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…

Delta Learning Rule for the Active Sites Model

OpenAIRE

Lingashetty, Krishna Chaithanya

2010-01-01

This paper reports the results on methods of comparing the memory retrieval capacity of the Hebbian neural network which implements the B-Matrix approach, by using the Widrow-Hoff rule of learning. We then, extend the recently proposed Active Sites model by developing a delta rule to increase memory capacity. Also, this paper extends the binary neural network to a multi-level (non-binary) neural network.

Can a model of study activity increase didactic dialogue and students' understanding of learning in IPE?

DEFF Research Database (Denmark)

Hansen, Bodil Winther; Hatt, Camusa

at Metropolitan University College. Since 2013 all UCS have worked with a nationally decided study activity model. The model outlines four different types of learning activities. Students are introduced to courses via the model to heighten their understanding of course design and the expectations...... combining quantitative surveys, interviews, observation and focus groups. Comparisonldiscussion: The presentation will discuss the ambition to optimize dialogue about learning between lecturers and students by using a model of study activity. Results related to the value and potential of the model as seen...... by both lecturers and students will be presented. Findings/results/outcomes/effects: Students point out that the model can be a useful tool to gain an overview of learning activities and the amount of time they are expected to spend in courses. When lecturers introduce courses via the model it deepens...

The Game Enhanced Learning Model

DEFF Research Database (Denmark)

Reng, Lars; Schoenau-Fog, Henrik

2016-01-01

will describe the levels of the model, which is based on our experience in teaching professional game development at university level. Furthermore, we have been using the model to inspire numerous educators to improve their students’ motivation and skills. The model presents various game-based learning...... activities, and depicts their required planning and expected outcome through eight levels. At its lower levels, the model contains the possibilities of using stand-alone analogue and digital games as teachers, utilizing games as a facilitator of learning activities, exploiting gamification and motivating......In this paper, we will introduce the Game Enhanced learning Model (GEM), which describes a range of gameoriented learning activities. The model is intended to give an overview of the possibilities of game-based learning in general and all the way up to purposive game productions. In the paper, we...

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

The Play Curricular Activity Reflection Discussion Model for Game-Based Learning

Science.gov (United States)

Foster, Aroutis; Shah, Mamta

2015-01-01

This article elucidates the process of game-based learning in classrooms through the use of the Play Curricular activity Reflection Discussion (PCaRD) model. A mixed-methods study was conducted at a high school to implement three games with the PCaRD model in a year-long elective course. Data sources included interviews and observations for…

Modeling learning and memory using verbal learning tests: results from ACTIVE.

Science.gov (United States)

Gross, Alden L; Rebok, George W; Brandt, Jason; Tommet, Doug; Marsiske, Michael; Jones, Richard N

2013-03-01

To investigate the influence of memory training on initial recall and learning. The Advanced Cognitive Training for Independent and Vital Elderly study of community-dwelling adults older than age 65 (n = 1,401). We decomposed trial-level recall in the Auditory Verbal Learning Test (AVLT) and Hopkins Verbal Learning Test (HVLT) into initial recall and learning across trials using latent growth models. Trial-level increases in words recalled in the AVLT and HVLT at each follow-up visit followed an approximately logarithmic shape. Over the 5-year study period, memory training was associated with slower decline in Trial 1 AVLT recall (Cohen's d = 0.35, p = .03) and steep pre- and posttraining acceleration in learning (d = 1.56, p learning, d = 3.10, p memory-trained group had a higher level of recall than the control group through the end of the 5-year study period despite faster decline in learning. This study contributes to the understanding of the mechanisms by which training benefits memory and expands current knowledge by reporting long-term changes in initial recall and learning, as measured from growth models and by characterization of the impact of memory training on these components. Results reveal that memory training delays the worsening of memory span and boosts learning.

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.

Learning from erroneous models using SCYDynamics

NARCIS (Netherlands)

Mulder, Y.G.; Bollen, Lars; de Jong, Anthonius J.M.

2014-01-01

Dynamic phenomena are common in science education. Students can learn about such system dynamic processes through model based learning activities. This paper describes a study on the effects of a learning from erroneous models approach using the learning environment SCYDynamics. The study compared

Learning Design of Problem Based Learning Model Based on Recommendations of Sintax Study and Contents Issues on Physics Impulse Materials with Experimental Activities

Directory of Open Access Journals (Sweden)

Kristia Agustina

2017-08-01

Full Text Available This study aims to design learning Problem Based Learning Model based on syntax study recommendations and content issues on Physics Impulse materials through experiments. This research is a development research with Kemp model. The reference for making the learning design is the result of the syntax study and the content of existing PBL implementation problems from Agustina research. This instructional design is applied to the physics material about Impulse done through experimental activity. Limited trials were conducted on the SWCU Physics Education Study Program students group Salatiga, while the validity test was conducted by high school teachers and physics education lecturers. The results of the trial evaluation are limited and the validity test is used to improve the designs that have been made. The conclusion of this research is the design of learning by using PBL model on Impuls material by referring the result of syntax study and the problem content of existing PBL implementation can be produced by learning activity designed in laboratory experiment activity. The actual problem for Impuls material can be used car crash test video at factory. The results of validation tests and limited trials conducted by researchers assessed that the design of learning made by researchers can be used with small revisions. Suggestions from this research are in making learning design by using PBL model to get actual problem can by collecting news that come from newspaper, YouTube, internet, and television.

Learning with hierarchical-deep models.

Science.gov (United States)

Salakhutdinov, Ruslan; Tenenbaum, Joshua B; Torralba, Antonio

2013-08-01

We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.

Individual Learning Accounts and Other Models of Financing Lifelong Learning

Science.gov (United States)

Schuetze, Hans G.

2007-01-01

To answer the question "Financing what?" this article distinguishes several models of lifelong learning as well as a variety of lifelong learning activities. Several financing methods are briefly reviewed, however the principal focus is on Individual Learning Accounts (ILAs) which were seen by some analysts as a promising model for…

Lessons learned from recent geomagnetic disturbance model validation activities

Science.gov (United States)

Pulkkinen, A. A.; Welling, D. T.

2017-12-01

Due to concerns pertaining to geomagnetically induced current impact on ground-based infrastructure, there has been significantly elevated interest in applying models for local geomagnetic disturbance or "delta-B" predictions. Correspondingly there has been elevated need for testing the quality of the delta-B predictions generated by the modern empirical and physics-based models. To address this need, community-wide activities were launched under the GEM Challenge framework and one culmination of the activities was the validation and selection of models that were transitioned into operations at NOAA SWPC. The community-wide delta-B action is continued under the CCMC-facilitated International Forum for Space Weather Capabilities Assessment and its "Ground Magnetic Perturbations: dBdt, delta-B, GICs, FACs" working group. The new delta-B working group builds on the past experiences and expands the collaborations to cover the entire international space weather community. In this paper, we discuss the key lessons learned from the past delta-B validation exercises and lay out the path forward for building on those experience under the new delta-B working group.

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.

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...

Learning in AN Oscillatory Cortical Model

Science.gov (United States)

Scarpetta, Silvia; Li, Zhaoping; Hertz, John

We study a model of generalized-Hebbian learning in asymmetric oscillatory neural networks modeling cortical areas such as hippocampus and olfactory cortex. The learning rule is based on the synaptic plasticity observed experimentally, in particular long-term potentiation and long-term depression of the synaptic efficacies depending on the relative timing of the pre- and postsynaptic activities during learning. The learned memory or representational states can be encoded by both the amplitude and the phase patterns of the oscillating neural populations, enabling more efficient and robust information coding than in conventional models of associative memory or input representation. Depending on the class of nonlinearity of the activation function, the model can function as an associative memory for oscillatory patterns (nonlinearity of class II) or can generalize from or interpolate between the learned states, appropriate for the function of input representation (nonlinearity of class I). In the former case, simulations of the model exhibits a first order transition between the "disordered state" and the "ordered" memory state.

THE USE OF BLENDED LEARNING MODELS IN THE PROCESS OF FOREIGN LANGUAGE LEARNING

Directory of Open Access Journals (Sweden)

Oleksandra Bezverkha

2017-09-01

Full Text Available In the article, the acute problem of implementation of pedagogical innovations and online technologies into the educational process is analyzed. The article explores the advantages of blended learning as a latter-day educational program in comparison with traditional campus learning. Blended learning is regarded worldwide as the combination of classroom face-to-face sessions with interactive learning opportunities created online. The purpose of the article is to identify blended learning transformational potential impacting students and teachers by ensuring a more personalized learning experience. The concept of blended learning, as a means to enhance foreign language teaching and learning in the classroom during the traditional face-to-face interaction between a teacher and a student, combined with computer-mediated activities, is examined. In the article, the main classification of blended learning models is established. There are four main blended learning models which include both face-to-face instruction time and online learning: Rotation Model, Flex Model, A La Carte Model, and Enriched Virtual Model. Once implemented successfully, a blended model can take advantage of both brick-and-mortar and digital worlds, providing significant benefits for the educational establishments and learners. To integrate any of the blended learning models, a teacher can create online activities that enable learners to explore the topic online at home, and then develop face-to-face interactions to dig deeper into the subject matter at the lesson. The use of blended learning models in order to expand educational opportunities for students while the foreign language acquisition, by increasing the availability and flexibility of education, taking into account student individual learning needs, with some element of student control over time, place and pace, is explored. The realization of blended learning models in regards to age and physiological peculiarities of

PENERAPAN MODEL ACTIVE LEARNING PERMAINAN CARD SORT UNTUK MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR MATEMATIKA SISWA KELAS IV SDN 05 METRO SELATAN

Directory of Open Access Journals (Sweden)

Muncarno Muncarno

2015-12-01

Full Text Available This research was motivated by the low activities and student learning outcomes on mathematics. The purpose of this research was to increase the activities and student learning outcomes on mathematics by applying the active learning model of card sort game. The method of this research was classroom action research that consist of planning, implementation, observation, and reflection. The instrument of data collection used was observation sheet and test questions. The technique of analysis data used were qualitative analysis and quantitative analysis. The results of this research showed that aplication of active learning model of card sort game on mathematics learning can increase the activities and student learning outcomes. It can be showed that students learning completeness reached 75%, the average activities of students in the first cycle were 59.80% and 78.39% in the second cycle with the increasing of 18.59%. The average student learning outcomes in the first cycle and the second cycle were 69.52 78.70, with an increase of 9.18.

A CONCEPTUAL MODEL FOR EFFECTIVE DISTANCE LEARNING IN HIGHER A CONCEPTUAL MODEL FOR EFFECTIVE DISTANCE LEARNING IN HIGHER A CONCEPTUAL MODEL FOR EFFECTIVE DISTANCE LEARNING IN HIGHER EDUCATION

Directory of Open Access Journals (Sweden)

Mehran FARAJOLLAHI

2010-07-01

Full Text Available The present research aims at presenting a conceptual model for effective distance learning in higher education. Findings of this research shows that an understanding of the technological capabilities and learning theories especially constructive theory and independent learning theory and communicative and interaction theory in Distance learning is an efficient factor in the planning of effective Distance learning in higher education. Considering the theoretical foundations of the present research, in the effective distance learning model, the learner is situated at the center of learning environment. For this purpose, the learner needs to be ready for successful learning and the teacher has to be ready to design the teaching- learning activities when they initially enter the environment. In the present model, group and individual active teaching-learning approach, timely feedback, using IT and eight types of interactions have been designed with respect to theoretical foundations and current university missions. From among the issues emphasized in this model, one can refer to the Initial, Formative and Summative evaluations. In an effective distance learning environment, evaluation should be part of the learning process and the feedback resulting from it should be used to improve learning. For validating the specified features, the opinions of Distance learning experts in Payame Noor, Shiraz, Science and Technology and Amirkabir Universities have been used which verified a high percentage of the statistical sample of the above mentioned features.

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...

Learning Activities That Combine Science Magic Activities with the 5E Instructional Model to Influence Secondary-School Students' Attitudes to Science

Science.gov (United States)

Lin, Jang-Long; Cheng, Meng-Fei; Chang, Ying-Chi; Li, Hsiao-Wen; Chang, Jih-Yuan; Lin, Deng-Min

2014-01-01

The purpose of this study was to investigate how learning materials based on Science Magic activities affect student attitudes to science. A quasi-experimental design was conducted to explore the combination of Science Magic with the 5E Instructional Model to develop learning materials for teaching a science unit about friction. The participants…

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 ...

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.

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.

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.

Active learning of constitutive relation from mesoscopic dynamics for macroscopic modeling of non-Newtonian flows

Science.gov (United States)

Zhao, Lifei; Li, Zhen; Caswell, Bruce; Ouyang, Jie; Karniadakis, George Em

2018-06-01

We simulate complex fluids by means of an on-the-fly coupling of the bulk rheology to the underlying microstructure dynamics. In particular, a continuum model of polymeric fluids is constructed without a pre-specified constitutive relation, but instead it is actively learned from mesoscopic simulations where the dynamics of polymer chains is explicitly computed. To couple the bulk rheology of polymeric fluids and the microscale dynamics of polymer chains, the continuum approach (based on the finite volume method) provides the transient flow field as inputs for the (mesoscopic) dissipative particle dynamics (DPD), and in turn DPD returns an effective constitutive relation to close the continuum equations. In this multiscale modeling procedure, we employ an active learning strategy based on Gaussian process regression (GPR) to minimize the number of expensive DPD simulations, where adaptively selected DPD simulations are performed only as necessary. Numerical experiments are carried out for flow past a circular cylinder of a non-Newtonian fluid, modeled at the mesoscopic level by bead-spring chains. The results show that only five DPD simulations are required to achieve an effective closure of the continuum equations at Reynolds number Re = 10. Furthermore, when Re is increased to 100, only one additional DPD simulation is required for constructing an extended GPR-informed model closure. Compared to traditional message-passing multiscale approaches, applying an active learning scheme to multiscale modeling of non-Newtonian fluids can significantly increase the computational efficiency. Although the method demonstrated here obtains only a local viscosity from the polymer dynamics, it can be extended to other multiscale models of complex fluids whose macro-rheology is unknown.

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

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...

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…

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.

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

A model of positive and negative learning : Learning demands and resources, learning engagement, critical thinking, and fake news detection

NARCIS (Netherlands)

Dormann, Christian; Demerouti, Eva; Bakker, Arnold; Zlatkin-Troitschanskaia, O.; Wittum, G.; Dengel, A.

2018-01-01

This chapter proposes a model of positive and negative learning (PNL model). We use the term negative learning when stress among students occurs, and when knowledge and abilities are not properly developed. We use the term positive learning if motivation is high and active learning occurs. The PNL

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.

A student-centered approach for developing active learning: the construction of physical models as a teaching tool in medical physiology.

Science.gov (United States)

Rezende-Filho, Flávio Moura; da Fonseca, Lucas José Sá; Nunes-Souza, Valéria; Guedes, Glaucevane da Silva; Rabelo, Luiza Antas

2014-09-15

Teaching physiology, a complex and constantly evolving subject, is not a simple task. A considerable body of knowledge about cognitive processes and teaching and learning methods has accumulated over the years, helping teachers to determine the most efficient way to teach, and highlighting student's active participation as a means to improve learning outcomes. In this context, this paper describes and qualitatively analyzes an experience of a student-centered teaching-learning methodology based on the construction of physiological-physical models, focusing on their possible application in the practice of teaching physiology. After having Physiology classes and revising the literature, students, divided in small groups, built physiological-physical models predominantly using low-cost materials, for studying different topics in Physiology. Groups were followed by monitors and guided by teachers during the whole process, finally presenting the results in a Symposium on Integrative Physiology. Along the proposed activities, students were capable of efficiently creating physiological-physical models (118 in total) highly representative of different physiological processes. The implementation of the proposal indicated that students successfully achieved active learning and meaningful learning in Physiology while addressing multiple learning styles. The proposed method has proved to be an attractive, accessible and relatively simple approach to facilitate the physiology teaching-learning process, while facing difficulties imposed by recent requirements, especially those relating to the use of experimental animals and professional training guidelines. Finally, students' active participation in the production of knowledge may result in a holistic education, and possibly, better professional practices.

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.

Structural reliability analysis under evidence theory using the active learning kriging model

Science.gov (United States)

Yang, Xufeng; Liu, Yongshou; Ma, Panke

2017-11-01

Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush-Kuhn-Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.

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

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.

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.

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

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...

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.

Development of Predictive QSAR Models of 4-Thiazolidinones Antitrypanosomal Activity using Modern Machine Learning Algorithms.

Science.gov (United States)

Kryshchyshyn, Anna; Devinyak, Oleg; Kaminskyy, Danylo; Grellier, Philippe; Lesyk, Roman

2017-11-14

This paper presents novel QSAR models for the prediction of antitrypanosomal activity among thiazolidines and related heterocycles. The performance of four machine learning algorithms: Random Forest regression, Stochastic gradient boosting, Multivariate adaptive regression splines and Gaussian processes regression have been studied in order to reach better levels of predictivity. The results for Random Forest and Gaussian processes regression are comparable and outperform other studied methods. The preliminary descriptor selection with Boruta method improved the outcome of machine learning methods. The two novel QSAR-models developed with Random Forest and Gaussian processes regression algorithms have good predictive ability, which was proved by the external evaluation of the test set with corresponding Q 2 ext =0.812 and Q 2 ext =0.830. The obtained models can be used further for in silico screening of virtual libraries in the same chemical domain in order to find new antitrypanosomal agents. Thorough analysis of descriptors influence in the QSAR models and interpretation of their chemical meaning allows to highlight a number of structure-activity relationships. The presence of phenyl rings with electron-withdrawing atoms or groups in para-position, increased number of aromatic rings, high branching but short chains, high HOMO energy, and the introduction of 1-substituted 2-indolyl fragment into the molecular structure have been recognized as trypanocidal activity prerequisites. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

Machine learning models identify molecules active against the Ebola virus in vitro [version 3; referees: 2 approved

Directory of Open Access Journals (Sweden)

Sean Ekins

2017-01-01

Full Text Available The search for small molecule inhibitors of Ebola virus (EBOV has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in

Machine learning models identify molecules active against the Ebola virus in vitro [version 1; referees: 2 approved

Directory of Open Access Journals (Sweden)

Sean Ekins

2015-10-01

Full Text Available The search for small molecule inhibitors of Ebola virus (EBOV has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in

Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved

Directory of Open Access Journals (Sweden)

Sean Ekins

2016-01-01

Full Text Available The search for small molecule inhibitors of Ebola virus (EBOV has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in

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...

Correlation Between Blended Learning Model With The Perspective Of Learning Effectiveness For Nursing Student

Directory of Open Access Journals (Sweden)

Susila Sumartiningsih

2015-06-01

Full Text Available ABSTRACT The learning model is one of the enabling factors that influence the achievement of students. That students have a good learning outcomes the lecturer must choose appropriate learning models. But in fact not all lecturers choose the most appropriate learning model with the demands of learning outcomes and student characteristics.The study design was descriptive quantitative correlation. Total population of 785 the number of samples are 202 were taken by purposive sampling. Techniques of data collection is done by cross-sectional and then processed through the Spearman test. The results showed no significant relationship between classroom lecture method in the context of blended learning models to study the effectiveness perspective the p value of 0.001. There is a significant relationship between e-learning methods in the context of blended learning models with perspective of activities study of nursing students the p value of 0.028. There is a significant relationship between learning model of blended learning with the perspective of nursing students learning effectiveness p value 0.167. Researchers recommend to future researchers conduct more research on the comparison between the effectiveness of the learning model based on student learning centers with the e-learning models and its impact on student achievement of learning competencies as well as to the implications for other dimensions of learning outcomes and others.

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

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.

Enhancing the T-shaped learning profile when teaching hydrology using data, modeling, and visualization activities

Science.gov (United States)

Sanchez, Christopher A.; Ruddell, Benjamin L.; Schiesser, Roy; Merwade, Venkatesh

2016-03-01

Previous research has suggested that the use of more authentic learning activities can produce more robust and durable knowledge gains. This is consistent with calls within civil engineering education, specifically hydrology, that suggest that curricula should more often include professional perspective and data analysis skills to better develop the "T-shaped" knowledge profile of a professional hydrologist (i.e., professional breadth combined with technical depth). It was expected that the inclusion of a data-driven simulation lab exercise that was contextualized within a real-world situation and more consistent with the job duties of a professional in the field, would provide enhanced learning and appreciation of job duties beyond more conventional paper-and-pencil exercises in a lower-division undergraduate course. Results indicate that while students learned in both conditions, learning was enhanced for the data-driven simulation group in nearly every content area. This pattern of results suggests that the use of data-driven modeling and visualization activities can have a significant positive impact on instruction. This increase in learning likely facilitates the development of student perspective and conceptual mastery, enabling students to make better choices about their studies, while also better preparing them for work as a professional in the field.

Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.

Science.gov (United States)

Nissim, Nir; Shahar, Yuval; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

2017-09-01

Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers' learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by using the labels provided by seven

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.

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

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.

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

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.

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.

Implementation of ICARE learning model using visualization animation on biotechnology course

Science.gov (United States)

Hidayat, Habibi

2017-12-01

ICARE is a learning model that directly ensure the students to actively participate in the learning process using animation media visualization. ICARE have five key elements of learning experience from children and adult that is introduction, connection, application, reflection and extension. The use of Icare system to ensure that participants have opportunity to apply what have been they learned. So that, the message delivered by lecture to students can be understood and recorded by students in a long time. Learning model that was deemed capable of improving learning outcomes and interest to learn in following learning process Biotechnology with applying the ICARE learning model using visualization animation. This learning model have been giving motivation to participate in the learning process and learning outcomes obtained becomes more increased than before. From the results of student learning in subjects Biotechnology by applying the ICARE learning model using Visualization Animation can improving study results of student from the average value of middle test amounted to 70.98 with the percentage of 75% increased value of final test to be 71.57 with the percentage of 68.63%. The interest to learn from students more increasing visits of student activities at each cycle, namely the first cycle obtained average value by 33.5 with enough category. The second cycle is obtained an average value of 36.5 to good category and third cycle the average value of 36.5 with a student activity to good category.

Inter-Labeler and Intra-Labeler Variability of Condition Severity Classification Models Using Active and Passive Learning Methods

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Nissim, Nir; Shahar, Yuval; Boland, Mary Regina; Tatonetti, Nicholas P; Elovici, Yuval; Hripcsak, George; Moskovitch, Robert

2018-01-01

Background and Objectives Labeling instances by domain experts for classification is often time consuming and expensive. To reduce such labeling efforts, we had proposed the application of active learning (AL) methods, introduced our CAESAR-ALE framework for classifying the severity of clinical conditions, and shown its significant reduction of labeling efforts. The use of any of three AL methods (one well known [SVM-Margin], and two that we introduced [Exploitation and Combination_XA]) significantly reduced (by 48% to 64%) condition labeling efforts, compared to standard passive (random instance-selection) SVM learning. Furthermore, our new AL methods achieved maximal accuracy using 12% fewer labeled cases than the SVM-Margin AL method. However, because labelers have varying levels of expertise, a major issue associated with learning methods, and AL methods in particular, is how to best to use the labeling provided by a committee of labelers. First, we wanted to know, based on the labelers’ learning curves, whether using AL methods (versus standard passive learning methods) has an effect on the Intra-labeler variability (within the learning curve of each labeler) and inter-labeler variability (among the learning curves of different labelers). Then, we wanted to examine the effect of learning (either passively or actively) from the labels created by the majority consensus of a group of labelers. Methods We used our CAESAR-ALE framework for classifying the severity of clinical conditions, the three AL methods and the passive learning method, as mentioned above, to induce the classifications models. We used a dataset of 516 clinical conditions and their severity labeling, represented by features aggregated from the medical records of 1.9 million patients treated at Columbia University Medical Center. We analyzed the variance of the classification performance within (intra-labeler), and especially among (inter-labeler) the classification models that were induced by

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

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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.

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

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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.

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

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

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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.

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

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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

Active Learning Methods

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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…

PENINGKATKAN HASIL BELAJAR PKN KELAS V MELALUI MODEL ACTIVE LEARNING (TIPE ROLE REVERSAL QUESTION SDN 4 DOPLANG KECAMATAN JATI KABUPATEN BLORA

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Ambar Susilo Murti

2016-12-01

Full Text Available Constitution No. 20 of 2003, concerning Citizenship Education (PKN is a compulsory subject for primary education, secondary, and compulsory subjects for higher education. The purpose of this study is to improve learning outcomes Civics using Active Learning Model Type Question Role Reversal in Class V SDN 4 Doplang Jati district of  Blora. This research was a class action (classroom action research. The stages as follows: (1 planning, (2 implementation, (3 observation and (4 reflection. The result of research indicating that students who received grades ≥70 the first cycle increased by 25%  from the initial 44% to 69%. Then students who scored ≥70 on the second cycle increased 28% to 97%. The average value of the first cycle increased by 8.75% from 66.53 into 75.28 early in the first cycle and then the second cycle of the average value increased again by 10.97% to 86.25. Researchers suggest teachers should encourage students to be more daring in expressing opinions, questions and ideas that are not held only in Civics alone but on other subjects. In addition, teachers are expected to use active learning model of the type of role reversal question in improving student learning outcomes in other subjects. As for the school is expected to provide training to teachers on implementing learning activities are innovative and creative. Keywords: active learning, civic education, learning outcomes.

The active learning hypothesis of the job-demand-control model: an experimental examination.

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Häusser, Jan Alexander; Schulz-Hardt, Stefan; Mojzisch, Andreas

2014-01-01

The active learning hypothesis of the job-demand-control model [Karasek, R. A. 1979. "Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign." Administration Science Quarterly 24: 285-307] proposes positive effects of high job demands and high job control on performance. We conducted a 2 (demands: high vs. low) × 2 (control: high vs. low) experimental office workplace simulation to examine this hypothesis. Since performance during a work simulation is confounded by the boundaries of the demands and control manipulations (e.g. time limits), we used a post-test, in which participants continued working at their task, but without any manipulation of demands and control. This post-test allowed for examining active learning (transfer) effects in an unconfounded fashion. Our results revealed that high demands had a positive effect on quantitative performance, without affecting task accuracy. In contrast, high control resulted in a speed-accuracy tradeoff, that is participants in the high control conditions worked slower but with greater accuracy than participants in the low control conditions.

Model-observer similarity, error modeling and social learning in rhesus macaques.

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Elisabetta Monfardini

Full Text Available Monkeys readily learn to discriminate between rewarded and unrewarded items or actions by observing their conspecifics. However, they do not systematically learn from humans. Understanding what makes human-to-monkey transmission of knowledge work or fail could help identify mediators and moderators of social learning that operate regardless of language or culture, and transcend inter-species differences. Do monkeys fail to learn when human models show a behavior too dissimilar from the animals' own, or when they show a faultless performance devoid of error? To address this question, six rhesus macaques trained to find which object within a pair concealed a food reward were successively tested with three models: a familiar conspecific, a 'stimulus-enhancing' human actively drawing the animal's attention to one object of the pair without actually performing the task, and a 'monkey-like' human performing the task in the same way as the monkey model did. Reward was manipulated to ensure that all models showed equal proportions of errors and successes. The 'monkey-like' human model improved the animals' subsequent object discrimination learning as much as a conspecific did, whereas the 'stimulus-enhancing' human model tended on the contrary to retard learning. Modeling errors rather than successes optimized learning from the monkey and 'monkey-like' models, while exacerbating the adverse effect of the 'stimulus-enhancing' model. These findings identify error modeling as a moderator of social learning in monkeys that amplifies the models' influence, whether beneficial or detrimental. By contrast, model-observer similarity in behavior emerged as a mediator of social learning, that is, a prerequisite for a model to work in the first place. The latter finding suggests that, as preverbal infants, macaques need to perceive the model as 'like-me' and that, once this condition is fulfilled, any agent can become an effective model.

Applying Active Learning to Assertion Classification of Concepts in Clinical Text

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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

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.

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.

Business Simulation as an Active Learning Activity for Developing Soft Skills

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Levant, Yves; Coulmont, Michel; Sandu, Raluca

2016-01-01

Business simulations are innovative instruction models for active or cooperative learning. In this paper, we look at the social constructionist roots of these education models in light of the current efforts to enhance employability skills in undergraduate and graduate studies. More specifically, we analyse the role of business simulations in…

Simulation modelling: educational development roles for learning technologists

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David Riley

2002-12-01

Full Text Available Simulation modelling was in the mainstream of CAL development in the 1980s when the late David Squires introduced this author to the Dynamic Modelling System. Since those early days, it seems that simulation modelling has drifted into a learning technology backwater to become a member of Laurillard's underutilized, 'adaptive and productive' media. Referring to her Conversational Framework, Laurillard constructs a pedagogic case for modelling as a productive student activity but provides few references to current practice and available resources. This paper seeks to complement her account by highlighting the pioneering initiatives of the Computers in the Curriculum Project and more recent developments in systems modelling within geographic and business education. The latter include improvements to system dynamics modelling programs such as STELLA®, the publication of introductory textbooks, and the emergence of online resources. The paper indicates several ways in which modelling activities may be approached and identifies some educational development roles for learning technologists. The paper concludes by advocating simulation modelling as an exemplary use of learning technologies - one that realizes their creative-transformative potential.

Representative Model of the Learning Process in Virtual Spaces Supported by ICT

Science.gov (United States)

Capacho, José

2014-01-01

This paper shows the results of research activities for building the representative model of the learning process in virtual spaces (e-Learning). The formal basis of the model are supported in the analysis of models of learning assessment in virtual spaces and specifically in Dembo´s teaching learning model, the systemic approach to evaluating…

Model brain based learning (BBL and whole brain teaching (WBT in learning

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Baiq Sri Handayani

2017-08-01

Full Text Available The learning process is a process of change in behavior as a form of the result of learning. The learning model is a crucial component of the success of the learning process. The learning model is growing fastly, and each model has different characteristics. Teachers are required to be able to understand each model to teach the students optimally by matching the materials and the learning model. The best of the learning model is the model that based on the brain system in learning that are the model of Brain Based Learning (BBL and the model of Whole Brain Teaching (WBT. The purposes of this article are to obtain information related to (1 the brain’s natural learning system, (2 analyze the characteristics of the model BBL and WBT based on theory, brain sections that play a role associated with syntax, similarities, and differences, (3 explain the distinctive characteristics of both models in comparison to other models. The results of this study are: (1 the brain’s natural learning system are: (a the nerves in each hemisphere do not work independently, (b doing more activities can connect more brain nerves, (c the right hemisphere controls the left side motoric sensor of the body, and vice versa; (2 the characteristics of BBL and WBT are: (a BBL is based on the brain’s structure and function, while the model WBT is based on the instructional approach, neurolinguistic, and body language, (b the parts of the brain that work in BBL are: cerebellum, cerebral cortex, frontal lobe, limbic system, and prefrontal cortex; whereas the parts that work WBT are: prefrontal cortex, visual cortex, motor cortex, limbic system, and amygdala, (c the similarities between them are that they both rely on the brain’s system and they both promote gesture in learning, whereas the differences are on the view of the purposes of gestures and the learning theory that they rely on. BBL relies on cognitive theory while WBT relies on social theory; (3 the typical

Cognitive components underpinning the development of model-based learning.

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Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

2017-06-01

Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

The Influence Of Learning Model Guided Findings Of Student Learning Outcomes

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A. SaefulBahri

2015-03-01

Full Text Available Abstract This study examines the influence of the learning model guided findings on student learning outcomes in subjects PAI eighth grade students of SMP Plus al Masoem. The research method used in this study is a quantitative method in the form of quasi-experiment Quasi-Experimental Design. The findings of the study are expected to demonstrate 1 the difference significant increase in learning outcomes between the experimental class using guided discovery method that uses the control class discussion of learning models 2 Constraints in the method of guided discovery activities and the limited ability of educators in the experimental class in implements the method of guided discovery and constraints faced by students while digging the information they need so we need special strategies to motivate students in the experimental class in order for them creatively find the right way to gather information that supports learning PAI.

On hierarchical models for visual recognition and learning of objects, scenes, and activities

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Spehr, Jens

2015-01-01

In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model...

The Implementation of Discovery Learning Model with Scientific Learning Approach to Improve Students’ Critical Thinking in Learning History

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Edi Nurcahyo

2018-03-01

Full Text Available Historical learning has not reached optimal in the learning process. It is caused by the history teachers’ learning model has not used the innovative learning models. Furthermore, it supported by the perception of students to the history subject because it does not become final exam (UN subject so it makes less improvement and builds less critical thinking in students’ daily learning. This is due to the lack of awareness of historical events and the availability of history books for students and teachers in the library are still lacking. Discovery learning with scientific approach encourages students to solve problems actively and able to improve students' critical thinking skills with scientific approach so student can build scientific thinking include observing, asking, reasoning, trying, and networking   Keywords: discovery learning, scientific, critical thinking

The behavioural motivation model in open distance learning

DEFF Research Database (Denmark)

Zaikin, Oleg; Malinowska, Magdalena; Kofoed, Lise B.

2014-01-01

The article contains the concept of developing a motivation model aimed at supporting activity of both students and teachers in the process of implementing and using an open and distance learning system. Proposed motivation model is focused on the task of filling the knowledge repository with high...... quality didactic material. Open and distance learning system assures a computer space for the teaching/learning process in open environment. The structure of the motivation model and formal assumptions are described. Additionally, there is presented a structure of the linguistic database, helping...... the teacher to assess the student's motivation and the basic simulation model to analysis the teaching/learning process constrains. The proposed approach is based on the games theory and simulation approach....

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...

Collective learning modeling based on the kinetic theory of active particles

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Burini, D.; De Lillo, S.; Gibelli, L.

2016-03-01

This paper proposes a systems approach to the theory of perception and learning in populations composed of many living entities. Starting from a phenomenological description of these processes, a mathematical structure is derived which is deemed to incorporate their complexity features. The modeling is based on a generalization of kinetic theory methods where interactions are described by theoretical tools of game theory. As an application, the proposed approach is used to model the learning processes that take place in a classroom.

The Use of Problem-Based Learning Model to Improve Quality Learning Students Morals

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Nurzaman

2017-01-01

Model of moral cultivation in MTsN Bangunharja done using three methods, classical cultivation methods, extra-curricular activities in the form of religious activities, scouting, sports, and Islamic art, and habituation of morals. Problem base learning models in MTsN Bangunharja applied using the following steps: find the problem, define the…

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

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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.

The Design of Effective ICT-Supported Learning Activities: Exemplary Models, Changing Requirements, and New Possibilities

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Cameron Richards

2005-01-01

Full Text Available Despite the imperatives of policy and rhetoric about their integration in formal education, Information and Communication Technologies (ICTs are often used as an "add-on" in many classrooms and in many lesson plans. Nevertheless, many teachers find that interesting and well-planned tasks, projects, and resources provide a key to harnessing the educational potential of digital resources, Internet communications and interactive multimedia to engage the interest, interaction, and knowledge construction of young learners. To the extent that such approaches go beyond and transform traditional "transmission" models of teaching and formal lesson planning, this paper investigates the changing requirements and new possibilities represented by the challenge of integrating ICTs in education in a way which at the same time connects more effectively with both the specific contents of the curriculum and the various stages and elements of the learning process. Case studies from teacher education foundation courses provide an exemplary focus of inquiry in order to better link relevant new theories or models of learning with practice, to build upon related learner-centered strategies for integrating ICT resources and tools, and to incorporate interdependent functions of learning as information access, communication, and applied interactions. As one possible strategy in this direction, the concept of an "ICT-supported learning activity" suggests the need for teachers to approach this increasing challenge more as "designers" of effective and integrated learning rather than mere "transmitters" of skills or information through an add-on use of ICTs.

The philosophical and pedagogical underpinnings of Active Learning in Engineering Education

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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.

Learning situation models in a smart home.

Science.gov (United States)

Brdiczka, Oliver; Crowley, James L; Reignier, Patrick

2009-02-01

This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.

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.

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.

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

Learning the ShamWow: Creating Infomercials to Teach the AIDA Model

Science.gov (United States)

Lee, Seung Hwan; Hoffman, K. Douglas

2015-01-01

The AIDA Model (Attention-Interest-Desire-Action) is one of the classical promotional theories in marketing. Through active-learning techniques and peer critiques, we use infomercials as an innovative educational tool to instruct the four components of the AIDA model. Student evaluations regarding this active-learning assignment reveal that the…

Modellus: Learning Physics with Mathematical Modelling

Science.gov (United States)

Teodoro, Vitor

Computers are now a major tool in research and development in almost all scientific and technological fields. Despite recent developments, this is far from true for learning environments in schools and most undergraduate studies. This thesis proposes a framework for designing curricula where computers, and computer modelling in particular, are a major tool for learning. The framework, based on research on learning science and mathematics and on computer user interface, assumes that: 1) learning is an active process of creating meaning from representations; 2) learning takes place in a community of practice where students learn both from their own effort and from external guidance; 3) learning is a process of becoming familiar with concepts, with links between concepts, and with representations; 4) direct manipulation user interfaces allow students to explore concrete-abstract objects such as those of physics and can be used by students with minimal computer knowledge. Physics is the science of constructing models and explanations about the physical world. And mathematical models are an important type of models that are difficult for many students. These difficulties can be rooted in the fact that most students do not have an environment where they can explore functions, differential equations and iterations as primary objects that model physical phenomena--as objects-to-think-with, reifying the formal objects of physics. The framework proposes that students should be introduced to modelling in a very early stage of learning physics and mathematics, two scientific areas that must be taught in very closely related way, as they were developed since Galileo and Newton until the beginning of our century, before the rise of overspecialisation in science. At an early stage, functions are the main type of objects used to model real phenomena, such as motions. At a later stage, rates of change and equations with rates of change play an important role. This type of equations

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 ...

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.

Collective learning modeling based on the kinetic theory of active particles.

Science.gov (United States)

Burini, D; De Lillo, S; Gibelli, L

2016-03-01

This paper proposes a systems approach to the theory of perception and learning in populations composed of many living entities. Starting from a phenomenological description of these processes, a mathematical structure is derived which is deemed to incorporate their complexity features. The modeling is based on a generalization of kinetic theory methods where interactions are described by theoretical tools of game theory. As an application, the proposed approach is used to model the learning processes that take place in a classroom. Copyright © 2015 Elsevier B.V. All rights reserved.

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

Learning Survival Models with On-Line Simulation Activities in the Actuarial Science Degree

Directory of Open Access Journals (Sweden)

Antonio Fernandez-Morales

2011-03-01

Full Text Available The aim of this paper is to describe an on-line survival laboratory designed to enhance teaching and learning in the Statistics courses of the Actuarial Science Degree of the Uni-versity of Málaga. The objective of the on-line survival lab is to help students through a guided program of simulation activities with the understanding of the most important statistical concepts of the stochastic modeling of human survival, from an Actuarial point of view. The graphical interactive simulator is implemented as Java applets for the web version, and as a Javascript animation for a lite iPhone/iPod touch version. Finally, the results of a survey carried out at the end of the course are discussed to have a preliminary assessment of the students’ satisfaction with the resources, and their perception about the usefulness for their learning process.

Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students.

Science.gov (United States)

Khumrin, Piyapong; Ryan, Anna; Judd, Terry; Verspoor, Karin

2017-01-01

Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e. personalised feedback) is both critical and challenging. In this paper, we describe the development of a machine learning model to support medical students' diagnostic decisions. Machine learning models were trained on 208 clinical cases presenting with abdominal pain, to predict five diagnoses. We assessed which of these models are likely to be most effective for use in an e-learning tool that allows students to interact with a virtual patient. The broader goal is to utilise these models to generate personalised feedback based on the specific patient information requested by students and their active diagnostic hypotheses.

Mosaic model for sensorimotor learning and control.

Science.gov (United States)

Haruno, M; Wolpert, D M; Kawato, M

2001-10-01

Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor) and inverse (controller) models. The architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the set of inverse models appropriate for a given environment. It combines both feedforward and feedback sensorimotor information so that the controllers can be selected both prior to movement and subsequently during movement. This article extends and evaluates the MOSAIC architecture in the following respects. The learning in the architecture was implemented by both the original gradient-descent method and the expectation-maximization (EM) algorithm. Unlike gradient descent, the newly derived EM algorithm is robust to the initial starting conditions and learning parameters. Second, simulations of an object manipulation task prove that the architecture can learn to manipulate multiple objects and switch between them appropriately. Moreover, after learning, the model shows generalization to novel objects whose dynamics lie within the polyhedra of already learned dynamics. Finally, when each of the dynamics is associated with a particular object shape, the model is able to select the appropriate controller before movement execution. When presented with a novel shape-dynamic pairing, inappropriate activation of modules is observed followed by on-line correction.

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).

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

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...

Beyond the Didactic Classroom: Educational Models to Encourage Active Student Involvement in Learning

OpenAIRE

Shreeve, Michael W.

2008-01-01

In a chiropractic college that utilizes a hybrid curriculum model composed of adult-based learning strategies along with traditional lecture-based course delivery, a literature search for educational delivery methods that would integrate the affective domain and the cognitive domain of learning provided some insights into the use of problem-based learning (PBL), experiential learning theory (ELT), and the emerging use of appreciative inquiry (AI) to enhance the learning experience. The purpos...

Effects of Problem-Based Learning Model versus Expository Model and Motivation to Achieve for Student's Physic Learning Result of Senior High School at Class XI

Science.gov (United States)

Prayekti

2016-01-01

"Problem-based learning" (PBL) is one of an innovative learning model which can provide an active learning to student, include the motivation to achieve showed by student when the learning is in progress. This research is aimed to know: (1) differences of physic learning result for student group which taught by PBL versus expository…

A developmental approach to learning causal models for cyber security

Science.gov (United States)

Mugan, Jonathan

2013-05-01

To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis.

Science.gov (United States)

Isupova, Olga; Kuzin, Danil; Mihaylova, Lyudmila

2017-09-27

Semisupervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators' load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this paper proposes new learning algorithms for activity analysis in video. The activities and behaviors are described by a dynamic topic model. Two novel learning algorithms based on the expectation maximization approach and variational Bayes inference are proposed. Theoretical derivations of the posterior estimates of model parameters are given. The designed learning algorithms are compared with the Gibbs sampling inference scheme introduced earlier in the literature. A detailed comparison of the learning algorithms is presented on real video data. We also propose an anomaly localization procedure, elegantly embedded in the topic modeling framework. It is shown that the developed learning algorithms can achieve 95% success rate. The proposed framework can be applied to a number of areas, including transportation systems, security, and surveillance.

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…

Active Learning for Directed Exploration of Complex Systems

Science.gov (United States)

Burl, Michael C.; Wang, Esther

2009-01-01

Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.

Probabilistic electricity price forecasting with variational heteroscedastic Gaussian process and active learning

International Nuclear Information System (INIS)

Kou, Peng; Liang, Deliang; Gao, Lin; Lou, Jianyong

2015-01-01

Highlights: • A novel active learning model for the probabilistic electricity price forecasting. • Heteroscedastic Gaussian process that captures the local volatility of the electricity price. • Variational Bayesian learning that avoids over-fitting. • Active learning algorithm that reduces the computational efforts. - Abstract: Electricity price forecasting is essential for the market participants in their decision making. Nevertheless, the accuracy of such forecasting cannot be guaranteed due to the high variability of the price data. For this reason, in many cases, rather than merely point forecasting results, market participants are more interested in the probabilistic price forecasting results, i.e., the prediction intervals of the electricity price. Focusing on this issue, this paper proposes a new model for the probabilistic electricity price forecasting. This model is based on the active learning technique and the variational heteroscedastic Gaussian process (VHGP). It provides the heteroscedastic Gaussian prediction intervals, which effectively quantify the heteroscedastic uncertainties associated with the price data. Because the high computational effort of VHGP hinders its application to the large-scale electricity price forecasting tasks, we design an active learning algorithm to select a most informative training subset from the whole available training set. By constructing the forecasting model on this smaller subset, the computational efforts can be significantly reduced. In this way, the practical applicability of the proposed model is enhanced. The forecasting performance and the computational time of the proposed model are evaluated using the real-world electricity price data, which is obtained from the ANEM, PJM, and New England ISO

Vodcasts and active-learning exercises in a "flipped classroom" model of a renal pharmacotherapy module.

Science.gov (United States)

Pierce, Richard; Fox, Jeremy

2012-12-12

To implement a "flipped classroom" model for a renal pharmacotherapy topic module and assess the impact on pharmacy students' performance and attitudes. Students viewed vodcasts (video podcasts) of lectures prior to the scheduled class and then discussed interactive cases of patients with end-stage renal disease in class. A process-oriented guided inquiry learning (POGIL) activity was developed and implemented that complemented, summarized, and allowed for application of the material contained in the previously viewed lectures. Students' performance on the final examination significantly improved compared to performance of students the previous year who completed the same module in a traditional classroom setting. Students' opinions of the POGIL activity and the flipped classroom instructional model were mostly positive. Implementing a flipped classroom model to teach a renal pharmacotherapy module resulted in improved student performance and favorable student perceptions about the instructional approach. Some of the factors that may have contributed to students' improved scores included: student mediated contact with the course material prior to classes, benchmark and formative assessments administered during the module, and the interactive class activities.

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

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.

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

THE EFFECTIVENESS OF CTL MODEL GUIDED INQUIRI-BASED IN THE TOPIC OF CHEMICALS IN DAILY LIFE TO IMPROVE STUDENTS’ LEARNING OUTCOMES AND ACTIVENESS

OpenAIRE

N. R. Fitriani; A. Widiyatmoko; M. Khusniati

2016-01-01

Science learning in school can be applied by connecting the material in the learning with real life. However in fact science learning process in SMP Negeri 10 Magelang has not emphasized students’ activity to relate science to real life. Learning science using CTL guided inquiry-based model implement the learning in where teacher provides initial questions related issues or events in everyday life, then students do experiments to prove concepts of science guided by teacher.The purpose of this...

The POL Model: Using a Social Constructivist Framework to Develop Blended and Online Learning

DEFF Research Database (Denmark)

Dalsgaard, Christian; Godsk, Mikkel

2007-01-01

The paper presents a model for developing blended and online learning based on a given curriculum and typical learning objectives for university courses. The model consists of a three-step-process in which the instructor formulates product-oriented tasks, develops and structures the learning...... materials and tools, outlines a schedule, and supports the students' learning activity in developing a product. The model is based on our experiences with transforming traditional lecture-based lessons into problem-based blended and online learning using a social constructivist approach and a standard...... virtual learning environment (VLE). Our initial experiments indicate that our model is useful to develop blended and online modules and, furthermore, it seems fruitful to use a social constructivist framework and orienting learning activities towards the development of products....

Student Modelling in Adaptive E-Learning Systems

Directory of Open Access Journals (Sweden)

Clemens Bechter

2011-09-01

Full Text Available Most e-Learning systems provide web-based learning so that students can access the same online courses via the Internet without adaptation, based on each student's profile and behavior. In an e-Learning system, one size does not fit all. Therefore, it is a challenge to make e-Learning systems that are suitably “adaptive”. The aim of adaptive e-Learning is to provide the students the appropriate content at the right time, means that the system is able to determine the knowledge level, keep track of usage, and arrange content automatically for each student for the best learning result. This study presents a proposed system which includes major adaptive features based on a student model. The proposed system is able to initialize the student model for determining the knowledge level of a student when the student registers for the course. After a student starts learning the lessons and doing many activities, the system can track information of the student until he/she takes a test. The student’s knowledge level, based on the test scores, is updated into the system for use in the adaptation process, which combines the student model with the domain model in order to deliver suitable course contents to the students. In this study, the proposed adaptive e-Learning system is implemented on an “Introduction to Java Programming Language” course, using LearnSquare software. After the system was tested, the results showed positive feedback towards the proposed system, especially in its adaptive capability.

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 =…

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.

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...

Active machine learning-driven experimentation to determine compound effects on protein patterns.

Science.gov (United States)

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-02-03

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

Improvements from a flipped classroom may simply be the fruits of active learning.

Science.gov (United States)

Jensen, Jamie L; Kummer, Tyler A; d M Godoy, Patricia D

2015-03-02

The "flipped classroom" is a learning model in which content attainment is shifted forward to outside of class, then followed by instructor-facilitated concept application activities in class. Current studies on the flipped model are limited. Our goal was to provide quantitative and controlled data about the effectiveness of this model. Using a quasi-experimental design, we compared an active nonflipped classroom with an active flipped classroom, both using the 5-E learning cycle, in an effort to vary only the role of the instructor and control for as many of the other potentially influential variables as possible. Results showed that both low-level and deep conceptual learning were equivalent between the conditions. Attitudinal data revealed equal student satisfaction with the course. Interestingly, both treatments ranked their contact time with the instructor as more influential to their learning than what they did at home. We conclude that the flipped classroom does not result in higher learning gains or better attitudes compared with the nonflipped classroom when both utilize an active-learning, constructivist approach and propose that learning gains in either condition are most likely a result of the active-learning style of instruction rather than the order in which the instructor participated in the learning process. © 2015 J. L. Jensen et al. CBE—Life Sciences Education © 2015 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).

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.

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.

Modeling individuals’ cognitive and affective responses in spatial learning behavior

NARCIS (Netherlands)

Han, Q.; Arentze, T.A.; Timmermans, H.J.P.; Janssens, D.; Wets, G.; Lo, H.P.; Leung, Stephen C.H.; Tan, Susanna M.L.

2008-01-01

Activity-based analysis has slowly shifted gear from analysis of daily activity patterns to analysis and modeling of dynamic activity-travel patterns. In this paper, we describe a dynamic model that is concerned with simulating cognitive and affective responses in spatial learning behavior for a

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

Science.gov (United States)

Lane, Thomas; Russo, Daniel P; Zorn, Kimberley M; Clark, Alex M; Korotcov, Alexandru; Tkachenko, Valery; Reynolds, Robert C; Perryman, Alexander L; Freundlich, Joel S; Ekins, Sean

2018-04-26

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

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…

Culture in Transition: A learning model

DEFF Research Database (Denmark)

Baca, Susan

2010-01-01

of organizational transition, and 3) demonstrating the efficacy of the model by using it to explain empirical research findings. It is argued that learning new cultural currency involves the use of active intelligence to locate and answer relevant questions, and further that this process requires the interplay......This paper addresses the problem of resistance to attempted changes in organizational culture, particularly those involving diversity, by 1) identifying precisely what is meant by organizational as opposed to societal culture, 2) developing a theoretical model of learning useful in contexts...... is useful for both management and labor in regulating transition processes, thus making a contribution to industrial relations....

Implementation of K-Means Clustering Method for Electronic Learning Model

Science.gov (United States)

Latipa Sari, Herlina; Suranti Mrs., Dewi; Natalia Zulita, Leni

2017-12-01

Teaching and Learning process at SMK Negeri 2 Bengkulu Tengah has applied e-learning system for teachers and students. The e-learning was based on the classification of normative, productive, and adaptive subjects. SMK Negeri 2 Bengkulu Tengah consisted of 394 students and 60 teachers with 16 subjects. The record of e-learning database was used in this research to observe students’ activity pattern in attending class. K-Means algorithm in this research was used to classify students’ learning activities using e-learning, so that it was obtained cluster of students’ activity and improvement of student’s ability. Implementation of K-Means Clustering method for electronic learning model at SMK Negeri 2 Bengkulu Tengah was conducted by observing 10 students’ activities, namely participation of students in the classroom, submit assignment, view assignment, add discussion, view discussion, add comment, download course materials, view article, view test, and submit test. In the e-learning model, the testing was conducted toward 10 students that yielded 2 clusters of membership data (C1 and C2). Cluster 1: with membership percentage of 70% and it consisted of 6 members, namely 1112438 Anggi Julian, 1112439 Anis Maulita, 1112441 Ardi Febriansyah, 1112452 Berlian Sinurat, 1112460 Dewi Anugrah Anwar and 1112467 Eka Tri Oktavia Sari. Cluster 2:with membership percentage of 30% and it consisted of 4 members, namely 1112463 Dosita Afriyani, 1112471 Erda Novita, 1112474 Eskardi and 1112477 Fachrur Rozi.

Active Player Modeling in the Iterated Prisoner's Dilemma.

Science.gov (United States)

Park, Hyunsoo; Kim, Kyung-Joong

2016-01-01

The iterated prisoner's dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents' actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player's behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent's behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent's behavior than when the data were collected through random actions.

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...

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...

A model of olfactory associative learning

Science.gov (United States)

Tavoni, Gaia; Balasubramanian, Vijay

We propose a mechanism, rooted in the known anatomy and physiology of the vertebrate olfactory system, by which presentations of rewarded and unrewarded odors lead to formation of odor-valence associations between piriform cortex (PC) and anterior olfactory nucleus (AON) which, in concert with neuromodulators release in the bulb, entrains a direct feedback from the AON representation of valence to a group of mitral cells (MCs). The model makes several predictions concerning MC activity during and after associative learning: (a) AON feedback produces synchronous divergent responses in a localized subset of MCs; (b) such divergence propagates to other MCs by lateral inhibition; (c) after learning, MC responses reconverge; (d) recall of the newly formed associations in the PC increases feedback inhibition in the MCs. These predictions have been confirmed in disparate experiments which we now explain in a unified framework. For cortex, our model further predicts that the response divergence developed during learning reshapes odor representations in the PC, with the effects of (a) decorrelating PC representations of odors with different valences, (b) increasing the size and reliability of those representations, and enabling recall correction and redundancy reduction after learning. Simons Foundation for Mathematical Modeling of Living Systems.

FACILITATING THE STATISTIC’S LEARNING: A B-LEARNING MODEL FOR OUR STUDENTS

Directory of Open Access Journals (Sweden)

Miguel Ángel Montero

2011-05-01

Full Text Available The experience which we count with in the university education, the development of the ICT (Information and Communications Technology, the integration in the ESSE, the new qualifications (or Grades and mainly the desire to improve push us to innovate and to put into practice new methodologies in the teaching and learning of the subjects of Mathematics and Statistic assigned to our department. These methods totally renovate the lecturer’s roll and the traditional teaching, introducing multimedia tools, support platforms and new resources that provide students an autonomy which before they did not have, modifying the organization of time and space, increasing modalities and strategies of teaching-learning-tutorization and therefore developing more flexible models. It is tried to facilitate the learning of these subjects, providing a model b-learning, a comple- ment or alternative to the attendance classes, reinforcing the student’s active self-training.

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.

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.

A model for hypermedia learning environments based on electronic books

Directory of Open Access Journals (Sweden)

Ignacio Aedo

1997-12-01

Full Text Available Current hypermedia learning environments do not have a common development basis. Their designers have often used ad-hoc solutions to solve the learning problems they have encountered. However, hypermedia technology can take advantage of employing a theoretical scheme - a model - which takes into account various kinds of learning activities, and solves some of the problems associated with its use in the learning process. The model can provide designers with the tools for creating a hypermedia learning system, by allowing the elements and functions involved in the definition of a specific application to be formally represented.

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.

THE PROPOSED MODEL OF COLLABORATIVE VIRTUAL LEARNING ENVIRONMENT FOR INTRODUCTORY PROGRAMMING COURSE

Directory of Open Access Journals (Sweden)

Mahfudzah OTHMAN

2012-01-01

Full Text Available This paper discusses the proposed model of the collaborative virtual learning system for the introductory computer programming course which uses one of the collaborative learning techniques known as the “Think-Pair-Share”. The main objective of this study is to design a model for an online learning system that facilitates the collaborative learning activities in a virtual environment such as online communications and pair or small group discussions. In order to model the virtual learning environment, the RUP methodology has been used where it involves the data collection phase and the analysis and design phase. Fifty respondents have been randomly selected to participate in the data collection phase to investigate the students’ interest and learning styles as well as their learning preferences. The results have shown the needs for the development of online small group discussions that can be used as an alternative learning style for programming courses. The proposed design of the virtual learning system named as the Online Collaborative Learning System or OCLS is being depicted using the object-oriented models which are the use-case model and class diagram in order to show the concise processes of virtual “Think-Pair-Share” collaborative activities. The “Think-Pair-Share” collaborative learning technique that is being used in this model has been chosen because of its simplicity and relatively low-risk. This paper also presents the proposed model of the system’s architecture that will become the guidelines for the physical development of OCLS using the web-based applications.

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.

A Pedagogical Model for Science Education through Blended Learning

NARCIS (Netherlands)

Bidarra, José; Rusman, Ellen

2015-01-01

This paper proposes a framework to support science education through blended learning, based on a participatory and interactive approach supported by ICT-based tools, called Science Learning Activities Model (SLAM). The study constitutes a work in progress and started as a response to complex

Fostering the development of effective person-centered healthcare communication skills: an interprofessional shared learning model.

Science.gov (United States)

Cavanaugh, James T; Konrad, Shelley Cohen

2012-01-01

To describe the implementation of an interprofessional shared learning model designed to promote the development of person-centered healthcare communication skills. Master of social work (MSW) and doctor of physical therapy (DPT) degree students. The model used evidence-based principles of effective healthcare communication and shared learning methods; it was aligned with student learning outcomes contained in MSW and DPT curricula. Students engaged in 3 learning sessions over 2 days. Sessions involved interactive reflective learning, simulated role-modeling with peer assessment, and context-specific practice of communication skills. The perspective of patients/clients was included in each learning activity. Activities were evaluated through narrative feedback. Students valued opportunities to learn directly from each other and from healthcare consumers. Important insights and directions for future interprofessional learning experiences were gleaned from model implementation. The interprofessional shared learning model shows promise as an effective method for developing person-centered communication skills.

Flipped classroom model for learning evidence-based medicine.

Science.gov (United States)

Rucker, Sydney Y; Ozdogan, Zulfukar; Al Achkar, Morhaf

2017-01-01

Journal club (JC), as a pedagogical strategy, has long been used in graduate medical education (GME). As evidence-based medicine (EBM) becomes a mainstay in GME, traditional models of JC present a number of insufficiencies and call for novel models of instruction. A flipped classroom model appears to be an ideal strategy to meet the demands to connect evidence to practice while creating engaged, culturally competent, and technologically literate physicians. In this article, we describe a novel model of flipped classroom in JC. We present the flow of learning activities during the online and face-to-face instruction, and then we highlight specific considerations for implementing a flipped classroom model. We show that implementing a flipped classroom model to teach EBM in a residency program not only is possible but also may constitute improved learning opportunity for residents. Follow-up work is needed to evaluate the effectiveness of this model on both learning and clinical practice.

Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning.

Science.gov (United States)

Doll, Bradley B; Bath, Kevin G; Daw, Nathaniel D; Frank, Michael J

2016-01-27

Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes

Electronic learning and constructivism: a model for nursing education.

Science.gov (United States)

Kala, Sasikarn; Isaramalai, Sang-Arun; Pohthong, Amnart

2010-01-01

Nurse educators are challenged to teach nursing students to become competent professionals, who have both in-depth knowledge and decision-making skills. The use of electronic learning methods has been found to facilitate the teaching-learning process in nursing education. Although learning theories are acknowledged as useful guides to design strategies and activities of learning, integration of these theories into technology-based courses appears limited. Constructivism is a theoretical paradigm that could prove to be effective in guiding the design of electronic learning experiences for the purpose of providing positive outcomes, such as the acquisition of knowledge and decision-making skills. Therefore, the purposes of this paper are to: describe electronic learning, present a brief overview of what is known about the outcomes of electronic learning, discuss constructivism theory, present a model for electronic learning using constructivism, and describe educators' roles emphasizing the utilization of the model in developing electronic learning experiences in nursing education.

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...

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.

Model-based machine learning.

Science.gov (United States)

Bishop, Christopher M

2013-02-13

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

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.

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.

Active Learning versus Traditional Teaching

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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.

Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression.

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Kim, Yongsoo; Perova, Zinaida; Mirrione, Martine M; Pradhan, Kith; Henn, Fritz A; Shea, Stephen; Osten, Pavel; Li, Bo

2016-01-01

Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP - a marker of neuronal activation - in c-fosGFP transgenic mice subjected to the learned helplessness (LH) procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing "helpless" behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing "resilient" behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole-brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

A cognitive learning model of clinical nursing leadership.

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Pepin, Jacinthe; Dubois, Sylvie; Girard, Francine; Tardif, Jacques; Ha, Laurence

2011-04-01

Cognitive modeling of competencies is important to facilitate learning and evaluation. Clinical nursing leadership is considered a competency, as it is a "complex know-act" that students and nurses develop for the quality of care of patients and their families. Previous research on clinical leadership describes the attributes and characteristics of leaders and leadership, but, to our knowledge, a cognitive learning model (CLM) has yet to be developed. The purpose of our research was to develop a CLM of the clinical nursing leadership competency, from the beginning of a nursing program to expertise. An interpretative phenomenological study design was used 1) to document the experience of learning and practicing clinical leadership, and 2) to identify critical-learning turning points. Data was gathered from interviews with 32 baccalaureate students and 21 nurses from two clinical settings. An inductive analysis of data was conducted to determine the learning stages experienced: awareness of clinical leadership in nursing; integration of clinical leadership in actions; active leadership with patient/family; active leadership with the team; and, embedded clinical leadership extended to organizational level and beyond. The resulting CLM could have significant impact on both basic and continuing nursing education. Copyright © 2010 Elsevier Ltd. All rights reserved.

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...

The Effect of Mastery Learning Model with Reflective Thinking Activities on Medical Students' Academic Achievement: An Experimental Study

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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…

A New Approach to Teaching Biomechanics Through Active, Adaptive, and Experiential Learning.

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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.

MODEL DISCOVERY LEARNING DENGAN PENDEKATAN SAINTIFIK BERMUATAN KARAKTER UNTUK MENINGKATKAN KEMAMPUAN BERPIKIR KREATIF

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Hendra Erik Rudyanto

2016-11-01

Full Text Available This Study aims to produce a model learning device discovery learning with scientific approach to improve the character charged valid creative thinking, practical and effective. The model refers to a model of learning development includes activities Plomp initial investigation, design, realization/contruction, testing, evaluation and revision. The results showed that (1 learning tools developed valid; syllabus ehit an average of 3,3 (very good; RPP with an average of 3,2 (good; LKS with an average of 3,2 (good; textbook student with an average of 3,3 (very good; and TKBK with an average of 3,5 (good.; (2 the stated learning practical , namely: 1 the activity of student on both criteria, an average score 74,1%; 2 the activity of the teacher are very good on the criterion, the average score of 98,25; 3 positive teacher response, a score of 97,14; 4 positive students response, average 89,73.; (3 the learning of mathematics is declared effective the indicator 1 traffic to think creatively achieve mastery with the average value of 71,55 and a classical completeness reaches 90%; 2 the average grade of creative thinking ability model of discovery learning with scientific approachis better than ekspositori class; 3 the character of the curiosity and skills to communicate a positive influence on the ability to think creatively; and 4 an increase in the ability to think creatively in class models discovery learning with scientific approach.   Keywords: discovery learning, scientific approach, creative thinking ability.

Integrating Learning Styles and Personality Traits into an Affective Model to Support Learner's Learning

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Leontidis, Makis; Halatsis, Constantin

The aim of this paper is to present a model in order to integrate the learning style and the personality traits of a learner into an enhanced Affective Style which is stored in the learner’s model. This model which can deal with the cognitive abilities as well as the affective preferences of the learner is called Learner Affective Model (LAM). The LAM is used to retain learner’s knowledge and activities during his interaction with a Web-based learning environment and also to provide him with the appropriate pedagogical guidance. The proposed model makes use of an ontological approach in combination with the Bayesian Network model and contributes to the efficient management of the LAM in an Affective Module.

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

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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.

Active Learning in the Era of Big Data

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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.

Empowering Prospective Teachers to Become Active Sense-Makers: Multimodal Modeling of the Seasons

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Kim, Mi Song

2015-10-01

Situating science concepts in concrete and authentic contexts, using information and communications technologies, including multimodal modeling tools, is important for promoting the development of higher-order thinking skills in learners. However, teachers often struggle to integrate emergent multimodal models into a technology-rich informal learning environment. Our design-based research co-designs and develops engaging, immersive, and interactive informal learning activities called "Embodied Modeling-Mediated Activities" (EMMA) to support not only Singaporean learners' deep learning of astronomy but also the capacity of teachers. As part of the research on EMMA, this case study describes two prospective teachers' co-design processes involving multimodal models for teaching and learning the concept of the seasons in a technology-rich informal learning setting. Our study uncovers four prominent themes emerging from our data concerning the contextualized nature of learning and teaching involving multimodal models in informal learning contexts: (1) promoting communication and emerging questions, (2) offering affordances through limitations, (3) explaining one concept involving multiple concepts, and (4) integrating teaching and learning experiences. This study has an implication for the development of a pedagogical framework for teaching and learning in technology-enhanced learning environments—that is empowering teachers to become active sense-makers using multimodal models.

Learning versus correct models: influence of model type on the learning of a free-weight squat lift.

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McCullagh, P; Meyer, K N

1997-03-01

It has been assumed that demonstrating the correct movement is the best way to impart task-relevant information. However, empirical verification with simple laboratory skills has shown that using a learning model (showing an individual in the process of acquiring the skill to be learned) may accelerate skill acquisition and increase retention more than using a correct model. The purpose of the present study was to compare the effectiveness of viewing correct versus learning models on the acquisition of a sport skill (free-weight squat lift). Forty female participants were assigned to four learning conditions: physical practice receiving feedback, learning model with model feedback, correct model with model feedback, and learning model without model feedback. Results indicated that viewing either a correct or learning model was equally effective in learning correct form in the squat lift.

Is Peer Interaction Necessary for Optimal Active Learning?

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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…

Student Perceptions of Active Learning

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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…

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

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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…

Automatic, Global and Dynamic Student Modeling in a Ubiquitous Learning Environment

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Sabine Graf

2009-03-01

Full Text Available Ubiquitous learning allows students to learn at any time and any place. Adaptivity plays an important role in ubiquitous learning, aiming at providing students with adaptive and personalized learning material, activities, and information at the right place and the right time. However, for providing rich adaptivity, the student model needs to be able to gather a variety of information about the students. In this paper, an automatic, global, and dynamic student modeling approach is introduced, which aims at identifying and frequently updating information about students’ progress, learning styles, interests and knowledge level, problem solving abilities, preferences for using the system, social connectivity, and current location. This information is gathered in an automatic way, using students’ behavior and actions in different learning situations provided by different components/services of the ubiquitous learning environment. By providing a comprehensive student model, students can be supported by rich adaptivity in every component/service of the learning environment. Furthermore, the information in the student model can help in giving teachers a better understanding about the students’ learning process.

Learning to Act: Qualitative Learning of Deterministic Action Models

DEFF Research Database (Denmark)

Bolander, Thomas; Gierasimczuk, Nina

2017-01-01

In this article we study learnability of fully observable, universally applicable action models of dynamic epistemic logic. We introduce a framework for actions seen as sets of transitions between propositional states and we relate them to their dynamic epistemic logic representations as action...... in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while arbitrary (non-deterministic) actions require more learning power—they are identifiable in the limit. We then move on to a particular learning method, i.e. learning via update......, which proceeds via restriction of a space of events within a learning-specific action model. We show how this method can be adapted to learn conditional and unconditional deterministic action models. We propose update learning mechanisms for the afore mentioned classes of actions and analyse...

The control of tonic pain by active relief learning

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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

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.

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

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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.

From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning

Science.gov (United States)

Yu, Shengquan; Yang, Xianmin; Cheng, Gang; Wang, Minjuan

2015-01-01

This paper presents a new model for organizing learning resources: Learning Cell. This model is open, evolving, cohesive, social, and context-aware. By introducing a time dimension into the organization of learning resources, Learning Cell supports the dynamic evolution of learning resources while they are being used. In addition, by introducing a…

EFFECT OF INQUIRY LEARNING MODEL AND MOTIVATION ON PHYSICS OUTCOMES LEARNING STUDENTS

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Dahlia Megawati Pardede

2016-06-01

Full Text Available The purposes of the research are: (a to determine differences in learning outcomes of students with Inquiry Training models and conventional models, (b to determine differences in physics learning outcomes of students who have high motivation and low motivation, (c to determine the interaction between learning models with the level of motivation in improving student Physics learning outcomes. The results were found: (a there are differences in physical students learning outcomes are taught by Inquiry Training models and conventional models. (b learning outcomes of students who are taught by Inquiry Learning Model Training better than student learning outcomes are taught with conventional model. (c there is a difference in student's learning outcomes that have high motivation and low motivation. (d Student learning outcomes that have a high motivation better than student learning outcomes than have a low motivation. (e there is interaction between learning and motivation to student learning outcomes. Learning outcomes of students who are taught by the model is influenced also by the motivation, while learning outcomes of students who are taught with conventional models are not affected by motivation.

Active Learning in the Middle Grades

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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…

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.

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

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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

Faculty motivations to use active learning among pharmacy educators.

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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.

Active learning methods for interactive image retrieval.

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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.

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.

Progress with modeling activity landscapes in drug discovery.

Science.gov (United States)

Vogt, Martin

2018-04-19

Activity landscapes (ALs) are representations and models of compound data sets annotated with a target-specific activity. In contrast to quantitative structure-activity relationship (QSAR) models, ALs aim at characterizing structure-activity relationships (SARs) on a large-scale level encompassing all active compounds for specific targets. The popularity of AL modeling has grown substantially with the public availability of large activity-annotated compound data sets. AL modeling crucially depends on molecular representations and similarity metrics used to assess structural similarity. Areas covered: The concepts of AL modeling are introduced and its basis in quantitatively assessing molecular similarity is discussed. The different types of AL modeling approaches are introduced. AL designs can broadly be divided into three categories: compound-pair based, dimensionality reduction, and network approaches. Recent developments for each of these categories are discussed focusing on the application of mathematical, statistical, and machine learning tools for AL modeling. AL modeling using chemical space networks is covered in more detail. Expert opinion: AL modeling has remained a largely descriptive approach for the analysis of SARs. Beyond mere visualization, the application of analytical tools from statistics, machine learning and network theory has aided in the sophistication of AL designs and provides a step forward in transforming ALs from descriptive to predictive tools. To this end, optimizing representations that encode activity relevant features of molecules might prove to be a crucial step.

Whole-brain mapping of neuronal activity in the learned helplessness model of depression

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Yongsoo eKim

2016-02-01

Full Text Available Some individuals are resilient, whereas others succumb to despair in repeated stressful situations. The neurobiological mechanisms underlying such divergent behavioral responses remain unclear. Here, we employed an automated method for mapping neuronal activity in search of signatures of stress responses in the entire mouse brain. We used serial two-photon tomography to detect expression of c-FosGFP – a marker of neuronal activation – in c-fosGFP transgenic mice subjected to the learned helplessness (LH procedure, a widely used model of stress-induced depression-like phenotype in laboratory animals. We found that mice showing helpless behavior had an overall brain-wide reduction in the level of neuronal activation compared with mice showing resilient behavior, with the exception of a few brain areas, including the locus coeruleus, that were more activated in the helpless mice. In addition, the helpless mice showed a strong trend of having higher similarity in whole brain activity profile among individuals, suggesting that helplessness is represented by a more stereotypic brain-wide activation pattern. This latter effect was confirmed in rats subjected to the LH procedure, using 2-deoxy-2[18F]fluoro-D-glucose positron emission tomography to assess neural activity. Our findings reveal distinct brain activity markings that correlate with adaptive and maladaptive behavioral responses to stress, and provide a framework for further studies investigating the contribution of specific brain regions to maladaptive stress responses.

Proposing a Model of Co-Regulated Learning for Graduate Medical Education.

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Rich, Jessica V

2017-08-01

Primarily grounded in Zimmerman's social cognitive model of self-regulation, graduate medical education is guided by principles that self-regulated learning takes place within social context and influence, and that the social context and physical environment reciprocally influence persons and their cognition, behavior, and development. However, contemporary perspectives on self-regulation are moving beyond Zimmerman's triadic reciprocal orientation to models that consider social transactions as the central core of regulated learning. Such co-regulated learning models emphasize shared control of learning and the role more advanced others play in scaffolding novices' metacognitive engagement.Models of co-regulated learning describe social transactions as periods of distributed regulation among individuals, which instrumentally promote or inhibit the capacity for individuals to independently self-regulate. Social transactions with other regulators, including attending physicians, more experienced residents, and allied health care professionals, are known to mediate residents' learning and to support or hamper the development of their self-regulated learning competence. Given that social transactions are at the heart of learning-oriented assessment and entrustment decisions, an appreciation for co-regulated learning is likely important for advancing medical education research and practice-especially given the momentum of new innovations such as entrustable professional activities.In this article, the author explains why graduate medical educators should consider adopting a model of co-regulated learning to complement and extend Zimmerman's models of self-regulated learning. In doing so, the author suggests a model of co-regulated learning and provides practical examples of how the model is relevant to graduate medical education research and practice.

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…

A Metadata Model for E-Learning Coordination through Semantic Web Languages

Science.gov (United States)

Elci, Atilla

2005-01-01

This paper reports on a study aiming to develop a metadata model for e-learning coordination based on semantic web languages. A survey of e-learning modes are done initially in order to identify content such as phases, activities, data schema, rules and relations, etc. relevant for a coordination model. In this respect, the study looks into the…

TEXT-BASED LEARNING (TBL TO ACTIVATE ADULT EFL LEARNERS IN LEARNING ENGLISH: A NARRATIVE INQUIRY

Directory of Open Access Journals (Sweden)

Erna Iftanti

2017-06-01

Full Text Available In response to the fact that college students complain on their unsuccessful story of their EFL learning experience such as the limited number of vocabulary, English Grammar confusion, low competence of English language skills, this article explores an alternative effective way of helping them to improve their English through Text-Based Learning (TBL model. This article is then intended to narrate the implementation of TBL to teach English for college students of non English Department of Post Graduate Program of State Islamic Institute of Tulungagung, Indonesia. The result of implementing this teaching model proves to be able to not only stimulate joyful learning atmosphere but to attract the students’ active participation during the EFL instructional process as well. This further brings about their better practical understanding on English language skills as their expectation. Therefore, for English lecturers, this model is pedagogically good to be implemented in their English instructional practices.

The (kinetic) theory of active particles applied to learning dynamics. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

Science.gov (United States)

Nieto, J.

2016-03-01

The learning phenomena, their complexity, concepts, structure, suitable theories and models, have been extensively treated in the mathematical literature in the last century, and [4] contains a very good introduction to the literature describing the many approaches and lines of research developed about them. Two main schools have to be pointed out [5] in order to understand the two -not exclusive- kinds of existing models: the stimulus sampling models and the stochastic learning models. Also [6] should be mentioned as a survey where two methods of learning are pointed out, the cognitive and the social, and where the knowledge looks like a mathematical unknown. Finally, as the authors do, we refer to the works [9,10], where the concept of population thinking was introduced and which motivate the game theory rules as a tool (both included in [4] to develop their theory) and [7], where the ideas of developing a mathematical kinetic theory of perception and learning were proposed.

Active Player Modeling in the Iterated Prisoner’s Dilemma

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Hyunsoo Park

2016-01-01

Full Text Available The iterated prisoner’s dilemma (IPD is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents’ actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player’s behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent’s behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent’s behavior than when the data were collected through random actions.

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

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.

Student Modeling and Machine Learning

OpenAIRE

Sison , Raymund; Shimura , Masamichi

1998-01-01

After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine le...

A Balanced Scorecard-Based Model for Evaluating E-Learning and Conventional Pedagogical Activities in Nursing

Science.gov (United States)

Hovelja, Tomaž; Vavpotic, Damjan; Žvanut, Boštjan

2016-01-01

The evaluation of e-learning and conventional pedagogical activities in nursing programmes has focused either on a single pedagogical activity or the entire curriculum, and only on students' or teachers' perspective. The goal of this study was to design and test a novel approach for evaluation of e-learning and conventional pedagogical activities…

The Usage of E-Learning Model To Optimize Learning System In Higher Education by Using Dick and Carey Design Approach

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Anak Agung Gde Satia Utama

2016-04-01

Full Text Available Nowadays many universities in the world apply technology enhanced learning in order to help learning activities. Due to the potentials technology enhanced learning offers, recent education using it and universities in particular are trying to apply it. One of the subjects of this research is The Accounting Department of Airlangga University in Surabaya. The idea of this research is to investigate the students about how they know deeply about e-learning system and learning objectives as a first step to conduct e-learning model. After the model completed, the next step is to prepare database learning. Entity Relationship Diagram (ERD can help to explain the model. The purpose of this research was done by using Dick and Carey Design Model. There are nine steps to conduct e-learning model. All steps can be categorized into three steps research: first is the introduction or empirical study, the next step is the design and the last is the feedback after the implementation. The methodology used in this research is using Qualitative Exploratory, by using questionnaire and interviews as data collection techniques. The analysis of the data shows organization requires information about e-learning content, user as a learning subject and information technology infrastructures. E-learning model as one of the alternative learning can help users to optimized learning.

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…

Active learning: views and actions of students and teachers in basic education

NARCIS (Netherlands)

Lunenberg, Mieke L.; Volman, Monique

1999-01-01

This article discusses how teachers and adult, female, immigrant students in basic education deal with active learning. The study orientations, mental models of learning and images of ideal students of the two groups are compared both with each other and with actual educational practice, in order to

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.

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

The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept.

Science.gov (United States)

Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf

2016-02-01

Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).

Attacking Machine Learning models as part of a cyber kill chain

OpenAIRE

Nguyen, Tam N.

2017-01-01

Machine learning is gaining popularity in the network security domain as many more network-enabled devices get connected, as malicious activities become stealthier, and as new technologies like Software Defined Networking emerge. Compromising machine learning model is a desirable goal. In fact, spammers have been quite successful getting through machine learning enabled spam filters for years. While previous works have been done on adversarial machine learning, none has been considered within...

The Prisoner's Dilemma and Economics 101: Do Active Learning Exercises Correlate with Student Performance?

Science.gov (United States)

Byun, Chong Hyun Christie

2014-01-01

The importance of active learning in the classroom has been well established in the field of Economic education. This paper examines the connection between active learning and performance outcomes in an Economics 101 course. Students participated in single play simultaneous move game with a clear dominant strategy, modeled after the Prisoner's…

The Implementation of C-ID, R2D2 Model on Learning Reading Comprehension

Science.gov (United States)

Rayanto, Yudi Hari; Rusmawan, Putu Ngurah

2016-01-01

The purposes of this research are to find out, (1) whether C-ID, R2D2 model is effective to be implemented on learning Reading comprehension, (2) college students' activity during the implementation of C-ID, R2D2 model on learning Reading comprehension, and 3) college students' learning achievement during the implementation of C-ID, R2D2 model on…

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…

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

Towards a semantic learning model fostering learning object reusability

OpenAIRE

Fernandes , Emmanuel; Madhour , Hend; Wentland Forte , Maia; Miniaoui , Sami

2005-01-01

We try in this paper to propose a domain model for both author's and learner's needs concerning learning objects reuse. First of all, we present four key criteria for an efficient authoring tool: adaptive level of granularity, flexibility, integration and interoperability. Secondly, we introduce and describe our six-level Semantic Learning Model (SLM) designed to facilitate multi-level reuse of learning materials and search by defining a multi-layer model for metadata. Finally, after mapping ...

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

A review of active learning approaches to experimental design for uncovering biological networks

Science.gov (United States)

2017-01-01

Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area. PMID:28570593

The Aalborg Model and participant directed learning

DEFF Research Database (Denmark)

Qvist, Palle

2009-01-01

Preparing students for a life as active citizens in a democratic society is one of the aims within the Bologna process. The Council of Europe has also stressed the importance of focus on democracy in Higher Education. Higher Education is seen as important to develop a democratic culture among...... students. Teaching democracy should be promoted in lessons and curricula. Creating democratic learning systems in institutions of higher education could be the answer to reaching the aim related to democracy. The Aalborg Model practised at Aalborg University is a learning system which has collaborative...

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.

Assessing the Effectiveness of a Hybrid-Flipped Model of Learning on Fluid Mechanics Instruction: Overall Course Performance, Homework, and Far- and Near-Transfer of Learning

Science.gov (United States)

Harrison, David J.; Saito, Laurel; Markee, Nancy; Herzog, Serge

2017-01-01

To examine the impact of a hybrid-flipped model utilising active learning techniques, the researchers inverted one section of an undergraduate fluid mechanics course, reduced seat time, and engaged in active learning sessions in the classroom. We compared this model to the traditional section on four performance measures. We employed a propensity…

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.

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....

Toward a Neurobiological Basis for Understanding Learning in University Modeling Instruction Physics Courses

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Eric Brewe

2018-05-01

Full Text Available Modeling Instruction (MI for University Physics is a curricular and pedagogical approach to active learning in introductory physics. A basic tenet of science is that it is a model-driven endeavor that involves building models, then validating, deploying, and ultimately revising them in an iterative fashion. MI was developed to provide students a facsimile in the university classroom of this foundational scientific practice. As a curriculum, MI employs conceptual scientific models as the basis for the course content, and thus learning in a MI classroom involves students appropriating scientific models for their own use. Over the last 10 years, substantial evidence has accumulated supporting MI's efficacy, including gains in conceptual understanding, odds of success, attitudes toward learning, self-efficacy, and social networks centered around physics learning. However, we still do not fully understand the mechanisms of how students learn physics and develop mental models of physical phenomena. Herein, we explore the hypothesis that the MI curriculum and pedagogy promotes student engagement via conceptual model building. This emphasis on conceptual model building, in turn, leads to improved knowledge organization and problem solving abilities that manifest as quantifiable functional brain changes that can be assessed with functional magnetic resonance imaging (fMRI. We conducted a neuroeducation study wherein students completed a physics reasoning task while undergoing fMRI scanning before (pre and after (post completing a MI introductory physics course. Preliminary results indicated that performance of the physics reasoning task was linked with increased brain activity notably in lateral prefrontal and parietal cortices that previously have been associated with attention, working memory, and problem solving, and are collectively referred to as the central executive network. Critically, assessment of changes in brain activity during the physics

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...

Learning About Climate and Atmospheric Models Through Machine Learning

Science.gov (United States)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Adaptable Web Modules to Stimulate Active Learning in Engineering Hydrology using Data and Model Simulations of Three Regional Hydrologic Systems

Science.gov (United States)

Habib, E. H.; Tarboton, D. G.; Lall, U.; Bodin, M.; Rahill-Marier, B.; Chimmula, S.; Meselhe, E. A.; Ali, A.; Williams, D.; Ma, Y.

2013-12-01

The hydrologic community has long recognized the need for broad reform in hydrologic education. A paradigm shift is critically sought in undergraduate hydrology and water resource education by adopting context-rich, student-centered, and active learning strategies. Hydrologists currently deal with intricate issues rooted in complex natural ecosystems containing a multitude of interconnected processes. Advances in the multi-disciplinary field include observational settings such as Critical Zone and Water, Sustainability and Climate Observatories, Hydrologic Information Systems, instrumentation and modeling methods. These research advances theory and practices call for similar efforts and improvements in hydrologic education. The typical, text-book based approach in hydrologic education has focused on specific applications and/or unit processes associated with the hydrologic cycle with idealizations, rather than the contextual relations in the physical processes and the spatial and temporal dynamics connecting climate and ecosystems. An appreciation of the natural variability of these processes will lead to graduates with the ability to develop independent learning skills and understanding. This appreciation cannot be gained in curricula where field components such as observational and experimental data are deficient. These types of data are also critical when using simulation models to create environments that support this type of learning. Additional sources of observations in conjunction with models and field data are key to students understanding of the challenges associated with using models to represent such complex systems. Recent advances in scientific visualization and web-based technologies provide new opportunities for the development of active learning techniques utilizing ongoing research. The overall goal of the current study is to develop visual, case-based, data and simulation driven learning experiences to instructors and students through a web

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...

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.

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.

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.

L.I.M.E. A recommendation model for informal and formal learning, engaged

Directory of Open Access Journals (Sweden)

Daniel Burgos

2013-06-01

Full Text Available In current eLearning models and implementations (e.g. Learning Management Systems-LMS there is a lack of engagement between formal and informal activities. Furthermore, the online methodology focuses on a standard set of units of learning and learning objects, along with pre-defined tests, and collateral resources like, i.e. discussion fora and message wall. They miss the huge potential of learning via the interlacement of social networks, LMS and external sources. Thanks to user behaviour, user interaction, and personalised counselling by a tutor, learning performance can be improved. We design and develop an adaptation eLearning model for restricted social networks, which supports this approach. In addition, we build an eLearning module that implements this conceptual model in a real application case, and present the preliminary analysis and positive results.

Pre-Service Teachers' Modelling Processes through Engagement with Model Eliciting Activities with a Technological Tool

Science.gov (United States)

Daher, Wajeeh M.; Shahbari, Juhaina Awawdeh

2015-01-01

Engaging mathematics students with modelling activities helps them learn mathematics meaningfully. This engagement, in the case of model eliciting activities, helps the students elicit mathematical models by interpreting real-world situation in mathematical ways. This is especially true when the students utilize technology to build the models.…

Flipped classroom model for learning evidence-based medicine

Directory of Open Access Journals (Sweden)

Rucker SY

2017-08-01

Full Text Available Sydney Y Rucker,1 Zulfukar Ozdogan,1 Morhaf Al Achkar2 1School of Education, Indiana University, Bloomington, IN, 2Department of Family Medicine, School of Medicine, University of Washington, Seattle, WA, USA Abstract: Journal club (JC, as a pedagogical strategy, has long been used in graduate medical education (GME. As evidence-based medicine (EBM becomes a mainstay in GME, traditional models of JC present a number of insufficiencies and call for novel models of instruction. A flipped classroom model appears to be an ideal strategy to meet the demands to connect evidence to practice while creating engaged, culturally competent, and technologically literate physicians. In this article, we describe a novel model of flipped classroom in JC. We present the flow of learning activities during the online and face-to-face instruction, and then we highlight specific considerations for implementing a flipped classroom model. We show that implementing a flipped classroom model to teach EBM in a residency program not only is possible but also may constitute improved learning opportunity for residents. Follow-up work is needed to evaluate the effectiveness of this model on both learning and clinical practice. Keywords: evidence-based medicine, flipped classroom, residency education

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…

Collaborative Inquiry Learning: Models, tools, and challenges

Science.gov (United States)

Bell, Thorsten; Urhahne, Detlef; Schanze, Sascha; Ploetzner, Rolf

2010-02-01

Collaborative inquiry learning is one of the most challenging and exciting ventures for today's schools. It aims at bringing a new and promising culture of teaching and learning into the classroom where students in groups engage in self-regulated learning activities supported by the teacher. It is expected that this way of learning fosters students' motivation and interest in science, that they learn to perform steps of inquiry similar to scientists and that they gain knowledge on scientific processes. Starting from general pedagogical reflections and science standards, the article reviews some prominent models of inquiry learning. This comparison results in a set of inquiry processes being the basis for cooperation in the scientific network NetCoIL. Inquiry learning is conceived in several ways with emphasis on different processes. For an illustration of the spectrum, some main conceptions of inquiry and their focuses are described. In the next step, the article describes exemplary computer tools and environments from within and outside the NetCoIL network that were designed to support processes of collaborative inquiry learning. These tools are analysed by describing their functionalities as well as effects on student learning known from the literature. The article closes with challenges for further developments elaborated by the NetCoIL network.

Investigating a model for lecturer training that enables lecturers to plan and carry out meaningful e-learning activities

DEFF Research Database (Denmark)

Kjær, Christopher; Hansen, Pernille Stenkil; Christensen, Inger-Marie F.

2014-01-01

This paper reports on the effect of a lecturer training model in the shape of an e-learning project based on research on adult and work-based learning. A survey was conducted to explore participants’ learning experiences. Findings show high overall satisfaction, motivation and engagement. Suggest......This paper reports on the effect of a lecturer training model in the shape of an e-learning project based on research on adult and work-based learning. A survey was conducted to explore participants’ learning experiences. Findings show high overall satisfaction, motivation and engagement...

Learning about hydrothermal volcanic activity by modeling induced geophysical changes

Science.gov (United States)

Currenti, Gilda M.; Napoli, Rosalba

2017-05-01

Motivated by ongoing efforts to understand the nature and the energy potential of geothermal resources, we devise a coupled numerical model (hydrological, thermal, mechanical), which may help in the characterization and monitoring of hydrothermal systems through computational experiments. Hydrothermal areas in volcanic regions arise from a unique combination of geological and hydrological features which regulate the movement of fluids in the vicinity of magmatic sources capable of generating large quantities of steam and hot water. Numerical simulations help in understanding and characterizing rock-fluid interaction processes and the geophysical observations associated with them. Our aim is the quantification of the response of different geophysical observables (i.e. deformation, gravity and magnetic field) to hydrothermal activity on the basis of a sound geological framework (e.g. distribution and pathways of the flows, the presence of fractured zones, caprock). A detailed comprehension and quantification of the evolution and dynamics of the geothermal systems and the definition of their internal state through a geophysical modeling approach are essential to identify the key parameters for which the geothermal system may fulfill the requirements to be exploited as a source of energy. For the sake of illustration only, the numerical computations are focused on a conceptual model of the hydrothermal system of Vulcano Island by simulating a generic 1-year unrest and estimating different geophysical changes. We solved (i) the mass and energy balance equations of flow in porous media for temperature, pressure and density changes, (ii) the elastostatic equation for the deformation field and (iii) the Poisson’s equations for gravity and magnetic potential fields. Under the model assumptions, a generic unrest of 1-year engenders on the ground surface low amplitude changes in the investigated geophysical observables, that are, however, above the accuracies of the modern

Learning about Hydrothermal Volcanic Activity by Modeling Induced Geophysical Changes

Directory of Open Access Journals (Sweden)

Gilda M. Currenti

2017-05-01

Full Text Available Motivated by ongoing efforts to understand the nature and the energy potential of geothermal resources, we devise a coupled numerical model (hydrological, thermal, mechanical, which may help in the characterization and monitoring of hydrothermal systems through computational experiments. Hydrothermal areas in volcanic regions arise from a unique combination of geological and hydrological features which regulate the movement of fluids in the vicinity of magmatic sources capable of generating large quantities of steam and hot water. Numerical simulations help in understanding and characterizing rock-fluid interaction processes and the geophysical observations associated with them. Our aim is the quantification of the response of different geophysical observables (i.e., deformation, gravity, and magnetic fields to hydrothermal activity on the basis of a sound geological framework (e.g., distribution and pathways of the flows, the presence of fractured zones, caprock. A detailed comprehension and quantification of the evolution and dynamics of the geothermal systems and the definition of their internal state through a geophysical modeling approach are essential to identify the key parameters for which the geothermal system may fulfill the requirements to be exploited as a source of energy. For the sake of illustration only, the numerical computations are focused on a conceptual model of the hydrothermal system of Vulcano Island by simulating a generic 1-year unrest and estimating different geophysical changes. We solved (i the mass and energy balance equations of flow in porous media for temperature, pressure and density changes, (ii the elastostatic equation for the deformation field and (iii the Poisson's equations for gravity and magnetic potential fields. Under the model assumptions, a generic unrest of 1-year engenders on the ground surface low amplitude changes in the investigated geophysical observables, that, being above the accuracies of

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…

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…

ACTIVE AND PARTICIPATORY METHODS IN BIOLOGY: MODELING

Directory of Open Access Journals (Sweden)

Brînduşa-Antonela SBÎRCEA

2011-01-01

Full Text Available By using active and participatory methods it is hoped that pupils will not only come to a deeper understanding of the issues involved, but also that their motivation will be heightened. Pupil involvement in their learning is essential. Moreover, by using a variety of teaching techniques, we can help students make sense of the world in different ways, increasing the likelihood that they will develop a conceptual understanding. The teacher must be a good facilitator, monitoring and supporting group dynamics. Modeling is an instructional strategy in which the teacher demonstrates a new concept or approach to learning and pupils learn by observing. In the teaching of biology the didactic materials are fundamental tools in the teaching-learning process. Reading about scientific concepts or having a teacher explain them is not enough. Research has shown that modeling can be used across disciplines and in all grade and ability level classrooms. Using this type of instruction, teachers encourage learning.

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.

Evaluation of quality of pedagogic model for blended learning on course “Life safety”

Directory of Open Access Journals (Sweden)

M. V. Legan

2016-01-01

Full Text Available The article is devoted to evaluating the quality of the pedagogical model for the students’ blended learning on the course «Health and Safety». A choice of the used technologies, forms, active and interactive methods in the learning process, learning tools is made; a new generation of multi-media resources is developed; the quality of teaching evaluation by blended learning model according to the process approach is carried out.The purpose of the study was to assess the quality blended learning model with different ratios of classroom and e-learning component of students on the course “Health and Safety”.Two groups of students were involved in the experiment:1st experimental group (152 persons were the students of all areas of training, learning on the course “Health and Safety”, using a blended approach. E-learning technologies were used to provide the access to information resources (electronic educational-methodical complex on discipline, placed in the electronic educational environment of the University, the organization of self-study, sending the lecturer coursework and examinations, passing the intermediate and final testing for the purpose of monitoring assessment of the knowledge quality. This kind of blended learning models («with a web-enabled learning» is characterized by the addition of electronic educational resources without reducing the hours of a traditional component.2nd experimental group: (164 persons – students enrolled in the course «Health and Safety» in the blended learning model. Students have been using the distance technology for self-study according to the curriculum – 80% of university hours. Personal communication («face to face» was with the lecturer during the session.We have tested two groups of students who participated in the experiment (final control via a module testing electronic environment. In order to assess the quality of blended learning model we used a process approach. An

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...

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.

An efficient cardiac mapping strategy for radiofrequency catheter ablation with active learning.

Science.gov (United States)

Feng, Yingjing; Guo, Ziyan; Dong, Ziyang; Zhou, Xiao-Yun; Kwok, Ka-Wai; Ernst, Sabine; Lee, Su-Lin

2017-07-01

A major challenge in radiofrequency catheter ablation procedures is the voltage and activation mapping of the endocardium, given a limited mapping time. By learning from expert interventional electrophysiologists (operators), while also making use of an active-learning framework, guidance on performing cardiac voltage mapping can be provided to novice operators or even directly to catheter robots. A learning from demonstration (LfD) framework, based upon previous cardiac mapping procedures performed by an expert operator, in conjunction with Gaussian process (GP) model-based active learning, was developed to efficiently perform voltage mapping over right ventricles (RV). The GP model was used to output the next best mapping point, while getting updated towards the underlying voltage data pattern as more mapping points are taken. A regularized particle filter was used to keep track of the kernel hyperparameter used by GP. The travel cost of the catheter tip was incorporated to produce time-efficient mapping sequences. The proposed strategy was validated on a simulated 2D grid mapping task, with leave-one-out experiments on 25 retrospective datasets, in an RV phantom using the Stereotaxis Niobe ® remote magnetic navigation system, and on a tele-operated catheter robot. In comparison with an existing geometry-based method, regression error was reduced and was minimized at a faster rate over retrospective procedure data. A new method of catheter mapping guidance has been proposed based on LfD and active learning. The proposed method provides real-time guidance for the procedure, as well as a live evaluation of mapping sufficiency.

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

Science.gov (United States)

Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

2018-04-01

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

The Effects of Learning Activities Corresponding with Students’ Learning Styles on Academic Success and Attitude within the Scope of Constructivist Learning Approach: The Case of the Concepts of Function and Derivative

Directory of Open Access Journals (Sweden)

Kemal Özgen

2014-04-01

Full Text Available The aim of this study was to identify the effects of learning activities according to students’ learning styles on students’ academic success and attitude towards mathematics within a scope of constructivist learning approach. The study had a semi-experimental research design based on the pre test-post test model with a control group. The participants of the study were students studying at a state high school in the 2010-2011 academic year. As part of the study, activities which were suitable to the students’ learning styles were developed within the scope of constructivist learning approach in line with McCarthy’s 4MAT system with 8 steps of learning and used for the learning of the concepts of function and derivative. Data were collected using data collection tools such as a personal information form, non-routine problems, and a mathematics attitude scale. Descriptive and non-parametric statistics were used for the analysis of quantitative data. Data analysis indicated that, the learning process in which activities appropriate for students’ learning styles were used to contribute to an increase in the students’ academic success and problem solving skills. Yet, there was no statistically significant difference in students’ attitudes towards mathematics.Key Words:    Constructivist learning approach, learning style, learning activity, success, attitude

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

Visual Perceptual Learning and Models.

Science.gov (United States)

Dosher, Barbara; Lu, Zhong-Lin

2017-09-15

Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.

Origami: An Active Learning Exercise for Scrum Project Management

Science.gov (United States)

Sibona, Christopher; Pourreza, Saba; Hill, Stephen

2018-01-01

Scrum is a popular project management model for iterative delivery of software that subscribes to Agile principles. This paper describes an origami active learning exercise to teach the principles of Scrum in management information systems courses. The exercise shows students how Agile methods respond to changes in requirements during project…

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.

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...

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.

Teaching Economics: A Cooperative Learning Model.

Science.gov (United States)

Caropreso, Edward J.; Haggerty, Mark

2000-01-01

Describes an alternative approach to introductory economics based on a cooperative learning model, "Learning Together." Discussion of issues in economics education and cooperative learning in higher education leads to explanation of how to adapt the Learning Together Model to lesson planning in economics. A flow chart illustrates the process for a…

Ready to learn physics: a team-based learning model for first year university

Science.gov (United States)

Parappilly, Maria; Schmidt, Lisa; De Ritter, Samantha

2015-09-01

Team-based learning (TBL) is an established model of group work which aims to improve students' ability to apply discipline-related content. TBL consists of a readiness assurance process (RAP), student groups and application activities. While TBL has not been implemented widely in science, technology, engineering and mathematics disciplines, it has been effective in improving student learning in other disciplines. This paper describes the incorporation of TBL activities into a non-calculus based introductory level physics topic—Physics for the Modern World. Students were given pre-class preparation materials and an individual RAP online test before the workshops. The pre-workshop individual RAP test ensured that all students were exposed to concept-based questions before their workshops and motivated them to use the preparatory materials in readiness for the workshop. The students were placed into random teams and during the first part of the workshop, the teams went through a subset of the quiz questions (team RAP test) and in the remaining time, teams completed an in-class assignment. After the workshop students were allowed another attempt at the individual RAP test to see if their knowledge had improved. The ability of TBL to promote student learning of key concepts was evaluated by experiment using pre- and post- testing. The students’ perception of TBL was monitored by discussion posts and survey responses. Finally, the ability of TBL to support peer-peer interaction was evaluated by video analysis of the class. We found that the TBL process improved student learning; students did interact with each other in class; and the students had a positive view of TBL. To assess the transferability of this model to other topics, we conducted a comparison study with an environmental science topic which produced similar results. Our study supports the use of this TBL model in science topics.

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 ...

What’s about Peer Tutoring Learning Model?

Science.gov (United States)

Muthma'innah, M.

2017-09-01

Mathematics learning outcomes in Indonesia in general is still far from satisfactory. One effort that could be expected to solve the problem is to apply the model of peer tutoring learning in mathematics. This study aims to determine whether the results of students’ mathematics learning can be enhanced through peer tutoring learning models. This type of research is the study of literature, so that the method used is to summarize and analyze the results of relevant research that has been done. Peer tutoring learning model is a model of learning in which students learn in small groups that are grouped with different ability levels, all group members to work together and help each other to understand the material. By paying attention to the syntax of the learning, then learning will be invaluable peer tutoring for students who served as teachers and students are taught. In mathematics, the implementation of this learning model can make students understand each other mathematical concepts and help students in solving mathematical problems that are poorly understood, due to the interaction between students in learning. Then it will be able to improve learning outcomes in mathematics. The impact, it can be applied in mathematics learning.

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

Science.gov (United States)

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

Beyond the didactic classroom: educational models to encourage active student involvement in learning.

Science.gov (United States)

Shreeve, Michael W

2008-01-01

In a chiropractic college that utilizes a hybrid curriculum model composed of adult-based learning strategies along with traditional lecture-based course delivery, a literature search for educational delivery methods that would integrate the affective domain and the cognitive domain of learning provided some insights into the use of problem-based learning (PBL), experiential learning theory (ELT), and the emerging use of appreciative inquiry (AI) to enhance the learning experience. The purpose of this literature review is to provide a brief overview of key components of PBL, ELT, and AI in educational methodology and to discuss how these might be used within the chiropractic curriculum to supplement traditional didactic lecture courses. A growing body of literature describes the use of PBL and ELT in educational settings across many disciplines, both at the undergraduate and graduate levels. The use of appreciative inquiry as an instructional methodology presents a new area for exploration and study in the academic environment. Educational research in the chiropractic classroom incorporating ELT and appreciative inquiry might provide some valuable insights for future curriculum development.

Inquiry based learning as didactic model in distant learning

NARCIS (Netherlands)

Rothkrantz, L.J.M.

2015-01-01

Recent years many universities are involved in development of Massive Open Online Courses (MOOCs). Unfortunately an appropriate didactic model for cooperated network learning is lacking. In this paper we introduce inquiry based learning as didactic model. Students are assumed to ask themselves

Are All Hands-On Activities Equally Effective? Effect of Using Plastic Models, Organ Dissections, and Virtual Dissections on Student Learning and Perceptions

Science.gov (United States)

Lombardi, Sara A.; Hicks, Reimi E.; Thompson, Katerina V.; Marbach-Ad, Gili

2014-01-01

This study investigated the impact of three commonly used cardiovascular model-assisted activities on student learning and student attitudes and perspectives about science. College students enrolled in a Human Anatomy and Physiology course were randomly assigned to one of three experimental groups (organ dissections, virtual dissections, or…

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…

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…

A New Mobile Learning Adaptation Model

OpenAIRE

Mohamd Hassan Hassan; Jehad Al-Sadi

2009-01-01

This paper introduces a new model for m- Learning context adaptation due to the need of utilizing mobile technology in education. Mobile learning; m-Learning for short; in considered to be one of the hottest topics in the educational community, many researches had been done to conceptualize this new form of learning. We are presenting a promising design for a model to adapt the learning content in mobile learning applications in order to match the learner context, preferences and the educatio...

Student-Centered Modules to Support Active Learning in Hydrology: Development Experiences and Users' Perspectives

Science.gov (United States)

Tarboton, D. G.; Habib, E. H.; Deshotel, M.; Merck, M. F.; Lall, U.; Farnham, D. J.

2016-12-01

Traditional approaches to undergraduate hydrology and water resource education are textbook based, adopt unit processes and rely on idealized examples of specific applications, rather than examining the contextual relations in the processes and the dynamics connecting climate and ecosystems. The overarching goal of this project is to address the needed paradigm shift in undergraduate education of engineering hydrology and water resources education to reflect parallel advances in hydrologic research and technology, mainly in the areas of new observational settings, data and modeling resources and web-based technologies. This study presents efforts to develop a set of learning modules that are case-based, data and simulation driven and delivered via a web user interface. The modules are based on real-world case studies from three regional hydrologic settings: Coastal Louisiana, Utah Rocky Mountains and Florida Everglades. These three systems provide unique learning opportunities on topics such as: regional-scale budget analysis, hydrologic effects of human and natural changes, flashflood protection, climate-hydrology teleconnections and water resource management scenarios. The technical design and contents of the modules aim to support students' ability for transforming their learning outcomes and skills to hydrologic systems other than those used by the specific activity. To promote active learning, the modules take students through a set of highly engaging learning activities that are based on analysis of hydrologic data and model simulations. The modules include user support in the form of feedback and self-assessment mechanisms that are integrated within the online modules. Module effectiveness is assessed through an improvement-focused evaluation model using a mixed-method research approach guiding collection and analysis of evaluation data. Both qualitative and quantitative data are collected through student learning data, product analysis, and staff interviews

Effect Of Inquiry Learning Model And Motivation On Physics Outcomes Learning Students

OpenAIRE

Pardede, Dahlia Megawati; Manurung, Sondang Rina

2016-01-01

The purposes of the research are: (a) to determine differences in learning outcomes of students with Inquiry Training models and conventional models, (b) to determine differences in physics learning outcomes of students who have high motivation and low motivation, (c) to determine the interaction between learning models with the level of motivation in improving student Physics learning outcomes. The results were found: (a) there are differences in physical students learning outcomes are taugh...

Analysis student self efficacy in terms of using Discovery Learning model with SAVI approach

Science.gov (United States)

Sahara, Rifki; Mardiyana, S., Dewi Retno Sari

2017-12-01

Often students are unable to prove their academic achievement optimally according to their abilities. One reason is that they often feel unsure that they are capable of completing the tasks assigned to them. For students, such beliefs are necessary. The term belief has called self efficacy. Self efficacy is not something that has brought about by birth or something with permanent quality of an individual, but is the result of cognitive processes, the meaning one's self efficacy will be stimulated through learning activities. Self efficacy has developed and enhanced by a learning model that can stimulate students to foster confidence in their capabilities. One of them is by using Discovery Learning model with SAVI approach. Discovery Learning model with SAVI approach is one of learning models that involves the active participation of students in exploring and discovering their own knowledge and using it in problem solving by utilizing all the sensory devices they have. This naturalistic qualitative research aims to analyze student self efficacy in terms of use the Discovery Learning model with SAVI approach. The subjects of this study are 30 students focused on eight students who have high, medium, and low self efficacy obtained through purposive sampling technique. The data analysis of this research used three stages, that were reducing, displaying, and getting conclusion of the data. Based on the results of data analysis, it was concluded that the self efficacy appeared dominantly on the learning by using Discovery Learning model with SAVI approach is magnitude dimension.

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

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.

The influence of extracurricular activities on middle school students' science learning in China

Science.gov (United States)

Zhang, Danhui; Tang, Xing

2017-07-01

Informal science learning has been found to have effects on students' science learning. Through the use of secondary data from a national assessment of 7410 middle school students in China, this study explores the relationship among five types of extracurricular science activities, learning interests, academic self-concept, and science achievement. Structural equation modelling was used to investigate the influence of students' self-chosen and school-organised extracurricular activities on science achievement through mediating interests and the academic self-concept. Chi-square tests were used to determine whether there was an opportunity gap in the student's engagement in extracurricular activities. The students' volunteer and school-organised participation in extracurricular science activities had a positive and indirect influence on their science achievement through the mediating variables of their learning interests and academic self-concept. However, there were opportunity gaps between different groups of students in terms of school location, family background, and especially the mother's education level. Students from urban areas with better-educated mothers or higher socioeconomic status are more likely to access diverse science-related extracurricular activities.

Business Models for E-Learning

OpenAIRE

Hoppe, Gabriela; Breitner, Michael H.

2003-01-01

E(Electronic)-learning becomes more and more important. Reasons are the paramount importance of knowledge, life-time learning, globalization and mobility. Not all providers of e-learning products succeed in closing the gap between production costs and revenues. Especially in the academic sector e-learning projects suffer more and more from decreasing funding. For many currently active research groups it is essential to market their research results, e. g. e-learning applications, in order to ...

Learning classification models with soft-label information.

Science.gov (United States)

Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos

2014-01-01

Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.

Active learning of cortical connectivity from two-photon imaging data.

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Martín A Bertrán

Full Text Available Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this "active learning" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

EFFECTS OF COOPERATIVE LEARNING MODEL TYPE STAD JUST-IN TIME BASED ON THE RESULTS OF LEARNING TEACHING PHYSICS COURSE IN PHYSICS SCHOOL IN PHYSICS PROGRAM FACULTY UNIMED

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Teguh Febri Sudarma

2013-06-01

Full Text Available Research was aimed to determine: (1 Students’ learning outcomes that was taught with just in time teaching based STAD cooperative learning method and STAD cooperative learning method (2 Students’ outcomes on Physics subject that had high learning activity compared with low learning activity. The research sample was random by raffling four classes to get two classes. The first class taught with just in time teaching based STAD cooperative learning method, while the second class was taught with STAD cooperative learning method. The instrument used was conceptual understanding that had been validated with 7 essay questions. The average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,47 higher than average gain values of students learning results with STAD cooperative learning method. The high learning activity and low learning activity gave different learning results. In this case the average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,48 higher than average gain values of students learning results with STAD cooperative learning method. There was interaction between learning model and learning activity to the physics learning result test in students

Implementasi Pendekatan Konstruktivisme melalui Model Pembelajaran CLIS (Children Learning In Science dan Pengaruhnya terhadap Aktivitas Belajar dan Kemampuan Kognitif Siswa

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Dewi Ratnasari

2017-06-01

Full Text Available Abstract The aim of this research is to implementation constructivism approach through CLIS (Children Learning In Science learning model and its immpact toward students’ learning activities and cognitive ability. This action resesarch was analyzed by descriptive quantitative. Research subjects is students in 11th grade in the academic year 2013/2014 in the static fluid material. The data of this research from observation technique when learning process and cognitive test in the end of learning. Students’ learning activities in this research consist of visual activities, oral activities dan writing activities that described into many indicators, whereas cognitive test uses multiple choice instrument that has been properness analyzed. Based on research result and data analysis get result as follows: 1 The value of normalization in visual activities is 0,78 (high, oral activities is 0,31 (adequate, writing activities is 0,49 (adequate; 2 The increase of average achievement of students’ cognitive ability based on gain normalization is 0,32 (adequate after learning with constructivism approach through CLIS (Children Learning In Science model. Keywords: learning activities, Children Learning In Science, cognitive ability. Abstrak Penelitian ini bertujuan untuk mengimplemantasikan pendekatan konstruktivisme melalui model pembelajaran CLIS (Children Learning In Science dan pengaruhnya terhadap aktivitas belajar dan kemampuan kognitif siswa. Penelitian tindakan ini dianalisis menggunakan deskriptif kuantitatif dengan subjek penelitian adalah siswa kelas XI pada tahun ajaran 2013/2014 pada materi fluida statis. Data penelitian diperoleh dari hasil observasi selama proses pembelajaran dan hasil tes kognitif pada akhir pembelajaran. Aktivitas belajar siswa pada penelitian ini terdiri dari visual activities, oral activities dan writing activities yang dijabarkan menjadi beberapa indikator, sedangkan tes kognitif siswa menggunakan instrumen pilihan

[Effect of serotonin-modulated anticonsolidation protein on formation of long-term memory in carps Cyprinus carpio in the model of active avoidance learning].

Science.gov (United States)

Garina, D V; Mekhtiev, A A

2014-01-01

Effect of serotonin-modulated anticonsolidation protein (SMAP) that has property of disturbing formation of memory trace in mammals and of learning and memory in teleost fish was studied in the model of active avoidance learning. The experiment was performed in three stages: (1) fry of carps Cyprinus carpio L. was injected intracerebrovenricularly with the SMAP protein at a dose of 0.3 μg/g; control individuals were administered with equal amount of the buffered saline for poikilothermic animals; (2) 24 h after the injection, fish were learnt during 8 sèances for 2 days the conditioned reflex of active avoidance; (3) 48 h after the learning the testing of the skill was performed. The administration of the protein was shown to lead to disturbance of reproduction of the skill in the fish: the latent time of the skill reproduction in experimental individuals exceeded that in control fish more than two times, while the number of individuals succeeding the task in the experimental group was non-significantly lower than in the control group. However, unlike mammals, injection of the SMAP protein in this model produced no effect on the process of learning in carps. Thus, there was first demonstrated the inhibiting effect of the SMAP protein whose concentration correlated positively with the content of the neurotransmitter serotonin in brain on consolidation of memory traces in teleost fish.

Active Learning in Language Study and Science: Transforming Teacher Practice in North Sumatra’s Elementary Schools

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Ely Djulia

2011-12-01

Full Text Available AbstractAn action research project to investigate the implementation of active learning strategies to improve the quality of teaching and learning was conducted in three government elementary schools (Sekolah Dasar in North Sumatra that had received training in teaching for active learning under the auspices of the USAID-sponsored project, Decentralized Basic Education 2. Three cycles of data collection utilizing classroom observations, focus group discussions, and participant observation were conducted in each school. Data were analyzed both holistically and categorily to develop a better understanding of teachers’ successes and challenges in teaching for active learning. Finally, an intervention strategy involving modeling of teaching for active learning strategies was designed and implemented by members of the research team in each school. Our results suggest that language and science teachers developed more confidence in utilizing active learning strategies in their classrooms as a result of the intervention. Students also appeared to respond positively to the new active learning teaching strategies employed by their teachers. We conclude that the DBE-2 training provided to these schools can be considered successful; however, more attention needs to be paid to concrete factors that facilitate or impede teaching for active learning in Indonesian elementary schools in order to continue improving the quality of instruction for Indonesian children. Key Words: Islamic Education, Active Learning, Religious Studies, Indonesia

The effectiveness of flipped classroom learning model in secondary physics classroom setting

Science.gov (United States)

Prasetyo, B. D.; Suprapto, N.; Pudyastomo, R. N.

2018-03-01

The research aimed to describe the effectiveness of flipped classroom learning model on secondary physics classroom setting during Fall semester of 2017. The research object was Secondary 3 Physics group of Singapore School Kelapa Gading. This research was initiated by giving a pre-test, followed by treatment setting of the flipped classroom learning model. By the end of the learning process, the pupils were given a post-test and questionnaire to figure out pupils' response to the flipped classroom learning model. Based on the data analysis, 89% of pupils had passed the minimum criteria of standardization. The increment level in the students' mark was analysed by normalized n-gain formula, obtaining a normalized n-gain score of 0.4 which fulfil medium category range. Obtains from the questionnaire distributed to the students that 93% of students become more motivated to study physics and 89% of students were very happy to carry on hands-on activity based on the flipped classroom learning model. Those three aspects were used to generate a conclusion that applying flipped classroom learning model in Secondary Physics Classroom setting is effectively applicable.

Integrated Model for E-Learning Acceptance

Science.gov (United States)

Ramadiani; Rodziah, A.; Hasan, S. M.; Rusli, A.; Noraini, C.

2016-01-01

E-learning is not going to work if the system is not used in accordance with user needs. User Interface is very important to encourage using the application. Many theories had discuss about user interface usability evaluation and technology acceptance separately, actually why we do not make it correlation between interface usability evaluation and user acceptance to enhance e-learning process. Therefore, the evaluation model for e-learning interface acceptance is considered important to investigate. The aim of this study is to propose the integrated e-learning user interface acceptance evaluation model. This model was combined some theories of e-learning interface measurement such as, user learning style, usability evaluation, and the user benefit. We formulated in constructive questionnaires which were shared at 125 English Language School (ELS) students. This research statistics used Structural Equation Model using LISREL v8.80 and MANOVA analysis.

Vicarious learning from human models in monkeys.

Science.gov (United States)

Falcone, Rossella; Brunamonti, Emiliano; Genovesio, Aldo

2012-01-01

We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was apparent from the first trial of the test phase, confirming the ability of monkeys to learn by vicarious observation of human models.

The Effect of Inquiry Training Learning Model Based on Just in Time Teaching for Problem Solving Skill

Science.gov (United States)

Turnip, Betty; Wahyuni, Ida; Tanjung, Yul Ifda

2016-01-01

One of the factors that can support successful learning activity is the use of learning models according to the objectives to be achieved. This study aimed to analyze the differences in problem-solving ability Physics student learning model Inquiry Training based on Just In Time Teaching [JITT] and conventional learning taught by cooperative model…

Teaching physical activities to students with significant disabilities using video modeling.

Science.gov (United States)

Cannella-Malone, Helen I; Mizrachi, Sharona V; Sabielny, Linsey M; Jimenez, Eliseo D

2013-06-01

The objective of this study was to examine the effectiveness of video modeling on teaching physical activities to three adolescents with significant disabilities. The study implemented a multiple baseline across six physical activities (three per student): jumping rope, scooter board with cones, ladder drill (i.e., feet going in and out), ladder design (i.e., multiple steps), shuttle run, and disc ride. Additional prompt procedures (i.e., verbal, gestural, visual cues, and modeling) were implemented within the study. After the students mastered the physical activities, we tested to see if they would link the skills together (i.e., complete an obstacle course). All three students made progress learning the physical activities, but only one learned them with video modeling alone (i.e., without error correction). Video modeling can be an effective tool for teaching students with significant disabilities various physical activities, though additional prompting procedures may be needed.

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.

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.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

Science.gov (United States)

Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

Directory of Open Access Journals (Sweden)

Hashem Salarzadeh Jenatabadi

Full Text Available Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Inverse problems in machine learning: An application to brain activity interpretation

International Nuclear Information System (INIS)

Prato, M; Zanni, L

2008-01-01

In a typical machine learning problem one has to build a model from a finite training set which is able to generalize the properties characterizing the examples of the training set to new examples. The model has to reflect as much as possible the set of training examples but, especially in real-world problems in which the data are often corrupted by different sources of noise, it has to avoid a too strict dependence on the training examples themselves. Recent studies on the relationship between this kind of learning problem and the regularization theory for ill-posed inverse problems have given rise to new regularized learning algorithms. In this paper we recall some of these learning methods and we propose an accelerated version of the classical Landweber iterative scheme which results particularly efficient from the computational viewpoint. Finally, we compare the performances of these methods with the classical Support Vector Machines learning algorithm on a real-world experiment concerning brain activity interpretation through the analysis of functional magnetic resonance imaging data.

Activation of β-adrenoceptor facilitates active avoidance learning through enhancement of glutamate levels in the hippocampal dentate gyrus.

Science.gov (United States)

Lv, Jing; Feng, Hao; Chen, Ling; Wang, Wei-Yao; Yue, Xue-Ling; Jin, Qing-Hua

2017-10-18

Long-term potentiation (LTP) is widely accepted as the best studied model for neurophysiological mechanisms that could underlie learning and memory formation. Despite a number of studies indicating that β-adrenoceptors in the hippocampal dentate gyrus (DG) is involved in the modulation of learning and memory as well as LTP, few studies have used glutamate release as a visual indicator in awake animals to explore the role of β-adrenoceptors in learning-dependent LTP. Therefore, in the present study, the effects of propranolol (an antagonist of β-adrenoceptor) and isoproterenol (an agonist of β-adrenoceptor) on extracellular concentrations of glutamate and amplitudes of field excitatory postsynaptic potential were measured in the DG region during active avoidance learning in freely moving conscious rats. In the control group, the glutamate level in the DG was significantly increased during the acquisition of active avoidance behavior and returned to basal level following extinction training. In propranolol group, antagonism of β-adrenoceptors in the DG significantly reduced the change in glutamate level, and the acquisition of the active avoidance behavior was significantly inhibited. In contrast, the change in glutamate level was significantly enhanced by isoproterenol, and the acquisition of the active avoidance behavior was significantly accelerated. Furthermore, in all groups, the changes in glutamate level were accompanied by corresponding changes in field excitatory postsynaptic potential amplitude and active avoidance behavior. Our results suggest that activation of β-adrenoceptors in the hippocampal DG facilitates active avoidance learning by modulations of glutamate level and synaptic efficiency in rats.

Keefektifan Model CIRC Berbasis Joyful Learning Terhadap Kemampuan Penalaran Matematis Siswa SMP

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Rian Triastuti

2013-12-01

class was better than average grade student’s mathematical reasoning skills of control. Based on observations indicate that the percentage of active students in the implementation of CIRC based Joyful Learning models is higher than the percentage of active students in expository teaching and learning activities of teachers in managing the CIRC based Joyful Learning models achieving the criteria very well.  Keywords:          CIRC; Joyful Learning; Mathematical Reasoning Skills.

Meta Analisis Model Pembelajaran Problem Based Learning dalam Meningkatkan Keterampilan Berpikir Kritis di Sekolah Dasar [A Meta-analysis of Problem-Based Learning Models in Increasing Critical Thinking Skills in Elementary Schools

Directory of Open Access Journals (Sweden)

Indri Anugraheni

2018-01-01

Full Text Available This study aims to analyze Problem-based Learning models intended to improve critical thinking skills in elementary school students. Problem-based learning models are learning processes where students are open minded, reflexive, active, reflective, and critical through real-world context activities. In this study the researcher used a meta-analysis method. First, the researcher formulated the research problem, then proceeded to review the existing relevant research for analysis. Data were collected by using a non-test technique by browsing electronic journals through Google Scholar and studying documentation in the library. Seven articles were found through Google Scholar and only one was found in the library. Based on the analysis of the results, the problem-based learning model can improve students' thinking ability from as little as 2.87% up to 33.56% with an average of 14.18%. BAHASA INDONESIA ABSTRAK: Penelitian ini bertujuan untuk menganalisis kembali tentang model pembelajaran Problem Based Learning untuk meningkatkan keterampilan berpikir kritis di Sekolah Dasar. Model pembelajaran Problem Based Learning adalah proses pembelajaran dimana siswa mampu memiliki pola pikir yang terbuka, refktif, aktif, reflektif dan kritis melalui kegiatan konteks dunia nyata. Dalam penelitian ini peneliti menggunakan metode meta analisis. Pertama-tama, peneliti merumuskan masalah penelitian, kemudian dilanjutkan dengan menelusuri penelitian yang sudah ada dan relevan untuk dianalisis. Teknik pengumpulan data dengan menggunakan non tes yaitu dengan menelusuri jurnal elektronik melalui google Cendekia dan studi dokumentasi di perpustakaan. Dari hasil penelusuran diperoleh 20 artikel dari jurnal dan 3 dari repository. Berdasarkan hasil analisis ternyata model pembelajaran Problem Based Learning mampu meningkatkan kemampuan berpikir Siswa mulai dari yang terendah 2,87% sampai yang tertinggi 33,56% dengan rata-rata 12,73%.

Toward A Dual-Learning Systems Model of Speech Category Learning

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Bharath eChandrasekaran

2014-07-01

Full Text Available More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article we describe a neurobiologically-constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. We find an age related deficit in reflective-optimal but not reflexive-optimal auditory category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, uni-dimensional rules to more complex, reflexive, multi-dimensional rules. In a second application we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice

OpenAIRE

Bathellier, Brice; Tee, Sui Poh; Hrovat, Christina; Rumpel, Simon

2013-01-01

Learning speed can strongly differ across individuals. This is seen in humans and animals. Here, we measured learning speed in mice performing a discrimination task and developed a theoretical model based on the reinforcement learning framework to account for differences between individual mice. We found that, when using a multiplicative learning rule, the starting connectivity values of the model strongly determine the shape of learning curves. This is in contrast to current learning models ...

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…

Cooperative learning model with high order thinking skills questions: an understanding on geometry

Science.gov (United States)

Sari, P. P.; Budiyono; Slamet, I.

2018-05-01

Geometry, a branch of mathematics, has an important role in mathematics learning. This research aims to find out the effect of learning model, emotional intelligence, and the interaction between learning model and emotional intelligence toward students’ mathematics achievement. This research is quasi-experimental research with 2 × 3 factorial design. The sample in this research included 179 Senior High School students on 11th grade in Sukoharjo Regency, Central Java, Indonesia in academic year of 2016/2017. The sample was taken by using stratified cluster random sampling. The results showed that: the student are taught by Thinking Aloud Pairs Problem-Solving using HOTs questions provides better mathematics learning achievement than Make A Match using HOTs questions. High emotional intelligence students have better mathematics learning achievement than moderate and low emotional intelligence students, and moderate emotional intelligence students have better mathematics learning achievement than low emotional intelligence students. There is an interaction between learning model and emotional intelligence, and these affect mathematics learning achievement. We conclude that appropriate learning model can support learning activities become more meaningful and facilitate students to understand material. For further research, we suggest to explore the contribution of other aspects in cooperative learning modification to mathematics achievement.

The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models

Science.gov (United States)

Sigala, Rodrigo; Haufe, Sebastian; Roy, Dipanjan; Dinse, Hubert R.; Ritter, Petra

2014-01-01

During the past two decades growing evidence indicates that brain oscillations in the alpha band (~10 Hz) not only reflect an “idle” state of cortical activity, but also take a more active role in the generation of complex cognitive functions. A recent study shows that more than 60% of the observed inter-subject variability in perceptual learning can be ascribed to ongoing alpha activity. This evidence indicates a significant role of alpha oscillations for perceptual learning and hence motivates to explore the potential underlying mechanisms. Hence, it is the purpose of this review to highlight existent evidence that ascribes intrinsic alpha oscillations a role in shaping our ability to learn. In the review, we disentangle the alpha rhythm into different neural signatures that control information processing within individual functional building blocks of perceptual learning. We further highlight computational studies that shed light on potential mechanisms regarding how alpha oscillations may modulate information transfer and connectivity changes relevant for learning. To enable testing of those model based hypotheses, we emphasize the need for multidisciplinary approaches combining assessment of behavior and multi-scale neuronal activity, active modulation of ongoing brain states and computational modeling to reveal the mathematical principles of the complex neuronal interactions. In particular we highlight the relevance of multi-scale modeling frameworks such as the one currently being developed by “The Virtual Brain” project. PMID:24772077

The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models

Directory of Open Access Journals (Sweden)

Rodrigo eSigala

2014-04-01

Full Text Available During the past two decades growing evidence indicates that brain oscillations in the alpha band (~10 Hz not only reflect an ‘idle’ state of cortical activity, but also take a more active role in the generation of complex cognitive functions. A recent study shows that more than 60% of the observed inter-subject variability in perceptual learning can be ascribed to ongoing alpha activity. This evidence indicates a significant role of alpha oscillations for perceptual learning and hence motivates to explore the potential underlying mechanisms. Hence, it is the purpose of this review to highlight existent evidence that ascribes intrinsic alpha oscillations a role in shaping our ability to learn. In the review, we disentangle the alpha rhythm into different neural signatures that control information processing within individual functional building blocks of perceptual learning. We further highlight computational studies that shed light on potential mechanisms regarding how alpha oscillations may modulate information transfer and connectivity changes relevant for learning. To enable testing of those model based hypotheses, we emphasize the need for multidisciplinary approaches combining assessment of behavior and multi-scale neuronal activity, active modulation of ongoing brain states and computational modeling to reveal the mathematical principles of the complex neuronal interactions. In particular we highlight the relevance of multi-scale modeling frameworks such as the one currently being developed by The Virtual Brain project.

Active Learning through Materials Development: A Project for the Advanced L2 Classroom

Directory of Open Access Journals (Sweden)

Katrina Daly Thompson

2008-01-01

Full Text Available Building on the notion of active learning, the assumption that students learn more when given opportunities to practice using their skills and to receive feedback on their performance, this article de-scribes a project undertaken in an Advanced (third-year Swahili course in which students were given the opportunity to develop L2 materials for computer-mediated peer instruction. The article exam-ines the goals, design and results of the project in light of the litera-ture on active learning and learner autonomy, and suggests how the project might be improved in order to serve as a model for other Ad-vanced L2 courses.

An Analysis of Creative Process Learning in Computer Game Activities Through Player Experiences

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Wilawan Inchamnan

2016-09-01

Full Text Available This research investigates the extent to which creative processes can be fostered through computer gaming. It focuses on creative components in games that have been specifically designed for educational purposes: Digital Game Based Learning (DGBL. A behavior analysis for measuring the creative potential of computer game activities and learning outcomes is described. Creative components were measured by examining task motivation and domain-relevant and creativity-relevant skill factors. The research approach applied heuristic checklists in the field of gameplay to analyze the stage of player activities involved in the performance of the task and to examine player experiences with the Player Experience of Need Satisfaction (PENS survey. Player experiences were influenced by competency, autonomy, intuitive controls, relatedness and presence. This study examines the impact of these activities on the player experience for evaluating learning outcomes through school records. The study is designed to better understand the creative potential of people who are engaged in learning knowledge and skills during the course while playing video games. The findings show the creative potential that occurred to yield levels of creative performance within game play activities to support learning. The anticipated outcome is knowledge on how video games foster creative thinking as an overview of the Creative Potential of Learning Model (CPLN. CPLN clearly describes the interrelationships between principles of learning and creative potential, the interpretation of the results is indispensable.

Problem Solving Model for Science Learning

Science.gov (United States)

Alberida, H.; Lufri; Festiyed; Barlian, E.

2018-04-01

This research aims to develop problem solving model for science learning in junior high school. The learning model was developed using the ADDIE model. An analysis phase includes curriculum analysis, analysis of students of SMP Kota Padang, analysis of SMP science teachers, learning analysis, as well as the literature review. The design phase includes product planning a science-learning problem-solving model, which consists of syntax, reaction principle, social system, support system, instructional impact and support. Implementation of problem-solving model in science learning to improve students' science process skills. The development stage consists of three steps: a) designing a prototype, b) performing a formative evaluation and c) a prototype revision. Implementation stage is done through a limited trial. A limited trial was conducted on 24 and 26 August 2015 in Class VII 2 SMPN 12 Padang. The evaluation phase was conducted in the form of experiments at SMPN 1 Padang, SMPN 12 Padang and SMP National Padang. Based on the development research done, the syntax model problem solving for science learning at junior high school consists of the introduction, observation, initial problems, data collection, data organization, data analysis/generalization, and communicating.

Learning Markov Decision Processes for Model Checking

DEFF Research Database (Denmark)

Mao, Hua; Chen, Yingke; Jaeger, Manfred

2012-01-01

. The proposed learning algorithm is adapted from algorithms for learning deterministic probabilistic finite automata, and extended to include both probabilistic and nondeterministic transitions. The algorithm is empirically analyzed and evaluated by learning system models of slot machines. The evaluation......Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm...... on learning probabilistic automata to reactive systems, where the observed system behavior is in the form of alternating sequences of inputs and outputs. We propose an algorithm for automatically learning a deterministic labeled Markov decision process model from the observed behavior of a reactive system...

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

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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.

Peer-to-Peer Learning and the Army Learning Model

Science.gov (United States)

2012-06-08

education will be delivered to the current and future force. This thesis examined the salient areas proposed by the ALM and its impact on P2P learning ...The Army Learning Model is the new educational model that develops adaptive leaders in an era of persistent conflict. Life-long, individual

Learning Computational Models of Video Memorability from fMRI Brain Imaging.

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Han, Junwei; Chen, Changyuan; Shao, Ling; Hu, Xintao; Han, Jungong; Liu, Tianming

2015-08-01

Generally, various visual media are unequally memorable by the human brain. This paper looks into a new direction of modeling the memorability of video clips and automatically predicting how memorable they are by learning from brain functional magnetic resonance imaging (fMRI). We propose a novel computational framework by integrating the power of low-level audiovisual features and brain activity decoding via fMRI. Initially, a user study experiment is performed to create a ground truth database for measuring video memorability and a set of effective low-level audiovisual features is examined in this database. Then, human subjects' brain fMRI data are obtained when they are watching the video clips. The fMRI-derived features that convey the brain activity of memorizing videos are extracted using a universal brain reference system. Finally, due to the fact that fMRI scanning is expensive and time-consuming, a computational model is learned on our benchmark dataset with the objective of maximizing the correlation between the low-level audiovisual features and the fMRI-derived features using joint subspace learning. The learned model can then automatically predict the memorability of videos without fMRI scans. Evaluations on publically available image and video databases demonstrate the effectiveness of the proposed framework.

Using an academic-community partnership model and blended learning to advance community health nursing pedagogy.

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Ezeonwu, Mabel; Berkowitz, Bobbie; Vlasses, Frances R

2014-01-01

This article describes a model of teaching community health nursing that evolved from a long-term partnership with a community with limited existing health programs. The partnership supported RN-BSN students' integration in the community and resulted in reciprocal gains for faculty, students and community members. Community clients accessed public health services as a result of the partnership. A blended learning approach that combines face-to-face interactions, service learning and online activities was utilized to enhance students' learning. Following classroom sessions, students actively participated in community-based educational process through comprehensive health needs assessments, planning and implementation of disease prevention and health promotion activities for community clients. Such active involvement in an underserved community deepened students' awareness of the fundamentals of community health practice. Students were challenged to view public health from a broader perspective while analyzing the impacts of social determinants of health on underserved populations. Through asynchronous online interactions, students synthesized classroom and community activities through critical thinking. This paper describes a model for teaching community health nursing that informs students' learning through blended learning, and meets the demands for community health nursing services delivery. © 2013 Wiley Periodicals, Inc.

Empathy and feedback processing in active and observational learning.

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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.

Translation of an Action Learning Collaborative Model Into a Community-Based Intervention to Promote Physical Activity and Healthy Eating.

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Schifferdecker, Karen E; Adachi-Mejia, Anna M; Butcher, Rebecca L; O'Connor, Sharon; Li, Zhigang; Bazos, Dorothy A

2016-01-01

Action Learning Collaboratives (ALCs), whereby teams apply quality improvement (QI) tools and methods, have successfully improved patient care delivery and outcomes. We adapted and tested the ALC model as a community-based obesity prevention intervention focused on physical activity and healthy eating. The intervention used QI tools (e.g., progress monitoring) and team-based activities and was implemented in three communities through nine monthly meetings. To assess process and outcomes, we used a longitudinal repeated-measures and mixed-methods triangulation approach with a quasi-experimental design including objective measures at three time points. Most of the 97 participants were female (85.4%), White (93.8%), and non-Hispanic/Latino (95.9%). Average age was 52 years; 28.0% had annual household income of $20,000 or less; and mean body mass index was 35. Through mixed-effects models, we found some physical activity outcomes improved. Other outcomes did not significantly change. Although participants favorably viewed the QI tools, components of the QI process such as sharing goals and data on progress in teams and during meetings were limited. Participants' requests for more education or activities around physical activity and healthy eating, rather than progress monitoring and data sharing required for QI activities, challenged ALC model implementation. An ALC model for community-based obesity prevention may be more effective when applied to preexisting teams in community-based organizations. © 2015 Society for Public Health Education.

Biology learning evaluation model in Senior High Schools

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Sri Utari

2017-06-01

Full Text Available The study was to develop a Biology learning evaluation model in senior high schools that referred to the research and development model by Borg & Gall and the logic model. The evaluation model included the components of input, activities, output and outcomes. The developing procedures involved a preliminary study in the form of observation and theoretical review regarding the Biology learning evaluation in senior high schools. The product development was carried out by designing an evaluation model, designing an instrument, performing instrument experiment and performing implementation. The instrument experiment involved teachers and Students from Grade XII in senior high schools located in the City of Yogyakarta. For the data gathering technique and instrument, the researchers implemented observation sheet, questionnaire and test. The questionnaire was applied in order to attain information regarding teacher performance, learning performance, classroom atmosphere and scientific attitude; on the other hand, test was applied in order to attain information regarding Biology concept mastery. Then, for the analysis of instrument construct, the researchers performed confirmatory factor analysis by means of Lisrel 0.80 software and the results of this analysis showed that the evaluation instrument valid and reliable. The construct validity was between 0.43-0.79 while the reliability of measurement model was between 0.88-0.94. Last but not the least, the model feasibility test showed that the theoretical model had been supported by the empirical data.

Analysis of creative mathematical thinking ability by using model eliciting activities (MEAs)

Science.gov (United States)

Winda, A.; Sufyani, P.; Elah, N.

2018-05-01

Lack of creative mathematical thinking ability can lead to not accustomed with open ended problem. Students’ creative mathematical thinking ability in the first grade at one of junior high school in Tangerang City is not fully developed. The reason of students’ creative mathematical thinking ability is not optimally developed is so related with learning process which has done by the mathematics teacher, maybe the learning design that teacher use is unsuitable for increasing students’ activity in the learning process. This research objective is to see the differences in students’ ways of answering the problems in terms of students’ creative mathematical thinking ability during the implementation of Model Eliciting Activities (MEAs). This research use post-test experimental class design. The indicators for creative mathematical thinking ability in this research arranged in three parts, as follow: (1) Fluency to answer the problems; (2) Flexibility to solve the problems; (3) Originality of answers. The result of this research found that by using the same learning model and same instrument from Model Eliciting Activities (MEAs) there are some differences in the way students answer the problems and Model Eliciting Activities (MEAs) can be one of approach used to increase students’ creative mathematical thinking ability.

Vicarious learning from human models in monkeys.

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Rossella Falcone

Full Text Available We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was apparent from the first trial of the test phase, confirming the ability of monkeys to learn by vicarious observation of human models.

Impact of Learning Model Based on Cognitive Conflict toward Student’s Conceptual Understanding

Science.gov (United States)

Mufit, F.; Festiyed, F.; Fauzan, A.; Lufri, L.

2018-04-01

The problems that often occur in the learning of physics is a matter of misconception and low understanding of the concept. Misconceptions do not only happen to students, but also happen to college students and teachers. The existing learning model has not had much impact on improving conceptual understanding and remedial efforts of student misconception. This study aims to see the impact of cognitive-based learning model in improving conceptual understanding and remediating student misconceptions. The research method used is Design / Develop Research. The product developed is a cognitive conflict-based learning model along with its components. This article reports on product design results, validity tests, and practicality test. The study resulted in the design of cognitive conflict-based learning model with 4 learning syntaxes, namely (1) preconception activation, (2) presentation of cognitive conflict, (3) discovery of concepts & equations, (4) Reflection. The results of validity tests by some experts on aspects of content, didactic, appearance or language, indicate very valid criteria. Product trial results also show a very practical product to use. Based on pretest and posttest results, cognitive conflict-based learning models have a good impact on improving conceptual understanding and remediating misconceptions, especially in high-ability students.

Identifying Key Features of Effective Active Learning: The Effects of Writing and Peer Discussion

Science.gov (United States)

Pangle, Wiline M.; Wyatt, Kevin H.; Powell, Karli N.; Sherwood, Rachel E.

2014-01-01

We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning. PMID:25185230

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…

An implementation of 7E Learning Cycle Model to Improve Student Self-esteem

Science.gov (United States)

Firdaus, F.; Priatna, N.; Suhendra, S.

2017-09-01

One of the affective factors that affect student learning outcomes is student self-esteem in mathematics, learning achievement and self-esteem influence each other. The purpose of this research is to know whether self-esteem students who get 7E learning cycle model is better than students who get conventional learning. This research method is a non-control group design. Based on the results obtained that the normal and homogeneous data so that the t test and from the test results showed there are significant differences in self-esteem students learning with 7E learning cycle model compared with students who get conventional learning. The implications of the results of this study are that students should be required to conduct many discussions, presentations and evaluations on classroom activities as these learning stages can improve students’ self-esteem especially pride in the results achieved.

Transforming an Introductory Programming Course: From Lectures to Active Learning via Wireless Laptops

Science.gov (United States)

Barak, Miri; Harward, Judson; Kocur, George; Lerman, Steven

2007-08-01

Within the framework of MIT's course 1.00: Introduction to Computers and Engineering Problem Solving, this paper describes an innovative project entitled: Studio 1.00 that integrates lectures with in-class demonstrations, active learning sessions, and on-task feedback, through the use of wireless laptop computers. This paper also describes a related evaluation study that investigated the effectiveness of different instructional strategies, comparing traditional teaching with two models of the studio format. Students' learning outcomes, specifically, their final grades and conceptual understanding of computational methods and programming, were examined. Findings indicated that Studio-1.00, in both its extensive- and partial-active learning modes, enhanced students' learning outcomes in Java programming. Comparing to the traditional courses, more students in the studio courses received "A" as their final grade and less failed. Moreover, students who regularly attended the active learning sessions were able to conceptualize programming principles better than their peers. We have also found two weaknesses in the teaching format of Studio-1.00 that can guide future versions of the course.

Seamless Language Learning: Second Language Learning with Social Media

Science.gov (United States)

Wong, Lung-Hsiang; Chai, Ching Sing; Aw, Guat Poh

2017-01-01

This conceptual paper describes a language learning model that applies social media to foster contextualized and connected language learning in communities. The model emphasizes weaving together different forms of language learning activities that take place in different learning contexts to achieve seamless language learning. it promotes social…

A Professionalism Curricular Model to Promote Transformative Learning Among Residents.

Science.gov (United States)

Foshee, Cecile M; Mehdi, Ali; Bierer, S Beth; Traboulsi, Elias I; Isaacson, J Harry; Spencer, Abby; Calabrese, Cassandra; Burkey, Brian B

2017-06-01