While the subject in this study is based on the curiosity of the students groups are classified Epistemic Curiosity (EC) and Perceptual Curiosity (PC). The results showed that the learning of Discovery Learning by using Hand on Activity is effective toward mathematics critical thinking abilities. Students of the EC type are able to complete six indicators of mathematics critical thinking abilities, although there are still two indicators that the result is less than the maximum. While students of PC type have not fully been able to complete the indicator of mathematics critical thinking abilities. They are only strong on indicators formulating questions, while on the other five indicators they are still weak. The critical thinking abilities of EC’s students is better than the critical thinking abilities of the PC’s students.

  19. Applicability of neutron activation analysis to geological samples

    Energy Technology Data Exchange (ETDEWEB)

    Ebihara, Mitsuru [Tokyo Metropolitan Univ., Graduate School of Science, Tokyo (Japan)

    2003-03-01

    The applicability of neutron activation analysis (NAA) to geological samples in space is discussed by referring to future space mission programs, by which the extraterrestrial samples are to be delivered to the earth for scientific inspections. It is concluded that both destructive and non-destructive NAA are highly effective in analyzing these samples. (author)

  20. Applicability of neutron activation analysis to geological samples

    International Nuclear Information System (INIS)

    Ebihara, Mitsuru

    2003-01-01

    The applicability of neutron activation analysis (NAA) to geological samples in space is discussed by referring to future space mission programs, by which the extraterrestrial samples are to be delivered to the earth for scientific inspections. It is concluded that both destructive and non-destructive NAA are highly effective in analyzing these samples. (author)

  1. Collaborative filtering for brain-computer interaction using transfer learning and active class selection.

    Science.gov (United States)

    Wu, Dongrui; Lance, Brent J; Parsons, Thomas D

    2013-01-01

    Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

  2. Collaborative filtering for brain-computer interaction using transfer learning and active class selection.

    Directory of Open Access Journals (Sweden)

    Dongrui Wu

    Full Text Available Brain-computer interaction (BCI and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL, active class selection (ACS, and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

  3. Enhanced Multisensory Integration and Motor Reactivation after Active Motor Learning of Audiovisual Associations

    Science.gov (United States)

    Butler, Andrew J.; James, Thomas W.; James, Karin Harman

    2011-01-01

    Everyday experience affords us many opportunities to learn about objects through multiple senses using physical interaction. Previous work has shown that active motor learning of unisensory items enhances memory and leads to the involvement of motor systems during subsequent perception. However, the impact of active motor learning on subsequent…

  4. Using Active-Learning Pedagogy to Develop Essay-Writing Skills in Introductory Political Theory Tutorials

    Science.gov (United States)

    Murphy, Michael P. A.

    2017-01-01

    Building on prior research into active learning pedagogy in political science, I discuss the development of a new active learning strategy called the "thesis-building carousel," designed for use in political theory tutorials. This use of active learning pedagogy in a graduate student-led political theory tutorial represents the overlap…

  5. PROCRASTINATION AS FACTOR OF THE EMOTIONAL ATTITUDE OF STUDENTS TO LEARNING ACTIVITY

    Directory of Open Access Journals (Sweden)

    M. A. Kuznetsov

    2016-04-01

    Manifestation of academic procrastination in the emotional attitude to learning activity is connected with students’ academic progress. High academic progress students’ emotional attitude to learning activity is broken by procrastination more than that of low academic progress students.

  6. Active Learning Strategies in Physics Teaching

    Science.gov (United States)

    Karamustafaoglu, Orhan

    2009-01-01

    The purpose of this study was to determine physics teachers' opinions about student-centered activities applicable in physics teaching and learning in context. A case study approach was used in this research. First, semi-structured interviews were carried out with 6 physics teachers. Then, a questionnaire was developed based on the data obtained…

  7. Paramedic Learning Style Preferences and Continuing Medical Education Activities: A Cross-Sectional Survey Study.

    Science.gov (United States)

    Staple, Louis; Carter, Alix; Jensen, Jan L; Walker, Mark

    2018-01-01

    Paramedics participate in continuing medical education (CME) to maintain their skills and knowledge. An understanding of learning styles is important for education to be effective. This study examined the preferred learning styles of ground ambulance paramedics and describes how their preferred learning styles relate to the elective CME activities these paramedics attend. All paramedics (n=1,036) employed in a provincial ground ambulance service were invited to participate in a survey containing three parts: demographics, learning style assessed by the Kolb Learning Style Inventory (LSI), and elective CME activity. 260 paramedics (25%) participated in the survey. Preferred learning styles were: assimilator, 28%; diverger, 25%; converger, 24%; and accommodator, 23%. Advanced life support (ALS) providers had a higher proportion of assimilators (36%), and basic life support (BLS) providers had a higher proportion of divergers (30%). The learning style categories of CME activities attended by paramedics were: assimilators, 25%; divergers, 26%; convergers, 25%; and accommodators, 24%. These results suggest that paramedics are a diverse group of learners, and learning style differs within their demographics. Paramedics attend CME activities that complement all learning styles. Organizations providing education opportunities to paramedics should consider paramedics a diverse learning group when designing their CME programs.

  8. Evaluation of a faculty development program aimed at increasing residents' active learning in lectures.

    Science.gov (United States)

    Desselle, Bonnie C; English, Robin; Hescock, George; Hauser, Andrea; Roy, Melissa; Yang, Tong; Chauvin, Sheila W

    2012-12-01

    Active engagement in the learning process is important to enhance learners' knowledge acquisition and retention and the development of their thinking skills. This study evaluated whether a 1-hour faculty development workshop increased the use of active teaching strategies and enhanced residents' active learning and thinking. Faculty teaching in a pediatrics residency participated in a 1-hour workshop (intervention) approximately 1 month before a scheduled lecture. Participants' responses to a preworkshop/postworkshop questionnaire targeted self-efficacy (confidence) for facilitating active learning and thinking and providing feedback about workshop quality. Trained observers assessed each lecture (3-month baseline phase and 3-month intervention phase) using an 8-item scale for use of active learning strategies and a 7-item scale for residents' engagement in active learning. Observers also assessed lecturer-resident interactions and the extent to which residents were asked to justify their answers. Responses to the workshop questionnaire (n  =  32/34; 94%) demonstrated effectiveness and increased confidence. Faculty in the intervention phase demonstrated increased use of interactive teaching strategies for 6 items, with 5 reaching statistical significance (P ≤ .01). Residents' active learning behaviors in lectures were higher in the intervention arm for all 7 items, with 5 reaching statistical significance. Faculty in the intervention group demonstrated increased use of higher-order questioning (P  =  .02) and solicited justifications for answers (P  =  .01). A 1-hour faculty development program increased faculty use of active learning strategies and residents' engagement in active learning during resident core curriculum lectures.

  9. Blended learning models for directing the self-learning activity of “Software Engineering” specialty students

    Directory of Open Access Journals (Sweden)

    Vera V. Lyubchenko

    2014-12-01

    Full Text Available The adoption of Law of Ukraine “On Higher Education” (2014 involves the increase in students’ self-learning activity part in the curriculum. Therefore the self-learning activities’ arrangement in a way augmenting the result quality becomes a top priority task. This research objective consists in elaborating the scenario for organization of the students’ qualitative self-study, based on blended learning models. The author analyzes four blended learning models: the rotation model, flex-model, self-blend model and online driver model, and gives examples of their use. It is shown that first two models are the most suitable for full-time students. A general scenario for the use of blended learning models is described. Although the use of blended learning models causes several difficulties, it also essentially contributes into students’ self-study monitoring and control support.

  10. Impact of problem-based, active learning on graduation rates for 10 generations of Dutch medical students.

    Science.gov (United States)

    Schmidt, Henk G; Cohen-Schotanus, Janke; Arends, Lidia R

    2009-03-01

    We aimed to study the effects of active-learning curricula on graduation rates of students and on the length of time needed to graduate. Graduation rates for 10 generations of students enrolling in the eight Dutch medical schools between 1989 and 1998 were analysed. In addition, time needed to graduate was recorded. Three of the eight schools had curricula emphasising active learning, small-group instruction and limited numbers of lectures; the other five had conventional curricula to varying degrees. Overall, the active-learning curricula graduated on average 8% more students per year, and these students graduated on average 5 months earlier than their colleagues from conventional curricula. Four hypotheses potentially explaining the effect of active learning on graduation rate and study duration were considered: (i) active-learning curricula promote the social and academic integration of students; (ii) active-learning curricula attract brighter students; (iii) active-learning curricula retain more poor students, and (iv) the active engagement of students with their study required by active-learning curricula induces better academic performance and, hence, lower dropout rates. The first three hypotheses had to be rejected. It was concluded that the better-learning hypothesis provides the most parsimonious account for the data.

  11. Cognitive and Social Aspects of Engagement in Active Learning

    Science.gov (United States)

    Koretsky, Milo

    2017-01-01

    This article reports analysis of students' written reflections as to what helps them learn in an active learning environment. Eight hundred and twenty seven responses from 403 students in four different studio courses over two years were analyzed. An emergent coding scheme identified 55% of the responses as associated with cognitive processes…

  12. Using human brain activity to guide machine learning.

    Science.gov (United States)

    Fong, Ruth C; Scheirer, Walter J; Cox, David D

    2018-03-29

    Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

  13. Why Is Active Learning so Difficult to Implement: The Turkish Case

    Science.gov (United States)

    Aksit, Fisun; Niemi, Hannele; Nevgi, Anne

    2016-01-01

    This article aims to report how teacher education may promote active learning which is demanded by the current educational reform of Turkish teacher education (TE). This article also examines the effectiveness of the recent reforms in Turkey from a student's perspective, and provides an understanding of the concept of active learning, how it is…

  14. Effective, Active Learning Strategies for the Oceanography Classroom

    Science.gov (United States)

    Dmochowski, J. E.; Marinov, I.

    2014-12-01

    A decline in enrollment in STEM fields at the university level has prompted extensive research on alternative ways of teaching and learning science. Inquiry-based learning as well as the related "flipped" or "active" lectures, and similar teaching methods and philosophies have been proposed as more effective ways to disseminate knowledge in science classes than the traditional lecture. We will provide a synopsis of our experiences in implementing some of these practices into our Introductory Oceanography, Global Climate Change, and Ocean Atmosphere Dynamics undergraduate courses at the University of Pennsylvania, with both smaller and larger enrollments. By implementing tools such as at-home modules; computer labs; incorporation of current research; pre- and post-lecture quizzes; reflective, qualitative writing assignments; peer review; and a variety of in-class learning strategies, we aim to increase the science literacy of the student population and help students gain a more comprehensive knowledge of the topic, enhance their critical thinking skills, and correct misconceptions. While implementing these teaching techniques with college students is not without complications, we argue that a blended class that flexibly and creatively accounts for class size and science level improves the learning experience and the acquired knowledge. We will present examples of student assignments and activities as well as describe the lessons we have learned, and propose ideas for moving forward to best utilize innovative teaching tools in order to increase science literacy in oceanography and other climate-related courses.

  15. Shielding design of highly activated sample storage at reactor TRIGA PUSPATI

    International Nuclear Information System (INIS)

    Naim Syauqi Hamzah; Julia Abdul Karim; Mohamad Hairie Rabir; Muhd Husamuddin Abdul Khalil; Mohd Amin Sharifuldin Salleh

    2010-01-01

    Radiation protection has always been one of the most important things considered in Reaktor Triga PUSPATI (RTP) management. Currently, demands on sample activation were increased from variety of applicant in different research field area. Radiological hazard may occur if the samples evaluation done were misjudge or miscalculated. At present, there is no appropriate storage for highly activated samples. For that purpose, special irradiated samples storage box should be provided in order to segregate highly activated samples that produce high dose level and typical activated samples that produce lower dose level (1 - 2 mR/ hr). In this study, thickness required by common shielding material such as lead and concrete to reduce highly activated radiotracer sample (potassium bromide) with initial exposure dose of 5 R/ hr to background level (0.05 mR/ hr) were determined. Analyses were done using several methods including conventional shielding equation, half value layer calculation and Micro shield computer code. Design of new irradiated samples storage box for RTP that capable to contain high level gamma radioactivity were then proposed. (author)

  16. Active Learning in a Large General Physics Classroom.

    Science.gov (United States)

    Trousil, Rebecca

    2008-04-01

    In 2004, we launched a new calculus-based, introductory physics sequence at Washington University. Designed as an alternative to our traditional lecture-based sequence, the primary objectives for this new course were to actively engage students in the learning process, to significantly strengthen students' conceptual reasoning skills, to help students develop higher level quantitative problem solving skills necessary for analyzing ``real world'' problems, and to integrate modern physics into the curriculum. This talk will describe our approach, using The Six Ideas That Shaped Physics text by Thomas Moore, to creating an active learning environment in large classes as well as share our perspective on key elements for success and challenges that we face in the large class environment.

  17. Facilitate Active Learning: The Role of Perceived Benefits of Using Technology

    Science.gov (United States)

    Zhuang, Weiling; Xiao, Qian

    2018-01-01

    The authors examine factors influencing student active learning and the ensuing class learning experience in the context of applying technologies in the classroom. The results suggest that the psychological benefit directly and indirectly influences class learning experience. In addition, the functional benefit only indirectly influences class…

  18. Active Collaborative Learning through Remote Tutoring

    Science.gov (United States)

    Gehret, Austin U.; Elliot, Lisa B.; MacDonald, Jonathan H. C.

    2017-01-01

    An exploratory case study approach was used to describe remote tutoring in biochemistry and general chemistry with students who are deaf or hard of hearing (D/HH). Data collected for analysis were based on the observations of the participant tutor. The research questions guiding this study included (1) How is active learning accomplished in…

  19. Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013 RADARSAT Data

    Directory of Open Access Journals (Sweden)

    Yongfeng Cao

    2017-10-01

    Full Text Available This paper intends to find a more cost-effective way for training oil spill classification systems by introducing active learning (AL and exploring its potential, so that satisfying classifiers could be learned with reduced number of labeled samples. The dataset used has 143 oil spills and 124 look-alikes from 198 RADARSAT images covering the east and west coasts of Canada from 2004 to 2013. Six uncertainty-based active sample selecting (ACS methods are designed to choose the most informative samples. A method for reducing information redundancy amongst the selected samples and a method with varying sample preference are considered. Four classifiers (k-nearest neighbor (KNN, support vector machine (SVM, linear discriminant analysis (LDA and decision tree (DT are coupled with ACS methods to explore the interaction and possible preference between classifiers and ACS methods. Three kinds of measures are adopted to highlight different aspect of classification performance of these AL-boosted classifiers. Overall, AL proves its strong potential with 4% to 78% reduction on training samples in different settings. The SVM classifier shows to be the best one for using in the AL frame, with perfect performance evolving curves in different kinds of measures. The exploration and exploitation criterion can further improve the performance of the AL-boosted SVM classifier but not of the other classifiers.

  20. Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.

    Science.gov (United States)

    Minor, Bryan; Doppa, Janardhan Rao; Cook, Diane J

    2017-12-01

    Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction , where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for 9 participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.

  1. Virtual Reality Learning Activities for Multimedia Students to Enhance Spatial Ability

    Directory of Open Access Journals (Sweden)

    Rafael Molina-Carmona

    2018-04-01

    Full Text Available Virtual Reality is an incipient technology that is proving very useful for training different skills. Our hypothesis is that it is possible to design virtual reality learning activities that can help students to develop their spatial ability. To prove the hypothesis, we have conducted an experiment consisting of training the students using an on-purpose learning activity based on a virtual reality application and assessing the possible improvement of the students’ spatial ability through a widely accepted spatial visualization test. The learning activity consists of a virtual environment where some simple polyhedral shapes are shown and manipulated by moving, rotating and scaling them. The students participating in the experiment are divided into a control and an experimental group, carrying out the same learning activity with the only difference of the device used for the interaction: a traditional computer with screen, keyboard and mouse for the control group, and virtual reality goggles with a smartphone for the experimental group. To assess the experience, all the students have completed a spatial visualization test twice: just before performing the activities and four weeks later, once all the activities were performed. Specifically, we have used the well-known and widely used Purdue Spatial Visualization Test—Rotation (PSVT-R, designed to test rotational visualization ability. The results of the test show that there is an improvement in the test results for both groups, but the improvement is significantly higher in the case of the experimental group. The conclusion is that the virtual reality learning activities have shown to improve the spatial ability of the experimental group.

  2. Analyzing the Impact of Using Optional Activities in Self-Regulated Learning

    Science.gov (United States)

    Ruipérez-Valiente, Jose A.; Muñoz-Merino, Pedro J.; Kloos, Carlos Delgado; Niemann, Katja; Scheffel, Maren; Wolpers, Martin

    2016-01-01

    Self-regulated learning (SRL) environments provide students with activities to improve their learning (e.g., by solving exercises), but they might also provide optional activities (e.g., changing an avatar image or setting goals) where students can decide whether they would like to use or do them and how. Few works have dealt with the use of…

  3. Student Buy-In to Active Learning in a College Science Course

    Science.gov (United States)

    Cavanagh, Andrew J.; Aragón, Oriana R.; Chen, Xinnian; Couch, Brian; Durham, Mary; Bobrownicki, Aiyana; Hanauer, David I.; Graham, Mark J.

    2016-01-01

    The benefits of introducing active learning in college science courses are well established, yet more needs to be understood about student buy-in to active learning and how that process of buy-in might relate to student outcomes. We test the exposure-persuasion-identification-commitment (EPIC) process model of buy-in, here applied to student (n =…

  4. Teaching Sociology of Sport: An Active Learning Approach.

    Science.gov (United States)

    Blinde, Elaine M.

    1995-01-01

    Asserts that sport is a pervasive aspect of society. Presents and describes four learning activities designed to help students understand the significance of sport as a social institution. Maintains that, while the activities focus on the institution of sport, they can be used in a variety of sociology courses. (CFR)

  5. Active Learning Strategies for Introductory Light and Optics

    Science.gov (United States)

    Sokoloff, David R.

    2016-01-01

    There is considerable evidence that traditional approaches are ineffective in teaching physics concepts, including light and optics concepts. A major focus of the work of the Activity Based Physics Group has been on the development of active learning curricula like RealTime Physics (RTP) labs and Interactive Lecture Demonstrations (ILDs). Among…

  6. Plastics in Our Environment: A Jigsaw Learning Activity

    Science.gov (United States)

    Hampton, Elaine; Wallace, Mary Ann; Lee, Wen-Yee

    2009-01-01

    In this lesson, a ready-to-teach cooperative reading activity, students learn about the effects of plastics in our environment, specifically that certain petrochemicals act as artificial estrogens and impact hormonal activities. Much of the content in this lesson was synthesized from recent medical research about the impact of xenoestrogens and…

  7. THE EFFECT OF LEARNING INQUIRY TRAINING MODEL ON STUDENT LEARNING OUTCOMES ON MEASUREMENT MATERIALS

    Directory of Open Access Journals (Sweden)

    Felisa Irawani Hutabarat

    2017-06-01

    Full Text Available This research aims to know the effect of learning model of inquiry learning results students training material measurement. This type of research is quasi experiment. Sampling done by cluster random sampling by taking 2 classes from grade 9 i.e. class X SCIENCE experiments as a class-B that add up to 35 people and class X SCIENCE-C as control classes that add up to 35 people. The instruments used to find out the results of student learning is the learning outcomes tests have been validated in multiple choice form numbered 15 reserved and activity sheets students. The results of the value obtained 37.71 pretes and postest 70.11. The t-test analysis retrieved thitung greater than ttabel so that it can be concluded no difference due to the influence of the learning model of inquiry learning results students training material measurement.

  8. Synthesizing Technology Adoption and Learners' Approaches towards Active Learning in Higher Education

    Science.gov (United States)

    Chan, Kevin; Cheung, George; Wan, Kelvin; Brown, Ian; Luk, Green

    2015-01-01

    In understanding how active and blended learning approaches with learning technologies engagement in undergraduate education, current research models tend to undermine the effect of learners' variations, particularly regarding their styles and approaches to learning, on intention and use of learning technologies. This study contributes to further…

  9. An active learning organisation: teaching projects in electrical engineering education

    NARCIS (Netherlands)

    Christensen, H.-P.; Vos, Henk; de Graaff, E.; Lemoult, B.

    2004-01-01

    The introduction of active learning in engineering education is often started by enthusiastic teachers or change agents. They usually encounter resistance from stakeholders such as colleagues, department boards or students. For a successful introduction these stakeholders all have to learn what

  10. The neural coding of expected and unexpected monetary performance outcomes: dissociations between active and observational learning.

    Science.gov (United States)

    Bellebaum, C; Jokisch, D; Gizewski, E R; Forsting, M; Daum, I

    2012-02-01

    Successful adaptation to the environment requires the learning of stimulus-response-outcome associations. Such associations can be learned actively by trial and error or by observing the behaviour and accompanying outcomes in other persons. The present study investigated similarities and differences in the neural mechanisms of active and observational learning from monetary feedback using functional magnetic resonance imaging. Two groups of 15 subjects each - active and observational learners - participated in the experiment. On every trial, active learners chose between two stimuli and received monetary feedback. Each observational learner observed the choices and outcomes of one active learner. Learning performance as assessed via active test trials without feedback was comparable between groups. Different activation patterns were observed for the processing of unexpected vs. expected monetary feedback in active and observational learners, particularly for positive outcomes. Activity for unexpected vs. expected reward was stronger in the right striatum in active learning, while activity in the hippocampus was bilaterally enhanced in observational and reduced in active learning. Modulation of activity by prediction error (PE) magnitude was observed in the right putamen in both types of learning, whereas PE related activations in the right anterior caudate nucleus and in the medial orbitofrontal cortex were stronger for active learning. The striatum and orbitofrontal cortex thus appear to link reward stimuli to own behavioural reactions and are less strongly involved when the behavioural outcome refers to another person's action. Alternative explanations such as differences in reward value between active and observational learning are also discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

  11. Active Learning through Online Quizzes: Better Learning and Less (Busy) Work

    Science.gov (United States)

    Cook, Brian Robert; Babon, Andrea

    2017-01-01

    Active learning is increasingly promoted within institutions of higher education to assist students develop higher order thinking and link knowledge to meaning. In this paper, the authors evaluate the use of weekly online quizzes based on prescribed preparatory material as a tool to incentivize preparatory reading in order to enable and encourage…

  12. Hilar GABAergic Interneuron Activity Controls Spatial Learning and Memory Retrieval

    Science.gov (United States)

    Andrews-Zwilling, Yaisa; Gillespie, Anna K.; Kravitz, Alexxai V.; Nelson, Alexandra B.; Devidze, Nino; Lo, Iris; Yoon, Seo Yeon; Bien-Ly, Nga; Ring, Karen; Zwilling, Daniel; Potter, Gregory B.; Rubenstein, John L. R.; Kreitzer, Anatol C.; Huang, Yadong

    2012-01-01

    Background Although extensive research has demonstrated the importance of excitatory granule neurons in the dentate gyrus of the hippocampus in normal learning and memory and in the pathogenesis of amnesia in Alzheimer's disease (AD), the role of hilar GABAergic inhibitory interneurons, which control the granule neuron activity, remains unclear. Methodology and Principal Findings We explored the function of hilar GABAergic interneurons in spatial learning and memory by inhibiting their activity through Cre-dependent viral expression of enhanced halorhodopsin (eNpHR3.0)—a light-driven chloride pump. Hilar GABAergic interneuron-specific expression of eNpHR3.0 was achieved by bilaterally injecting adeno-associated virus containing a double-floxed inverted open-reading frame encoding eNpHR3.0 into the hilus of the dentate gyrus of mice expressing Cre recombinase under the control of an enhancer specific for GABAergic interneurons. In vitro and in vivo illumination with a yellow laser elicited inhibition of hilar GABAergic interneurons and consequent activation of dentate granule neurons, without affecting pyramidal neurons in the CA3 and CA1 regions of the hippocampus. We found that optogenetic inhibition of hilar GABAergic interneuron activity impaired spatial learning and memory retrieval, without affecting memory retention, as determined in the Morris water maze test. Importantly, optogenetic inhibition of hilar GABAergic interneuron activity did not alter short-term working memory, motor coordination, or exploratory activity. Conclusions and Significance Our findings establish a critical role for hilar GABAergic interneuron activity in controlling spatial learning and memory retrieval and provide evidence for the potential contribution of GABAergic interneuron impairment to the pathogenesis of amnesia in AD. PMID:22792368

  13. World War II Memorial Learning Activities.

    Science.gov (United States)

    Tennessee State Dept. of Education, Nashville.

    These learning activities can help students get the most out of a visit to the Tennessee World War II Memorial, a group of ten pylons located in Nashville (Tennessee). Each pylon contains informational text about the events of World War II. The ten pylons are listed as: (1) "Pylon E-1--Terror: America Enters the War against Fascism, June…

  14. ANALYTiC: An Active Learning System for Trajectory Classification.

    Science.gov (United States)

    Soares Junior, Amilcar; Renso, Chiara; Matwin, Stan

    2017-01-01

    The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.

  15. Advancing the skill set of SCM graduates – An active learning approach

    NARCIS (Netherlands)

    Scholten, Kirstin; Dubois, Anna

    2017-01-01

    Purpose Drawing on a novel approach to active learning in supply chain management, the purpose of this paper is to describe and analyze how the students’ learning process as well as their learning outcomes are influenced by the learning and teaching contexts. Design/methodology/approach A case study

  16. Advancing the skill set of SCM graduates – An active learning approach

    NARCIS (Netherlands)

    Scholten, Kirstin; Dubois, Anna

    Purpose Drawing on a novel approach to active learning in supply chain management, the purpose of this paper is to describe and analyze how the students’ learning process as well as their learning outcomes are influenced by the learning and teaching contexts. Design/methodology/approach A case study

  17. Public health genetic counselors: activities, skills, and sources of learning.

    Science.gov (United States)

    McWalter, Kirsty M; Sdano, Mallory R; Dave, Gaurav; Powell, Karen P; Callanan, Nancy

    2015-06-01

    Specialization within genetic counseling is apparent, with 29 primary specialties listed in the National Society of Genetic Counselors' 2012 Professional Status Survey (PSS). PSS results show a steady proportion of genetic counselors primarily involved in public health, yet do not identify all those performing public health activities. Little is known about the skills needed to perform activities outside of "traditional" genetic counselor roles and the expertise needed to execute those skills. This study aimed to identify genetic counselors engaging in public health activities, the skills used, and the most influential sources of learning for those skills. Participants (N = 155) reported involvement in several public health categories: (a) Education of Public and/or Health Care Providers (n = 80, 52 %), (b) Population-Based Screening Programs (n = 70, 45 %), (c) Lobbying/Public Policy (n = 62, 40 %), (d) Public Health Related Research (n = 47, 30 %), and (e) State Chronic Disease Programs (n = 12, 8 %). Regardless of category, "on the job" was the most common primary source of learning. Genetic counseling training program was the most common secondary source of learning. Results indicate that the number of genetic counselors performing public health activities is likely higher than PSS reports, and that those who may not consider themselves "public health genetic counselors" do participate in public health activities. Genetic counselors learn a diverse skill set in their training programs; some skills are directly applicable to public health genetics, while other public health skills require additional training and/or knowledge.

  18. Developing students' listening metacognitive strategies using online videotext self-dictation-generation learning activity

    Directory of Open Access Journals (Sweden)

    Ching Chang

    2014-03-01

    Full Text Available The study is based on the use of a flexible learning framework to help students improve information processes underlying strategy instruction in EFL listening. By exploiting the online videotext self-dictation-generation (video-SDG learning activity implemented on the YouTube caption manager platform, the learning cycle was emphasized to promote metacognitive listening development. Two theories were used to guide the online video-SDG learning activity: a student question-generation method and a metacognitive listening training model in a second language (L2. The study investigated how college students in the online video-SDG activity enhanced the use of listening strategies by developing metacognitive listening skills. With emphasis on the metacognitive instructional process, students could promote their listening comprehension of advertisement videos (AVs. Forty-eight students were recruited to participate in the study. Through data collected from the online learning platform, questionnaires, a focus-group interview, and pre- and post- achievement tests, the results revealed that the online video-SDG learning activity could effectively engage students in reflecting upon their perceptions of specific problems countered, listening strategy usages, and strategic knowledge exploited in the metacognitive instructional process. The importance of employing cost-effective online video-SGD learning activities is worthy of consideration in developing students’ metacognitive listening knowledge for enhancing EFL listening strategy instruction.

  19. A Context-Aware Ubiquitous Learning Approach for Providing Instant Learning Support in Personal Computer Assembly Activities

    Science.gov (United States)

    Hsu, Ching-Kun; Hwang, Gwo-Jen

    2014-01-01

    Personal computer assembly courses have been recognized as being essential in helping students understand computer structure as well as the functionality of each computer component. In this study, a context-aware ubiquitous learning approach is proposed for providing instant assistance to individual students in the learning activity of a…

  20. Children’s Resistance in the Emergence of Learning as Leading Activity

    DEFF Research Database (Denmark)

    Hrepich, Paula Alejandra Cavada

    2017-01-01

    Learning becomes the main activity of the traditional school practice from first-year primary school, and children are required to develop a new set of skills, attitudes and knowledge in the new classroom arrangements. As a consequence, a variety of reactions can be observed in children, from eng...... to respond to this question by drawing from research on children’s transition to the first year of primary in two educational systems.......Learning becomes the main activity of the traditional school practice from first-year primary school, and children are required to develop a new set of skills, attitudes and knowledge in the new classroom arrangements. As a consequence, a variety of reactions can be observed in children, from...... engagement to struggle, resistance and rebellion. This chapter presents observation of children actively engaged in changing and creating conditions of learning and development and the role played by resistance in the learning process. Inspired by a sociocultural theoretical perspective, this chapter seeks...