Representation Learning from Time Labelled Heterogeneous Data for Mobile Crowdsensing

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Chunmei Ma

2016-01-01

Full Text Available Mobile crowdsensing is a new paradigm that can utilize pervasive smartphones to collect and analyze data to benefit users. However, sensory data gathered by smartphone usually involves different data types because of different granularity and multiple sensor sources. Besides, the data are also time labelled. The heterogeneous and time sequential data raise new challenges for data analyzing. Some existing solutions try to learn each type of data one by one and analyze them separately without considering time information. In addition, the traditional methods also have to determine phone orientation because some sensors equipped in smartphone are orientation related. In this paper, we think that a combination of multiple sensors can represent an invariant feature for a crowdsensing context. Therefore, we propose a new representation learning method of heterogeneous data with time labels to extract typical features using deep learning. We evaluate that our proposed method can adapt data generated by different orientations effectively. Furthermore, we test the performance of the proposed method by recognizing two group mobile activities, walking/cycling and driving/bus with smartphone sensors. It achieves precisions of 98.6% and 93.7% in distinguishing cycling from walking and bus from driving, respectively.

Projection specificity in heterogeneous locus coeruleus cell populations: implications for learning and memory

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Uematsu, Akira; Tan, Bao Zhen

2015-01-01

Noradrenergic neurons in the locus coeruleus (LC) play a critical role in many functions including learning and memory. This relatively small population of cells sends widespread projections throughout the brain including to a number of regions such as the amygdala which is involved in emotional associative learning and the medial prefrontal cortex which is important for facilitating flexibility when learning rules change. LC noradrenergic cells participate in both of these functions, but it is not clear how this small population of neurons modulates these partially distinct processes. Here we review anatomical, behavioral, and electrophysiological studies to assess how LC noradrenergic neurons regulate these different aspects of learning and memory. Previous work has demonstrated that subpopulations of LC noradrenergic cells innervate specific brain regions suggesting heterogeneity of function in LC neurons. Furthermore, noradrenaline in mPFC and amygdala has distinct effects on emotional learning and cognitive flexibility. Finally, neural recording data show that LC neurons respond during associative learning and when previously learned task contingencies change. Together, these studies suggest a working model in which distinct and potentially opposing subsets of LC neurons modulate particular learning functions through restricted efferent connectivity with amygdala or mPFC. This type of model may provide a general framework for understanding other neuromodulatory systems, which also exhibit cell type heterogeneity and projection specificity. PMID:26330494

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks.

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Li, Ming; Miao, Chunyan; Leung, Cyril

2015-12-04

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.

PEDLA: predicting enhancers with a deep learning-based algorithmic framework.

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Liu, Feng; Li, Hao; Ren, Chao; Bo, Xiaochen; Shu, Wenjie

2016-06-22

Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from many issues. We developed a deep learning-based algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and demonstrated that PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to iteratively learn from 22 training cell types/tissues. Our results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) of sensitivity and specificity across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues.

Improving Collaborative Learning in the Classroom: Text Mining Based Grouping and Representing

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Erkens, Melanie; Bodemer, Daniel; Hoppe, H. Ulrich

2016-01-01

Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful…

Boundedly rational learning and heterogeneous trading strategies with hybrid neuro-fuzzy models

NARCIS (Netherlands)

Bekiros, S.D.

2009-01-01

The present study deals with heterogeneous learning rules in speculative markets where heuristic strategies reflect the rules-of-thumb of boundedly rational investors. The major challenge for "chartists" is the development of new models that would enhance forecasting ability particularly for time

A resilient and efficient CFD framework: Statistical learning tools for multi-fidelity and heterogeneous information fusion

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Lee, Seungjoon; Kevrekidis, Ioannis G.; Karniadakis, George Em

2017-09-01

Exascale-level simulations require fault-resilient algorithms that are robust against repeated and expected software and/or hardware failures during computations, which may render the simulation results unsatisfactory. If each processor can share some global information about the simulation from a coarse, limited accuracy but relatively costless auxiliary simulator we can effectively fill-in the missing spatial data at the required times by a statistical learning technique - multi-level Gaussian process regression, on the fly; this has been demonstrated in previous work [1]. Based on the previous work, we also employ another (nonlinear) statistical learning technique, Diffusion Maps, that detects computational redundancy in time and hence accelerate the simulation by projective time integration, giving the overall computation a "patch dynamics" flavor. Furthermore, we are now able to perform information fusion with multi-fidelity and heterogeneous data (including stochastic data). Finally, we set the foundations of a new framework in CFD, called patch simulation, that combines information fusion techniques from, in principle, multiple fidelity and resolution simulations (and even experiments) with a new adaptive timestep refinement technique. We present two benchmark problems (the heat equation and the Navier-Stokes equations) to demonstrate the new capability that statistical learning tools can bring to traditional scientific computing algorithms. For each problem, we rely on heterogeneous and multi-fidelity data, either from a coarse simulation of the same equation or from a stochastic, particle-based, more "microscopic" simulation. We consider, as such "auxiliary" models, a Monte Carlo random walk for the heat equation and a dissipative particle dynamics (DPD) model for the Navier-Stokes equations. More broadly, in this paper we demonstrate the symbiotic and synergistic combination of statistical learning, domain decomposition, and scientific computing in

Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.

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Honnorat, Nicolas; Dong, Aoyan; Meisenzahl-Lechner, Eva; Koutsouleris, Nikolaos; Davatzikos, Christos

2017-12-20

Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a semi-supervised clustering method. We apply this strategy to a cohort of patients with schizophrenia of varying extends of disease duration, and we describe the neuroanatomical, demographic and clinical characteristics of the subtypes discovered. We analyze the neuroanatomical heterogeneity of 157 patients diagnosed with Schizophrenia, relative to a control population of 169 subjects, using a machine learning method called CHIMERA. CHIMERA clusters the differences between patients and a demographically-matched population of healthy subjects, rather than clustering patients themselves, thereby specifically assessing disease-related neuroanatomical alterations. Voxel-Based Morphometry was conducted to visualize the neuroanatomical patterns associated with each group. The clinical presentation and the demographics of the groups were then investigated. Three subgroups were identified. The first two differed substantially, in that one involved predominantly temporal-thalamic-peri-Sylvian regions, whereas the other involved predominantly frontal regions and the thalamus. Both subtypes included primarily male patients. The third pattern was a mix of these two and presented milder neuroanatomic alterations and comprised a comparable number of men and women. VBM and statistical analyses suggest that these groups could correspond to different neuroanatomical dimensions of schizophrenia. Our analysis suggests that schizophrenia presents distinct neuroanatomical variants. This variability points to the need for a dimensional neuroanatomical approach using data-driven, mathematically principled multivariate pattern analysis methods, and should be taken into account in clinical studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Learning to Learn: A Case for the Heterogeneous Expectations Hypothesis in Industrialized Markets

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Desmond W. Ng

2016-07-01

Full Text Available A cobweb model is developed where the heterogeneous expectation hypothesis is examined. An agent’s heterogeneous expectation involves the development of a “higher ordered learning” process in which agents over time develop expectations that are consistent with rational expectations. In addition, as cob web models are production based systems, an agents’ heterogeneous expectations are influenced by a specialization of activities. The case of the industrialization of the U.S. hog-pork industry is used to illustrate the influence of these features on the equilibrium and non-equilibrium properties of a modified cob-web model.

Group Composition of Cooperative Learning: Does Heterogeneous Grouping Work in Asian Classrooms?

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Thanh, Pham Thi Hong; Gillies, Robyn

2010-01-01

Constructing an appropriate group is important to teamwork success. Although, heterogeneous grouping is widely recommended in Western countries, this method of grouping is questioned in Asian classrooms because Asian and Western students have different cultures of learning. Unfortunately, this issue has not been addressed in any research to date.…

"The Coat Traps All Your Body Heat": Heterogeneity as Fundamental to Learning

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Rosebery, Ann S.; Ogonowski, Mark; DiSchino, Mary; Warren, Beth

2010-01-01

This article explores heterogeneity as fundamental to learning. Inspired by Bakhtin's notion of heteroglossia, a design team consisting of an experienced classroom teacher and 2 researchers investigated how a class of 3rd and 4th graders came to understand disciplinary points of view on heat, heat transfer, and the particulate nature of matter.…

Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

KAUST Repository

AlQuerm, Ismail A.

2018-02-21

resources management in diverse wireless networks. The core operation of the proposed architecture is decision-making for resource allocation and system’s parameters adaptation. Thus, we develop the decision-making mechanism using different artificial intelligence techniques, evaluate the performance achieved and determine the tradeoff of using one technique over the others. The techniques include decision-trees, genetic algorithm, hybrid engine based on decision-trees and case based reasoning, and supervised engine with machine learning contribution to determine the ultimate technique that suits the current environment conditions. All the proposed techniques are evaluated using testbed implementation in different topologies and scenarios. LTE networks have been considered as a potential environment for demonstration of our proposed cognitive based resource allocation techniques as they lack of radio resource management. In addition, we explore the use of enhanced online learning to perform efficient resource allocation in the upcoming 5G networks to maximize energy efficiency and data rate. The considered 5G structures are heterogeneous multi-tier networks with device to device communication and heterogeneous cloud radio access networks. We propose power and resource blocks allocation schemes to maximize energy efficiency and data rate in heterogeneous 5G networks. Moreover, traffic offloading from large cells to small cells in 5G heterogeneous networks is investigated and an online learning based traffic offloading strategy is developed to enhance energy efficiency. Energy efficiency problem in heterogeneous cloud radio access networks is tackled using online learning in centralized and distributed fashions. The proposed online learning comprises improvement features that reduce the algorithms complexities and enhance the performance achieved.

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

OpenAIRE

Abadi, Martín; Agarwal, Ashish; Barham, Paul; Brevdo, Eugene; Chen, Zhifeng; Citro, Craig; Corrado, Greg S.; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Goodfellow, Ian; Harp, Andrew; Irving, Geoffrey; Isard, Michael

2016-01-01

TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algo...

Sophisticated Online Learning Scheme for Green Resource Allocation in 5G Heterogeneous Cloud Radio Access Networks

KAUST Repository

Alqerm, Ismail

2018-01-23

5G is the upcoming evolution for the current cellular networks that aims at satisfying the future demand for data services. Heterogeneous cloud radio access networks (H-CRANs) are envisioned as a new trend of 5G that exploits the advantages of heterogeneous and cloud radio access networks to enhance spectral and energy efficiency. Remote radio heads (RRHs) are small cells utilized to provide high data rates for users with high quality of service (QoS) requirements, while high power macro base station (BS) is deployed for coverage maintenance and low QoS users service. Inter-tier interference between macro BSs and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRANs. Therefore, we propose an efficient resource allocation scheme using online learning, which mitigates interference and maximizes energy efficiency while maintaining QoS requirements for all users. The resource allocation includes resource blocks (RBs) and power. The proposed scheme is implemented using two approaches: centralized, where the resource allocation is processed at a controller integrated with the baseband processing unit and decentralized, where macro BSs cooperate to achieve optimal resource allocation strategy. To foster the performance of such sophisticated scheme with a model free learning, we consider users\\' priority in RB allocation and compact state representation learning methodology to improve the speed of convergence and account for the curse of dimensionality during the learning process. The proposed scheme including both approaches is implemented using software defined radios testbed. The obtained results and simulation results confirm that the proposed resource allocation solution in H-CRANs increases the energy efficiency significantly and maintains users\\' QoS.

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

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Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

2017-08-03

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

Activities of Heterogeneous Acid-Base Catalysts for Fragrances Synthesis: A Review

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Hartati Hartati

2013-06-01

Full Text Available This paper reviews various types of heterogeneous acid-base catalysts for fragrances preparation. Catalytic activities of various types of heterogeneous acid and base catalysts in fragrances preparation, i.e. non-zeolitic, zeolitic, and mesoporous molecular sieves have been reported. Generally, heterogeneous acid catalysts are commonly used in fragrance synthesis as compared to heterogeneous base catalysts. Heteropoly acids and hydrotalcites type catalysts are widely used as heterogeneous acid and base catalysts, respectively. © 2013 BCREC UNDIP. All rights reservedReceived: 20th January 2013; Revised: 31st March 2013; Accepted: 1st April 2013[How to Cite: Hartati, H., Santoso, M., Triwahyono, S., Prasetyoko, D. (2013. Activities of Heterogeneous Acid-Base Catalysts for Fragrances Synthesis: A Review. Bulletin of Chemical Reaction Engineering & Catalysis, 8 (1: 14-33. (doi:10.9767/bcrec.8.1.4394.14-33][Permalink/DOI: http://dx.doi.org/10.9767/bcrec.8.1.4394.14-33] | View in  |

Interpretable Categorization of Heterogeneous Time Series Data

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Lee, Ritchie; Kochenderfer, Mykel J.; Mengshoel, Ole J.; Silbermann, Joshua

2017-01-01

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

How Design of Online Learning Materials can Accommodate the Heterogeneity in Student Abilities, Aptitudes and Aspirations

OpenAIRE

Bates, Simon; Hardy, Judy; Hill, Jon; McKain, David; University of Gloucestershire

2008-01-01

We describe the challenges facing higher education in terms of the heterogeneity of the cohort of students that arrive at university. The reasons why such diversity exists are many: students differ widely in terms of their preparedness for study at university, their degree choice aspirations and the issue of motivation for study of a particular subject. We illustrate how well-designed e-learning course materials can support many of the particular facets of heterogeneity by offering an inheren...

Synchronous message-based communication for distributed heterogeneous systems

International Nuclear Information System (INIS)

Wilkinson, N.; Dohan, D.

1992-01-01

The use of a synchronous, message-based real-time operating system (Unison) as the basis of transparent interprocess and inter-processor communication over VME-bus is described. The implementation of a synchronous, message-based protocol for network communication between heterogeneous systems is discussed. In particular, the design and implementation of a message-based session layer over a virtual circuit transport layer protocol using UDP/IP is described. Inter-process communication is achieved via a message-based semantic which is portable by virtue of its ease of implementation in other operating system environments. Protocol performance for network communication among heterogeneous architecture is presented, including VMS, Unix, Mach and Unison. (author)

Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

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Johan Parent

2004-01-01

Full Text Available We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

Heterogeneous Users in MOOC and their Adaptive Learning Needs

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María Luisa SEIN-ECHALUCE LACLETA

2017-02-01

Full Text Available Many research works point out the overcrowding and the heterogeneity of participant’s profiles in Massive Open Online Courses (MOOC as the main causes of their low completion rate. On the other hand, the methodologies of personalization of the learning, along next to the technologies of the information, that allows to realize techniques of adaptativity, appear in international reports as an effective way to improve the learning. This paper explores the participante’ perception of their adaptive needs in this tupe of course, as well as their relationship with different aspects of the participants, such as: profiles (gender, age, geographical location and academic level, previous experience and knowledge about the topic of the MOOC and motivation to enroll the MOOC. The study is carried out through a survey completes by the participants in the MOOC Campus of Educational Innovation. We conclude that the age or gender of the participants does not significantly influence their need for adaptive techniques in a MOOC. However, living in a Latin American country, working as a manager or enrolling in a MOOC with a specific motivation, are some of the factors that influence in the desire for adaptive techniques in a MOOC. The obtained results will contribute to improve the adaptive designs of the MOOC and will be easily transferable to any online training course, in blended or virtual learning.

Associations Between Academic and Motor Performance in a Heterogeneous Sample of Children With Learning Disabilities

NARCIS (Netherlands)

Vuijk, Pieter Jelle; Hartman, Esther; Mombarg, Remo; Scherder, Erik; Visscher, Chris

2011-01-01

A heterogeneous sample of 137 school-aged children with learning disabilities (IQ > 80) attending special needs schools was examined on the Movement Assessment Battery for Children (MABC). The results show that compared to the available norm scores, 52.6% of the children tested performed below the

A Web-Based Learning Support System for Inquiry-Based Learning

Science.gov (United States)

Kim, Dong Won; Yao, Jingtao

The emergence of the Internet and Web technology makes it possible to implement the ideals of inquiry-based learning, in which students seek truth, information, or knowledge by questioning. Web-based learning support systems can provide a good framework for inquiry-based learning. This article presents a study on a Web-based learning support system called Online Treasure Hunt. The Web-based learning support system mainly consists of a teaching support subsystem, a learning support subsystem, and a treasure hunt game. The teaching support subsystem allows instructors to design their own inquiry-based learning environments. The learning support subsystem supports students' inquiry activities. The treasure hunt game enables students to investigate new knowledge, develop ideas, and review their findings. Online Treasure Hunt complies with a treasure hunt model. The treasure hunt model formalizes a general treasure hunt game to contain the learning strategies of inquiry-based learning. This Web-based learning support system empowered with the online-learning game and founded on the sound learning strategies furnishes students with the interactive and collaborative student-centered learning environment.

A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System

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Jilin Zhang

2017-01-01

Full Text Available With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.

Application-specific mesh-based heterogeneous FPGA architectures

CERN Document Server

Parvez, Husain

2011-01-01

This volume presents a new exploration environment for mesh-based, heterogeneous FPGA architectures. Readers will find a description of state-of-the-art techniques for reducing area requirements, which both increase performance and enable power reduction.

Interfacial mechanisms of heterogeneous Fenton reactions catalyzed by iron-based materials: A review.

Science.gov (United States)

He, Jie; Yang, Xiaofang; Men, Bin; Wang, Dongsheng

2016-01-01

The heterogeneous Fenton reaction can generate highly reactive hydroxyl radicals (OH) from reactions between recyclable solid catalysts and H2O2 at acidic or even circumneutral pH. Hence, it can effectively oxidize refractory organics in water or soils and has become a promising environmentally friendly treatment technology. Due to the complex reaction system, the mechanism behind heterogeneous Fenton reactions remains unresolved but fascinating, and is crucial for understanding Fenton chemistry and the development and application of efficient heterogeneous Fenton technologies. Iron-based materials usually possess high catalytic activity, low cost, negligible toxicity and easy recovery, and are a superior type of heterogeneous Fenton catalysts. Therefore, this article reviews the fundamental but important interfacial mechanisms of heterogeneous Fenton reactions catalyzed by iron-based materials. OH, hydroperoxyl radicals/superoxide anions (HO2/O2(-)) and high-valent iron are the three main types of reactive oxygen species (ROS), with different oxidation reactivity and selectivity. Based on the mechanisms of ROS generation, the interfacial mechanisms of heterogeneous Fenton systems can be classified as the homogeneous Fenton mechanism induced by surface-leached iron, the heterogeneous catalysis mechanism, and the heterogeneous reaction-induced homogeneous mechanism. Different heterogeneous Fenton systems catalyzed by characteristic iron-based materials are comprehensively reviewed. Finally, related future research directions are also suggested. Copyright © 2015. Published by Elsevier B.V.

The Effect of Cooperative Learning on the Learning Approaches of Students with Different Learning Styles

Science.gov (United States)

Çolak, Esma

2015-01-01

Problem Statement: For this study, a cooperative learning process was designed in which students with different learning styles could help each other in heterogeneous groups to perform teamwork-based activities. One aspect deemed important in this context was whether the instructional environment designed to reach students with different learning…

Personalizing Access to Learning Networks

DEFF Research Database (Denmark)

Dolog, Peter; Simon, Bernd; Nejdl, Wolfgang

2008-01-01

In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address in this fra......In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address...... in this framework, enabling personalized access to federated learning repositories with a vast number of learning offers. Our infrastructure includes personalization strategies both at the query and the query results level. Query rewriting is based on learning and language preferences; rule-based and ranking...

Supervised Filter Learning for Representation Based Face Recognition.

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Chao Bi

Full Text Available Representation based classification methods, such as Sparse Representation Classification (SRC and Linear Regression Classification (LRC have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm.

Micromechanics Based Failure Analysis of Heterogeneous Materials

Science.gov (United States)

Sertse, Hamsasew M.

In recent decades, heterogeneous materials are extensively used in various industries such as aerospace, defense, automotive and others due to their desirable specific properties and excellent capability of accumulating damage. Despite their wide use, there are numerous challenges associated with the application of these materials. One of the main challenges is lack of accurate tools to predict the initiation, progression and final failure of these materials under various thermomechanical loading conditions. Although failure is usually treated at the macro and meso-scale level, the initiation and growth of failure is a complex phenomena across multiple scales. The objective of this work is to enable the mechanics of structure genome (MSG) and its companion code SwiftComp to analyze the initial failure (also called static failure), progressive failure, and fatigue failure of heterogeneous materials using micromechanics approach. The initial failure is evaluated at each numerical integration point using pointwise and nonlocal approach for each constituent of the heterogeneous materials. The effects of imperfect interfaces among constituents of heterogeneous materials are also investigated using a linear traction-displacement model. Moreover, the progressive and fatigue damage analyses are conducted using continuum damage mechanics (CDM) approach. The various failure criteria are also applied at a material point to analyze progressive damage in each constituent. The constitutive equation of a damaged material is formulated based on a consistent irreversible thermodynamics approach. The overall tangent modulus of uncoupled elastoplastic damage for negligible back stress effect is derived. The initiation of plasticity and damage in each constituent is evaluated at each numerical integration point using a nonlocal approach. The accumulated plastic strain and anisotropic damage evolution variables are iteratively solved using an incremental algorithm. The damage analyses

Research on Heterogeneous Data Exchange based on XML

Science.gov (United States)

Li, Huanqin; Liu, Jinfeng

Integration of multiple data sources is becoming increasingly important for enterprises that cooperate closely with their partners for e-commerce. OLAP enables analysts and decision makers fast access to various materialized views from data warehouses. However, many corporations have internal business applications deployed on different platforms. This paper introduces a model for heterogeneous data exchange based on XML. The system can exchange and share the data among the different sources. The method used to realize the heterogeneous data exchange is given in this paper.

Research on distributed heterogeneous data PCA algorithm based on cloud platform

Science.gov (United States)

Zhang, Jin; Huang, Gang

2018-05-01

Principal component analysis (PCA) of heterogeneous data sets can solve the problem that centralized data scalability is limited. In order to reduce the generation of intermediate data and error components of distributed heterogeneous data sets, a principal component analysis algorithm based on heterogeneous data sets under cloud platform is proposed. The algorithm performs eigenvalue processing by using Householder tridiagonalization and QR factorization to calculate the error component of the heterogeneous database associated with the public key to obtain the intermediate data set and the lost information. Experiments on distributed DBM heterogeneous datasets show that the model method has the feasibility and reliability in terms of execution time and accuracy.

Learning second language vocabulary: neural dissociation of situation-based learning and text-based learning.

Science.gov (United States)

Jeong, Hyeonjeong; Sugiura, Motoaki; Sassa, Yuko; Wakusawa, Keisuke; Horie, Kaoru; Sato, Shigeru; Kawashima, Ryuta

2010-04-01

Second language (L2) acquisition necessitates learning and retrieving new words in different modes. In this study, we attempted to investigate the cortical representation of an L2 vocabulary acquired in different learning modes and in cross-modal transfer between learning and retrieval. Healthy participants learned new L2 words either by written translations (text-based learning) or in real-life situations (situation-based learning). Brain activity was then measured during subsequent retrieval of these words. The right supramarginal gyrus and left middle frontal gyrus were involved in situation-based learning and text-based learning, respectively, whereas the left inferior frontal gyrus was activated when learners used L2 knowledge in a mode different from the learning mode. Our findings indicate that the brain regions that mediate L2 memory differ according to how L2 words are learned and used. Copyright 2009 Elsevier Inc. All rights reserved.

An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments

Directory of Open Access Journals (Sweden)

Xiaohong Li

2018-03-01

Full Text Available Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users select optimal networks to access is challenging. Prior implementations of network selection are usually applicable for the environment with static radio resources, while they cannot handle the unpredictable dynamics in 5G network environments. To this end, this paper considers both the fluctuation of radio resources and the variation of user demand. We model the access network selection scenario as a multiagent coordination problem, in which a bunch of rationally terminal users compete to maximize their benefits with incomplete information about the environment (no prior knowledge of network resource and other users’ choices. Then, an adaptive learning based strategy is proposed, which enables users to adaptively adjust their selections in response to the gradually or abruptly changing environment. The system is experimentally shown to converge to Nash equilibrium, which also turns out to be both Pareto optimal and socially optimal. Extensive simulation results show that our approach achieves significantly better performance compared with two learning and non-learning based approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency. In addition, the system has a good robustness performance under the condition with non-compliant terminal users.

A CFD Heterogeneous Parallel Solver Based on Collaborating CPU and GPU

Science.gov (United States)

Lai, Jianqi; Tian, Zhengyu; Li, Hua; Pan, Sha

2018-03-01

Since Graphic Processing Unit (GPU) has a strong ability of floating-point computation and memory bandwidth for data parallelism, it has been widely used in the areas of common computing such as molecular dynamics (MD), computational fluid dynamics (CFD) and so on. The emergence of compute unified device architecture (CUDA), which reduces the complexity of compiling program, brings the great opportunities to CFD. There are three different modes for parallel solution of NS equations: parallel solver based on CPU, parallel solver based on GPU and heterogeneous parallel solver based on collaborating CPU and GPU. As we can see, GPUs are relatively rich in compute capacity but poor in memory capacity and the CPUs do the opposite. We need to make full use of the GPUs and CPUs, so a CFD heterogeneous parallel solver based on collaborating CPU and GPU has been established. Three cases are presented to analyse the solver’s computational accuracy and heterogeneous parallel efficiency. The numerical results agree well with experiment results, which demonstrate that the heterogeneous parallel solver has high computational precision. The speedup on a single GPU is more than 40 for laminar flow, it decreases for turbulent flow, but it still can reach more than 20. What’s more, the speedup increases as the grid size becomes larger.

DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer.

Science.gov (United States)

Saha, Abhijoy; Banerjee, Sayantan; Kurtek, Sebastian; Narang, Shivali; Lee, Joonsang; Rao, Ganesh; Martinez, Juan; Bharath, Karthik; Rao, Arvind U K; Baladandayuthapani, Veerabhadran

2016-01-01

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher-Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

An open, object-based modeling approach for simulating subsurface heterogeneity

Science.gov (United States)

Bennett, J.; Ross, M.; Haslauer, C. P.; Cirpka, O. A.

2017-12-01

Characterization of subsurface heterogeneity with respect to hydraulic and geochemical properties is critical in hydrogeology as their spatial distribution controls groundwater flow and solute transport. Many approaches of characterizing subsurface heterogeneity do not account for well-established geological concepts about the deposition of the aquifer materials; those that do (i.e. process-based methods) often require forcing parameters that are difficult to derive from site observations. We have developed a new method for simulating subsurface heterogeneity that honors concepts of sequence stratigraphy, resolves fine-scale heterogeneity and anisotropy of distributed parameters, and resembles observed sedimentary deposits. The method implements a multi-scale hierarchical facies modeling framework based on architectural element analysis, with larger features composed of smaller sub-units. The Hydrogeological Virtual Reality simulator (HYVR) simulates distributed parameter models using an object-based approach. Input parameters are derived from observations of stratigraphic morphology in sequence type-sections. Simulation outputs can be used for generic simulations of groundwater flow and solute transport, and for the generation of three-dimensional training images needed in applications of multiple-point geostatistics. The HYVR algorithm is flexible and easy to customize. The algorithm was written in the open-source programming language Python, and is intended to form a code base for hydrogeological researchers, as well as a platform that can be further developed to suit investigators' individual needs. This presentation will encompass the conceptual background and computational methods of the HYVR algorithm, the derivation of input parameters from site characterization, and the results of groundwater flow and solute transport simulations in different depositional settings.

A Distributed Dynamic Super Peer Selection Method Based on Evolutionary Game for Heterogeneous P2P Streaming Systems

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Jing Chen

2013-01-01

Full Text Available Due to high efficiency and good scalability, hierarchical hybrid P2P architecture has drawn more and more attention in P2P streaming research and application fields recently. The problem about super peer selection, which is the key problem in hybrid heterogeneous P2P architecture, is becoming highly challenging because super peers must be selected from a huge and dynamically changing network. A distributed super peer selection (SPS algorithm for hybrid heterogeneous P2P streaming system based on evolutionary game is proposed in this paper. The super peer selection procedure is modeled based on evolutionary game framework firstly, and its evolutionarily stable strategies are analyzed. Then a distributed Q-learning algorithm (ESS-SPS according to the mixed strategies by analysis is proposed for the peers to converge to the ESSs based on its own payoff history. Compared to the traditional randomly super peer selection scheme, experiments results show that the proposed ESS-SPS algorithm achieves better performance in terms of social welfare and average upload rate of super peers and keeps the upload capacity of the P2P streaming system increasing steadily with the number of peers increasing.

Problem Based Learning

DEFF Research Database (Denmark)

de Graaff, Erik; Guerra, Aida

, the key principles remain the same everywhere. Graaff & Kolmos (2003) identify the main PBL principles as follows: 1. Problem orientation 2. Project organization through teams or group work 3. Participant-directed 4. Experiental learning 5. Activity-based learning 6. Interdisciplinary learning and 7...... model and in general problem based and project based learning. We apply the principle of teach as you preach. The poster aims to outline the visitors’ workshop programme showing the results of some recent evaluations.......Problem-Based Learning (PBL) is an innovative method to organize the learning process in such a way that the students actively engage in finding answers by themselves. During the past 40 years PBL has evolved and diversified resulting in a multitude in variations in models and practices. However...

Heterogeneous Community-based mobility model for human opportunistic network

DEFF Research Database (Denmark)

Hu, Liang; Dittmann, Lars

2009-01-01

a heterogeneous community-based random way-point (HC-RWP) mobility model that captures the four important properties of real human mobility. These properties are based on both intuitive observations of daily human mobility and analysis of empirical mobility traces. By discrete event simulation, we show HC...

Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network.

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Wan, Mengting; Ouyang, Yunbo; Kaplan, Lance; Han, Jiawei

2015-01-01

A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because network based information for unlabeled objects is usually limited to make precise estimations. In this paper, we consider a graph regularized meta-path based transductive regression model ( Grempt ), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks. The computation of our method is time and space efficient and the precision of our model can be verified by numerical experiments.

In-situ characterization of heterogeneous catalysts

CERN Document Server

Rodriguez, Jose A; Chupas, Peter J

2013-01-01

Helps researchers develop new catalysts for sustainable fuel and chemical production Reviewing the latest developments in the field, this book explores the in-situ characterization of heterogeneous catalysts, enabling readers to take full advantage of the sophisticated techniques used to study heterogeneous catalysts and reaction mechanisms. In using these techniques, readers can learn to improve the selectivity and the performance of catalysts and how to prepare catalysts as efficiently as possible, with minimum waste. In-situ Characterization of Heterogeneous Catalysts feat

Heterogeneous base-catalyzed methanolysis of vegetable oils: State of art

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Miladinović Marija R.

2010-01-01

Full Text Available Today, homogeneous base-catalyzed methanolysis is most frequently used method for industrial biodiesel production. High requirements for the quality of feedstocks and the problems related to a huge amount of wastewaters have led to the development of novel biodiesel production technologies. Among them, the most important is heterogeneous base-catalyzed methanolysis, which has been intensively investigated in the last decade in order to develop new catalytic systems, to optimize the reaction conditions and to recycle catalysts. These studies are a base for developing continuous biodiesel production on industrial scale in near future. The present work summarizes up-to-date studies on biodiesel production by heterogeneous base-catalyzed methanolysis. The main goals were to point out the application of different base compounds as catalysts, the methods of catalyst preparation, impregnation on carriers and recycling as well as the possibilities to improve existing base-catalyzed biodiesel production processes and to develop novel ones.

Strengths-based Learning

DEFF Research Database (Denmark)

Ledertoug, Mette Marie

-being. The Ph.D.-project in Strength-based learning took place in a Danish school with 750 pupils age 6-16 and a similar school was functioning as a control group. The presentation will focus on both the aware-explore-apply processes and the practical implications for the schools involved, and on measurable......Strength-based learning - Children͛s Character Strengths as Means to their Learning Potential͛ is a Ph.D.-project aiming to create a strength-based mindset in school settings and at the same time introducing strength-based interventions as specific tools to improve both learning and well...

CLASS-PAIR-GUIDED MULTIPLE KERNEL LEARNING OF INTEGRATING HETEROGENEOUS FEATURES FOR CLASSIFICATION

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Q. Wang

2017-10-01

Full Text Available In recent years, many studies on remote sensing image classification have shown that using multiple features from different data sources can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL can conveniently be embedded in a variety of characteristics. The conventional combined kernel learned by MKL can be regarded as the compromise of all basic kernels for all classes in classification. It is the best of the whole, but not optimal for each specific class. For this problem, this paper proposes a class-pair-guided MKL method to integrate the heterogeneous features (HFs from multispectral image (MSI and light detection and ranging (LiDAR data. In particular, the one-against-one strategy is adopted, which converts multiclass classification problem to a plurality of two-class classification problem. Then, we select the best kernel from pre-constructed basic kernels set for each class-pair by kernel alignment (KA in the process of classification. The advantage of the proposed method is that only the best kernel for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on two real data sets, and the experimental results show that the proposed method achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

Heterogeneous classifier fusion for ligand-based virtual screening: or, how decision making by committee can be a good thing.

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Riniker, Sereina; Fechner, Nikolas; Landrum, Gregory A

2013-11-25

The concept of data fusion - the combination of information from different sources describing the same object with the expectation to generate a more accurate representation - has found application in a very broad range of disciplines. In the context of ligand-based virtual screening (VS), data fusion has been applied to combine knowledge from either different active molecules or different fingerprints to improve similarity search performance. Machine-learning (ML) methods based on fusion of multiple homogeneous classifiers, in particular random forests, have also been widely applied in the ML literature. The heterogeneous version of classifier fusion - fusing the predictions from different model types - has been less explored. Here, we investigate heterogeneous classifier fusion for ligand-based VS using three different ML methods, RF, naïve Bayes (NB), and logistic regression (LR), with four 2D fingerprints, atom pairs, topological torsions, RDKit fingerprint, and circular fingerprint. The methods are compared using a previously developed benchmarking platform for 2D fingerprints which is extended to ML methods in this article. The original data sets are filtered for difficulty, and a new set of challenging data sets from ChEMBL is added. Data sets were also generated for a second use case: starting from a small set of related actives instead of diverse actives. The final fused model consistently outperforms the other approaches across the broad variety of targets studied, indicating that heterogeneous classifier fusion is a very promising approach for ligand-based VS. The new data sets together with the adapted source code for ML methods are provided in the Supporting Information .

An FPGA-based heterogeneous image fusion system design method

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Song, Le; Lin, Yu-chi; Chen, Yan-hua; Zhao, Mei-rong

2011-08-01

Taking the advantages of FPGA's low cost and compact structure, an FPGA-based heterogeneous image fusion platform is established in this study. Altera's Cyclone IV series FPGA is adopted as the core processor of the platform, and the visible light CCD camera and infrared thermal imager are used as the image-capturing device in order to obtain dualchannel heterogeneous video images. Tailor-made image fusion algorithms such as gray-scale weighted averaging, maximum selection and minimum selection methods are analyzed and compared. VHDL language and the synchronous design method are utilized to perform a reliable RTL-level description. Altera's Quartus II 9.0 software is applied to simulate and implement the algorithm modules. The contrast experiments of various fusion algorithms show that, preferably image quality of the heterogeneous image fusion can be obtained on top of the proposed system. The applied range of the different fusion algorithms is also discussed.

Heterogeneity, histological features and DNA ploidy in oral carcinoma by image-based analysis.

Science.gov (United States)

Diwakar, N; Sperandio, M; Sherriff, M; Brown, A; Odell, E W

2005-04-01

Oral squamous carcinomas appear heterogeneous on DNA ploidy analysis. However, this may be partly a result of sample dilution or the detection limit of techniques. The aim of this study was to determine whether oral squamous carcinomas are heterogeneous for ploidy status using image-based ploidy analysis and to determine whether ploidy status correlates with histological parameters. Multiple samples from 42 oral squamous carcinomas were analysed for DNA ploidy using an image-based system and scored for histological parameters. 22 were uniformly aneuploid, 1 uniformly tetraploid and 3 uniformly diploid. 16 appeared heterogeneous but only 8 appeared to be genuinely heterogeneous when minor ploidy histogram peaks were taken into account. Ploidy was closely related to nuclear pleomorphism but not differentiation. Sample variation, detection limits and diagnostic criteria account for much of the ploidy heterogeneity observed. Confident diagnosis of diploid status in an oral squamous cell carcinoma requires a minimum of 5 samples.

Model of Recommendation System for for Indexing and Retrieving the Learning Object based on Multiagent System

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Ronaldo Lima Rocha Campos

2012-07-01

Full Text Available This paper proposes a multiagent system application model for indexing, retrieving and recommendation learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the signification of the results we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered.

An Autonomous Mobile Agent-Based Distributed Learning Architecture-A Proposal and Analytical Analysis

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I. Ahmed M. J. SADIIG

2005-10-01

Full Text Available An Autonomous Mobile Agent-Based Distributed Learning Architecture-A Proposal and Analytical Analysis Dr. I. Ahmed M. J. SADIIG Department of Electrical & Computer EngineeringInternational Islamic University GombakKuala Lumpur-MALAYSIA ABSTRACT The traditional learning paradigm invoving face-to-face interaction with students is shifting to highly data-intensive electronic learning with the advances in Information and Communication Technology. An important component of the e-learning process is the delivery of the learning contents to their intended audience over a network. A distributed learning system is dependent on the network for the efficient delivery of its contents to the user. However, as the demand of information provision and utilization increases on the Internet, the current information service provision and utilization methods are becoming increasingly inefficient. Although new technologies have been employed for efficient learning methodologies within the context of an e-learning environment, the overall efficiency of the learning system is dependent on the mode of distribution and utilization of its learning contents. It is therefore imperative to employ new techniques to meet the service demands of current and future e-learning systems. In this paper, an architecture based on autonomous mobile agents creating a Faded Information Field is proposed. Unlike the centralized information distribution in a conventional e-learning system, the information is decentralized in the proposed architecture resulting in increased efficiency of the overall system for distribution and utilization of system learning contents efficiently and fairly. This architecture holds the potential to address the heterogeneous user requirements as well as the changing conditions of the underlying network.

An Erbium-Based Bifuctional Heterogeneous Catalyst: A Cooperative Route Towards C-C Bond Formation

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Manuela Oliverio

2014-07-01

Full Text Available Heterogeneous bifuctional catalysts are multifunctional synthetic catalysts enabling efficient organic transformations by exploiting two opposite functionalities without mutual destruction. In this paper we report the first Er(III-based metallorganic heterogeneous catalyst, synthesized by post-calcination MW-assisted grafting and modification of the natural aminoacid L-cysteine. The natural acid–base distance between sites was maintained to assure the cooperation. The applicability of this new bifunctional heterogeneous catalyst to C-C bond formation and the supposed mechanisms of action are discussed as well.

Spatial Heterogeneity of the Forest Canopy Scales with the Heterogeneity of an Understory Shrub Based on Fractal Analysis

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Catherine K. Denny

2017-04-01

Full Text Available Spatial heterogeneity of vegetation is an important landscape characteristic, but is difficult to assess due to scale-dependence. Here we examine how spatial patterns in the forest canopy affect those of understory plants, using the shrub Canada buffaloberry (Shepherdia canadensis (L. Nutt. as a focal species. Evergreen and deciduous forest canopy and buffaloberry shrub presence were measured with line-intercept sampling along ten 2-km transects in the Rocky Mountain foothills of west-central Alberta, Canada. Relationships between overstory canopy and understory buffaloberry presence were assessed for scales ranging from 2 m to 502 m. Fractal dimensions of both canopy and buffaloberry were estimated and then related using box-counting methods to evaluate spatial heterogeneity based on patch distribution and abundance. Effects of canopy presence on buffaloberry were scale-dependent, with shrub presence negatively related to evergreen canopy cover and positively related to deciduous cover. The effect of evergreen canopy was significant at a local scale between 2 m and 42 m, while that of deciduous canopy was significant at a meso-scale between 150 m and 358 m. Fractal analysis indicated that buffaloberry heterogeneity positively scaled with evergreen canopy heterogeneity, but was unrelated to that of deciduous canopy. This study demonstrates that evergreen canopy cover is a determinant of buffaloberry heterogeneity, highlighting the importance of spatial scale and canopy composition in understanding canopy-understory relationships.

Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity.

Science.gov (United States)

Zhong, Qing; Rüschoff, Jan H; Guo, Tiannan; Gabrani, Maria; Schüffler, Peter J; Rechsteiner, Markus; Liu, Yansheng; Fuchs, Thomas J; Rupp, Niels J; Fankhauser, Christian; Buhmann, Joachim M; Perner, Sven; Poyet, Cédric; Blattner, Miriam; Soldini, Davide; Moch, Holger; Rubin, Mark A; Noske, Aurelia; Rüschoff, Josef; Haffner, Michael C; Jochum, Wolfram; Wild, Peter J

2016-04-07

Recent large-scale genome analyses of human tissue samples have uncovered a high degree of genetic alterations and tumour heterogeneity in most tumour entities, independent of morphological phenotypes and histopathological characteristics. Assessment of genetic copy-number variation (CNV) and tumour heterogeneity by fluorescence in situ hybridization (ISH) provides additional tissue morphology at single-cell resolution, but it is labour intensive with limited throughput and high inter-observer variability. We present an integrative method combining bright-field dual-colour chromogenic and silver ISH assays with an image-based computational workflow (ISHProfiler), for accurate detection of molecular signals, high-throughput evaluation of CNV, expressive visualization of multi-level heterogeneity (cellular, inter- and intra-tumour heterogeneity), and objective quantification of heterogeneous genetic deletions (PTEN) and amplifications (19q12, HER2) in diverse human tumours (prostate, endometrial, ovarian and gastric), using various tissue sizes and different scanners, with unprecedented throughput and reproducibility.

Design And Planning Of E- Learning EnvironmentE-Education System On Heterogeneous Wireless Network Control System

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ThandarOo

2015-06-01

Full Text Available Abstract The purpose of this research is to provide a more efficient and effective communication method between teacher and student with the use of heterogeneous network. Moreover the effective use of heterogeneous network can be emphasized. The system of e-education can develop utilizing wireless network.The e-Education system can help students to communicate with their teacher more easily and effectively using a heterogeneous wireless network system. In this wireless network system students who are blind or dumb will also be able to communicate and learn from the teacher as normal students can do. All the devices or laptops will be connected on wireless LAN. Even when the teacher is not around he will be able to help his students with their study or give instructions easily by using the mobile phone to send text or voice signal. When the teacher sends information to the dumb student it will be converted into sign language for the student to be able to understand. When the dumb student sends the information to the teacher it will be converted into text for the teacher to understand. For the blind student text instructions from the teacher will be converted into audio signal using text-to-speech conversion.Thus the performance of heterogeneous wireless network model can evaluate by using Robust Optimization Method. Therefore the e-Education systems performance improves by evaluating Robust Optimization Method.

Approach to Dynamic Assembling of Individualized Learning Paths

Science.gov (United States)

Lubchak, Vladimir; Kupenko, Olena; Kuzikov, Borys

2012-01-01

E-learning students are generally heterogeneous and have different capabilities knowledge base and needs. The aim of the Sumy State University (SSU) e-learning system project is to cater to these individual needs by assembling individual learning path. This paper shows current situation with e-learning in Ukraine, state-of-art of development of…

Mobile Inquiry Based Learning

NARCIS (Netherlands)

Specht, Marcus

2012-01-01

Specht, M. (2012, 8 November). Mobile Inquiry Based Learning. Presentation given at the Workshop "Mobile inquiry-based learning" at the Mobile Learning Day 2012 at the Fernuniversität Hagen, Hagen, Germany.

Differentiating case-based learning from problem-based learning after a twoday introductory workshop on case-based learning

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Aqil Mohammad Daher

2017-12-01

Full Text Available Background Considerable overlap exists between case-based learning (CBL and problem-based learning (PBL and differentiating between the two can be difficult for a lot of the academicians. Aims This study gauged the ability of members of medical school, familiar with a problem-based learning (PBL curriculum, to differentiate between case-based learning (CBL and PBL after a two-day workshop on CBL. Methods A questionnaire was distributed to all participants, attending the introductory course on CBL. It was designed to document the basic characteristics of the respondents, their preference for either CBL or PBL, their ability to recognize differences between CBL and PBL, and their overall perception of the course. Results Of the total workshop participants, 80.5 per cent returned the completed questionnaire. The mean age of the respondents was 44.12±12.31 years and women made up a slight majority. Majority favoured CBL over PBL and felt it was more clinical, emphasizes on self-directed learning, provides more opportunities for learning, permits in-depth exploration of cases, has structured environment and encourages the use of all learning resources. On the respondents’ ability to discriminate CBL from PBL, a weighted score of 39.9 per cent indicated a failure on the part of the respondents to correctly identify differences between CBL and PBL. Less than half opined that CBL was a worthwhile progression from PBL and about third would recommend CBL over PBL. Conclusion It seems that majority of the respondents failed to adequately differentiate between CBL and PBL and didn’t favour CBL over PBL.

Genetically heterogeneous and selected lines of rats: behavioral and reproductive comparison.

Science.gov (United States)

Satinder, K P

1980-03-01

Avoidance learning, open-field, and reproductive behaviors of a genetically heterogeneous stock (derived from a four-way cross of selected lines) were compared with the corresponding behaviors of the parental lines. The heterogeneous stock showed heterosis on the body development, fertility rate, litter size at birth and at weaning, and directional dominance on the avoidance learning and open-field measures.

Heterogeneous Information about the Term Structure of Interest rates, Least-Squares Learning and Optimal Interest Rate Rules for Inflation Forecast Targeting

NARCIS (Netherlands)

Schaling, E.; Eijffinger, S.C.W.; Tesfaselassie, M.F.

2004-01-01

In this paper we incorporate the term structure of interest rates in a standard inflation forecast targeting framework.Learning about the transmission process of monetary policy is introduced by having heterogeneous agents - i.e. the central bank and private agents - who have different information

A link prediction method for heterogeneous networks based on BP neural network

Science.gov (United States)

Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

2018-04-01

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

DIGITAL ONCOLOGY PATIENT RECORD - HETEROGENEOUS FILE BASED APPROACH

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Nikolay Sapundzhiev

2010-12-01

Full Text Available Introduction: Oncology patients need extensive follow-up and meticulous documentation. The aim of this study was to introduce a simple, platform independent file based system for documentation of diagnostic and therapeutic procedures in oncology patients and test its function.Material and methods: A file-name based system of the type M1M2M3.F2 was introduced, where M1 is a unique identifier for the patient, M2 is the date of the clinical intervention/event, M3 is an identifier for the author of the medical record and F2 is the specific software generated file-name extension.Results: This system is in use at 5 institutions, where a total of 11 persons on 14 different workstations inputted 16591 entries (files for 2370. The merge process was tested on 2 operating systems - when copied together all files sort up as expected by patient, and for each patient in a chronological order, providing a digital cumulative patient record, which contains heterogeneous file formats.Conclusion: The file based approach for storing heterogeneous digital patient related information is an reliable system, which can handle open-source, proprietary, general and custom file formats and seems to be easily scalable. Further development of software for automatic checks of the integrity and searching and indexing of the files is expected to produce a more user-friendly environment

Study on evaluation method for heterogeneous sedimentary rocks based on forward model

International Nuclear Information System (INIS)

Masui, Yasuhiro; Kawada, Koji; Katoh, Arata; Tsuji, Takashi; Suwabe, Mizue

2004-02-01

It is very important to estimate the facies distribution of heterogeneous sedimentary rocks for geological disposal of high level radioactive waste. The heterogeneousness of sedimentary rocks is due to variable distribution of grain size and mineral composition. The objective of this study is to establish the evaluation method for heterogeneous sedimentary rocks based on forward model. This study consisted of geological study for Horonobe area and the development of soft wear for sedimentary model. Geological study was composed of following items. 1. The sedimentary system for Koetoi and Wakkanai formations in Horonobe area was compiled based on papers. 2. The cores of HDB-1 were observed mainly from sedimentological view. 3. The facies and compaction property of argillaceous rocks were studied based on physical logs and core analysis data of wells. 4. The structure maps, isochrone maps, isopach maps and restored geological sections were made. The soft wear for sedimentary model to show sedimentary system on a basin scale was developed. This soft wear estimates the facies distribution and hydraulic conductivity of sedimentary rocks on three dimensions scale by numerical simulation. (author)

Meta-analysis on the effectiveness of team-based learning on medical education in China.

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Chen, Minjian; Ni, Chunhui; Hu, Yanhui; Wang, Meilin; Liu, Lu; Ji, Xiaoming; Chu, Haiyan; Wu, Wei; Lu, Chuncheng; Wang, Shouyu; Wang, Shoulin; Zhao, Liping; Li, Zhong; Zhu, Huijuan; Wang, Jianming; Xia, Yankai; Wang, Xinru

2018-04-10

Team-based learning (TBL) has been adopted as a new medical pedagogical approach in China. However, there are no studies or reviews summarizing the effectiveness of TBL on medical education. This study aims to obtain an overall estimation of the effectiveness of TBL on outcomes of theoretical teaching of medical education in China. We retrieved the studies from inception through December, 2015. Chinese National Knowledge Infrastructure, Chinese Biomedical Literature Database, Chinese Wanfang Database, Chinese Scientific Journal Database, PubMed, EMBASE and Cochrane Database were searched. The quality of included studies was assessed by the Newcastle-Ottawa scale. Standardized mean difference (SMD) was applied for the estimation of the pooled effects. Heterogeneity assumption was detected by I 2 statistics, and was further explored by meta-regression analysis. A total of 13 articles including 1545 participants eventually entered into the meta-analysis. The quality scores of these studies ranged from 6 to 10. Altogether, TBL significantly increased students' theoretical examination scores when compared with lecture-based learning (LBL) (SMD = 2.46, 95% CI: 1.53-3.40). Additionally, TBL significantly increased students' learning attitude (SMD = 3.23, 95% CI: 2.27-4.20), and learning skill (SMD = 2.70, 95% CI: 1.33-4.07). The meta-regression results showed that randomization, education classification and gender diversity were the factors that caused heterogeneity. TBL in theoretical teaching of medical education seems to be more effective than LBL in improving the knowledge, attitude and skill of students in China, providing evidence for the implement of TBL in medical education in China. The medical schools should implement TBL with the consideration on the practical teaching situations such as students' education level.

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

Science.gov (United States)

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

[Verification of Learning Effects by Team-based Learning].

Science.gov (United States)

Ono, Shin-Ichi; Ito, Yoshihisa; Ishige, Kumiko; Inokuchi, Norio; Kosuge, Yasuhiro; Asami, Satoru; Izumisawa, Megumi; Kobayashi, Hiroko; Hayashi, Hiroyuki; Suzuki, Takashi; Kishikawa, Yukinaga; Hata, Harumi; Kose, Eiji; Tabata, Kei-Ichi

2017-11-01

 It has been recommended that active learning methods, such as team-based learning (TBL) and problem-based learning (PBL), be introduced into university classes by the Central Council for Education. As such, for the past 3 years, we have implemented TBL in a medical therapeutics course for 4-year students. Based upon our experience, TBL is characterized as follows: TBL needs fewer teachers than PBL to conduct a TBL module. TBL enables both students and teachers to recognize and confirm the learning results from preparation and reviewing. TBL grows students' responsibility for themselves and their teams, and likely facilitates learning activities through peer assessment.

A model-based approach to studying changes in compositional heterogeneity

NARCIS (Netherlands)

Baeten, L.; Warton, D.; Calster, van H.; Frenne, De P.; Verstraeten, G.; Bonte, D.; Bernhardt-Romermann, M.; Cornelis, R.; Decocq, G.; Eriksson, O.; Hommel, P.W.F.M.

2014-01-01

1. Non-random species loss and gain in local communities change the compositional heterogeneity between communities over time, which is traditionally quantified with dissimilarity-based approaches. Yet, dissimilarities summarize the multivariate species data into a univariate index and obscure the

Sharing e-Learning Experiences: A Personalised Approach

Science.gov (United States)

Clematis, Andrea; Forcheri, Paola; Ierardi, Maria Grazia; Quarati, Alfonso

A two-tier architecture is presented, based on hybrid peer-to-peer technology, aimed at providing personalized access to heterogeneous learning sources. The architecture deploys a conceptual model that is superimposed over logically and physically separated repositories. The model is based on the interactions between users and learning resources, described by means of coments. To support users to find out material satisfying their needs, mechanisms for ranking resources and for extracting personalized views of the learning space are provided.

Achievement of learning outcome after implemented physical modules based on problem based learning

Science.gov (United States)

Isna, R.; Masykuri, M.; Sukarmin

2018-03-01

Implementation of Problem BasedLearning (PBL) modules can grow the students' thinking skills to solve the problems in daily life and equip the students into higher education levels. The purpose of this research is to know the achievement of learning outcome after implementation physics module based on PBL in Newton,s Law of Gravity. This research method use the experimental method with posttest only group design. To know the achievement of student learning outcomes was analyzed using t test through application of SPSS 18. Based on research result, it is found that the average of student learning outcomes after appliying physics module based on PBL has reached the minimal exhaustiveness criteria. In addition, students' scientific attitudes also improved at each meeting. Presentation activities which contained at learning sync are also able to practice speaking skills and broaden their knowledge. Looking at some shortcomings during the study, it is suggested the issues raised into learning should be a problem close to the life of students so that, the students are more active and enthusiastic in following the learning of physics.

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...

Intelligent Web-Based Learning System with Personalized Learning Path Guidance

Science.gov (United States)

Chen, C. M.

2008-01-01

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths…

Virtual Learning Environments and Learning Forms -experiments in ICT-based learning

DEFF Research Database (Denmark)

Helbo, Jan; Knudsen, Morten

2004-01-01

This paper report the main results of a three year experiment in ICT-based distance learning. The results are based on a full scale experiment in the education, Master of Industrial Information Technology (MII) and is one of many projects deeply rooted in the project Virtual Learning Environments...... and Learning forms (ViLL). The experiment was to transfer a well functioning on-campus engineering program based on project organized collaborative learning to a technology supported distance education program. After three years the experiments indicate that adjustments are required in this transformation....... The main problem is that we do not find the same self regulatoring learning effect in the group work among the off-campus students as is the case for on-campus students. Based on feedback from evaluation questionnaires and discussions with the students didactic adjustments have been made. The revised...

Web-Based Instruction, Learning Effectiveness and Learning Behavior: The Impact of Relatedness

Science.gov (United States)

Shieh, Chich-Jen; Liao, Ying; Hu, Ridong

2013-01-01

This study aims to discuss the effects of Web-based Instruction and Learning Behavior on Learning Effectiveness. Web-based Instruction contains the dimensions of Active Learning, Simulation-based Learning, Interactive Learning, and Accumulative Learning; and, Learning Behavior covers Learning Approach, Learning Habit, and Learning Attitude. The…

A Study into the Effects of Competitive Team-Based Learning and 'Learning Together' on the Oral Performance of Intermediate EFL Learners

Directory of Open Access Journals (Sweden)

Mahdi Mardani

2015-08-01

Full Text Available The present study intended to look into and compare the possible effects of Competitive Team-Based Learning (CTBL with Learning Together (LT or Cooperative Group-Based Learning (CGBL – the most popular method of Cooperative Learning (CL -- on oral performance of Iranian EFL intermediate students. After administering the oral interview, this researcher selected a group of 40 almost homogeneous Iranian intermediate students and randomly assigned them to control and experimental groups – 20 per group. Based on their scores, the experimental class were divided into 5 almost heterogeneous teams - four members each. But in the control group, the participants were allowed to shape their own favourable groups. For six weeks (18 sessions of 90 minutes each, both the groups received the same course materials, instructor, curriculum, out of-class and in-class assignments, schedule of instruction and equivalent methods of evaluation, but the experimental group experienced language learning via CTBL rather than via the CGBL as their counterparts in the control group. At the end of the course again the oral interview was administered to both the groups. Then the obtained scores on pre-test and post-test were analyzed through different statistical procedures. The results of the study rejected the null hypothesis and provided evidence supporting the hypothesis that CTBL can have a more significant effect on improving the oral performance of Iranian intermediate students. This researcher will discuss the probable causes for the results of the study, and will shed light on the pedagogical implications. She will also suggest recommendations for further research.

Consensus of heterogeneous multi-agent systems based on sampled data with a small sampling delay

International Nuclear Information System (INIS)

Wang Na; Wu Zhi-Hai; Peng Li

2014-01-01

In this paper, consensus problems of heterogeneous multi-agent systems based on sampled data with a small sampling delay are considered. First, a consensus protocol based on sampled data with a small sampling delay for heterogeneous multi-agent systems is proposed. Then, the algebra graph theory, the matrix method, the stability theory of linear systems, and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing heterogeneous multi-agent systems to asymptotically achieve the stationary consensus. Finally, simulations are performed to demonstrate the correctness of the theoretical results. (interdisciplinary physics and related areas of science and technology)

A SCORM Thin Client Architecture for E-Learning Systems Based on Web Services

Science.gov (United States)

Casella, Giovanni; Costagliola, Gennaro; Ferrucci, Filomena; Polese, Giuseppe; Scanniello, Giuseppe

2007-01-01

In this paper we propose an architecture of e-learning systems characterized by the use of Web services and a suitable middleware component. These technical infrastructures allow us to extend the system with new services as well as to integrate and reuse heterogeneous software e-learning components. Moreover, they let us better support the…

Web-Based Learning Environment Based on Students’ Needs

Science.gov (United States)

Hamzah, N.; Ariffin, A.; Hamid, H.

2017-08-01

Traditional learning needs to be improved since it does not involve active learning among students. Therefore, in the twenty-first century, the development of internet technology in the learning environment has become the main needs of each student. One of the learning environments to meet the needs of the teaching and learning process is a web-based learning environment. This study aims to identify the characteristics of a web-based learning environment that supports students’ learning needs. The study involved 542 students from fifteen faculties in a public higher education institution in Malaysia. A quantitative method was used to collect the data via a questionnaire survey by randomly. The findings indicate that the characteristics of a web-based learning environment that support students’ needs in the process of learning are online discussion forum, lecture notes, assignments, portfolio, and chat. In conclusion, the students overwhelmingly agreed that online discussion forum is the highest requirement because the tool can provide a space for students and teachers to share knowledge and experiences related to teaching and learning.

Annual sums of carbon dioxide exchange over a heterogeneous urban landscape through machine learning based gap-filling

Science.gov (United States)

Menzer, Olaf; Meiring, Wendy; Kyriakidis, Phaedon C.; McFadden, Joseph P.

2015-01-01

A small, but growing, number of flux towers in urban environments measure surface-atmospheric exchanges of carbon dioxide by the eddy covariance method. As in all eddy covariance studies, obtaining annual sums of urban CO2 exchange requires imputation of data gaps due to low turbulence and non-stationary conditions, adverse weather, and instrument failures. Gap-filling approaches that are widely used for measurements from towers in natural vegetation are based on light and temperature response models. However, they do not account for key features of the urban environment including tower footprint heterogeneity and localized CO2 sources. Here, we present a novel gap-filling modeling framework that uses machine learning to select explanatory variables, such as continuous traffic counts and temporal variables, and then constrains models separately for spatially classified subsets of the data. We applied the modeling framework to a three year time series of measurements from a tall broadcast tower in a suburban neighborhood of Minneapolis-Saint Paul, Minnesota, USA. The gap-filling performance was similar to that reported for natural measurement sites, explaining 64% to 88% of the variability in the fluxes. Simulated carbon budgets were in good agreement with an ecophysiological bottom-up study at the same site. Total annual carbon dioxide flux sums for the tower site ranged from 1064 to 1382 g C m-2 yr-1, across different years and different gap-filling methods. Bias errors of annual sums resulting from gap-filling did not exceed 18 g C m-2 yr-1 and random uncertainties did not exceed ±44 g C m-2 yr-1 (or ±3.8% of the annual flux). Regardless of the gap-filling method used, the year-to-year differences in carbon exchange at this site were small. In contrast, the modeled annual sums of CO2 exchange differed by a factor of two depending on wind direction. This indicated that the modeled time series captured the spatial variability in both the biogenic and

EFFECT OF PROBLEM BASED LEARNING IN COMPARISION WITH LECTURE BASED LEARNING IN FORENSIC MEDICINE

Directory of Open Access Journals (Sweden)

Padmakumar

2015-09-01

Full Text Available BACKGROUND: Problem based learning (PBL is an approach to learning and instruction in which students tackle problems in small groups under the supervision of a teacher. This style of learning assumed to foster increased retention of knowledge, improve student’s gene ral problem solving skills, enhance integration of basic science concepts in to clinical problems, foster the development of self - directed learning skills and strengthen student’s intrinsic motivation. AIM: The study was conducted to compare the effect of Problem based learning in comparison with lecture based learning. SETTING: A cross - sectional study was conducted among 2nd year MBBS students of Jubilee Mission Medical College and Research Institute, Thrissur during the period of December 2014 to March 20 15. METHODOLOGY: The batch is divided into two groups (A & B, 45 in each group. By using PBL method, blunt force injuries were taught to Group - A and sharp weapon injuries to group - B. By using lecture based learning (LBL method blunt force injuries were t aught to Group - B and sharp weapon injuries to group - A. At the end of the session a test in the form of MCQ was conducted on the students to evaluate their learning outcome. OBSERVATION AND RESU LTS: In session I, the average test score of LBL group was 8.16 and PBL group was 12. The difference was statistically significant. In session - II also 45 students has participated each in LBL and PBL classes. The average of test score of LBL group was 7.267 and PBL was 11.289, which was highly significant statistical ly . CONCLUSION: Study has proven that problem based learning is an effective teaching learning method when compared to conventional lecture based learning.

Project- Based Learning and Problem-Based Learning: Are They Effective to Improve Student's Thinking Skills?

OpenAIRE

Anazifa, R. D; Djukri, D

2017-01-01

The study aims at finding (1) the effect of project-based learning and problem-based learning on student's creativity and critical thinking and (2) the difference effect of project-based learning and problem-based learning on student's creativity and critical thinking. This study is quasi experiment using non-equivalent control-group design. Research population of this study was all classes in eleventh grade of mathematics and natural science program of SMA N 1 Temanggung. The participants we...

Elucidating the impact of neurofibromatosis-1 germline mutations on neurofibromin function and dopamine-based learning.

Science.gov (United States)

Anastasaki, Corina; Woo, Albert S; Messiaen, Ludwine M; Gutmann, David H

2015-06-15

Neurofibromatosis type 1 (NF1) is a common autosomal dominant neurologic condition characterized by significant clinical heterogeneity, ranging from malignant cancers to cognitive deficits. Recent studies have begun to reveal rare genotype-phenotype correlations, suggesting that the specific germline NF1 gene mutation may be one factor underlying disease heterogeneity. The purpose of this study was to define the impact of the germline NF1 gene mutation on brain neurofibromin function relevant to learning. Herein, we employ human NF1-patient primary skin fibroblasts, induced pluripotent stem cells and derivative neural progenitor cells (NPCs) to demonstrate that NF1 germline mutations have dramatic effects on neurofibromin expression. Moreover, while all NF1-patient NPCs exhibit increased RAS activation and reduced cyclic AMP generation, there was a neurofibromin dose-dependent reduction in dopamine (DA) levels. Additionally, we leveraged two complementary Nf1 genetically-engineered mouse strains in which hippocampal-based learning and memory is DA-dependent to establish that neuronal DA levels and signaling as well as mouse spatial learning are controlled in an Nf1 gene dose-dependent manner. Collectively, this is the first demonstration that different germline NF1 gene mutations differentially dictate neurofibromin function in the brain. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

RuleML-Based Learning Object Interoperability on the Semantic Web

Science.gov (United States)

Biletskiy, Yevgen; Boley, Harold; Ranganathan, Girish R.

2008-01-01

Purpose: The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions. Design/methodology/approach: As proof of concept, a rule…

Cognitive Heterogeneous Reconfigurable Optical Networks (CHRON): Enabling Technologies and Techniques

DEFF Research Database (Denmark)

Tafur Monroy, Idelfonso; Zibar, Darko; Guerrero Gonzalez, Neil

2011-01-01

We present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive Heterogeneous Reconfigurable Optical Network. We introduce and discuss in particular the technologies and techniques that will enable a cognitive optical...... network to observe, act, learn and optimizes its performance, taking into account its high degree of heterogeneity with respect to quality of service, transmission and switching techniques....

Meta-path based heterogeneous combat network link prediction

Science.gov (United States)

Li, Jichao; Ge, Bingfeng; Yang, Kewei; Chen, Yingwu; Tan, Yuejin

2017-09-01

The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

How to Enhance Interdisciplinary Competence--Interdisciplinary Problem-Based Learning versus Interdisciplinary Project-Based Learning

Science.gov (United States)

Brassler, Mirjam; Dettmers, Jan

2017-01-01

Interdisciplinary competence is important in academia for both employability and sustainable development. However, to date, there are no specific interdisciplinary education models and, naturally, no empirical studies to assess them. Since problem-based learning (PBL) and project-based learning (PjBL) are learning approaches that emphasize…

Problem-based learning and radiology

International Nuclear Information System (INIS)

Thurley, P.; Dennick, R.

2008-01-01

The Royal College of Radiologists recently published documents setting out guidelines to improve the teaching of radiology to medical students. These included recommendations that clinicians who teach radiology should be aware of newer educational techniques, such as problem-based learning, and should be involved in the development of curricula and assessment in medical schools. This review aims to introduce the educational theories behind problem-based learning and describe how a problem-based learning tutorial is run. The relevance of problem-based learning to radiology and the potential advantages and disadvantages are discussed

Teaching Problem Based Learning as Blended Learning

DEFF Research Database (Denmark)

Kolbæk, Ditte; Nortvig, Anne-Mette

2018-01-01

Problem-based and project organized learning (PBL) was originally developed for collaboration between physically present students, but political decisions at many universities require that collaboration, dialogues, and other PBL activities take place online as well. With a theoretical point...... of departure in Dewey and a methodological point of departure in netnography, this study focuses on an online module at Aalborg University where teaching is based on PBL. With the research question ‘How can teachers design for PBL online,’ this study explores the teacher’s role in a six weeks’ blended learning...... program, and we present suggestions for designs for blended learning PBL based on case studies from two PBL courses...

Pathway-based Analysis of the Hidden Genetic Heterogeneities in Cancers

Directory of Open Access Journals (Sweden)

Xiaolei Zhao

2014-02-01

Full Text Available Many cancers apparently showing similar phenotypes are actually distinct at the molecular level, leading to very different responses to the same treatment. It has been recently demonstrated that pathway-based approaches are robust and reliable for genetic analysis of cancers. Nevertheless, it remains unclear whether such function-based approaches are useful in deciphering molecular heterogeneities in cancers. Therefore, we aimed to test this possibility in the present study. First, we used a NCI60 dataset to validate the ability of pathways to correctly partition samples. Next, we applied the proposed method to identify the hidden subtypes in diffuse large B-cell lymphoma (DLBCL. Finally, the clinical significance of the identified subtypes was verified using survival analysis. For the NCI60 dataset, we achieved highly accurate partitions that best fit the clinical cancer phenotypes. Subsequently, for a DLBCL dataset, we identified three hidden subtypes that showed very different 10-year overall survival rates (90%, 46% and 20% and were highly significantly (P = 0.008 correlated with the clinical survival rate. This study demonstrated that the pathway-based approach is promising for unveiling genetic heterogeneities in complex human diseases.

Socially Aware Heterogeneous Wireless Networks.

Science.gov (United States)

Kosmides, Pavlos; Adamopoulou, Evgenia; Demestichas, Konstantinos; Theologou, Michael; Anagnostou, Miltiades; Rouskas, Angelos

2015-06-11

The development of smart cities has been the epicentre of many researchers' efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities. One common solution proposed in the recent literature is the use of software defined networks (SDNs), in order to enhance interoperability among the various heterogeneous wireless networks. In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure. In this paper, we propose an architecture combining heterogeneous wireless networks with social networks using SDNs. Specifically, we exploit the information retrieved from location based social networks regarding users' locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques. By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation.

XML-based approaches for the integration of heterogeneous bio-molecular data.

Science.gov (United States)

Mesiti, Marco; Jiménez-Ruiz, Ernesto; Sanz, Ismael; Berlanga-Llavori, Rafael; Perlasca, Paolo; Valentini, Giorgio; Manset, David

2009-10-15

The today's public database infrastructure spans a very large collection of heterogeneous biological data, opening new opportunities for molecular biology, bio-medical and bioinformatics research, but raising also new problems for their integration and computational processing. In this paper we survey the most interesting and novel approaches for the representation, integration and management of different kinds of biological data by exploiting XML and the related recommendations and approaches. Moreover, we present new and interesting cutting edge approaches for the appropriate management of heterogeneous biological data represented through XML. XML has succeeded in the integration of heterogeneous biomolecular information, and has established itself as the syntactic glue for biological data sources. Nevertheless, a large variety of XML-based data formats have been proposed, thus resulting in a difficult effective integration of bioinformatics data schemes. The adoption of a few semantic-rich standard formats is urgent to achieve a seamless integration of the current biological resources.

Human disease MiRNA inference by combining target information based on heterogeneous manifolds.

Science.gov (United States)

Ding, Pingjian; Luo, Jiawei; Liang, Cheng; Xiao, Qiu; Cao, Buwen

2018-04-01

The emergence of network medicine has provided great insight into the identification of disease-related molecules, which could help with the development of personalized medicine. However, the state-of-the-art methods could neither simultaneously consider target information and the known miRNA-disease associations nor effectively explore novel gene-disease associations as a by-product during the process of inferring disease-related miRNAs. Computational methods incorporating multiple sources of information offer more opportunities to infer disease-related molecules, including miRNAs and genes in heterogeneous networks at a system level. In this study, we developed a novel algorithm, named inference of Disease-related MiRNAs based on Heterogeneous Manifold (DMHM), to accurately and efficiently identify miRNA-disease associations by integrating multi-omics data. Graph-based regularization was utilized to obtain a smooth function on the data manifold, which constitutes the main principle of DMHM. The novelty of this framework lies in the relatedness between diseases and miRNAs, which are measured via heterogeneous manifolds on heterogeneous networks integrating target information. To demonstrate the effectiveness of DMHM, we conducted comprehensive experiments based on HMDD datasets and compared DMHM with six state-of-the-art methods. Experimental results indicated that DMHM significantly outperformed the other six methods under fivefold cross validation and de novo prediction tests. Case studies have further confirmed the practical usefulness of DMHM. Copyright © 2018 Elsevier Inc. All rights reserved.

A Learning Object Approach To Evidence based learning

Directory of Open Access Journals (Sweden)

Zabin Visram

2005-06-01

Full Text Available This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has meant increasingly, there is a desperate need to adopt wireless schemes, whereby bespoke courses can be developed to help practitioners keep up with expanding knowledge base. Evidently, without current best evidence, practice risks becoming rapidly out of date, to the detriment of the patient. There is a need to provide a tactical, operational and effective environment, which allows professional to update their education, and complete specialised training, just-in-time, in their own time and location. Following this demand in the marketplace the information engineering group, in combination with several medical and dental schools, set out to develop and design a conceptual framework which form the basis of pioneering research, which at last, enables practitioner's to adopt a philosophy of life long learning. The body and structure of this framework is subsumed under the term Object oriented approach to Evidence Based learning, Just-in-time, via Internet sustained by Reusable Learning Objects (The OEBJIRLO Progression. The technical pillars which permit this concept of life long learning are pivoted by the foundations of object oriented technology, Learning objects, Just-in-time education, Data Mining, intelligent Agent technology, Flash interconnectivity and remote wireless technology, which allow practitioners to update their professional skills, complete specialised training which leads to accredited qualifications. This paper sets out to develop and

Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

Science.gov (United States)

Zong, Nansu; Kim, Hyeoneui; Ngo, Victoria; Harismendy, Olivier

2017-08-01

A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies. The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction . nazong@ucsd.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Students Learn How Nonprofits Utilize Volunteers through Inquiry-Based Learning

Science.gov (United States)

Bolton, Elizabeth B.; Brennan, M. A.; Terry, Bryan D.

2009-01-01

This article highlights how undergraduate students implemented inquiry-based learning strategies to learn how nonprofit organizations utilize volunteers. In inquiry-based learning, students begin with a problem or question with some degree of focus or structure provided by the professor. The student inquiry showcased in this article was based on a…

Study on distributed generation algorithm of variable precision concept lattice based on ontology heterogeneous database

Science.gov (United States)

WANG, Qingrong; ZHU, Changfeng

2017-06-01

Integration of distributed heterogeneous data sources is the key issues under the big data applications. In this paper the strategy of variable precision is introduced to the concept lattice, and the one-to-one mapping mode of variable precision concept lattice and ontology concept lattice is constructed to produce the local ontology by constructing the variable precision concept lattice for each subsystem, and the distributed generation algorithm of variable precision concept lattice based on ontology heterogeneous database is proposed to draw support from the special relationship between concept lattice and ontology construction. Finally, based on the standard of main concept lattice of the existing heterogeneous database generated, a case study has been carried out in order to testify the feasibility and validity of this algorithm, and the differences between the main concept lattice and the standard concept lattice are compared. Analysis results show that this algorithm above-mentioned can automatically process the construction process of distributed concept lattice under the heterogeneous data sources.

Quantum-dot-based immunofluorescent imaging of HER2 and ER provides new insights into breast cancer heterogeneity

International Nuclear Information System (INIS)

Chen Chuang; Li Yan; Peng Jun; Xu Hao; Tang Hongwu; Zhang Zhiling; Pang Daiwen; Xia Heshun; Wu Qiongshui; Zeng Libo; Zhu Xiaobo

2010-01-01

Breast cancer (BC) is a heterogeneous tumor, and better understanding of its heterogeneity is essential to improving treatment effect. Quantum dot (QD)-based immunofluorescent nanotechnology (QD-IHC) for molecular pathology has potential advantages in delineating tumor heterogeneity. This potential is explored in this paper by QD-IHC imaging of HER2 and ER. BC heterogeneity can be displayed more clearly and sensitively by QD-IHC than conventional IHC in BC tissue microarrays. Furthermore, the simultaneous imaging of ER and HER2 might help understand their interactions during the process of evolution of heterogeneous BC.

Web Service Architecture for e-Learning

Directory of Open Access Journals (Sweden)

Xiaohong Qiu

2005-10-01

Full Text Available Message-based Web Service architecture provides a unified approach to applications and Web Services that incorporates the flexibility of messaging and distributed components. We propose SMMV and MMMV collaboration as the general architecture of collaboration based on a Web service model, which accommodates both instructor-led learning and participatory learning. This approach derives from our message-based Model-View-Controller (M-MVC architecture of Web applications, comprises an event-driven Publish/Subscribe scheme, and provides effective collaboration with high interactivity of rich Web content for diverse clients over heterogeneous network environments.

Ontology based heterogeneous materials database integration and semantic query

Science.gov (United States)

Zhao, Shuai; Qian, Quan

2017-10-01

Materials digital data, high throughput experiments and high throughput computations are regarded as three key pillars of materials genome initiatives. With the fast growth of materials data, the integration and sharing of data is very urgent, that has gradually become a hot topic of materials informatics. Due to the lack of semantic description, it is difficult to integrate data deeply in semantic level when adopting the conventional heterogeneous database integration approaches such as federal database or data warehouse. In this paper, a semantic integration method is proposed to create the semantic ontology by extracting the database schema semi-automatically. Other heterogeneous databases are integrated to the ontology by means of relational algebra and the rooted graph. Based on integrated ontology, semantic query can be done using SPARQL. During the experiments, two world famous First Principle Computational databases, OQMD and Materials Project are used as the integration targets, which show the availability and effectiveness of our method.

Problem-based learning

NARCIS (Netherlands)

Loyens, Sofie; Kirschner, Paul A.; Paas, Fred

2010-01-01

Loyens, S. M. M., Kirschner, P. A., & Paas, F. (2011). Problem-based learning. In S. Graham (Editor-in-Chief), A. Bus, S. Major, & L. Swanson (Associate Editors), APA educational psychology handbook: Vol. 3. Application to learning and teaching (pp. 403-425). Washington, DC: American Psychological

A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.

Science.gov (United States)

Luo, Yunan; Zhao, Xinbin; Zhou, Jingtian; Yang, Jinglin; Zhang, Yanqing; Kuang, Wenhua; Peng, Jian; Chen, Ligong; Zeng, Jianyang

2017-09-18

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.

The effect of discovery learning and problem-based learning on middle school students’ self-regulated learning

Science.gov (United States)

Miatun, A.; Muntazhimah

2018-01-01

The aim of this research was to determine the effect of learning models on mathematics achievement viewed from student’s self-regulated learning. The learning model compared were discovery learning and problem-based learning. The population was all students at the grade VIII of Junior High School in Boyolali regency. The samples were students of SMPN 4 Boyolali, SMPN 6 Boyolali, and SMPN 4 Mojosongo. The instruments used were mathematics achievement tests and self-regulated learning questionnaire. The data were analyzed using unbalanced two-ways Anova. The conclusion was as follows: (1) discovery learning gives better achievement than problem-based learning. (2) Achievement of students who have high self-regulated learning was better than students who have medium and low self-regulated learning. (3) For discovery learning, achievement of students who have high self-regulated learning was better than students who have medium and low self-regulated learning. For problem-based learning, students who have high and medium self-regulated learning have the same achievement. (4) For students who have high self-regulated learning, discovery learning gives better achievement than problem-based learning. Students who have medium and low self-regulated learning, both learning models give the same achievement.

Learning Object Retrieval and Aggregation Based on Learning Styles

Science.gov (United States)

Ramirez-Arellano, Aldo; Bory-Reyes, Juan; Hernández-Simón, Luis Manuel

2017-01-01

The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students' learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step…

Features and characteristics of problem based learning

Directory of Open Access Journals (Sweden)

Eser Ceker

2016-12-01

Full Text Available Throughout the years, there appears to be an increase in Problem Based Learning applications in education; and Problem Based Learning related research areas. The main aim of this research is to underline the fundamentals (basic elements of Problem Based Learning, investigate the dimensions of research approached to PBL oriented areas (with a look for the latest technology supported tools of Problem Based Learning. This research showed that the most researched characteristics of PBL are; teacher and student assessments on Problem Based Learning, Variety of disciplines in which Problem Based Learning strategies were tried and success evaluated, Using Problem Based Learning alone or with other strategies (Hybrid or Mix methods, Comparing Problem Based Learning with other strategies, and new trends and tendencies in Problem Based Learning related research. Our research may help us to identify the latest trends and tendencies referred to in the published studies related to “problem based learning” areas. In this research, Science Direct and Ulakbim were used as our main database resources. The sample of this study consists of 150 articles.

Merging Problem-Based Learning with Simulation-Based Learning in the Medical Undergraduate Curriculum: The PAIRED Framework for Enhancing Lifelong Learning

Science.gov (United States)

Koh, Jansen

2016-01-01

Lifelong learning is an essential trait that is expected of every physician. The CanMeds 2005 Physician Competency Framework emphasizes lifelong learning as a key competency that physicians must achieve in becoming better physicians. However, many physicians are not competent at engaging in lifelong learning. The current medical education system is deficient in preparing medical students to develop and carry out their own lifelong learning curriculum upon graduation. Despite understanding how physicians learn at work, medical students are not trained to learn while working. Similarly, although barriers to lifelong learning are known, medical students are not adequately skilled in overcoming these barriers. Learning to learn is just as important, if not more, as acquiring the skills and knowledge required of a physician. The medical undergraduate curriculum lacks a specific learning strategy to prepare medical students in becoming an adept lifelong learner. In this article, we propose a learning strategy for lifelong learning at the undergraduate level. In developing this novel strategy, we paid particular attention to two parameters. First, this strategy should be grounded on literature describing a physician’s lifelong learning process. Second, the framework for implementing this strategy must be based on existing undergraduate learning strategies to obviate the need for additional resources, learner burden, and faculty time. In this paper, we propose a Problem, Analysis, Independent Research Reporting, Experimentation Debriefing (PAIRED) framework that follows the learning process of a physician and serves to synergize the components of problem-based learning and simulation-based learning in specifically targeting the barriers to lifelong learning. PMID:27446767

Conformity-based cooperation in online social networks: The effect of heterogeneous social influence

International Nuclear Information System (INIS)

Xu, Bo; Wang, Jianwei; Zhang, Xuejun

2015-01-01

This paper extends the conformity model by introducing heterogeneous social influence into the analysis. We associate the influence of a player in the network with its degree centrality assuming that players of higher degree exhibit more social influence on its neighbors. The results show that the equilibrium level of cooperators can be dramatically enhanced if the conformity-driven players are preferentially influenced by neighbors of higher degree. We attribute this finding to two elementary mechanisms in the evolutionary process: (1) degree-based social influence facilitates the formation of strategic clusters around hubs; and (2) payoff-heterogeneity between cooperative clusters and defective clusters contributes to the promotion of cooperation. This research reveals the important role of heterogeneous social influence on the emergence of cooperation in social networks.

Improving Video Game Development: Facilitating Heterogeneous Team Collaboration through Flexible Software Processes

Science.gov (United States)

Musil, Juergen; Schweda, Angelika; Winkler, Dietmar; Biffl, Stefan

Based on our observations of Austrian video game software development (VGSD) practices we identified a lack of systematic processes/method support and inefficient collaboration between various involved disciplines, i.e. engineers and artists. VGSD includes heterogeneous disciplines, e.g. creative arts, game/content design, and software. Nevertheless, improving team collaboration and process support is an ongoing challenge to enable a comprehensive view on game development projects. Lessons learned from software engineering practices can help game developers to increase game development processes within a heterogeneous environment. Based on a state of the practice survey in the Austrian games industry, this paper presents (a) first results with focus on process/method support and (b) suggests a candidate flexible process approach based on Scrum to improve VGSD and team collaboration. Results showed (a) a trend to highly flexible software processes involving various disciplines and (b) identified the suggested flexible process approach as feasible and useful for project application.

FEATURE SELECTION METHODS BASED ON MUTUAL INFORMATION FOR CLASSIFYING HETEROGENEOUS FEATURES

Directory of Open Access Journals (Sweden)

Ratri Enggar Pawening

2016-06-01

Full Text Available Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI for classifying heterogeneous features. We use unsupervised feature transformation (UFT methods and joint mutual information maximation (JMIM methods. UFT methods is used to transform non-numerical features into numerical features. JMIM methods is used to select feature subset with a consideration of the class label. The transformed and the original features are combined entirely, then determine features subset by using JMIM methods, and classify them using support vector machine (SVM algorithm. The classification accuracy are measured for any number of selected feature subset and compared between UFT-JMIM methods and Dummy-JMIM methods. The average classification accuracy for all experiments in this study that can be achieved by UFT-JMIM methods is about 84.47% and Dummy-JMIM methods is about 84.24%. This result shows that UFT-JMIM methods can minimize information loss between transformed and original features, and select feature subset to avoid redundant and irrelevant features.

Virtual Learning Environments and Learning Forms -experiments in ICT-based learning

DEFF Research Database (Denmark)

Helbo, Jan; Knudsen, Morten

2004-01-01

This paper report the main results of a three year experiment in ICT-based distance learning. The results are based on a full scale experiment in the education, Master of Industrial Information Technology (MII) and is one of many projects deeply rooted in the project Virtual Learning Environments...... didactic model has until now been a positive experience........ The main problem is that we do not find the same self regulatoring learning effect in the group work among the off-campus students as is the case for on-campus students. Based on feedback from evaluation questionnaires and discussions with the students didactic adjustments have been made. The revised...

Artificial Immune Ecosystems: the role of expert-based learning in artificial cognition

Directory of Open Access Journals (Sweden)

Pierre Parrend

2018-03-01

Full Text Available The rapid evolution of IT ecosystems significantly challenges the security models our infrastructures rely on. Beyond the old dichotomy between open and closed systems, it is now necessary to handle securely the interaction between heterogeneous devices building dynamic ecosystems. To this regard, bio-inspired approaches provide a rich set of conceptual tools, but they have failed to lay the basis for robust and efficient solutions. Our research effort intends to revisit the contribution of artificial immune system research to bring immune properties: security, resilience, distribution, memory, into IT infrastructures. Artificial immune ecosystems support a comprehensive model for anomaly detection and characterization, but their cognitive capacity are limited by the state of the art in machine learning and the rapid evolution of cybersecurity threats so far. We therefore propose to enrich the cognitive process with expert-based learning for reinforcement, classification and investigation. Application to system supervision using system logs and supervision time series confirms the relevance and performance of this model.

The effectiveness of problem-based learning on development of nursing students' critical thinking: a systematic review and meta-analysis.

Science.gov (United States)

Kong, Ling-Na; Qin, Bo; Zhou, Ying-qing; Mou, Shao-yu; Gao, Hui-Ming

2014-03-01

The objective of this systematic review and meta-analysis was to estimate the effectiveness of problem-based learning in developing nursing students' critical thinking. Searches of PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Proquest, Cochrane Central Register of Controlled Trials (CENTRAL) and China National Knowledge Infrastructure (CNKI) were undertaken to identify randomized controlled trails from 1965 to December 2012, comparing problem-based learning with traditional lectures on the effectiveness of development of nursing students' critical thinking, with no language limitation. The mesh-terms or key words used in the search were problem-based learning, thinking, critical thinking, nursing, nursing education, nurse education, nurse students, nursing students and pupil nurse. Two reviewers independently assessed eligibility and extracted data. Quality assessment was conducted independently by two reviewers using the Cochrane Collaboration's Risk of Bias Tool. We analyzed critical thinking scores (continuous outcomes) using a standardized mean difference (SMD) or weighted mean difference (WMD) with a 95% confidence intervals (CIs). Heterogeneity was assessed using the Cochran's Q statistic and I(2) statistic. Publication bias was assessed by means of funnel plot and Egger's test of asymmetry. Nine articles representing eight randomized controlled trials were included in the meta-analysis. Most studies were of low risk of bias. The pooled effect size showed problem-based learning was able to improve nursing students' critical thinking (overall critical thinking scores SMD=0.33, 95%CI=0.13-0.52, P=0.0009), compared with traditional lectures. There was low heterogeneity (overall critical thinking scores I(2)=45%, P=0.07) in the meta-analysis. No significant publication bias was observed regarding overall critical thinking scores (P=0.536). Sensitivity analysis showed that the result of our meta-analysis was reliable. Most

Hot news recommendation system from heterogeneous websites based on bayesian model.

Science.gov (United States)

Xia, Zhengyou; Xu, Shengwu; Liu, Ningzhong; Zhao, Zhengkang

2014-01-01

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.

Inquiry-Based Learning in China: Lesson Learned for School Science Practices

Science.gov (United States)

Nuangchalerm, Prasart

2014-01-01

Inquiry-based learning is widely considered for science education in this era. This study aims to explore inquiry-based learning in teacher preparation program and the findings will help us to understanding what inquiry-based classroom is and how inquiry-based learning are. Data were collected by qualitative methods; classroom observation,…

Effects of Heterogeneity in Residential Preferences on an Agent-Based Model of Urban Sprawl

Directory of Open Access Journals (Sweden)

Daniel G. Brown

2006-06-01

Full Text Available The ability of agent-based models (ABMs to represent heterogeneity in the characteristics and behaviors of actors enables analyses about the implications of this heterogeneity for system behavior. The importance of heterogeneity in the specification of ABMs, however, creates new demands for empirical support. An earlier analysis of a survey of residential preferences within southeastern Michigan revealed seven groups of residents with similar preferences on similar characteristics of location. In this paper, we present an ABM that represents the process of residential development within an urban system and run it for a hypothetical pattern of environmental variation. Residential locations are selected by residential agents, who evaluate locations on the basis of preference for nearness to urban services, including jobs, aesthetic quality of the landscape, and their similarity to their neighbors. We populate our ABM with a population of residential preferences drawn from the survey results in five different ways: (1 preferences drawn at random; (2 equal preferences based on the mean from the entire survey sample; (3 preferences drawn from a single distribution, whose mean and standard deviation are derived from the survey sample; (4 equal preferences within each of seven groups, based on the group means; and (5 preferences drawn from distributions for each of seven groups, defined by group means and standard deviations. Model sensitivity analysis, based on multiple runs of our model under each case, revealed that adding heterogeneity to agents has a significant effect on model outcomes, measured by aggregate patterns of development sprawl and clustering.

An operational framework for object-based land use classification of heterogeneous rural landscapes

DEFF Research Database (Denmark)

Watmough, Gary Richard; Palm, Cheryl; Sullivan, Clare

2017-01-01

The characteristics of very high resolution (VHR) satellite data are encouraging development agencies to investigate its use in monitoring and evaluation programmes. VHR data pose challenges for land use classification of heterogeneous rural landscapes as it is not possible to develop generalised...... and transferable land use classification definitions and algorithms. We present an operational framework for classifying VHR satellite data in heterogeneous rural landscapes using an object-based and random forest classifier. The framework overcomes the challenges of classifying VHR data in anthropogenic...

Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.

Science.gov (United States)

Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda

2017-08-20

Alzheimer's disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer's and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93

Heterogeneous network architectures

DEFF Research Database (Denmark)

Christiansen, Henrik Lehrmann

2006-01-01

is flexibility. This thesis investigates such heterogeneous network architectures and how to make them flexible. A survey of algorithms for network design is presented, and it is described how using heuristics can increase the speed. A hierarchical, MPLS based network architecture is described......Future networks will be heterogeneous! Due to the sheer size of networks (e.g., the Internet) upgrades cannot be instantaneous and thus heterogeneity appears. This means that instead of trying to find the olution, networks hould be designed as being heterogeneous. One of the key equirements here...... and it is discussed that it is advantageous to heterogeneous networks and illustrated by a number of examples. Modeling and simulation is a well-known way of doing performance evaluation. An approach to event-driven simulation of communication networks is presented and mixed complexity modeling, which can simplify...

Use of positioning information for performance enhancement of uncoordinated heterogeneous network deployment

DEFF Research Database (Denmark)

Semov, Plamen T.; Mihovska, Albena D.; Prasad, Ramjee

2013-01-01

with information but the locations of the mobile users and neighboring cells to solve the problem of dynamic physical resource assignment in uncoordinated scenario while accounting for improved allocation and scheduling. The results are compared to the performance when known scheduling algorithms are employed......This paper proposes a novel algorithm for dynamic physical resource allocation based on the use of positioning information during carrier aggregation (CA) in a semi-and uncoordinated deployment of heterogeneous networks (HetNet). The algorithm uses the known Q-learning method enhanced...... and show increased cell throughput, while maintaining an adequate user throughput when employing Q-learning with positioning information....

A review on machine learning principles for multi-view biological data integration.

Science.gov (United States)

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Agent-based land markets: Heterogeneous agents, land proces and urban land use change

NARCIS (Netherlands)

Filatova, Tatiana; Parker, Dawn C.; van der Veen, A.; Amblard, F.

2007-01-01

We construct a spatially explicit agent-based model of a bilateral land market. Heterogeneous agents form their bid and ask prices for land based on the utility that they obtain from a certain location (houte/land) and base on the state of the market (an excess of demand or supply). We underline the

Dimensions of problem based learning

DEFF Research Database (Denmark)

Nielsen, Jørgen Lerche; Andreasen, Lars Birch

2013-01-01

The article contributes to the literature on problem based learning and problem-oriented project work, building on and reflecting the experiences of the authors through decades of work with problem-oriented project pedagogy. The article explores different dimensions of problem based learning such...... and Learning (MIL). We discuss changes in the roles of the teachers as supervisors within this learning environment, and we explore the involvement of students as active participants and co-designers of how course and project activities unfold....

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.

eLearning techniques supporting problem based learning in clinical simulation.

Science.gov (United States)

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2005-08-01

This paper details the results of the first phase of a project using eLearning to support students' learning within a simulated environment. The locus was a purpose built clinical simulation laboratory (CSL) where the School's philosophy of problem based learning (PBL) was challenged through lecturers using traditional teaching methods. a student-centred, problem based approach to the acquisition of clinical skills that used high quality learning objects embedded within web pages, substituting for lecturers providing instruction and demonstration. This encouraged student nurses to explore, analyse and make decisions within the safety of a clinical simulation. Learning was facilitated through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that eLearning techniques can help students acquire clinical skills in the safety of a simulated environment within the context of a problem based learning curriculum.

Graph embedding with rich information through heterogeneous graph

KAUST Repository

Sun, Guolei

2017-11-12

Graph embedding, aiming to learn low-dimensional representations for nodes in graphs, has attracted increasing attention due to its critical application including node classification, link prediction and clustering in social network analysis. Most existing algorithms for graph embedding only rely on the topology information and fail to use the copious information in nodes as well as edges. As a result, their performance for many tasks may not be satisfactory. In this thesis, we proposed a novel and general framework for graph embedding with rich text information (GERI) through constructing a heterogeneous network, in which we integrate node and edge content information with graph topology. Specially, we designed a novel biased random walk to explore the constructed heterogeneous network with the notion of flexible neighborhood. Our sampling strategy can compromise between BFS and DFS local search on heterogeneous graph. To further improve our algorithm, we proposed semi-supervised GERI (SGERI), which learns graph embedding in an discriminative manner through heterogeneous network with label information. The efficacy of our method is demonstrated by extensive comparison experiments with 9 baselines over multi-label and multi-class classification on various datasets including Citeseer, Cora, DBLP and Wiki. It shows that GERI improves the Micro-F1 and Macro-F1 of node classification up to 10%, and SGERI improves GERI by 5% in Wiki.

Simulation and case-based learning

DEFF Research Database (Denmark)

Ørngreen, Rikke; Guralnick, David

2008-01-01

Abstract- This paper has its origin in the authors' reflection on years of practical experiences combined with literature readings in our preparation for a workshop on learn-by-doing simulation and case-based learning to be held at the ICELW 2008 conference (the International Conference on E-Learning...... in the Workplace). The purpose of this paper is to describe the two online learning methodologies and to raise questions for future discussion. In the workshop, the organizers and participants work with and discuss differences and similarities within the two pedagogical methodologies, focusing on how...... they are applied in workplace related and e-learning contexts. In addition to the organizers, a small number of invited presenters will attend, giving demonstrations of their work within learn-by-doing simulation and cases-based learning, but still leaving ample of time for discussion among all participants....

Design for game based learning platforms

DEFF Research Database (Denmark)

Sørensen, Birgitte Holm; Meyer, Bente

2010-01-01

This paper focuses on the challenges related to the design of game based learning platforms for formal learning contexts that are inspired by the pupil's leisure time related use of web 2.0. The paper is based on the project Serious Games on a Global Market Place (2007-2011) founded by the Danish...... of web 2.0 and integrates theories of learning, didactics, games, play, communication, multimodality and different pedagogical approaches. In relation to the introduced model the teacher role is discussed.......This paper focuses on the challenges related to the design of game based learning platforms for formal learning contexts that are inspired by the pupil's leisure time related use of web 2.0. The paper is based on the project Serious Games on a Global Market Place (2007-2011) founded by the Danish...... Council for Strategic Research, in which an online game-based platform for English as a foreign language in primary school is studied. The paper presents a model for designing for game based learning platforms. This design is based on cultural and ethnographic based research on children's leisure time use...

Group learning

DEFF Research Database (Denmark)

Pimentel, Ricardo; Noguira, Eloy Eros da Silva; Elkjær, Bente

The article presents a study that aims at the apprehension of the group learning in a top management team composed by teachers in a Brazilian Waldorf school whose management is collective. After deciding to extend the school, they had problems recruiting teachers who were already trained based...... on the Steiner´s ideas, which created practical problems for conducting management activities. The research seeks to understand how that group of teachers collectively manage the school, facing the lack of resources, a significant heterogeneity in the relationships, and the conflicts and contradictions......, and they are interrelated to the group learning as the construction, maintenance and reconstruction of the intelligibility of practices. From this perspective, it can be said that learning is a practice and not an exceptional phenomenon. Building, maintaining and rebuilding the intelligibility is the group learning...

Recommender systems for technology enhanced learning research trends and applications

CERN Document Server

Manouselis, Nikos; Verbert, Katrien

2014-01-01

Presents cutting edge research from leading experts in the growing field of Recommender Systems for Technology Enhanced Learning (RecSys TEL) International contributions are included to demonstrate the merging of various efforts and communities Topics include: Linked Data and the Social Web as Facilitators for TEL Recommender Systems in Research and Practice, Personalised Learning-Plan Recommendations in Game-Based Learning and Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem

Seamless and secure communications over heterogeneous wireless networks

CERN Document Server

Cao, Jiannong

2014-01-01

This brief provides an overview of the requirements, challenges, design issues and major techniques for seamless and secure communications over heterogeneous wireless networks. It summarizes and provides detailed insights into the latest research on handoff management, mobility management, fast authentication and security management to support seamless and secure roaming for mobile clients. The reader will also learn about the challenges in developing relevant technologies and providing ubiquitous Internet access over heterogeneous wireless networks. The authors have extensive experience in im

Distance learning, problem based learning and dynamic knowledge networks.

Science.gov (United States)

Giani, U; Martone, P

1998-06-01

This paper is an attempt to develop a distance learning model grounded upon a strict integration of problem based learning (PBL), dynamic knowledge networks (DKN) and web tools, such as hypermedia documents, synchronous and asynchronous communication facilities, etc. The main objective is to develop a theory of distance learning based upon the idea that learning is a highly dynamic cognitive process aimed at connecting different concepts in a network of mutually supporting concepts. Moreover, this process is supposed to be the result of a social interaction that has to be facilitated by the web. The model was tested by creating a virtual classroom of medical and nursing students and activating a learning session on the concept of knowledge representation in health sciences.

Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk.

Science.gov (United States)

Mahrooghy, Majid; Ashraf, Ahmed B; Daye, Dania; Mies, Carolyn; Feldman, Michael; Rosen, Mark; Kontos, Despina

2013-01-01

Breast tumors are heterogeneous lesions. Intra-tumor heterogeneity presents a major challenge for cancer diagnosis and treatment. Few studies have worked on capturing tumor heterogeneity from imaging. Most studies to date consider aggregate measures for tumor characterization. In this work we capture tumor heterogeneity by partitioning tumor pixels into subregions and extracting heterogeneity wavelet kinetic (HetWave) features from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to obtain the spatiotemporal patterns of the wavelet coefficients and contrast agent uptake from each partition. Using a genetic algorithm for feature selection, and a logistic regression classifier with leave one-out cross validation, we tested our proposed HetWave features for the task of classifying breast cancer recurrence risk. The classifier based on our features gave an ROC AUC of 0.78, outperforming previously proposed kinetic, texture, and spatial enhancement variance features which give AUCs of 0.69, 0.64, and 0.65, respectively.

Unsupervised multiple kernel learning for heterogeneous data integration.

Science.gov (United States)

Mariette, Jérôme; Villa-Vialaneix, Nathalie

2018-03-15

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

Measuring the Differences between Traditional Learning and Game-Based Learning Using Electroencephalography (EEG) Physiologically Based Methodology

Science.gov (United States)

Chen, Ching-Huei

2017-01-01

Students' cognitive states can reflect a learning experience that results in engagement in an activity. In this study, we used electroencephalography (EEG) physiologically based methodology to evaluate students' levels of attention and relaxation, as well as their learning performance within a traditional and game-based learning context. While no…

Gameplay Engagement and Learning in Game-Based Learning: A Systematic Review

Science.gov (United States)

Abdul Jabbar, Azita Iliya; Felicia, Patrick

2015-01-01

In this review, we investigated game design features that promote engagement and learning in game-based learning (GBL) settings. The aim was to address the lack of empirical evidence on the impact of game design on learning outcomes, identify how the design of game-based activities may affect learning and engagement, and develop a set of general…

DEVELOPMENT OF SCIENCE PROCESS SKILLS STUDENTS WITH PROJECT BASED LEARNING MODEL- BASED TRAINING IN LEARNING PHYSICS

Directory of Open Access Journals (Sweden)

Ratna Malawati

2016-06-01

Full Text Available This study aims to improve the physics Science Process Skills Students on cognitive and psychomotor aspects by using model based Project Based Learning training.The object of this study is the Project Based Learning model used in the learning process of Computationa Physics.The method used is classroom action research through two learning cycles, each cycle consisting of the stages of planning, implementation, observation and reflection. In the first cycle of treatment with their emphasis given training in the first phase up to third in the model Project Based Learning, while the second cycle is given additional treatment with emphasis discussion is collaboration in achieving the best results for each group of products. The results of data analysis showed increased ability to think Students on cognitive and Science Process Skills in the psychomotor.

Problem-based learning in pre-clinical medical education: 22 years of outcome research.

Science.gov (United States)

Hartling, Lisa; Spooner, Carol; Tjosvold, Lisa; Oswald, Anna

2010-01-01

To conduct a systematic review of problem-based learning (PBL) in undergraduate, pre-clinical medical education. A research librarian developed comprehensive search strategies for MEDLINE, PSYCINFO, and ERIC (1985-2007). Two reviewers independently screened search results and applied inclusion criteria. Studies were included if they had a comparison group and reported primary data for evaluative outcomes. One reviewer extracted data and a second reviewer checked data for accuracy. Two reviewers independently assessed methodological quality. Quantitative synthesis was not performed due to heterogeneity. A qualitative review with detailed evidence tables is provided. Thirty unique studies were included. Knowledge acquisition measured by exam scores was the most frequent outcome reported; 12 of 15 studies found no significant differences. Individual studies demonstrated either improved clerkship (N = 3) or residency (N = 1) performance, or benefits on some clinical competencies during internships for PBL (N = 1). Three of four studies found some benefits for PBL when evaluating diagnostic accuracy. Three studies found few differences of clinical (or practical) importance on the impact of PBL on practicing physicians. Twenty-two years of research shows that PBL does not impact knowledge acquisition; evidence for other outcomes does not provide unequivocal support for enhanced learning. Work is needed to determine the most appropriate outcome measures to capture and quantify the effects of PBL. General conclusions are limited by methodological weaknesses and heterogeneity across studies. The critical appraisal of previous studies, conducted as part of this review, provides direction for future research in this area.

Competition-Based Learning: A Model for the Integration of Competitions with Project-Based Learning Using Open Source LMS

Science.gov (United States)

Issa, Ghassan; Hussain, Shakir M.; Al-Bahadili, Hussein

2014-01-01

In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning (CBL) is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning (PBL) and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as…

A family-based joint test for mean and variance heterogeneity for quantitative traits.

Science.gov (United States)

Cao, Ying; Maxwell, Taylor J; Wei, Peng

2015-01-01

Traditional quantitative trait locus (QTL) analysis focuses on identifying loci associated with mean heterogeneity. Recent research has discovered loci associated with phenotype variance heterogeneity (vQTL), which is important in studying genetic association with complex traits, especially for identifying gene-gene and gene-environment interactions. While several tests have been proposed to detect vQTL for unrelated individuals, there are no tests for related individuals, commonly seen in family-based genetic studies. Here we introduce a likelihood ratio test (LRT) for identifying mean and variance heterogeneity simultaneously or for either effect alone, adjusting for covariates and family relatedness using a linear mixed effect model approach. The LRT test statistic for normally distributed quantitative traits approximately follows χ(2)-distributions. To correct for inflated Type I error for non-normally distributed quantitative traits, we propose a parametric bootstrap-based LRT that removes the best linear unbiased prediction (BLUP) of family random effect. Simulation studies show that our family-based test controls Type I error and has good power, while Type I error inflation is observed when family relatedness is ignored. We demonstrate the utility and efficiency gains of the proposed method using data from the Framingham Heart Study to detect loci associated with body mass index (BMI) variability. © 2014 John Wiley & Sons Ltd/University College London.

Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model

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Zhengyou Xia

2014-01-01

Full Text Available The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs. In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.

Foundations of Game-Based Learning

Science.gov (United States)

Plass, Jan L.; Homer, Bruce D.; Kinzer, Charles K.

2015-01-01

In this article we argue that to study or apply games as learning environments, multiple perspectives have to be taken into account. We first define game-based learning and gamification, and then discuss theoretical models that describe learning with games, arguing that playfulness is orthogonal to learning theory. We then review design elements…

Characteristics of Problem-Based Learning

DEFF Research Database (Denmark)

Kolmos, Anette

2003-01-01

Problem BAsed LEarning (PBL) is widely regarded as a successful and innovative method for engineering education. The article highlights the Dutch approach of directing the learning process throuogh problem analysis and the Danish model of project-organised learning...

What students learn in problem-based learning: a process analysis

NARCIS (Netherlands)

E.H.J. Yew (Elaine); H.G. Schmidt (Henk)

2012-01-01

textabstractThis study aimed to provide an account of how learning takes place in problem-based learning (PBL), and to identify the relationships between the learning-oriented activities of students with their learning outcomes. First, the verbal interactions and computer resources studied by nine

Learning Object Metadata in a Web-Based Learning Environment

NARCIS (Netherlands)

Avgeriou, Paris; Koutoumanos, Anastasios; Retalis, Symeon; Papaspyrou, Nikolaos

2000-01-01

The plethora and variance of learning resources embedded in modern web-based learning environments require a mechanism to enable their structured administration. This goal can be achieved by defining metadata on them and constructing a system that manages the metadata in the context of the learning

Digital Microdroplet Ejection Technology-Based Heterogeneous Objects Prototyping

OpenAIRE

Li, Na; Yang, Jiquan; Feng, Chunmei; Yang, Jianfei; Zhu, Liya; Guo, Aiqing

2016-01-01

An integrate fabrication framework is presented to build heterogeneous objects (HEO) using digital microdroplets injecting technology and rapid prototyping. The heterogeneous materials part design and manufacturing method in structure and material was used to change the traditional process. The net node method was used for digital modeling that can configure multimaterials in time. The relationship of material, color, and jetting nozzle was built. The main important contributions are to combi...

International Military Practice Amidst Ethical Heterogeneity

Science.gov (United States)

2013-12-13

importance of the multicultural environment cannot be understated, and the ethical make-up, moral philosophy, and cultural relativism (Robertson and...determinism, moral relativism . Rather, the development of students’ ethical and moral strength is achieved by engaging in learning opportunites...INTERNATIONAL MILITARY PRACTICE AMIDST ETHICAL HETEROGENEITY A thesis presented to the Faculty of the U.S. Army Command and

Team-based learning for midwifery education.

Science.gov (United States)

Moore-Davis, Tonia L; Schorn, Mavis N; Collins, Michelle R; Phillippi, Julia; Holley, Sharon

2015-01-01

Many US health care and education stakeholder groups, recognizing the need to prepare learners for collaborative practice in complex care environments, have called for innovative approaches in health care education. Team-based learning is an educational method that relies on in-depth student preparation prior to class, individual and team knowledge assessment, and use of small-group learning to apply knowledge to complex scenarios. Although team-based learning has been studied as an approach to health care education, its application to midwifery education is not well described. A master's-level, nurse-midwifery, didactic antepartum course was revised to a team-based learning format. Student grades, course evaluations, and aggregate American Midwifery Certification Board examination pass rates for 3 student cohorts participating in the team-based course were compared with 3 student cohorts receiving traditional, lecture-based instruction. Students had mixed responses to the team-based learning format. Student evaluations improved when faculty added recorded lectures as part of student preclass preparation. Statistical comparisons were limited by variations across cohorts; however, student grades and certification examination pass rates did not change substantially after the course revision. Although initial course revision was time-consuming for faculty, subsequent iterations of the course required less effort. Team-based learning provides students with more opportunity to interact during on-site classes and may spur application of knowledge into practice. However, it is difficult to assess the effect of the team-based learning approach with current measures. Further research is needed to determine the effects of team-based learning on communication and collaboration skills, as well as long-term performance in clinical practice. This article is part of a special series of articles that address midwifery innovations in clinical practice, education, interprofessional

Cloud-Based Mobile Learning

Directory of Open Access Journals (Sweden)

Alexandru BUTOI

2013-01-01

Full Text Available As the cloud technologies are largely studied and mobile technologies are evolving, new di-rections for development of mobile learning tools deployed on cloud are proposed.. M-Learning is treated as part of the ubiquitous learning paradigm and is a pervasive extension of E-Learning technologies. Development of such learning tools requires specific development strategies for an effective abstracting of pedagogical principles at the software design and implementation level. Current paper explores an interdisciplinary approach for designing and development of cloud based M-Learning tools by mapping a specific development strategy used for educational programs to software prototyping strategy. In order for such instruments to be user effective from the learning outcome point of view, the evaluation process must be rigorous as we propose a metric model for expressing the trainee’s overall learning experience with evaluated levels of interactivity, content presentation and graphical user interface usability.

Effects of team-based learning on self-regulated online learning.

Science.gov (United States)

Whittaker, Alice A

2015-04-10

Online learning requires higher levels of self-regulation in order to achieve optimal learning outcomes. As nursing education moves further into the blended and online learning venue, new teaching/learning strategies will be required to develop and enhance self-regulated learning skills in nursing students. The purpose of this study was to compare the effectiveness of team-based learning (TBL) with traditional instructor-led (IL) learning, on self-regulated online learning outcomes, in a blended undergraduate research and evidence-based practice course. The nonrandomized sample consisted of 98 students enrolled in the IL control group and 86 students enrolled in the TBL intervention group. The percentage of total possible online viewing time was used as the measure of self-regulated online learning activity. The TBL group demonstrated a significantly higher percentage (p learning activities than the IL control group. The TBL group scored significantly higher on the course examinations (p = 0.003). The findings indicate that TBL is an effective instructional strategy that can be used to achieve the essential outcomes of baccalaureate nursing education by increasing self-regulated learning capabilities in nursing students.

Personalised Learning Object System Based on Self-Regulated Learning Theories

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Ali Alharbi

2014-06-01

Full Text Available Self-regulated learning has become an important construct in education research in the last few years. Selfregulated learning in its simple form is the learner’s ability to monitor and control the learning process. There is increasing research in the literature on how to support students become more self-regulated learners. However, the advancement in the information technology has led to paradigm changes in the design and development of educational content. The concept of learning object instructional technology has emerged as a result of this shift in educational technology paradigms. This paper presents the results of a study that investigated the potential educational effectiveness of a pedagogical framework based on the self-regulated learning theories to support the design of learning object systems to help computer science students. A prototype learning object system was developed based on the contemporary research on self-regulated learning. The system was educationally evaluated in a quasi-experimental study over two semesters in a core programming languages concepts course. The evaluation revealed that a learning object system that takes into consideration contemporary research on self-regulated learning can be an effective learning environment to support computer science education.

Supporting Case-Based Learning in Information Security with Web-Based Technology

Science.gov (United States)

He, Wu; Yuan, Xiaohong; Yang, Li

2013-01-01

Case-based learning has been widely used in many disciplines. As an effective pedagogical method, case-based learning is also being used to support teaching and learning in the domain of information security. In this paper, we demonstrate case-based learning in information security by sharing our experiences in using a case study to teach security…

Managing the Gap between Curriculum Based and Problem Based Learning

DEFF Research Database (Denmark)

Bygholm, Ann; Buus, Lillian

2009-01-01

/or but rather both/and. In this paper we describe an approach to design and delivery of online courses in computer science which on the one hand is based on a specified curriculum and on the other hand gives room for different learning strategies, problem based learning being one of them. We discuss......Traditionally there has been a clear distinction between curriculum based and problem based approaches to accomplish learning. Preferred approaches depend of course on conviction, culture, traditions and also on the specific learning situation. We will argue that it is not a question of either...

The use of a mobile assistant learning system for health education based on project-based learning.

Science.gov (United States)

Wu, Ting-Ting

2014-10-01

With the development of mobile devices and wireless technology, mobile technology has gradually infiltrated nursing practice courses to facilitate instruction. Mobile devices save manpower and reduce errors while enhancing nursing students' professional knowledge and skills. To achieve teaching objectives and address the drawbacks of traditional education, this study presents a mobile assistant learning system to help nursing students prepare health education materials. The proposed system is based on a project-based learning strategy to assist nursing students with internalizing professional knowledge and developing critical thinking skills. Experimental results show that the proposed mobile system and project-based learning strategy can promote learning effectiveness and efficiency. Most nursing students and nursing educators showed positive attitudes toward this mobile learning system and looked forward to using it again in related courses in the future.

Learning discriminant face descriptor.

Science.gov (United States)

Lei, Zhen; Pietikäinen, Matti; Li, Stan Z

2014-02-01

Local feature descriptor is an important module for face recognition and those like Gabor and local binary patterns (LBP) have proven effective face descriptors. Traditionally, the form of such local descriptors is predefined in a handcrafted way. In this paper, we propose a method to learn a discriminant face descriptor (DFD) in a data-driven way. The idea is to learn the most discriminant local features that minimize the difference of the features between images of the same person and maximize that between images from different people. In particular, we propose to enhance the discriminative ability of face representation in three aspects. First, the discriminant image filters are learned. Second, the optimal neighborhood sampling strategy is soft determined. Third, the dominant patterns are statistically constructed. Discriminative learning is incorporated to extract effective and robust features. We further apply the proposed method to the heterogeneous (cross-modality) face recognition problem and learn DFD in a coupled way (coupled DFD or C-DFD) to reduce the gap between features of heterogeneous face images to improve the performance of this challenging problem. Extensive experiments on FERET, CAS-PEAL-R1, LFW, and HFB face databases validate the effectiveness of the proposed DFD learning on both homogeneous and heterogeneous face recognition problems. The DFD improves POEM and LQP by about 4.5 percent on LFW database and the C-DFD enhances the heterogeneous face recognition performance of LBP by over 25 percent.

Adding Social Elements to Game-Based Learning

Directory of Open Access Journals (Sweden)

Chien-Hung Lai

2014-05-01

Full Text Available Game-based learning is to present the instruction by games in learning, with the main purpose of triggering learners’ motives instead of instructing the courses. Thus, increasing learning motive by game-based learning becomes a common instructional strategy to enhance learning achievement. However, it is not easy to design interesting games combined with courses. In 2011, Echeverria proposed a design to combine characteristics of games with elements of courses by matching the virtual scenarios in games with proper courses. However, in the past game-based learning, students were gathered in regular places for several times of game-based learning. Students’ learning was limited by time and space. Therefore, for students’ game-based learning at any time and in any places, based on theories of design elements of online community game Aki Järvinen, this study treats Facebook as the platform of games. The development by online community game is easier, faster and cheaper than traditional video games. In 2006, Facebook allowed API program of the third party. Therefore, by Facebook, this study provides the platform for students to learn in social lives to explore students’ activities in online community games. Questionnaire survey is conducted to find out if the design of non-single user game is attractive for students to participate in game-based learning. In order to make sure that the questionnaires can be the criteria to investigate students’ intention to play games, by statistical program of social science; this study validates reliability and validity of items of questionnaire to effectively control the effect of online community games on students’ learning intention.

Learning Theory Foundations of Simulation-Based Mastery Learning.

Science.gov (United States)

McGaghie, William C; Harris, Ilene B

2018-06-01

Simulation-based mastery learning (SBML), like all education interventions, has learning theory foundations. Recognition and comprehension of SBML learning theory foundations are essential for thoughtful education program development, research, and scholarship. We begin with a description of SBML followed by a section on the importance of learning theory foundations to shape and direct SBML education and research. We then discuss three principal learning theory conceptual frameworks that are associated with SBML-behavioral, constructivist, social cognitive-and their contributions to SBML thought and practice. We then discuss how the three learning theory frameworks converge in the course of planning, conducting, and evaluating SBML education programs in the health professions. Convergence of these learning theory frameworks is illustrated by a description of an SBML education and research program in advanced cardiac life support. We conclude with a brief coda.

Bare-Bones Teaching-Learning-Based Optimization

Directory of Open Access Journals (Sweden)

Feng Zou

2014-01-01

Full Text Available Teaching-learning-based optimization (TLBO algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

Lessons learned from IOR steamflooding in a bitumen-light oil heterogeneous reservoir

NARCIS (Netherlands)

Al Mudhafar, W.J.M.; Hosseini Nasab, S.M.

2015-01-01

The Steamflooding was considered in this research to extract the discontinuous bitumen layers that are located at the oil-water contact for the heterogeneous light oil sandstone reservoir of South Rumaila Field. The reservoir heterogeneity and the bitumen layers impede water aquifer approaching into

Heterogeneity in Perceptual Category Learning by High Functioning Children with Autism Spectrum Disorder

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Eduardo eMercado

2015-06-01

Full Text Available Previous research suggests that high functioning children with Autism Spectrum Disorder (ASD sometimes have problems learning categories, but often appear to perform normally in categorization tasks. The deficits that individuals with ASD show when learning categories have been attributed to executive dysfunction, general deficits in implicit learning, atypical cognitive strategies, or abnormal perceptual biases and abilities. Several of these psychological explanations for category learning deficits have been associated with neural abnormalities such as cortical underconnectivity. The present study evaluated how well existing neurally-based theories account for atypical perceptual category learning shown by high functioning children with ASD across multiple category learning tasks involving novel, abstract shapes. Consistent with earlier results, children’s performances revealed two distinct patterns of learning and generalization associated with ASD: one was indistinguishable from performance in typically developing children; the other revealed dramatic impairments. These two patterns were evident regardless of training regimen or stimulus set. Surprisingly, some children with ASD showed both patterns. Simulations of perceptual category learning could account for the two observed patterns in terms of differences in neural plasticity. However, no current psychological or neural theory adequately explains why a child with ASD might show such large fluctuations in category learning ability across training conditions or stimulus sets.

Heterogeneity in perceptual category learning by high functioning children with autism spectrum disorder.

Science.gov (United States)

Mercado, Eduardo; Church, Barbara A; Coutinho, Mariana V C; Dovgopoly, Alexander; Lopata, Christopher J; Toomey, Jennifer A; Thomeer, Marcus L

2015-01-01

Previous research suggests that high functioning (HF) children with autism spectrum disorder (ASD) sometimes have problems learning categories, but often appear to perform normally in categorization tasks. The deficits that individuals with ASD show when learning categories have been attributed to executive dysfunction, general deficits in implicit learning, atypical cognitive strategies, or abnormal perceptual biases and abilities. Several of these psychological explanations for category learning deficits have been associated with neural abnormalities such as cortical underconnectivity. The present study evaluated how well existing neurally based theories account for atypical perceptual category learning shown by HF children with ASD across multiple category learning tasks involving novel, abstract shapes. Consistent with earlier results, children's performances revealed two distinct patterns of learning and generalization associated with ASD: one was indistinguishable from performance in typically developing children; the other revealed dramatic impairments. These two patterns were evident regardless of training regimen or stimulus set. Surprisingly, some children with ASD showed both patterns. Simulations of perceptual category learning could account for the two observed patterns in terms of differences in neural plasticity. However, no current psychological or neural theory adequately explains why a child with ASD might show such large fluctuations in category learning ability across training conditions or stimulus sets.

Model-Based Learning Environment Based on The Concept IPS School-Based Management

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Hamid Darmadi

2017-03-01

Full Text Available The results showed: (1 learning model IPS-oriented environment can grow and not you love the cultural values of the area as a basis for the development of national culture, (2 community participation, and the role of government in implementing learning model of IPS-based environment provides a positive impact for the improvement of management school resources, (3 learning model IPS-based environment effectively creating a way of life together peacefully, increase the intensity of togetherness and mutual respect (4 learning model IPS-based environment can improve student learning outcomes, (5 there are differences in the expression of attitudes and results learning among students who are located in the area of conflict with students who are outside the area of conflict (6 analysis of the scale of attitudes among school students da SMA result rewards high school students to the values of unity and nation, respect for diversity and peaceful coexistence, It is recommended that the Department of Education authority as an institution of Trustees and the development of social and cultural values in the province can apply IPS learning model based environments.

Confabulation Based Real-time Anomaly Detection for Wide-area Surveillance Using Heterogeneous High Performance Computing Architecture

Science.gov (United States)

2015-06-01

CONFABULATION BASED REAL-TIME ANOMALY DETECTION FOR WIDE-AREA SURVEILLANCE USING HETEROGENEOUS HIGH PERFORMANCE COMPUTING ARCHITECTURE SYRACUSE...DETECTION FOR WIDE-AREA SURVEILLANCE USING HETEROGENEOUS HIGH PERFORMANCE COMPUTING ARCHITECTURE 5a. CONTRACT NUMBER FA8750-12-1-0251 5b. GRANT...processors including graphic processor units (GPUs) and Intel Xeon Phi processors. Experimental results showed significant speedups, which can enable

Integration of heterogeneous features for remote sensing scene classification

Science.gov (United States)

Wang, Xin; Xiong, Xingnan; Ning, Chen; Shi, Aiye; Lv, Guofang

2018-01-01

Scene classification is one of the most important issues in remote sensing (RS) image processing. We find that features from different channels (shape, spectral, texture, etc.), levels (low-level and middle-level), or perspectives (local and global) could provide various properties for RS images, and then propose a heterogeneous feature framework to extract and integrate heterogeneous features with different types for RS scene classification. The proposed method is composed of three modules (1) heterogeneous features extraction, where three heterogeneous feature types, called DS-SURF-LLC, mean-Std-LLC, and MS-CLBP, are calculated, (2) heterogeneous features fusion, where the multiple kernel learning (MKL) is utilized to integrate the heterogeneous features, and (3) an MKL support vector machine classifier for RS scene classification. The proposed method is extensively evaluated on three challenging benchmark datasets (a 6-class dataset, a 12-class dataset, and a 21-class dataset), and the experimental results show that the proposed method leads to good classification performance. It produces good informative features to describe the RS image scenes. Moreover, the integration of heterogeneous features outperforms some state-of-the-art features on RS scene classification tasks.

Brain-Based Learning and Standards-Based Elementary Science.

Science.gov (United States)

Konecki, Loretta R.; Schiller, Ellen

This paper explains how brain-based learning has become an area of interest to elementary school science teachers, focusing on the possible relationships between, and implications of, research on brain-based learning to the teaching of science education standards. After describing research on the brain, the paper looks at three implications from…

Coordinated SLNR based Precoding in Large-Scale Heterogeneous Networks

KAUST Repository

Boukhedimi, Ikram

2017-03-06

This work focuses on the downlink of large-scale two-tier heterogeneous networks composed of a macro-cell overlaid by micro-cell networks. Our interest is on the design of coordinated beamforming techniques that allow to mitigate the inter-cell interference. Particularly, we consider the case in which the coordinating base stations (BSs) have imperfect knowledge of the channel state information. Under this setting, we propose a regularized SLNR based precoding design in which the regularization factor is used to allow better resilience with respect to the channel estimation errors. Based on tools from random matrix theory, we provide an analytical analysis of the SINR and SLNR performances. These results are then exploited to propose a proper setting of the regularization factor. Simulation results are finally provided in order to validate our findings and to confirm the performance of the proposed precoding scheme.

Coordinated SLNR based Precoding in Large-Scale Heterogeneous Networks

KAUST Repository

Boukhedimi, Ikram; Kammoun, Abla; Alouini, Mohamed-Slim

2017-01-01

This work focuses on the downlink of large-scale two-tier heterogeneous networks composed of a macro-cell overlaid by micro-cell networks. Our interest is on the design of coordinated beamforming techniques that allow to mitigate the inter-cell interference. Particularly, we consider the case in which the coordinating base stations (BSs) have imperfect knowledge of the channel state information. Under this setting, we propose a regularized SLNR based precoding design in which the regularization factor is used to allow better resilience with respect to the channel estimation errors. Based on tools from random matrix theory, we provide an analytical analysis of the SINR and SLNR performances. These results are then exploited to propose a proper setting of the regularization factor. Simulation results are finally provided in order to validate our findings and to confirm the performance of the proposed precoding scheme.

Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code

Directory of Open Access Journals (Sweden)

Guillermo Vigueras

2017-01-01

Full Text Available The current trends in next-generation exascale systems go towards integrating a wide range of specialized (co-processors into traditional supercomputers. Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. However, heterogeneous platforms limit the portability of the applications and increase development complexity due to the programming skills required. Program transformation can help make programming heterogeneous systems easier by defining a step-wise transformation process that translates a given initial code into a semantically equivalent final code, but adapted to a specific platform. Program transformation systems require the definition of efficient transformation strategies to tackle the combinatorial problem that emerges due to the large set of transformations applicable at each step of the process. In this paper we propose a machine learning-based approach to learn heuristics to define program transformation strategies. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of this approach.

Characterizing heterogeneous cellular responses to perturbations.

Science.gov (United States)

Slack, Michael D; Martinez, Elisabeth D; Wu, Lani F; Altschuler, Steven J

2008-12-09

Cellular populations have been widely observed to respond heterogeneously to perturbation. However, interpreting the observed heterogeneity is an extremely challenging problem because of the complexity of possible cellular phenotypes, the large dimension of potential perturbations, and the lack of methods for separating meaningful biological information from noise. Here, we develop an image-based approach to characterize cellular phenotypes based on patterns of signaling marker colocalization. Heterogeneous cellular populations are characterized as mixtures of phenotypically distinct subpopulations, and responses to perturbations are summarized succinctly as probabilistic redistributions of these mixtures. We apply our method to characterize the heterogeneous responses of cancer cells to a panel of drugs. We find that cells treated with drugs of (dis-)similar mechanism exhibit (dis-)similar patterns of heterogeneity. Despite the observed phenotypic diversity of cells observed within our data, low-complexity models of heterogeneity were sufficient to distinguish most classes of drug mechanism. Our approach offers a computational framework for assessing the complexity of cellular heterogeneity, investigating the degree to which perturbations induce redistributions of a limited, but nontrivial, repertoire of underlying states and revealing functional significance contained within distinct patterns of heterogeneous responses.

Personalised learning object based on multi-agent model and learners’ learning styles

Directory of Open Access Journals (Sweden)

Noppamas Pukkhem

2011-09-01

Full Text Available A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i non-personalised, ii preferred feature-based, and iii neighbour-based collaborative filtering. The analysis of preference error (PE was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.

A numerical homogenization method for heterogeneous, anisotropic elastic media based on multiscale theory

KAUST Repository

Gao, Kai

2015-06-05

The development of reliable methods for upscaling fine-scale models of elastic media has long been an important topic for rock physics and applied seismology. Several effective medium theories have been developed to provide elastic parameters for materials such as finely layered media or randomly oriented or aligned fractures. In such cases, the analytic solutions for upscaled properties can be used for accurate prediction of wave propagation. However, such theories cannot be applied directly to homogenize elastic media with more complex, arbitrary spatial heterogeneity. Therefore, we have proposed a numerical homogenization algorithm based on multiscale finite-element methods for simulating elastic wave propagation in heterogeneous, anisotropic elastic media. Specifically, our method used multiscale basis functions obtained from a local linear elasticity problem with appropriately defined boundary conditions. Homogenized, effective medium parameters were then computed using these basis functions, and the approach applied a numerical discretization that was similar to the rotated staggered-grid finite-difference scheme. Comparisons of the results from our method and from conventional, analytical approaches for finely layered media showed that the homogenization reliably estimated elastic parameters for this simple geometry. Additional tests examined anisotropic models with arbitrary spatial heterogeneity in which the average size of the heterogeneities ranged from several centimeters to several meters, and the ratio between the dominant wavelength and the average size of the arbitrary heterogeneities ranged from 10 to 100. Comparisons to finite-difference simulations proved that the numerical homogenization was equally accurate for these complex cases.

Digital game-based learning in secondary education

NARCIS (Netherlands)

Huizenga, J.C.

2017-01-01

This PhD thesis presents research on digital game-based learning in secondary education. The main research question is: How do digital games contribute to learning, engagement and motivation to learn? The thesis contains seven chapters. Chapter one is an introduction to digital game-based learning

Learning-based diagnosis and repair

NARCIS (Netherlands)

Roos, Nico

2017-01-01

This paper proposes a new form of diagnosis and repair based on reinforcement learning. Self-interested agents learn locally which agents may provide a low quality of service for a task. The correctness of learned assessments of other agents is proved under conditions on exploration versus

Music Learning Based on Computer Software

Directory of Open Access Journals (Sweden)

Baihui Yan

2017-12-01

Full Text Available In order to better develop and improve students’ music learning, the authors proposed the method of music learning based on computer software. It is still a new field to use computer music software to assist teaching. Hereby, we conducted an in-depth analysis on the computer-enabled music learning and the music learning status in secondary schools, obtaining the specific analytical data. Survey data shows that students have many cognitive problems in the current music classroom, and yet teachers have not found a reasonable countermeasure to them. Against this background, the introduction of computer music software to music learning is a new trial that can not only cultivate the students’ initiatives of music learning, but also enhance their abilities to learn music. Therefore, it is concluded that the computer software based music learning is of great significance to improving the current music learning modes and means.

Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement

DEFF Research Database (Denmark)

Manoonpong, Poramate; Kolodziejski, Christoph; Wörgötter, Florentin

2013-01-01

Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about...... associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor–critic reinforcement...... learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control...

A Learning Object Approach To Evidence based learning

OpenAIRE

Zabin Visram; Bruce Elson; Patricia Reynolds

2005-01-01

This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has mea...

Toward Project-based Learning and Team Formation in Open Learning Environments

NARCIS (Netherlands)

Spoelstra, Howard; Van Rosmalen, Peter; Sloep, Peter

2014-01-01

Open Learning Environments, MOOCs, as well as Social Learning Networks, embody a new approach to learning. Although both emphasise interactive participation, somewhat surprisingly, they do not readily support bond creating and motivating collaborative learning opportunities. Providing project-based

Motion Learning Based on Bayesian Program Learning

Directory of Open Access Journals (Sweden)

Cheng Meng-Zhen

2017-01-01

Full Text Available The concept of virtual human has been highly anticipated since the 1980s. By using computer technology, Human motion simulation could generate authentic visual effect, which could cheat human eyes visually. Bayesian Program Learning train one or few motion data, generate new motion data by decomposing and combining. And the generated motion will be more realistic and natural than the traditional one.In this paper, Motion learning based on Bayesian program learning allows us to quickly generate new motion data, reduce workload, improve work efficiency, reduce the cost of motion capture, and improve the reusability of data.

Concept-Based Learning in Clinical Experiences: Bringing Theory to Clinical Education for Deep Learning.

Science.gov (United States)

Nielsen, Ann

2016-07-01

Concept-based learning is used increasingly in nursing education to support the organization, transfer, and retention of knowledge. Concept-based learning activities (CBLAs) have been used in clinical education to explore key aspects of the patient situation and principles of nursing care, without responsibility for total patient care. The nature of best practices in teaching and the resultant learning are not well understood. The purpose of this multiple-case study research was to explore and describe concept-based learning in the context of clinical education in inpatient settings. Four clinical groups (each a case) were observed while they used CBLAs in the clinical setting. Major findings include that concept-based learning fosters deep learning, connection of theory with practice, and clinical judgment. Strategies used to support learning, major teaching-learning foci, and preconditions for concept-based teaching and learning will be described. Concept-based learning is promising to support integration of theory with practice and clinical judgment through application experiences with patients. [J Nurs Educ. 2016;55(7):365-371.]. Copyright 2016, SLACK Incorporated.

The Effect of Animation in Multimedia Computer-Based Learning and Learning Style to the Learning Results

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Muhammad RUSLI

2017-10-01

Full Text Available The effectiveness of a learning depends on four main elements, they are content, desired learning outcome, instructional method and the delivery media. The integration of those four elements can be manifested into a learning modul which is called multimedia learning or learning by using multimedia. In learning context by using computer-based multimedia, there are two main things that need to be noticed so that the learning process can run effectively: how the content is presented, and what the learner’s chosen way in accepting and processing the information into a meaningful knowledge. First it is related with the way to visualize the content and how people learn. The second one is related with the learning style of the learner. This research aims to investigate the effect of the type of visualization—static vs animated—on a multimedia computer-based learning, and learning styles—visual vs verbal, towards the students’ capability in applying the concepts, procedures, principles of Java programming. Visualization type act as independent variables, and learning styles of the students act as a moderator variable. Moreover, the instructional strategies followed the Component Display Theory of Merril, and the format of presentation of multimedia followed the Seven Principles of Multimedia Learning of Mayer and Moreno. Learning with the multimedia computer-based learning has been done in the classroom. The subject of this research was the student of STMIK-STIKOM Bali in odd semester 2016-2017 which followed the course of Java programming. The Design experiments used multivariate analysis of variance, MANOVA 2 x 2, with a large sample of 138 students in 4 classes. Based on the results of the analysis, it can be concluded that the animation in multimedia interactive learning gave a positive effect in improving students’ learning outcomes, particularly in the applying the concepts, procedures, and principles of Java programming. The

Fuzzy Logic-Based Model That Incorporates Personality Traits for Heterogeneous Pedestrians

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Zhuxin Xue

2017-10-01

Full Text Available Most models designed to simulate pedestrian dynamical behavior are based on the assumption that human decision-making can be described using precise values. This study proposes a new pedestrian model that incorporates fuzzy logic theory into a multi-agent system to address cognitive behavior that introduces uncertainty and imprecision during decision-making. We present a concept of decision preferences to represent the intrinsic control factors of decision-making. To realize the different decision preferences of heterogeneous pedestrians, the Five-Factor (OCEAN personality model is introduced to model the psychological characteristics of individuals. Then, a fuzzy logic-based approach is adopted for mapping the relationships between the personality traits and the decision preferences. Finally, we have developed an application using our model to simulate pedestrian dynamical behavior in several normal or non-panic scenarios, including a single-exit room, a hallway with obstacles, and a narrowing passage. The effectiveness of the proposed model is validated with a user study. The results show that the proposed model can generate more reasonable and heterogeneous behavior in the simulation and indicate that individual personality has a noticeable effect on pedestrian dynamical behavior.

From scientifically based research to evidence based learning

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Rosa Cera

2016-02-01

Full Text Available This essay is a reflection on the peculiarities of the scientifically based research and on the distinctive elements of the EBL (evidence based learning, methodology used in the study on the “Relationship between Metacognition, Self-efficacy and Self-regulation in Learning”. The EBL method, based on the standardization of data, explains how the students’ learning experience can be considered as a set of “data” and can be used to explain how and when the research results can be considered generalizable and transferable to other learning situations. The reflections present in this study have also allowed us to illustrate the impact that its results have had on the micro and macro level of reality. They helped to fill in the gaps concerning the learning/teaching processes, contributed to the enrichment of the scientific literature on this subject and allowed to establish standards through rigorous techniques such as systematic reviews and meta-analysis.

Polymer-based 2D/3D wafer level heterogeneous integration for SSL module

NARCIS (Netherlands)

Yuan, C.; Wei, J.; Ye, H.; Koh, S.; Harianto, S.; Nieuwenhof, M.A. van den; Zhang, G.Q.

2012-01-01

This paper demonstrates a heterogeneous integration of solid state lighting (SSL) module, including light source (LED) and driver/control components. Such integration has been realized by the polymer-based reconfigured wafer level package technologies and such structure has been prototyped and

Constructivism Based Learning: Design and Practice

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Lia Kurniawati

2016-06-01

Full Text Available Abstract One of many problems in the madrasahs is that learning processes less-involve students actively (teacher-centered, thus, it affects to the improvement of learning outcomes and quality of the graduates. The purposes of this study are , firstly, to analyze what type of constructivism learning models, which can be developed to overcome madrasahs’ problems. Secondly, how to design and implement a learning plan based on the developed constructivism models. This research was conducted at Private Islamic Elementary School  (Madrasah Ad-Diyanah Ciputat, South Tangerang. Research method used in this study is descriptive-qualitative research. The results showed that the active learning models based on constructivism are suitable to be developed in the Madarasah, which were the models of Problem Based Learning (PBM, Realistic Learning, Inquiry Learning and Thematic Learning and also how the development of the learning processes from the lesson plans to the learning implementation showed a paradigm shifting from teacher-centered to student-centered. Abstrak Salah satu permasalahan di madrasah-madrasah adalah proses pembelajaran yang kurang melibatkan siswa secara aktif (berpusat pada guru, sehingga hal ini mengakibatkan pada peningkatan hasil belajar dan kualitas lulusan. Tujuan dari penelitian ini adalah, pertama, untuk menganalisis jenis model pembelajaran konstruktivisme apa yang dapat dikembangkan untuk mengatasi permasalahan di madrasah. Ke dua, bagaimana merancang dan melaksanakan rencana pembelajaran berdasarkan model konstruktivisme yang dikembangkan. Penelitian ini dilaksanakan di Sekolah Dasar Swasta (madrasah Ad-Diayanah Ciputat, Tangerang Selatan. Metode penelitian yang digunakan adalah metode deskriptif-kualitatif. Hasil penelitian menunjukkan bahwa model pembelajaran aktif yang berbasis konstruktivisme sesuai untuk dikembangkan di madrasah, yakni model pembelajaran Problem Based Learning (PBL, Pembelajaran Realistis, Pembelajaran

Development Of Entrepreneur Learning Model Based On Problem Based Learning To Increase Competency Independence And Creativity Students Of Industrial Engineering

Directory of Open Access Journals (Sweden)

Leola Dewiyani

2017-10-01

Full Text Available Currently it is undeniable that the competition to get a job is very tight and of course universities have an important role in printing human resources that can compete globally not least with the Department of Industrial Engineering Faculty of Engineering Muhammadiyah University of Jakarta FT UMJ. Problems that occur is based on the analysis obtained from the track record of graduates researchers found that 60 percent of students of Industrial Engineering FT UMJ work not in accordance with the level of education owned so financially their income is still below the standard. This study aims to improve the competence of students of Industrial Engineering Department FT UMJ in entrepreneurship courses especially through the development of Problem Based Learning based learning model. Specific targets of this research were conducted with the aim to identify and analyze the need to implement learning model based on Problem Based Learning Entrepreneurship and to design and develop the model of entrepreneurship based on Problem Based Learning to improve the competence independence and creativity of Industrial Engineering students of FT UMJ in Entrepreneurship course. To achieve the above objectives this research uses research and development R amp D method. The product produced in this research is the detail of learning model of entrepreneurial model based on Problem Based Learning entrepreneurship model based on Problem Based Learning and international journals

in Heterogeneous Media

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Saeed Balouchi

2013-01-01

Full Text Available Fractured reservoirs contain about 85 and 90 percent of oil and gas resources respectively in Iran. A comprehensive study and investigation of fractures as the main factor affecting fluid flow or perhaps barrier seems necessary for reservoir development studies. High degrees of heterogeneity and sparseness of data have incapacitated conventional deterministic methods in fracture network modeling. Recently, simulated annealing (SA has been applied to generate stochastic realizations of spatially correlated fracture networks by assuming that the elastic energy of fractures follows Boltzmann distribution. Although SA honors local variability, the objective function of geometrical fracture modeling is defined for homogeneous conditions. In this study, after the introduction of SA and the derivation of the energy function, a novel technique is presented to adjust the model with highly heterogeneous data for a fractured field from the southwest of Iran. To this end, the regular object-based model is combined with a grid-based technique to cover the heterogeneity of reservoir properties. The original SA algorithm is also modified by being constrained in different directions and weighting the energy function to make it appropriate for heterogeneous conditions. The simulation results of the presented approach are in good agreement with the observed field data.

Generative Adversarial Networks Based Heterogeneous Data Integration and Its Application for Intelligent Power Distribution and Utilization

Directory of Open Access Journals (Sweden)

Yuanpeng Tan

2018-01-01

Full Text Available Heterogeneous characteristics of a big data system for intelligent power distribution and utilization have already become more and more prominent, which brings new challenges for the traditional data analysis technologies and restricts the comprehensive management of distribution network assets. In order to solve the problem that heterogeneous data resources of power distribution systems are difficult to be effectively utilized, a novel generative adversarial networks (GANs based heterogeneous data integration method for intelligent power distribution and utilization is proposed. In the proposed method, GANs theory is introduced to expand the distribution of completed data samples. Then, a so-called peak clustering algorithm is proposed to realize the finite open coverage of the expanded sample space, and repair those incomplete samples to eliminate the heterogeneous characteristics. Finally, in order to realize the integration of the heterogeneous data for intelligent power distribution and utilization, the well-trained discriminator model of GANs is employed to check the restored data samples. The simulation experiments verified the validity and stability of the proposed heterogeneous data integration method, which provides a novel perspective for the further data quality management of power distribution systems.

Features and Characteristics of Problem Based Learning

Science.gov (United States)

Ceker, Eser; Ozdamli, Fezile

2016-01-01

Throughout the years, there appears to be an increase in Problem Based Learning applications in education; and Problem Based Learning related research areas. The main aim of this research is to underline the fundamentals (basic elements) of Problem Based Learning, investigate the dimensions of research approached to PBL oriented areas (with a look…

Service Learning to Promote Brain-Based Learning in Undergraduate Teaching

Science.gov (United States)

Nwokah, Eva E.; Leafblad, Stefanie

2013-01-01

In this study 44 undergraduate students in a language development course participated in service learning with preschool homeless and low-income children as a course requirement. Students completed a survey, questionnaires, reflective journaling, and small-group debriefing sessions. Based on current views on brain-based learning from cortical…

Project-Based Learning in Programmable Logic Controller

Science.gov (United States)

Seke, F. R.; Sumilat, J. M.; Kembuan, D. R. E.; Kewas, J. C.; Muchtar, H.; Ibrahim, N.

2018-02-01

Project-based learning is a learning method that uses project activities as the core of learning and requires student creativity in completing the project. The aims of this study is to investigate the influence of project-based learning methods on students with a high level of creativity in learning the Programmable Logic Controller (PLC). This study used experimental methods with experimental class and control class consisting of 24 students, with 12 students of high creativity and 12 students of low creativity. The application of project-based learning methods into the PLC courses combined with the level of student creativity enables the students to be directly involved in the work of the PLC project which gives them experience in utilizing PLCs for the benefit of the industry. Therefore, it’s concluded that project-based learning method is one of the superior learning methods to apply on highly creative students to PLC courses. This method can be used as an effort to improve student learning outcomes and student creativity as well as to educate prospective teachers to become reliable educators in theory and practice which will be tasked to create qualified human resources candidates in order to meet future industry needs.

Heterogeneous catalysis: on bathroom mirrors and boiling stones

NARCIS (Netherlands)

Philipse, A.P.

2011-01-01

A catalyst is defined as a substance that accelerates a process without undergoing a net change due to that process. Most chemistry students learn about catalysts in the context of chemical reactions, such as the enzymes in biochemistry or the heterogeneous metal catalysts in inorganic chemistry (1,

E-learning: Web-based education.

Science.gov (United States)

Sajeva, Marco

2006-12-01

This review introduces state-of-the-art Web-based education and shows how the e-learning model can be applied to an anaesthesia department using Open Source solutions, as well as lifelong learning programs, which is happening in several European research projects. The definition of the term e-learning is still a work in progress due to the fact that technologies are evolving every day and it is difficult to improve teaching methodologies or to adapt traditional methods to a new or already existing educational model. The European Community is funding several research projects to define the new common market place for tomorrow's educational system; this is leading to new frontiers like virtual Erasmus inter-exchange programs based on e-learning. The first step when adapting a course to e-learning is to re-define the educational/learning model adopted: cooperative learning and tutoring are the two key concepts. This means that traditional lecture notes, books and exercises are no longer effective; teaching files must use rich multimedia content and have to be developed using the new media. This can lead to several pitfalls that can be avoided with an accurate design phase.

ENVIRONMENTAL LEARNING APPROACHES IN IMPROVING LEARNING OUTCOMES IN ACID-BASE SUBJECT

Directory of Open Access Journals (Sweden)

Rachmat Sahputra

2016-03-01

Full Text Available Learning in the understanding of acid-base chemistry in schools needs to be improved so research to determine differences in learning outcomes between students taught using environmental approaches and methods lectures in class XI SMA on acid-base subject needs to be done. In this study, using a quasi-experimental method using a data collection tool achievement test essay form. The test statistic results of the post-test learning has been obtained Asymp value. Sig (2-tailed 0,026 that showed the differences between students' learning outcomes with a control experimental class with effect size of 0.63 or much influence difference with the percentage 23.57% which indicated that the learning environment approach can improve learning outcomes of high school students.

Consumer Product Perceptions and Salmon Consumption Frequency: The Role of Heterogeneity Based on Food Lifestyle Segments

OpenAIRE

Yuko Onozaka; Håvard Hansen; Arne Sørvig

2014-01-01

Seafood consumers are vastly heterogeneous in terms of their knowledge, confidence, and perceptions about seafood. This article examines the relationship between consumer perceptions (healthiness, value for money, and convenience) and salmon consumption frequencies while modeling unobserved consumer heterogeneity by segmenting consumers based on their food-related lifestyle. We employ latent class analysis (LCA) that embeds the structural equation modeling (SEM) to ensure the latent nature of...

Learning and Motivational Processes When Students Design Curriculum-Based Digital Learning Games

DEFF Research Database (Denmark)

Weitze, Charlotte Lærke

2015-01-01

This design-based research (DBR) project has developed an overall gamified learning design (big Game) to facilitate the learning process for adult students by inviting them to be their own learning designers through designing digital learning games (small games) in cross-disciplinary subject...... matters. The DBR project has investigated and experimented with which elements, methods, and processes are important when aiming at creating a cognitive complex (Anderson and Krathwohl, 2001) and motivating learning process within a reusable game-based learning design. This project took place in a co......, or programming provide a rich context for learning, since the construction of artefacts, in this case learning games, enables reflection and new ways of thinking. The students learned from reflection and interaction with the tools alone as well as in collaboration with peers. After analysing the students...

Students' learning processes during school-based learning and workplace learning in vocational education : a review

NARCIS (Netherlands)

Dr. Harmen Schaap; Dr. Liesbeth Baartman; Prof.Dr. Elly de Bruijn

2012-01-01

This article reviews 24 articles in order to get a structured view on student's learning processes when dealing with a combination of school-based learning and workplace learning in vocational education. It focuses on six main themes: students' expertise development, students' learning styles,

Adding Social Elements to Game-Based Learning

OpenAIRE

Chien-Hung Lai; Yu-Chang Lin; Bin-Shyan Jong; Yen-Teh Hsia

2014-01-01

Game-based learning is to present the instruction by games in learning, with the main purpose of triggering learners’ motives instead of instructing the courses. Thus, increasing learning motive by game-based learning becomes a common instructional strategy to enhance learning achievement. However, it is not easy to design interesting games combined with courses. In 2011, Echeverria proposed a design to combine characteristics of games with elements of courses by matching the virtual scenario...

Student Perceptions of Team-based Learning vs Traditional Lecture-based Learning.

Science.gov (United States)

Frame, Tracy R; Cailor, Stephanie M; Gryka, Rebecca J; Chen, Aleda M; Kiersma, Mary E; Sheppard, Lorin

2015-05-25

To evaluate pharmacy student perceptions of team-based learning (TBL) vs traditional lecture-based learning formats. First professional year pharmacy students (N=111) at two universities used TBL in different courses during different semesters (fall vs spring). Students completed a 22-item team perceptions instrument before and after the fall semester. A 14-item teaching style preference instrument was completed at the end of the spring semester. Data were analyzed using Wilcoxon signed rank test and Mann-Whitney U test. Students who experienced TBL in the fall and went back to traditional format in the spring reported improved perceptions of teams and preferred TBL format over a traditional format more than students who experienced a traditional format followed by TBL. Students at both universities agreed that the TBL format assists with critical-thinking, problem-solving, and examination preparation. Students also agreed that teams should consist of individuals with different personalities and learning styles. When building teams, faculty members should consider ways to diversify teams by considering different views, perspectives, and strengths. Offering TBL early in the curriculum prior to traditional lecture-based formats is better received by students, as evidenced by anecdotal reports from students possibly because it allows students time to realize the benefits and assist them in building teamwork-related skills.

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

Science.gov (United States)

Balasubramanian, R.

2007-12-01

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

[E-learning and problem based learning integration in cardiology education].

Science.gov (United States)

Gürpinar, Erol; Zayim, Neşe; Başarici, Ibrahim; Gündüz, Filiz; Asar, Mevlüt; Oğuz, Nurettin

2009-06-01

The aim of this study was to determine students' satisfaction with an e-learning environment which is developed to support classical problem-based learning (PBL) in medical education and its effect on academic achievement. In this cross-sectional study, students were provided with a web-based learning environment including learning materials related to objectives of the subject of PBL module, which could be used during independent study period. The study group comprised of all of the second year students (164 students) of Akdeniz University, Medical Faculty, during 2007-2008 education period. In order to gather data about students' satisfaction with learning environment, a questionnaire was administered to the students. Comparison of students' academic achievement was based on their performance score in PBL exam. Statistical analyses were performed using unpaired t test and Mann Whitney U test. Findings indicated that 72.6% of the students used e-learning practice. There is no statistically significant difference between mean PBL performance scores of users and non-users of e-learning practice (103.58 vs. 100.88) (t=-0.998, p=0.320). It is found that frequent users of e-learning application had statistically significant higher scores than non-frequent users (106.28 vs. 100.59) (t=-2.373, p=0.01). In addition, 72.6% of the students declared they were satisfied with the application. Our study demonstrated that the most of the students use e-learning application and are satisfied with it. In addition, it is observed that e-learning application positively affects the academic achievement of the students. This study gains special importance by providing contribution to limited literature in the area of instructional technology in PBL and Cardiology teaching.

Problem-Based Learning and Learning Approach: Is There a Relationship?

Science.gov (United States)

Groves, Michele

2005-01-01

Aim: To assess the influence of a graduate-entry PBL (problem-based learning) curriculum on individual learning style; and to investigate the relationship between learning style, academic achievement and clinical reasoning skill. Method: Subjects were first-year medical students completed the Study Process Questionnaire at the commencement, and…

Combining heterogeneous features for colonic polyp detection in CTC based on semi-definite programming

Science.gov (United States)

Wang, Shijun; Yao, Jianhua; Petrick, Nicholas A.; Summers, Ronald M.

2009-02-01

Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible combination for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features, called histogram of curvature features, are rotation, translation and scale invariant and can be treated as complementing our existing feature set. Then in order to make full use of the traditional features (defined as group A) and the new features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to identify an optimized classification kernel based on the combined set of features. We did leave-one-patient-out test on a CTC dataset which contained scans from 50 patients (with 90 6-9mm polyp detections). Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per patient rate of 7, the sensitivity on 6-9mm polyps using the combined features improved from 0.78 (Group A) and 0.73 (Group B) to 0.82 (p<=0.01).

Problem-Based Learning: Student Engagement, Learning and Contextualized Problem-Solving. Occasional Paper

Science.gov (United States)

Mossuto, Mark

2009-01-01

The adoption of problem-based learning as a teaching method in the advertising and public relations programs offered by the Business TAFE (Technical and Further Education) School at RMIT University is explored in this paper. The effect of problem-based learning on student engagement, student learning and contextualised problem-solving was…

Mining Learning Social Networks for Cooperative Learning with Appropriate Learning Partners in a Problem-Based Learning Environment

Science.gov (United States)

Chen, Chih-Ming; Chang, Chia-Cheng

2014-01-01

Many studies have identified web-based cooperative learning as an increasingly popular educational paradigm with potential to increase learner satisfaction and interactions. However, peer-to-peer interaction often suffers barriers owing to a failure to explore useful social interaction information in web-based cooperative learning environments.…

Learning and Motivational Processes When Students Design Curriculum‐Based Digital Learning Games

DEFF Research Database (Denmark)

Weitze, Charlotte Lærke

2016-01-01

This design‐based research (DBR) project has developed an overall gamified learning design (big Game) to facilitate the learning process for adult students by inviting them to be their own learning designers through designing digital learning games (small games) in cross‐disciplinary subject...... matters. The DBR project has investigated and experimented with which elements, methods, and processes are important when aiming at creating a cognitive complex (Anderson and Krathwohl, 2001) and motivating learning process within a reusable game‐based learning design. This project took place in a co......, or programming provide a rich context for learning, since the construction of artefacts, in this case learning games, enables reflection and new ways of thinking. The students learned from reflection and interaction with the tools alone as well as in collaboration with peers. After analysing the students...

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing

Science.gov (United States)

Fang, Ye; Ding, Yun; Feinstein, Wei P.; Koppelman, David M.; Moreno, Juana; Jarrell, Mark; Ramanujam, J.; Brylinski, Michal

2016-01-01

Computational modeling of drug binding to proteins is an integral component of direct drug design. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Consequently, highly optimized docking codes are of paramount importance for the effectiveness of virtual screening methods. In this communication, we describe the implementation, tuning and performance characteristics of GeauxDock, a recently developed molecular docking program. GeauxDock is built upon the Monte Carlo algorithm and features a novel scoring function combining physics-based energy terms with statistical and knowledge-based potentials. Developed specifically for heterogeneous computing platforms, the current version of GeauxDock can be deployed on modern, multi-core Central Processing Units (CPUs) as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU). First, we carried out a thorough performance tuning of the high-level framework and the docking kernel to produce a fast serial code, which was then ported to shared-memory multi-core CPUs yielding a near-ideal scaling. Further, using Xeon Phi gives 1.9× performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5×. On that account, GeauxDock can take advantage of modern heterogeneous architectures to considerably accelerate structure-based virtual screening applications. GeauxDock is open-sourced and publicly available at www.brylinski.org/geauxdock and https://figshare.com/articles/geauxdock_tar_gz/3205249. PMID:27420300

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing.

Directory of Open Access Journals (Sweden)

Ye Fang

Full Text Available Computational modeling of drug binding to proteins is an integral component of direct drug design. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Consequently, highly optimized docking codes are of paramount importance for the effectiveness of virtual screening methods. In this communication, we describe the implementation, tuning and performance characteristics of GeauxDock, a recently developed molecular docking program. GeauxDock is built upon the Monte Carlo algorithm and features a novel scoring function combining physics-based energy terms with statistical and knowledge-based potentials. Developed specifically for heterogeneous computing platforms, the current version of GeauxDock can be deployed on modern, multi-core Central Processing Units (CPUs as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU. First, we carried out a thorough performance tuning of the high-level framework and the docking kernel to produce a fast serial code, which was then ported to shared-memory multi-core CPUs yielding a near-ideal scaling. Further, using Xeon Phi gives 1.9× performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5×. On that account, GeauxDock can take advantage of modern heterogeneous architectures to considerably accelerate structure-based virtual screening applications. GeauxDock is open-sourced and publicly available at www.brylinski.org/geauxdock and https://figshare.com/articles/geauxdock_tar_gz/3205249.

Neuromodulatory Adaptive Combination of Correlation-based Learning in Cerebellum and Reward-based Learning in Basal Ganglia for Goal-directed Behavior Control

DEFF Research Database (Denmark)

Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational...... and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role...... in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We...

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

Science.gov (United States)

Acar Sesen, Burcin; Tarhan, Leman

2011-07-01

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

ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance.

Science.gov (United States)

Soekadar, Surjo R; Witkowski, Matthias; Mellinger, Jürgen; Ramos, Ander; Birbaumer, Niels; Cohen, Leonardo G

2011-10-01

Event-related desynchronization (ERD) of sensori-motor rhythms (SMR) can be used for online brain-machine interface (BMI) control, but yields challenges related to the stability of ERD and feedback strategy to optimize BMI learning.Here, we compared two approaches to this challenge in 20 right-handed healthy subjects (HS, five sessions each, S1-S5) and four stroke patients (SP, 15 sessions each, S1-S15). ERD was recorded from a 275-sensor MEG system. During daily training,motor imagery-induced ERD led to visual and proprioceptive feedback delivered through an orthotic device attached to the subjects' hand and fingers. Group A trained with a heterogeneous reference value (RV) for ERD detection with binary feedback and Group B with a homogenous RV and graded feedback (10 HS and 2 SP in each group). HS in Group B showed better BMI performance than Group A (p learning was significantly better (p learning relative to use of a heterogeneous RV and binary feedback.

Domesticating Digital Game-based Learning

Directory of Open Access Journals (Sweden)

Helga Dís Sigurdardottir

2016-07-01

Full Text Available This paper analyses the use of digital game-based learning (DGBL in schools in Norway. It investigates the types of games used in Norwegian schools and how pupils experience that practice. Digital game-based learning is being widely employed throughout Norway as a result of the increased focus on digital skills in Norwegian education. This paper analyses that development by way of focus group interviews with a total of sixty-four pupils at four schools. Drawing upon domestication and actor-network theory, the paper provides a novel approach to the study of DGBL. The broad empirical investigation into DGBL practices furthermore provides a contribution to scholarly literature on the subject. A noteworthy finding of this study is the diversity of games employed in schools—around 30 different titles— indicating that the choice of games lies at the discretion of individual teachers. Findings from this research show that the domestication of digital game-based learning occurs through the construction of complex game-based learning assemblages. This includes the classroom and home as gaming sites, group work and individual assignments as practices, and PCs and iPads as platforms.

Web-based Cooperative Learning in College Chemistry Teaching

Directory of Open Access Journals (Sweden)

Bin Jiang

2014-03-01

Full Text Available With the coming of information era, information process depend on internet and multi-media technology in education becomes the new approach of present teaching model reform. Web-based cooperative learning is becoming a popular learning approach with the rapid development of web technology. The paper aims to how to carry out the teaching strategy of web-based cooperative learning and applied in the foundation chemistry teaching.It was shown that with the support of modern web-based teaching environment, students' cooperative learning capacity and overall competence can be better improved and the problems of interaction in large foundation chemistry classes can be solved. Web-based cooperative learning can improve learning performance of students, what's more Web-based cooperative learning provides students with cooperative skills, communication skills, creativity, critical thinking skills and skills in information technology application.

Learning material recommendation based on case-based reasoning similarity scores

Science.gov (United States)

Masood, Mona; Mokmin, Nur Azlina Mohamed

2017-10-01

A personalized learning material recommendation is important in any Intelligent Tutoring System (ITS). Case-based Reasoning (CBR) is an Artificial Intelligent Algorithm that has been widely used in the development of ITS applications. This study has developed an ITS application that applied the CBR algorithm in the development process. The application has the ability to recommend the most suitable learning material to the specific student based on information in the student profile. In order to test the ability of the application in recommending learning material, two versions of the application were created. The first version displayed the most suitable learning material and the second version displayed the least preferable learning material. The results show the application has successfully assigned the students to the most suitable learning material.

Design of learner-centred constructivism based learning process

OpenAIRE

Schreurs, Jeanne; Al-Huneidi, Ahmad

2012-01-01

A Learner-centered learning is constructivism based and Competence directed. We define general competencies, domain competencies and specific course competencies. Constructivism based learning activities are based on constructivism theory. For each course module the intended learning level will be defined. A model is built for the design of a learner centered constructivism based and competency directed learning process. The application of it in two courses are presented. Constructivism ba...

3D Game-Based Learning System for Improving Learning Achievement in Software Engineering Curriculum

Science.gov (United States)

Su,Chung-Ho; Cheng, Ching-Hsue

2013-01-01

The advancement of game-based learning has encouraged many related studies, such that students could better learn curriculum by 3-dimension virtual reality. To enhance software engineering learning, this paper develops a 3D game-based learning system to assist teaching and assess the students' motivation, satisfaction and learning achievement. A…

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

Maintaining collaborative, democratic and dialogue-based learning processes in virtual and game-based learning environments

DEFF Research Database (Denmark)

Gyldendahl Jensen, Camilla; Sorensen, Elsebeth Korsgaard

2017-01-01

The incorporation and use of virtual learning platforms, including computer games, in the education sector, challenge these years the complexity of the learning environment regarding maintaining collaborative, democratic and dialogue-based learning processes that support a high degree of reflection....... When virtual learning platforms are used in an educational context, a fundamental paradox appears as the student needs an active and practice-oriented participation identity to learn while at the same time needing to learn to acquire a participation identity. This identity is raised and trained...... by being a continuous part of a community that recalls the scenarios of reality. It is therefore crucial that the learning environment reflects the reality of which the students' professionalism is unfolded. Learning is, therefore, something more and not just the acquisition of knowledge and past actions...

[Discovery-based teaching and learning strategies in health: problematization and problem-based learning].

Science.gov (United States)

Cyrino, Eliana Goldfarb; Toralles-Pereira, Maria Lúcia

2004-01-01

Considering the changes in teaching in the health field and the demand for new ways of dealing with knowledge in higher learning, the article discusses two innovative methodological approaches: problem-based learning (PBL) and problematization. Describing the two methods' theoretical roots, the article attempts to identify their main foundations. As distinct proposals, both contribute to a review of the teaching and learning process: problematization, focused on knowledge construction in the context of the formation of a critical awareness; PBL, focused on cognitive aspects in the construction of concepts and appropriation of basic mechanisms in science. Both problematization and PBL lead to breaks with the traditional way of teaching and learning, stimulating participatory management by actors in the experience and reorganization of the relationship between theory and practice. The critique of each proposal's possibilities and limits using the analysis of their theoretical and methodological foundations leads us to conclude that pedagogical experiences based on PBL and/or problematization can represent an innovative trend in the context of health education, fostering breaks and more sweeping changes.

Cancer heterogeneity and imaging.

Science.gov (United States)

O'Connor, James P B

2017-04-01

There is interest in identifying and quantifying tumor heterogeneity at the genomic, tissue pathology and clinical imaging scales, as this may help better understand tumor biology and may yield useful biomarkers for guiding therapy-based decision making. This review focuses on the role and value of using x-ray, CT, MRI and PET based imaging methods that identify, measure and map tumor heterogeneity. In particular we highlight the potential value of these techniques and the key challenges required to validate and qualify these biomarkers for clinical use. Copyright © 2016. Published by Elsevier Ltd.

Fuzzy-logic based learning style prediction in e-learning using web ...

Indian Academy of Sciences (India)

tion, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in .... learning in safe and supportive environment ... working of the proposed Fuzzy-logic based learning style prediction in e-learning. Section 4.

Kinespell: Kinesthetic Learning Activity and Assessment in a Digital Game-Based Learning Environment

Science.gov (United States)

Cariaga, Ada Angeli; Salvador, Jay Andrae; Solamo, Ma. Rowena; Feria, Rommel

Various approaches in learning are commonly classified into visual, auditory and kinesthetic (VAK) learning styles. One way of addressing the VAK learning styles is through game-based learning which motivates learners pursue knowledge holistically. The paper presents Kinespell, an unconventional method of learning through digital game-based learning. Kinespell is geared towards enhancing not only the learner’s spelling abilities but also the motor skills through utilizing wireless controllers. It monitors player’s performance through integrated assessment scheme. Results show that Kinespell may accommodate the VAK learning styles and is a promising alternative to established methods in learning and assessing students’ performance in Spelling.

Gender-related model for mobile-based learning

Science.gov (United States)

Simanjuntak, R. R.; Dewi, U. P.; Rifai, I.

2018-03-01

The study investigates gender influence on mobile-based learning. This case study of university students in Jakarta involved 235 students (128 male, 97 female). Results of this qualitative study showed 96% preference for mobile-based learning. A further 94% showed the needs for collaboration and authenticity for 92%. Hofstede’s cultural dimensions were used to identify the gender aspects of MALL. Preference for Masculinity (65%) was showed rather than Femininity (35%), even among the female respondents (70% of the population). Professions and professionalism received strongest preference (70%) while Individuality and Collectivism had equal preferences among students. Both female and male respondents requested Indulgence (84%) for mobile-based learning with more male respondents opted for Indulgence. The study provided a model for this gender sensitive mobile-based learning. Implications of implementing mobile-based learning as an ideal alternative for well-accommodated education are is also discussed.

Comparison of technology-based cooperative learning with technology-based individual learning in enhancing fundamental nursing proficiency.

Science.gov (United States)

Lin, Zu-Chun

2013-05-01

The aim of nursing education is to prepare students with critical thinking, high interests in profession and high proficiency in patient care. Cooperative learning promotes team work and encourages knowledge building upon discussion. It has been viewed as one of the most powerful learning methods. Technology has been considered an influential tool in teaching and learning. It assists students in gathering more information to solve the problems and master skills better. The purpose of this study was to compare the effect of technology-based cooperative learning with technology-based individual learning in nursing students' critical thinking in catheterization knowledge gaining, error discovering, skill acquisitions, and overall scores. This study used a pretest-posttest experimental design. Ninety-eight students were assigned randomly to one of two groups. Questionnaires and tests were collected at baseline and after completion of intervention. The results of this study showed that there was no significant difference in related catheterization skill performance. However, the remaining variables differed greatly between the two groups. CONCLUSIONS AND APPLICATIONS: This study's findings guide the researchers and instructors to use technology-based cooperative learning more appropriately. Future research should address the design of the course module and the availability of mobile devices to reach student-centered and learn on the move goals. Copyright © 2011 Elsevier Ltd. All rights reserved.

Research and Design of Dynamic Migration Access Control Technology Based on Heterogeneous Network

Directory of Open Access Journals (Sweden)

Wang Feng

2017-01-01

Full Text Available With the continuous development of wireless networks, the amount of privacy services in heterogeneous mobile networks is increasing, such as information storage, user access, and so on. Access control security issues for heterogeneous mobile radio network, this paper proposes a dynamic migration access control technology based on heterogeneous network. Through the system architecture of the mutual trust system, we can understand the real-time mobile node failure or abnormal state. To make the service can be terminated for the node. And adopt the 802.1X authentication way to improve the security of the system. Finally, it by combining the actual running test data, the trust update algorithm of the system is optimized to reduce the actual security threats in the environment. Experiments show that the system’s anti-attack, the success rate of access, bit error rate is in line with the expected results. This system can effectively reduce the system authentication information is illegally obtained after the network security protection mechanism failure and reduce the risk of user data leakage.

A least-effort principle based model for heterogeneous pedestrian flow considering overtaking behavior

Science.gov (United States)

Liu, Chi; Ye, Rui; Lian, Liping; Song, Weiguo; Zhang, Jun; Lo, Siuming

2018-05-01

In the context of global aging, how to design traffic facilities for a population with a different age composition is of high importance. For this purpose, we propose a model based on the least effort principle to simulate heterogeneous pedestrian flow. In the model, the pedestrian is represented by a three-disc shaped agent. We add a new parameter to realize pedestrians' preference to avoid changing their direction of movement too quickly. The model is validated with numerous experimental data on unidirectional pedestrian flow. In addition, we investigate the influence of corridor width and velocity distribution of crowds on unidirectional heterogeneous pedestrian flow. The simulation results reflect that widening corridors could increase the specific flow for the crowd composed of two kinds of pedestrians with significantly different free velocities. Moreover, compared with a unified crowd, the crowd composed of pedestrians with great mobility differences requires a wider corridor to attain the same traffic efficiency. This study could be beneficial in providing a better understanding of heterogeneous pedestrian flow, and quantified outcomes could be applied in traffic facility design.

Inquiry based learning: a student centered learning to develop mathematical habits of mind

Science.gov (United States)

Handayani, A. D.; Herman, T.; Fatimah, S.; Setyowidodo, I.; Katminingsih, Y.

2018-05-01

Inquiry based learning is learning that based on understanding constructivist mathematics learning. Learning based on constructivism is the Student centered learning. In constructivism, students are trained and guided to be able to construct their own knowledge on the basis of the initial knowledge that they have before. This paper explained that inquiry based learning can be used to developing student’s Mathematical habits of mind. There are sixteen criteria Mathematical Habits of mind, among which are diligent, able to manage time well, have metacognition ability, meticulous, etc. This research method is qualitative descriptive. The result of this research is that the instruments that have been developed to measure mathematical habits of mind are validated by the expert. The conclusion is the instrument of mathematical habits of mind are valid and it can be used to measure student’s mathematical habits of mind.

Investigating the Learning-Theory Foundations of Game-Based Learning: A Meta-Analysis

Science.gov (United States)

Wu, W-H.; Hsiao, H-C.; Wu, P-L.; Lin, C-H.; Huang, S-H.

2012-01-01

Past studies on the issue of learning-theory foundations in game-based learning stressed the importance of establishing learning-theory foundation and provided an exploratory examination of established learning theories. However, we found research seldom addressed the development of the use or failure to use learning-theory foundations and…

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).

Game-Based Life-Long Learning

NARCIS (Netherlands)

Kelle, Sebastian; Sigurðarson, Steinn; Westera, Wim; Specht, Marcus

2010-01-01

Kelle, S., Sigurðarson, S., Westera, W., & Specht, M. (2011). Game-Based Life-Long Learning. In G. D. Magoulas (Ed.), E-Infrastructures and Technologies for Lifelong Learning: Next Generation Environments (pp. 337-349). Hershey, PA: IGI Global.

The Design and Analysis of Learning Effects for a Game-based Learning System

OpenAIRE

Wernhuar Tarng; Weichian Tsai

2010-01-01

The major purpose of this study is to use network and multimedia technologies to build a game-based learning system for junior high school students to apply in learning “World Geography" through the “role-playing" game approaches. This study first investigated the motivation and habits of junior high school students to use the Internet and online games, and then designed a game-based learning system according to situated and game-based learning theories. A teaching experiment was conducted to...

Digital case-based learning system in school.

Science.gov (United States)

Gu, Peipei; Guo, Jiayang

2017-01-01

With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework.

Digital case-based learning system in school.

Directory of Open Access Journals (Sweden)

Peipei Gu

Full Text Available With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework.

PENGGUNAAN MODEL PROBLEM BASED LEARNING BERBANTUAN E-LEARNING TERHADAP KEMANDIRIAN BELAJAR MAHASISWA

Directory of Open Access Journals (Sweden)

Jusep Saputra

2017-11-01

Full Text Available Self-regulated learning of learners can be achieved, if in the process of learning mathematics provides an open opportunity for students to learn independently. This research is a mixed method type embedded design, which aims to do studies focused on the use of the Problem Based Learning (PBL model assisted e-learning to student self-regulated learning. Sample selection is done on the purposive sampling and was taken 2 class contracting courses of school math III. Class A numbered 50 members, 24 the superior group and 26 the low group, given the treatment with PBL models assisted e-learning and class B numbered 50, 27 the superior group and 23 the low group, with expository. Instruments used in this research is self-regulated learning questionnaire with Likert scale. Based on data analysis we concluded that (1 Self-regulated learning of superior and low student who obtains aided PBL models assisted e-learning is better than self-regulated learning of superior and low superior students who obtain expository.

The Credentials of Brain-Based Learning

Science.gov (United States)

Davis, Andrew

2004-01-01

This paper discusses the current fashion for brain-based learning, in which value-laden claims about learning are grounded in neurophysiology. It argues that brain science cannot have the authority about learning that some seek to give it. It goes on to discuss whether the claim that brain science is relevant to learning involves a category…

Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.

Science.gov (United States)

Dinov, Ivo D; Heavner, Ben; Tang, Ming; Glusman, Gustavo; Chard, Kyle; Darcy, Mike; Madduri, Ravi; Pa, Judy; Spino, Cathie; Kesselman, Carl; Foster, Ian; Deutsch, Eric W; Price, Nathan D; Van Horn, John D; Ames, Joseph; Clark, Kristi; Hood, Leroy; Hampstead, Benjamin M; Dauer, William; Toga, Arthur W

2016-01-01

A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches

Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.

Directory of Open Access Journals (Sweden)

Ivo D Dinov

Full Text Available A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI. The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data.Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i introduce methods for rebalancing imbalanced cohorts, (ii utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based

Problem based Learning versus Design Thinking in Team based Project work

DEFF Research Database (Denmark)

Denise J. Stokholm, Marianne

2014-01-01

project based learning issues, which has caused a need to describe and compare the two models; in specific the understandings, approaches and organization of learning in project work. The PBL model viewing the process as 3 separate project stages including; problem analysis, problem solving and project......All educations at Aalborg University has since 1974 been rooted in Problem Based Learning (PBL). In 1999 a new education in Industrial design was set up, introducing Design Based Learning (DBL). Even though the two approaches have a lot in common they also hold different understandings of core...... report, with focus on problem solving through analysis. Design Based Learning viewing the process as series of integrated design spaces including; alignment, research, mission, vision, concept, product and process report, with focus on innovative ideation though integration. There is a need of renewing...

Improving self-regulated learning junior high school students through computer-based learning

Science.gov (United States)

Nurjanah; Dahlan, J. A.

2018-05-01

This study is back grounded by the importance of self-regulated learning as an affective aspect that determines the success of students in learning mathematics. The purpose of this research is to see how the improvement of junior high school students' self-regulated learning through computer based learning is reviewed in whole and school level. This research used a quasi-experimental research method. This is because individual sample subjects are not randomly selected. The research design used is Pretest-and-Posttest Control Group Design. Subjects in this study were students of grade VIII junior high school in Bandung taken from high school (A) and middle school (B). The results of this study showed that the increase of the students' self-regulated learning who obtain learning with computer-based learning is higher than students who obtain conventional learning. School-level factors have a significant effect on increasing of the students' self-regulated learning.

Learning-based identification and iterative learning control of direct-drive robots

NARCIS (Netherlands)

Bukkems, B.H.M.; Kostic, D.; Jager, de A.G.; Steinbuch, M.

2005-01-01

A combination of model-based and Iterative Learning Control is proposed as a method to achieve high-quality motion control of direct-drive robots in repetitive motion tasks. We include both model-based and learning components in the total control law, as their individual properties influence the

EFFECTS OF INQUIRY TRAINING LEARNING MODEL BASED MULTIMEDIA AND MOTIVATION OF PHYSICS STUDENT LEARNING OUTCOMES

Directory of Open Access Journals (Sweden)

Hayati .

2013-06-01

Full Text Available The objective in this research: (1 Determine a better learning model to improve learning outcomes physics students among learning model Inquiry Training based multimedia and Inquiry Training learning model. (2 Determine the level of motivation to learn in affects physics student learning outcomes. (3 Knowing the interactions between the model of learning and motivation in influencing student learning outcomes. This research is a quasi experimental. The population in this research was all students in class XI SMA Negeri 1 T.P Sunggal Semester I 2012/2013. The sample of this research was consisted of two classes with a sample of 70 peoples who are determined by purposive sampling, the IPA XI-2 as a class experiment using a model-based multimedia learning Training Inquiry as many as 35 peoples and XI IPA-3 as a control class using learning model Inquiry Training 35 peoples. Hypotheses were analyzed using the GLM at significant level of 0.05 using SPSS 17.0 for Windows. Based on data analysis and hypothesis testing conducted found that: (1 Training Inquiry-based multimedia learning model in improving student learning outcomes rather than learning model physics Inquiry Training. (2 The results of studying physics students who have high motivation to learn better than students who have a low learning motivation. (3 From this research there was an interaction between learning model inquiry-based multimedia training and motivation to study on learning outcomes of students.

Component-Based Approach in Learning Management System Development

Science.gov (United States)

Zaitseva, Larisa; Bule, Jekaterina; Makarov, Sergey

2013-01-01

The paper describes component-based approach (CBA) for learning management system development. Learning object as components of e-learning courses and their metadata is considered. The architecture of learning management system based on CBA being developed in Riga Technical University, namely its architecture, elements and possibilities are…

Thesaurus-based search in large heterogeneous collections

NARCIS (Netherlands)

J. Wielemaker (Jan); M. Hildebrand (Michiel); J.R. van Ossenbruggen (Jacco); G. Schreiber (Guus); A. Sheth; not CWI et al

2008-01-01

htmlabstractIn cultural heritage, large virtual collections are coming into existence. Such collections contain heterogeneous sets of metadata and vocabulary concepts, originating from multiple sources. In the context of the E-Culture demonstrator we have shown earlier that such virtual

Investigating the Efficiency of Scenario Based Learning and Reflective Learning Approaches in Teacher Education

Science.gov (United States)

Hursen, Cigdem; Fasli, Funda Gezer

2017-01-01

The main purpose of this research is to investigate the efficiency of scenario based learning and reflective learning approaches in teacher education. The impact of applications of scenario based learning and reflective learning on prospective teachers' academic achievement and views regarding application and professional self-competence…

Experimental oligopolies modeling: A dynamic approach based on heterogeneous behaviors

Science.gov (United States)

Cerboni Baiardi, Lorenzo; Naimzada, Ahmad K.

2018-05-01

In the rank of behavioral rules, imitation-based heuristics has received special attention in economics (see [14] and [12]). In particular, imitative behavior is considered in order to understand the evidences arising in experimental oligopolies which reveal that the Cournot-Nash equilibrium does not emerge as unique outcome and show that an important component of the production at the competitive level is observed (see e.g.[1,3,9] or [7,10]). By considering the pioneering groundbreaking approach of [2], we build a dynamical model of linear oligopolies where heterogeneous decision mechanisms of players are made explicit. In particular, we consider two different types of quantity setting players characterized by different decision mechanisms that coexist and operate simultaneously: agents that adaptively adjust their choices towards the direction that increases their profit are embedded with imitator agents. The latter ones use a particular form of proportional imitation rule that considers the awareness about the presence of strategic interactions. It is noteworthy that the Cournot-Nash outcome is a stationary state of our models. Our thesis is that the chaotic dynamics arousing from a dynamical model, where heterogeneous players are considered, are capable to qualitatively reproduce the outcomes of experimental oligopolies.

Heterogeneity within autism spectrum disorders: what have we learned from neuroimaging studies?

Directory of Open Access Journals (Sweden)

Rhoshel Krystyna Lenroot

2013-10-01

Full Text Available Autism spectrum disorders (ASD display significant heterogeneity. Although most neuroimaging studies in ASD have been designed to identify commonalities among affected individuals, rather than differences, some studies have explored variation within ASD. There have been two general types of approaches used for this in the neuroimaging literature to date: comparison of subgroups within ASD, and analyses using dimensional measures to link clinical variation to brain differences. This review focuses on structural and functional magnetic resonance imaging studies that have used these approaches to begin to explore heterogeneity between individuals with ASD. Although this type of data is yet sparse, recognition is growing of the limitations of behaviourally defined categorical diagnoses for understanding neurobiology. Study designs that are more informative regarding the sources of heterogeneity in ASD have the potential to improve our understanding of the neurobiological processes underlying ASD.

Acceptance of Game-Based Learning and Intrinsic Motivation as Predictors for Learning Success and Flow Experience

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Manuel Ninaus

2017-09-01

Full Text Available There is accumulating evidence that engagement with digital math games can improve students’ learning. However, in what way individual variables critical to game-based learning influence students' learning success still needs to be explored. Therefore, the aim of this study was to investigate the influence of students’ acceptance of game-based learning (e.g., perceived usefulness of a game as a learning tool, perceived ease of use, as well as their intrinsic motivation for math (e.g., their math interest, self-efficacy and quality of playing experience on learning success in a game-based rational number training. Additionally, we investigated the influence of the former variables on quality of playing experience (operationalized as perceived flow. Results indicated that the game-based training was effective. Moreover, students’ learning success and their quality of playing experience were predicted by measures of acceptance of game-based learning and intrinsic motivation for math. These findings indicated that learning success in game-based learning approaches are driven by students’ acceptance of the game as a learning tool and content-specific intrinsic motivation. Therefore, the present work is of particular interest to researchers, developers, and practitioners working with game-based learning environments.

A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data

Directory of Open Access Journals (Sweden)

Ruzzo Walter L

2006-03-01

Full Text Available Abstract Background As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. Methods In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. Results We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly. Conclusion Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets.

The development of learning material using learning cycle 5E model based stem to improve students’ learning outcomes in Thermochemistry

Science.gov (United States)

sugiarti, A. C.; suyatno, S.; Sanjaya, I. G. M.

2018-04-01

The objective of this study is describing the feasibility of Learning Cycle 5E STEM (Science, Technology, Engineering, and Mathematics) based learning material which is appropriate to improve students’ learning achievement in Thermochemistry. The study design used 4-D models and one group pretest-posttest design to obtain the information about the improvement of sudents’ learning outcomes. The subject was learning cycle 5E based STEM learning materials which the data were collected from 30 students of Science class at 11th Grade. The techniques used in this study were validation, observation, test, and questionnaire. Some result attain: (1) all the learning materials contents were valid, (2) the practicality and the effectiveness of all the learning materials contents were classified as good. The conclution of this study based on those three condition, the Learnig Cycle 5E based STEM learning materials is appropriate to improve students’ learning outcomes in studying Thermochemistry.

Problem-Based Learning Approaches in Meteorology

Science.gov (United States)

Charlton-Perez, Andrew James

2013-01-01

Problem-Based Learning, despite recent controversies about its effectiveness, is used extensively as a teaching method throughout higher education. In meteorology, there has been little attempt to incorporate Problem-Based Learning techniques into the curriculum. Motivated by a desire to enhance the reflective engagement of students within a…

Can Learning Motivation Predict Learning Achievement? A Case Study of a Mobile Game-Based English Learning Approach

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Tsai, Chia-Hui; Cheng, Ching-Hsue; Yeh, Duen-Yian; Lin, Shih-Yun

2017-01-01

This study applied a quasi-experimental design to investigate the influence and predictive power of learner motivation for achievement, employing a mobile game-based English learning approach. A system called the Happy English Learning System, integrating learning material into a game-based context, was constructed and installed on mobile devices…

Inference of Cell Mechanics in Heterogeneous Epithelial Tissue Based on Multivariate Clone Shape Quantification

Science.gov (United States)

Tsuboi, Alice; Umetsu, Daiki; Kuranaga, Erina; Fujimoto, Koichi

2017-01-01

Cell populations in multicellular organisms show genetic and non-genetic heterogeneity, even in undifferentiated tissues of multipotent cells during development and tumorigenesis. The heterogeneity causes difference of mechanical properties, such as, cell bond tension or adhesion, at the cell–cell interface, which determine the shape of clonal population boundaries via cell sorting or mixing. The boundary shape could alter the degree of cell–cell contacts and thus influence the physiological consequences of sorting or mixing at the boundary (e.g., tumor suppression or progression), suggesting that the cell mechanics could help clarify the physiology of heterogeneous tissues. While precise inference of mechanical tension loaded at each cell–cell contacts has been extensively developed, there has been little progress on how to distinguish the population-boundary geometry and identify the cause of geometry in heterogeneous tissues. We developed a pipeline by combining multivariate analysis of clone shape with tissue mechanical simulations. We examined clones with four different genotypes within Drosophila wing imaginal discs: wild-type, tartan (trn) overexpression, hibris (hbs) overexpression, and Eph RNAi. Although the clones were previously known to exhibit smoothed or convoluted morphologies, their mechanical properties were unknown. By applying a multivariate analysis to multiple criteria used to quantify the clone shapes based on individual cell shapes, we found the optimal criteria to distinguish not only among the four genotypes, but also non-genetic heterogeneity from genetic one. The efficient segregation of clone shape enabled us to quantitatively compare experimental data with tissue mechanical simulations. As a result, we identified the mechanical basis contributed to clone shape of distinct genotypes. The present pipeline will promote the understanding of the functions of mechanical interactions in heterogeneous tissue in a non-invasive manner. PMID

Machine Learning, Statistical Learning and the Future of Biological Research in Psychiatry

OpenAIRE

Iniesta, Raquel; Stahl, Daniel Richard; McGuffin, Peter

2016-01-01

Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous vari- ables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other ‘omic’ measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may be further complicated by missing data for some subjects and variables that are highly correlated. Statistical learning- based models are a n...

Considerations regarding system engineering in large scale projects with heterogeneous contexts

Science.gov (United States)

Cremonini, A.; Caiazzo, M.; Hayden, D.; Labate, M. G.; Oulgin, R.; Santander-Vela, J.

2016-08-01

In this paper we would like to share some considerations and lessons learned based on our direct experience as system engineer at the SKA project, with emphasis in the personal experiences of the first author. This is a very wide and ambitious program, which involves several stakeholders with a level of heterogeneity in cultural backgrounds, technological heritages, multidisciplinary interplays, motivations and competences without precedents. The role of the leading author is to amalgamate efforts in order to deliver the "MID telescope" and in that role, he has often discovered that, Systems Engineering means far more than purely a disciplined sets of processes.

Metaheuristic Based Scheduling Meta-Tasks in Distributed Heterogeneous Computing Systems

Directory of Open Access Journals (Sweden)

Hesam Izakian

2009-07-01

Full Text Available Scheduling is a key problem in distributed heterogeneous computing systems in order to benefit from the large computing capacity of such systems and is an NP-complete problem. In this paper, we present a metaheuristic technique, namely the Particle Swarm Optimization (PSO algorithm, for this problem. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. The scheduler aims at minimizing makespan, which is the time when finishes the latest task. Experimental studies show that the proposed method is more efficient and surpasses those of reported PSO and GA approaches for this problem.

Group-Based Active Learning of Classification Models.

Science.gov (United States)

Luo, Zhipeng; Hauskrecht, Milos

2017-05-01

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

Thesaurus-based search in large heterogeneous collections

NARCIS (Netherlands)

Wielemaker, J.; Hildebrand, M.; van Ossenbruggen, J.; Schreiber, G.

2008-01-01

In cultural heritage, large virtual collections are coming into existence. Such collections contain heterogeneous sets of metadata and vocabulary concepts, originating from multiple sources. In the context of the E-Culture demonstrator we have shown earlier that such virtual collections can be

A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information.

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Wei, Wei; Gao, Bin; Liu, Tie-Yan; Wang, Taifeng; Li, Guohui; Li, Hang

2016-04-01

Graph-based ranking has been extensively studied and frequently applied in many applications, such as webpage ranking. It aims at mining potentially valuable information from the raw graph-structured data. Recently, with the proliferation of rich heterogeneous information (e.g., node/edge features and prior knowledge) available in many real-world graphs, how to effectively and efficiently leverage all information to improve the ranking performance becomes a new challenging problem. Previous methods only utilize part of such information and attempt to rank graph nodes according to link-based methods, of which the ranking performances are severely affected by several well-known issues, e.g., over-fitting or high computational complexity, especially when the scale of graph is very large. In this paper, we address the large-scale graph-based ranking problem and focus on how to effectively exploit rich heterogeneous information of the graph to improve the ranking performance. Specifically, we propose an innovative and effective semi-supervised PageRank (SSP) approach to parameterize the derived information within a unified semi-supervised learning framework (SSLF-GR), then simultaneously optimize the parameters and the ranking scores of graph nodes. Experiments on the real-world large-scale graphs demonstrate that our method significantly outperforms the algorithms that consider such graph information only partially.

Evaluating Web-Based Learning Systems

Science.gov (United States)

Pergola, Teresa M.; Walters, L. Melissa

2011-01-01

Accounting educators continuously seek ways to effectively integrate instructional technology into accounting coursework as a means to facilitate active learning environments and address the technology-driven learning preferences of the current generation of students. Most accounting textbook publishers now provide interactive, web-based learning…

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.

A Dimensionally Reduced Clustering Methodology for Heterogeneous Occupational Medicine Data Mining.

Science.gov (United States)

Saâdaoui, Foued; Bertrand, Pierre R; Boudet, Gil; Rouffiac, Karine; Dutheil, Frédéric; Chamoux, Alain

2015-10-01

Clustering is a set of techniques of the statistical learning aimed at finding structures of heterogeneous partitions grouping homogenous data called clusters. There are several fields in which clustering was successfully applied, such as medicine, biology, finance, economics, etc. In this paper, we introduce the notion of clustering in multifactorial data analysis problems. A case study is conducted for an occupational medicine problem with the purpose of analyzing patterns in a population of 813 individuals. To reduce the data set dimensionality, we base our approach on the Principal Component Analysis (PCA), which is the statistical tool most commonly used in factorial analysis. However, the problems in nature, especially in medicine, are often based on heterogeneous-type qualitative-quantitative measurements, whereas PCA only processes quantitative ones. Besides, qualitative data are originally unobservable quantitative responses that are usually binary-coded. Hence, we propose a new set of strategies allowing to simultaneously handle quantitative and qualitative data. The principle of this approach is to perform a projection of the qualitative variables on the subspaces spanned by quantitative ones. Subsequently, an optimal model is allocated to the resulting PCA-regressed subspaces.

Reinforcement Learning Based Novel Adaptive Learning Framework for Smart Grid Prediction

Directory of Open Access Journals (Sweden)

Tian Li

2017-01-01

Full Text Available Smart grid is a potential infrastructure to supply electricity demand for end users in a safe and reliable manner. With the rapid increase of the share of renewable energy and controllable loads in smart grid, the operation uncertainty of smart grid has increased briskly during recent years. The forecast is responsible for the safety and economic operation of the smart grid. However, most existing forecast methods cannot account for the smart grid due to the disabilities to adapt to the varying operational conditions. In this paper, reinforcement learning is firstly exploited to develop an online learning framework for the smart grid. With the capability of multitime scale resolution, wavelet neural network has been adopted in the online learning framework to yield reinforcement learning and wavelet neural network (RLWNN based adaptive learning scheme. The simulations on two typical prediction problems in smart grid, including wind power prediction and load forecast, validate the effectiveness and the scalability of the proposed RLWNN based learning framework and algorithm.

Increasing Throughput and Fairness for Users in Heterogeneous Semi Coordinated Deployments

DEFF Research Database (Denmark)

Semov, Plamen; Poulkov, Vladimir; Mihovska, Albena D.

2014-01-01

Incorporation of the geographical positions of mobile users into the resource assignment process in uncoordinated heterogeneous cell deployments, can lead to significant improvements of cell and user throughputs. This paper proposes a novel algorithm that combines the knowledge of the users......’ positions with a Q-learning and game-theoretic approaches to enhance the dynamic physical resource allocation during carrier aggregation (CA) in a semi-and uncoordinated deployment of Heterogeneous Networks (HetNet). The algorithm is evaluated through MATLAB simulation setup and in terms of macro-and pico......- cell and user throughputs. It has been shown that regardless of the approach chosen for physical resource assignment, positioning information increases the system and user performances. Use of Q-learning and positioning information leads to increased cell throughput without degrading the user...

Performance Evaluation of a Cluster-Based Service Discovery Protocol for Heterogeneous Wireless Sensor Networks

NARCIS (Netherlands)

Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

2006-01-01

Abstract—This paper evaluates the performance in terms of resource consumption of a service discovery protocol proposed for heterogeneous Wireless Sensor Networks (WSNs). The protocol is based on a clustering structure, which facilitates the construction of a distributed directory. Nodes with higher

The Effectiveness of Collaborative Academic Online Based Learning through Students’ Self-Regulated Learning

Directory of Open Access Journals (Sweden)

Erfan Priyambodo

2016-11-01

Full Text Available Nowdays, learning through e-learning is going rapidly, including the application BeSmart UNY. This application is providing collaborative method in teaching and learning. The aim of this study was to determine the effectiveness of the Collaborative Academic Online Based Learning method in teaching and learning toward students’ Self-Regulated Learning (SRL on Vocational School Chemistry courses. This study was quasi-experimental research method with one group pretest posttest design. Instruments used in this study were lesson plan and questionnaire of students’ SRL. This questionnaire is filled by students through BeSmart UNY.  In determining the differences SRL before and after teaching and learning processes, the data was analized by stastitical method.  The results showed that the implementation of the Collaborative Academic Online Based Learning method in teaching and learning was effective for improving students’ SRL.

Designing and Evaluating Conative Game-Based Learning Scenarios

DEFF Research Database (Denmark)

Schønau-Fog, Henrik

2014-01-01

It is an essential prerequisite to design for motivation in game-based learning applications, tools and activities. However, how is it possible to design and evaluate motivational game-based learning scenarios in a systematic process-oriented manner based on conation and player engagement? While...... of ‘continuation desire’ such as interfacing with the scenario, exploration and socialising. This paper aims to combine the concepts of Player Engagement, Conation and Continuation Desire by focusing on the conative aspects which are the essential drivers for the desire to continue any learning activity......-based learning scenarios....

Influence of learning strategy on response time during complex value-based learning and choice.

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Shiva Farashahi

Full Text Available Measurements of response time (RT have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning. Alternatively, they could learn reward values of options' features (e.g. color, shape and combine these values to estimate reward values for individual options (feature-based learning. We found that RT was slower when the difference between subjects' estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.

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

Science.gov (United States)

Sesen, Burcin Acar; Tarhan, Leman

2011-01-01

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

Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

Science.gov (United States)

Dinov, Ivo D.; Heavner, Ben; Tang, Ming; Glusman, Gustavo; Chard, Kyle; Darcy, Mike; Madduri, Ravi; Pa, Judy; Spino, Cathie; Kesselman, Carl; Foster, Ian; Deutsch, Eric W.; Price, Nathan D.; Van Horn, John D.; Ames, Joseph; Clark, Kristi; Hood, Leroy; Hampstead, Benjamin M.; Dauer, William; Toga, Arthur W.

2016-01-01

Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several

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

Directory of Open Access Journals (Sweden)

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

Development of Scientific Approach Based on Discovery Learning Module

Science.gov (United States)

Ellizar, E.; Hardeli, H.; Beltris, S.; Suharni, R.

2018-04-01

Scientific Approach is a learning process, designed to make the students actively construct their own knowledge through stages of scientific method. The scientific approach in learning process can be done by using learning modules. One of the learning model is discovery based learning. Discovery learning is a learning model for the valuable things in learning through various activities, such as observation, experience, and reasoning. In fact, the students’ activity to construct their own knowledge were not optimal. It’s because the available learning modules were not in line with the scientific approach. The purpose of this study was to develop a scientific approach discovery based learning module on Acid Based, also on electrolyte and non-electrolyte solution. The developing process of this chemistry modules use the Plomp Model with three main stages. The stages are preliminary research, prototyping stage, and the assessment stage. The subject of this research was the 10th and 11th Grade of Senior High School students (SMAN 2 Padang). Validation were tested by the experts of Chemistry lecturers and teachers. Practicality of these modules had been tested through questionnaire. The effectiveness had been tested through experimental procedure by comparing student achievement between experiment and control groups. Based on the findings, it can be concluded that the developed scientific approach discovery based learning module significantly improve the students’ learning in Acid-based and Electrolyte solution. The result of the data analysis indicated that the chemistry module was valid in content, construct, and presentation. Chemistry module also has a good practicality level and also accordance with the available time. This chemistry module was also effective, because it can help the students to understand the content of the learning material. That’s proved by the result of learning student. Based on the result can conclude that chemistry module based on

Quantitative learning strategies based on word networks

Science.gov (United States)

Zhao, Yue-Tian-Yi; Jia, Zi-Yang; Tang, Yong; Xiong, Jason Jie; Zhang, Yi-Cheng

2018-02-01

Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

iPad Learning Ecosystem: Developing Challenge-Based Learning Using Design Thinking

Science.gov (United States)

Marin, Catalina; Hargis, Jace; Cavanaugh, Cathy

2013-01-01

In order to maximize college English language students' learning, product development, 21st Century skills and engagement with real world meaningful challenges, a course was designed to integrate Challenge Based Learning (CBL) and iPad mobile learning technology. This article describes the course design, which was grounded in design thinking, and…

The Effects of Students' Learning Anxiety and Motivation on the Learning Achievement in the Activity Theory Based Gamified Learning Environment

Science.gov (United States)

Su, Chung-Ho

2017-01-01

The advancement of mobile game-based learning has encouraged many related studies, which has enabled students to learn more and faster. To enhance the clinical path of cardiac catheterization learning, this paper has developed a mobile 3D-CCGBLS (3D Cardiac Catheterization Game-Based Learning System) with a learning assessment for cardiac…

Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control.

Science.gov (United States)

Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.

SVM and PCA Based Learning Feature Classification Approaches for E-Learning System

Science.gov (United States)

Khamparia, Aditya; Pandey, Babita

2018-01-01

E-learning and online education has made great improvements in the recent past. It has shifted the teaching paradigm from conventional classroom learning to dynamic web based learning. Due to this, a dynamic learning material has been delivered to learners, instead ofstatic content, according to their skills, needs and preferences. In this…

The Effectiveness of the Problem-Based Learning Teaching Model for Use in Introductory Chinese Undergraduate Medical Courses: A Systematic Review and Meta-Analysis

Science.gov (United States)

Zhang, Yanqi; Zhou, Liang; Liu, Xiaoyu; Liu, Ling; Wu, Yazhou; Zhao, Zengwei; Yi, Dali; Yi, Dong

2015-01-01

Background Although the problem-based learning (PBL) emerged in 1969 and was soon widely applied internationally, the rapid development in China only occurred in the last 10 years. This study aims to compare the effect of PBL and lecture-based learning (LBL) on student course examination results for introductory Chinese undergraduate medical courses. Methods Randomized and nonrandomized controlled trial studies on PBL use in Chinese undergraduate medical education were retrieved through PubMed, the Excerpta Medica Database (EMBASE), Chinese National Knowledge Infrastructure (CNKI) and VIP China Science and Technology Journal Database (VIP-CSTJ) with publication dates from 1st January 1966 till 31 August 2014. The pass rate, excellence rate and examination scores of course examination were collected. Methodological quality was evaluated based on the modified Jadad scale. The I-square statistic and Chi-square test of heterogeneity were used to assess the statistical heterogeneity. Overall RRs or SMDs with their 95% CIs were calculated in meta-analysis. Meta-regression and subgroup meta-analyses were also performed based on comparators and other confounding factors. Funnel plots and Egger’s tests were performed to assess degrees of publication bias. Results The meta-analysis included 31studies and 4,699 subjects. Fourteen studies were of high quality with modified Jadad scores of 4 to 6, and 17 studies were of low quality with scores of 1 to 3. Relative to the LBL model, the PBL model yielded higher course examination pass rates [RR = 1.09, 95%CI (1.03, 1.17)], excellence rates [RR = 1.66, 95%CI (1.33, 2.06)] and examination scores [SMD = 0.82, 95%CI (0.63, 1.01)]. The meta-regression results show that course type was the significant confounding factor that caused heterogeneity in the examination-score meta-analysis (t = 0.410, Pteaching model application in introductory undergraduate medical courses can increase course examination excellence rates and scores in

Creative Writing, Problem-Based Learning, and Game-Based Learning Principles

Science.gov (United States)

Trekles, Anastasia M.

2012-01-01

This paper examines how virtual worlds and other advanced social media can be married with problem-based learning to encourage creativity and critical thinking in the English/Language Arts classroom, particularly for middle school, high school, and undergraduate college education. Virtual world experiences such as "Second Life," Jumpstart.com, and…

Mobile Learning for Higher Education in Problem-Based Learning Environments

DEFF Research Database (Denmark)

Rongbutsri, Nikorn

2011-01-01

This paper describes the PhD project on Mobile Learning for Higher Education in Problem-Based Learning Environment which aims to understand how students gain benefit from using mobile devices in the aspect of project work collaboration. It demonstrates research questions, theoretical perspective...

Heterogeneity: multilingualism and democracy

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Hans-Jürgen Krumm

2004-01-01

Full Text Available Linguistic diversity and multilingualism on the part of individuals are aprerequisite and a constitutive condition of enabling people to live togetherin a world of growing heterogeneity. Foreign language teaching plays animportant part in democratic education because it can be seen as a trainingin respecting otherness and developing an intercultural, non-ethnocentricperception and attitude. This is all the more important because of the neces-sity of integrating children from migrant families into school life.My article argues that language education policy has to take this per-spective into account, i.e., of establishing a planned diversification so thatpupils (and their parents will not feel satisfied with learning English only,but also become motivated to learn languages of their own neighbourhood,such as migrant and minority languages. However, in order to make use ofthe linguistic resources in the classroom, relating it to the democratic impetusof foreign language education, it is necessary to revise existing languagepolicies and to develop a multilingual perspective for all educational institutions.

Incorporating Problem-Based Learning in Physical Education Teacher Education

Science.gov (United States)

Hushman, Glenn; Napper-Owen, Gloria

2011-01-01

Problem-based learning (PBL) is an educational method that identifies a problem as a context for student learning. Critical-thinking skills, deductive reasoning, knowledge, and behaviors are developed as students learn how theory can be applied to practical settings. Problem-based learning encourages self-direction, lifelong learning, and sharing…

The Effect of Multimedia Based Learning in Chemistry Teaching and Learning on Students’ Self-Regulated Learning

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Erfan Priyambodo

2014-11-01

Full Text Available In recent years, the uses of Multimedia Based Learning (MBL in classroom instruction increased widely. Overall, this implementation aims to improve students’ motivation and also their learning outcomes. This study was answering the effect of MBL toward students’ Self-Regulated Learning (SRL in chemistry teaching and learning. The experiment was conducted in class XI of senior high school in Yogyakarta. Researchers create some computer based media for chemistry materials and continued with expert judgement of the media. Students’ data SRL were measured using validated questionnaire. The questionnaire consists of three components, i.e. metacognitive, motivation and behavior. The results showed that there was significant differences in SRL of students before and after participating in chemistry teaching and learning which applying MBL.

Meta-analysis of fluid intelligence tests of children from the Chinese mainland with learning difficulties.

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Fang Tong

Full Text Available OBJECTIVE: To evaluate the differences in fluid intelligence tests between normal children and children with learning difficulties in China. METHOD: PubMed, MD Consult, and other Chinese Journal Database were searched from their establishment to November 2012. After finding comparative studies of Raven measurements of normal children and children with learning difficulties, full Intelligent Quotation (FIQ values and the original values of the sub-measurement were extracted. The corresponding effect model was selected based on the results of heterogeneity and parallel sub-group analysis was performed. RESULTS: Twelve documents were included in the meta-analysis, and the studies were all performed in mainland of China. Among these, two studies were performed at child health clinics, the other ten sites were schools and control children were schoolmates or classmates. FIQ was evaluated using a random effects model. WMD was -13.18 (95% CI: -16.50- -9.85. Children with learning difficulties showed significantly lower FIQ scores than controls (P<0.00001; Type of learning difficulty and gender differences were evaluated using a fixed-effects model (I² = 0%. The sites and purposes of the studies evaluated here were taken into account, but the reasons of heterogeneity could not be eliminated; The sum IQ of all the subgroups showed considerable heterogeneity (I² = 76.5%. The sub-measurement score of document A showed moderate heterogeneity among all documents, and AB, B, and E showed considerable heterogeneity, which was used in a random effect model. Individuals with learning difficulties showed heterogeneity as well. There was a moderate delay in the first three items (-0.5 to -0.9, and a much more pronounced delay in the latter three items (-1.4 to -1.6. CONCLUSION: In the Chinese mainland, the level of fluid intelligence of children with learning difficulties was lower than that of normal children. Delayed development in sub-items of C, D

Cloudification of mmwave-based and packet-based fronthaul for future heterogeneous mobile networks

DEFF Research Database (Denmark)

Artuso, Matteo; Marcano, Andrea; Christiansen, Henrik Lehrmann

2015-01-01

is seen as an enabler for next-generation heterogeneous mobile networks. This allows for simpler base stations and savings in deployment costs, but introduces challenges in the fronthaul network connecting the sites to the processing pool. The fronthaul needs to have very low latency and high capacity......, but the traditional architecture of this network uses point-to-point links between each site and the pool, thus making it impossible to share capacity as the demands change. To address these challenges, a flexible network architecture for the fronthaul is presented that is based on Ethernet to carry the baseband......Current deployments of mobile networks are seriously challenged by increasing capacity demands, and traditional solutions are no longer practical. The use of small cells is considered as a viable technique to meet these demands. In this context, the use of centralized signal processing in a pool...

Effect of Worksheet Scaffolds on Student Learning in Problem-Based Learning

Science.gov (United States)

Choo, Serene S. Y.; Rotgans, Jerome I.; Yew, Elaine H. J.; Schmidt, Henk G.

2011-01-01

The purpose of this study was to investigate the effect of worksheets as a scaffolding tool on students' learning achievement in a problem-based learning (PBL) environment. Seventeen PBL classes (N = 241) were randomly assigned to two experimental groups--one with a worksheet provided and the other without. Students' learning of the topic at hand…

A simple network agreement-based approach for combining evidences in a heterogeneous sensor network

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Raúl Eusebio-Grande

2015-12-01

Full Text Available In this research we investigate how the evidences provided by both static and mobile nodes that are part of a heterogenous sensor network can be combined to have trustworthy results. A solution relying on a network agreement-based approach was implemented and tested.

Changing the Curriculum to Problem-Based and Project-Based Learning

DEFF Research Database (Denmark)

Kolmos, Anette

2012-01-01

Problem based and project based learning (PBL) models are implemented all over the world in various versions at curriculum or course level. Due to this development, the conceptual understanding of PBL has become more diverse and sometimes confusing. This chapter summarizes the conceptual work done...... by the UNESCO Chair in PBL in engineering education in order to define PBL as a set of core learning principles that can be applied in practice. The PBL learning principles are formulated within three aspects: learning, social, and content of study. Furthermore, the chapter contains a PBL curriculum model......, which can be used for analysis and development of the curriculum or single courses. Seven elements are identified as important for the planning and implementation of PBL learning principles, and for each of the elements there are several choices to be made. Finally, the chapter presents concrete advice...

Learning Tools for Knowledge Nomads: Using Personal Digital Assistants (PDAs) in Web-based Learning Environments.

Science.gov (United States)

Loh, Christian Sebastian

2001-01-01

Examines how mobile computers, or personal digital assistants (PDAs), can be used in a Web-based learning environment. Topics include wireless networks on college campuses; online learning; Web-based learning technologies; synchronous and asynchronous communication via the Web; content resources; Web connections; and collaborative learning. (LRW)

Digital Microdroplet Ejection Technology-Based Heterogeneous Objects Prototyping

Science.gov (United States)

Yang, Jiquan; Feng, Chunmei; Yang, Jianfei; Zhu, Liya; Guo, Aiqing

2016-01-01

An integrate fabrication framework is presented to build heterogeneous objects (HEO) using digital microdroplets injecting technology and rapid prototyping. The heterogeneous materials part design and manufacturing method in structure and material was used to change the traditional process. The net node method was used for digital modeling that can configure multimaterials in time. The relationship of material, color, and jetting nozzle was built. The main important contributions are to combine the structure, material, and visualization in one process and give the digital model for manufacture. From the given model, it is concluded that the method is effective for HEO. Using microdroplet rapid prototyping and the model given in the paper HEO could be gotten basically. The model could be used in 3D biomanufacturing. PMID:26981110

Heterogeneous Catalysis of Polyoxometalate Based Organic–Inorganic Hybrids

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Yuanhang Ren

2015-03-01

Full Text Available Organic–inorganic hybrid polyoxometalate (POM compounds are a subset of materials with unique structures and physical/chemical properties. The combination of metal-organic coordination complexes with classical POMs not only provides a powerful way to gain multifarious new compounds but also affords a new method to modify and functionalize POMs. In parallel with the many reports on the synthesis and structure of new hybrid POM compounds, the application of these compounds for heterogeneous catalysis has also attracted considerable attention. The hybrid POM compounds show noteworthy catalytic performance in acid, oxidation, and even in asymmetric catalytic reactions. This review summarizes the design and synthesis of organic–inorganic hybrid POM compounds and particularly highlights their recent progress in heterogeneous catalysis.

Digital Microdroplet Ejection Technology-Based Heterogeneous Objects Prototyping

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Na Li

2016-01-01

Full Text Available An integrate fabrication framework is presented to build heterogeneous objects (HEO using digital microdroplets injecting technology and rapid prototyping. The heterogeneous materials part design and manufacturing method in structure and material was used to change the traditional process. The net node method was used for digital modeling that can configure multimaterials in time. The relationship of material, color, and jetting nozzle was built. The main important contributions are to combine the structure, material, and visualization in one process and give the digital model for manufacture. From the given model, it is concluded that the method is effective for HEO. Using microdroplet rapid prototyping and the model given in the paper HEO could be gotten basically. The model could be used in 3D biomanufacturing.

Digital Microdroplet Ejection Technology-Based Heterogeneous Objects Prototyping.

Science.gov (United States)

Li, Na; Yang, Jiquan; Feng, Chunmei; Yang, Jianfei; Zhu, Liya; Guo, Aiqing

2016-01-01

An integrate fabrication framework is presented to build heterogeneous objects (HEO) using digital microdroplets injecting technology and rapid prototyping. The heterogeneous materials part design and manufacturing method in structure and material was used to change the traditional process. The net node method was used for digital modeling that can configure multimaterials in time. The relationship of material, color, and jetting nozzle was built. The main important contributions are to combine the structure, material, and visualization in one process and give the digital model for manufacture. From the given model, it is concluded that the method is effective for HEO. Using microdroplet rapid prototyping and the model given in the paper HEO could be gotten basically. The model could be used in 3D biomanufacturing.

Editorial: Web-Based Learning: Innovations and Challenges

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Mudasser F. Wyne

2010-12-01

Full Text Available This special issue of the Knowledge Management & E-Learning: an international journal(KM&EL aims to stimulate interest in the web based issues in both teaching and learning, expose natural collaboration among the authors and readers, inform the larger research community of the interest and importance of this area and create a forum for evaluating innovations and challenges. We intend to bring together researchers and practitioners interested in developing and enhancing web-based learning environment. The objectives for this attempt are to provide a forum for discussion of ideas and techniques developed and used in web based learning. In addition the issue can also be used for educators and developers to discuss requirements for web-based education. Both theoretical papers and papers reporting implementation models, technology used and practical results are included in the issue.

Keefektifan setting TPS dalam pendekatan discovery learning dan problem-based learning pada pembelajaran materi lingkaran SMP

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Rahmi Hidayati

2017-05-01

The purpose of this study was to describe the effectiveness of setting Think Pair Share (TPS in the approach to discovery learning and problem-based learning in terms of student achievement, mathematical communication skills, and interpersonal skills of the student.  This study was a quasi-experimental study using the pretest-posttest nonequivalent group design. The research population comprised all Year VIII students of SMP Negeri 1 Yogyakarta. The research sample was randomly selected from eight classes, two classes were elected. The instrument used in this study is the learning achievement test, a test of mathematical communication skills, and interpersonal skills student questionnaires. To test the effectiveness of setting Think Pair Share (TPS in the approach to discovery learning and problem-based learning, the one sample t-test was carried out. Then, to investigate the difference in effectiveness between the setting Think Pair Share (TPS in the approach to discovery learning and problem-based learning, the Multivariate Analysis of Variance (MANOVA was carried out. The research findings indicate that the setting TPS discovery approach to learning and problem-based approach to learning (PBL is effective in terms of learning achievement, mathematical communication skills, and interpersonal skills of the students. No difference in effectiveness between setting TPS discovery approach to learning and problem-based learning (PBL in terms of learning achievement, mathematical communication skills, and interpersonal skills of the students. Keywords: TPS setting in discovery learning approach, in problem-based learning, academic achievement, mathematical communication skills, and interpersonal skills of the student

Constructivism Based Blended Learning in Higher Education

OpenAIRE

Al-Huneidi, Ahmad

2011-01-01

Blended Learning, which is a mix of online and face-to-face learning, can combine the benefits of both, traditional classroom learning and e-learning environments.3 The aim of this thesis is to explore how to design and implement Blended Learning environment based on Constructivism theory, which focuses on students’ experience to construct the knowledge, in order to increase learning outcomes, performance, and quality in academic institutions. An affective and successful learni...

[Applying Game-Based Learning in Nursing Education: Empathy Board Game Learning].

Science.gov (United States)

Lu, Chueh-Fen; Wu, Shu-Mei; Shu, Ying-Mei; Yeh, Mei-Yu

2018-02-01

Attending lectures and reading are two common approaches to acquiring knowledge, while repetitive practice is a common approach to acquiring skills. Nurturing proper attitudes in students is one of the greatest challenges for educators. Health professionals must incorporate empathy into their practice. Creative teaching strategies may offer a feasible approach to enhancing empathy-related competence. The present article focuses on analyzing current, empathy-related curriculums in nursing education in Taiwan, exploring the concepts of empathy and game-based learning, presenting the development of an empathy board game as a teaching aid, and, finally, evaluating the developed education application. Based on the learner-centered principle, this aid was designed with peer learning, allowing learners to influence the learning process, to simulate the various roles of clients, and to develop diverse interpersonal dialogues. The continuous learning loops were formed using the gamification mechanism and transformation, enabling students to connect and practice the three elements of empathy ability: emotion, cognition and expression. Via the game elements of competition, interaction, storytelling, real-time responses, concretizing feedback, integrated peer learning, and equality between teachers and students, students who play patient roles are able to perceive different levels of comfort, which encourages the development of insight into the meaning of empathy. Thereby, the goals of the empathy lesson is achievable within a creative game-based learning environment.

Music Learning Based on Computer Software

OpenAIRE

Baihui Yan; Qiao Zhou

2017-01-01

In order to better develop and improve students’ music learning, the authors proposed the method of music learning based on computer software. It is still a new field to use computer music software to assist teaching. Hereby, we conducted an in-depth analysis on the computer-enabled music learning and the music learning status in secondary schools, obtaining the specific analytical data. Survey data shows that students have many cognitive problems in the current music classroom, and yet teach...

Spinocerebellar ataxia type 2 neurodegeneration differentially affects error-based and strategic-based visuomotor learning.

Science.gov (United States)

Vaca-Palomares, Israel; Díaz, Rosalinda; Rodríguez-Labrada, Roberto; Medrano-Montero, Jacqeline; Vázquez-Mojena, Yaimé; Velázquez-Pérez, Luis; Fernandez-Ruiz, Juan

2013-12-01

There are different types of visuomotor learning. Among the most studied is motor error-based learning where the sign and magnitude of the error are used to update motor commands. However, there are other instances where individuals show visuomotor learning even if the sign or magnitude of the error is precluded. Studies with patients suggest that the former learning is impaired after cerebellar lesions, while basal ganglia lesions disrupt the latter. Nevertheless, the cerebellar role is not restricted only to error-based learning, but it also contributes to several cognitive processes. Therefore, here, we tested if cerebellar ataxia patients are affected in two tasks, one that depends on error-based learning and the other that prevents the use of error-based learning. Our results showed that cerebellar patients have deficits in both visuomotor tasks; however, while error-based learning tasks deficits correlated with the motor impairments, the motor error-dependent task did not correlate with any motor measure.

An Adaptive E-Learning System Based on Students' Learning Styles: An Empirical Study

Science.gov (United States)

Drissi, Samia; Amirat, Abdelkrim

2016-01-01

Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia.…

A Survey of Technologies Supporting Virtual Project Based Learning

DEFF Research Database (Denmark)

Dirckinck-Holmfeld, Lone

2002-01-01

This paper describes a survey of technologies and to what extent they support virtual project based learning. The paper argues that a survey of learning technologies should be related to concrete learning tasks and processes. Problem oriented project pedagogy (POPP) is discussed, and a framework...... for evaluation is proposed where negotiation of meaning, coordination and resource management are identified as the key concepts in virtual project based learning. Three e-learning systems are selected for the survey, Virtual-U, Lotus Learningspace and Lotus Quickplace, as each system offers different strategies...... for e-learning. The paper concludes that virtual project based learning may benefit from facilities of all these systems....

Comparison the Application of PBL (Project Based Learning and PBL (Problem Based Learning Learning Model on Online Marketing Subjects

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Agnes Dini Mardani

2017-09-01

Full Text Available Purpose of this study are (1 the application of learning PjBL with PBL to improve study results students, (2 assessing the domain affective, cognitive, and psychomotor, (3 the difference study results use the PjBL with PBL to improve study results students. The research is research quantitative and including research apparent experiment (quasi eksperiment by taking sample class two classes X PM 1 as a class experiment and class X PM 2 as a class control. Research instruments used for data collection namely: (1 tests to pretes and postest used to determine the cognitive assessment, (2 sheets observation affective, (3 sheets of the process for the psychomotor. The trial research instruments use the validity and reabilitas. Analysis techniques data using: (1 test a prerequisite analysis consisting of normality test and the homogeneity (2 T test unpaired which ended with the help of computer programs spss. Based on the result of this research can be concluded that: (1 the application of PjBL (Project Based Learning and PBL (Problem Based Learning should be conducted well in accordance syntax learning, (2 assessing the cognitive students have a difference and class experiment having an average higher than class control, (3 assessing the results affective students have a difference and on the application of PjBL is better than PBL.

Explanation-based learning in infancy.

Science.gov (United States)

Baillargeon, Renée; DeJong, Gerald F

2017-10-01

In explanation-based learning (EBL), domain knowledge is leveraged in order to learn general rules from few examples. An explanation is constructed for initial exemplars and is then generalized into a candidate rule that uses only the relevant features specified in the explanation; if the rule proves accurate for a few additional exemplars, it is adopted. EBL is thus highly efficient because it combines both analytic and empirical evidence. EBL has been proposed as one of the mechanisms that help infants acquire and revise their physical rules. To evaluate this proposal, 11- and 12-month-olds (n = 260) were taught to replace their current support rule (that an object is stable when half or more of its bottom surface is supported) with a more sophisticated rule (that an object is stable when half or more of the entire object is supported). Infants saw teaching events in which asymmetrical objects were placed on a base, followed by static test displays involving a novel asymmetrical object and a novel base. When the teaching events were designed to facilitate EBL, infants learned the new rule with as few as two (12-month-olds) or three (11-month-olds) exemplars. When the teaching events were designed to impede EBL, however, infants failed to learn the rule. Together, these results demonstrate that even infants, with their limited knowledge about the world, benefit from the knowledge-based approach of EBL.

Applications of Task-Based Learning in TESOL

Science.gov (United States)

Shehadeh, Ali, Ed.; Coombe, Christine, Ed.

2010-01-01

Why are many teachers around the world moving toward task-based learning (TBL)? This shift is based on the strong belief that TBL facilitates second language acquisition and makes second language learning and teaching more principled and effective. Based on insights gained from using tasks as research tools, this volume shows how teachers can use…

Representation of Coordination Mechanisms in IMS Learning Design to Support Group-based Learning

NARCIS (Netherlands)

Miao, Yongwu; Burgos, Daniel; Griffiths, David; Koper, Rob

2007-01-01

Miao, Y., Burgos, D., Griffiths, D., & Koper, R. (2008). Representation of Coordination Mechanisms in IMS Learning Design to Support Group-based Learning. In L. Lockyer, S. Bennet, S. Agostinho & B. Harper (Eds.), Handbook of Research on Learning Design and Learning Objects: Issues, Applications and

Gene ontology based transfer learning for protein subcellular localization

Directory of Open Access Journals (Sweden)

Zhou Shuigeng

2011-02-01

Full Text Available Abstract Background Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as GO, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the GO terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology. Results In this paper, we propose a Gene Ontology Based Transfer Learning Model (GO-TLM for large-scale protein subcellular localization. The model transfers the signature-based homologous GO terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false GO terms that are resulted from evolutionary divergence. We derive three GO kernels from the three aspects of gene ontology to measure the GO similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for

Arts-Based Learning: A New Approach to Nursing Education Using Andragogy.

Science.gov (United States)

Nguyen, Megan; Miranda, Joyal; Lapum, Jennifer; Donald, Faith

2016-07-01

Learner-oriented strategies focusing on learning processes are needed to prepare nursing students for complex practice situations. An arts-based learning approach uses art to nurture cognitive and emotional learning. Knowles' theory of andragogy aims to develop the skill of learning and can inform the process of implementing arts-based learning. This article explores the use and evaluation of andragogy-informed arts-based learning for teaching nursing theory at the undergraduate level. Arts-based learning activities were implemented and then evaluated by students and instructors using anonymous questionnaires. Most students reported that the activities promoted learning. All instructors indicated an interest in integrating arts-based learning into the curricula. Facilitators and barriers to mainstreaming arts-based learning were highlighted. Findings stimulate implications for prospective research and education. Findings suggest that arts-based learning approaches enhance learning by supporting deep inquiry and different learning styles. Further exploration of andragogy-informed arts-based learning in nursing and other disciplines is warranted. [J Nurs Educ. 2016;55(7):407-410.]. Copyright 2016, SLACK Incorporated.

Effects of mobile phone-based app learning compared to computer-based web learning on nursing students: pilot randomized controlled trial.

Science.gov (United States)

Lee, Myung Kyung

2015-04-01

This study aimed to determine the effect of mobile-based discussion versus computer-based discussion on self-directed learning readiness, academic motivation, learner-interface interaction, and flow state. This randomized controlled trial was conducted at one university. Eighty-six nursing students who were able to use a computer, had home Internet access, and used a mobile phone were recruited. Participants were randomly assigned to either the mobile phone app-based discussion group (n = 45) or a computer web-based discussion group (n = 41). The effect was measured at before and after an online discussion via self-reported surveys that addressed academic motivation, self-directed learning readiness, time distortion, learner-learner interaction, learner-interface interaction, and flow state. The change in extrinsic motivation on identified regulation in the academic motivation (p = 0.011) as well as independence and ability to use basic study (p = 0.047) and positive orientation to the future in self-directed learning readiness (p = 0.021) from pre-intervention to post-intervention was significantly more positive in the mobile phone app-based group compared to the computer web-based discussion group. Interaction between learner and interface (p = 0.002), having clear goals (p = 0.012), and giving and receiving unambiguous feedback (p = 0.049) in flow state was significantly higher in the mobile phone app-based discussion group than it was in the computer web-based discussion group at post-test. The mobile phone might offer more valuable learning opportunities for discussion teaching and learning methods in terms of self-directed learning readiness, academic motivation, learner-interface interaction, and the flow state of the learning process compared to the computer.

Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles.

Science.gov (United States)

Zhang, Duona; Ding, Wenrui; Zhang, Baochang; Xie, Chunyu; Li, Hongguang; Liu, Chunhui; Han, Jungong

2018-03-20

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network.

Project based learning in organizations: towards a methodology for learning in groups

NARCIS (Netherlands)

Poell, R.F.; Krogt, F.J. van der

2003-01-01

This article introduces a methodology for employees in organizations to set up and carry out their own group learning projects. It is argued that employees can use project-based learning to make their everyday learning more systematic at times, without necessarily formalizing it. The article

Project-based learning in organizations : Towards a methodology for learning in groups

NARCIS (Netherlands)

Poell, R.F.; van der Krogt, F.J.

2003-01-01

This article introduces a methodology for employees in organizations to set up and carry out their own group learning projects. It is argued that employees can use project-based learning to make their everyday learning more systematic at times, without necessarily formalizing it. The article

How Do Teachers Learn Together? A Study of School-Based Teacher Learning in China from the Perspective of Organisational Learning

Science.gov (United States)

Zhang, Xiaolei; Wong, Jocelyn L. N.

2018-01-01

Studies of professional development have examined the influence of school-based approaches on in-service teacher learning and change but have seldom investigated teachers' job-embedded learning processes. This paper explores the dynamic processes of teacher learning in school-based settings. A qualitative comparative case study based on the…

Does individual learning styles influence the choice to use a web-based ECG learning programme in a blended learning setting?

OpenAIRE

Nilsson, Mikael; ?stergren, Jan; Fors, Uno; Rickenlund, Anette; Jorfeldt, Lennart; Caidahl, Kenneth; Bolinder, Gunilla

2012-01-01

Abstract Background The compressed curriculum in modern knowledge-intensive medicine demands useful tools to achieve approved learning aims in a limited space of time. Web-based learning can be used in different ways to enhance learning. Little is however known regarding its optimal utilisation. Our aim was to investigate if the individual learning styles of medical students influence the choice to use a web-based ECG learning programme in a blended learning setting. Methods The programme, wi...

Dopamine selectively remediates 'model-based' reward learning: a computational approach.

Science.gov (United States)

Sharp, Madeleine E; Foerde, Karin; Daw, Nathaniel D; Shohamy, Daphna

2016-02-01

Patients with loss of dopamine due to Parkinson's disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from 'model-free' learning. The other, 'model-based' learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson's disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson's disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson's disease may be related to an inability to pursue reward based on complete representations of the environment. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

DEVELOPMENT MODEL OF PATISSERIE PROJECT-BASED LEARNING

OpenAIRE

Ana Ana; Lutfhiyah Nurlaela

2013-01-01

The study aims to find a model of patisserie project-based learning with production approach that can improve effectiveness of patisserie learning. Delphi Technique, Cohen's Kappa and percentages of agreements were used to assess model of patisserie project based learning. Data collection techniques employed in the study were questionnaire, check list worksheet, observation, and interview sheets. Subjects were 13 lectures of expertise food and nutrition and 91 students of Food and Nutrition ...

PROCESS-BASED LEARNING: TOWARDS THEORETICAL AND LECTURE-BASED COURSEWORK IN STUDIO STYLE

Directory of Open Access Journals (Sweden)

Hatem Ezzat Nabih

2010-07-01

Full Text Available This article presents a process-based learning approach to design education where theoretical coursework is taught in studio-style. Lecture-based coursework is sometimes regarded as lacking in challenge and broadening the gap between theory and practice. Furthermore, lecture-based curricula tend to be detached from the studio and deny students from applying their theoretically gained knowledge. Following the belief that student motivation is increased by establishing a higher level of autonomy in the learning process, I argue for a design education that links theory with applied design work within the studio setting. By synthesizing principles of Constructivist Learning and Problem-Based Learning, PBL students are given greater autonomy by being actively involved in their education. Accordingly, I argue for a studio setting that incorporates learning in studio style by presenting three design applications involving students in investigation and experimentation in order to self-experience the design process.

The scientific learning approach using multimedia-based maze game to improve learning outcomes

Science.gov (United States)

Setiawan, Wawan; Hafitriani, Sarah; Prabawa, Harsa Wara

2016-02-01

The objective of curriculum 2013 is to improve the quality of education in Indonesia, which leads to improving the quality of learning. The scientific approach and supported empowerment media is one approach as massaged of curriculum 2013. This research aims to design a labyrinth game based multimedia and apply in the scientific learning approach. This study was conducted in one of the Vocational School in Subjects of Computer Network on 2 (two) classes of experimental and control. The method used Mix Method Research (MMR) which combines qualitative in multimedia design, and quantitative in the study of learning impact. The results of a survey showed that the general of vocational students like of network topology material (68%), like multimedia (74%), and in particular, like interactive multimedia games and flash (84%). Multimediabased maze game developed good eligibility based on media and material aspects of each value 840% and 82%. Student learning outcomes as a result of using a scientific approach to learning with a multimediabased labyrinth game increase with an average of gain index about (58%) and higher than conventional multimedia with index average gain of 0.41 (41%). Based on these results the scientific approach to learning by using multimediabased labyrinth game can improve the quality of learning and increase understanding of students. Multimedia of learning based labyrinth game, which developed, got a positive response from the students with a good qualification level (75%).

Promoting Student Collaboration in a Detracked, Heterogeneous Secondary Mathematics Classroom

Science.gov (United States)

Staples, Megan E.

2008-01-01

Detracking and heterogeneous groupwork are two educational practices that have been shown to have promise for affording all students needed learning opportunities to develop mathematical proficiency. However, teachers face significant pedagogical challenges in organizing productive groupwork in these settings. This study offers an analysis of one…

Silica-based PLC with heterogeneously-integrated PDs for one-chip DP-QPSK receiver.

Science.gov (United States)

Kurata, Yu; Nasu, Yusuke; Tamura, Munehisa; Kasahara, Ryoichi; Aozasa, Shinichi; Mizuno, Takayuki; Yokoyama, Haruki; Tsunashima, Satoshi; Muramoto, Yoshifumi

2012-12-10

To realize a DP-QPSK receiver PLC, we heterogeneously integrated eight high-speed PDs on a silica-based PLC platform with a PBS, 90-degree optical hybrids and a VOA. The use of a 2.5%-Δ waveguide reduced the receiver PLC size to 11 mm x 11 mm. We successfully demonstrated 32 Gbaud DP-QPSK signal demodulation with the receiver PLC.

A New Design Approach to Game-Based learning

DEFF Research Database (Denmark)

Larsen, Lasse Juel

2012-01-01

to ground the student’s reason to learn. This paper proposes a different approach: using visualisation in immersive 3D worlds as both documentation of learning progress and as a reward system which motivates further learning. The overall design idea is to build a game based learning system from three......This paper puts forward a new design perspective for gamebased learning. The general idea is to abandon the long sought-after dream of designing a closed learning system, where students in both primary and secondary school could learn – without the interference of teachers – whatever subject......-based learning system, but will also confront aspects of modern learning theory, especially the notion of reference between the content of an assignment and the reality with which it should or could be connected (situated learning). The second idea promotes a way of tackling the common experience of the average...

Characteristics of the Web-Based Learning Environment in Distance Education: Students' Perceptions of Their Learning Needs

Science.gov (United States)

Atan, Hanafi; Rahman, Zuraidah; Idrus, Rozhan M.

2004-01-01

A study was conducted regarding students' perceptions on the characteristics of the learning requirements in a web-based learning environment. Various aspects of on-line learning were studied including the general web-based support system for the students, the learning materials, instructional strategies of the learning materials and the learning…

Investigative Primary Science: A Problem-Based Learning Approach

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Etherington, Matthew B.

2011-01-01

This study reports on the success of using a problem-based learning approach (PBL) as a pedagogical mode of learning open inquiry science within a traditional four-year undergraduate elementary teacher education program. In 2010, a problem-based learning approach to teaching primary science replaced the traditional content driven syllabus. During…

ICT-Supported Problem-Based Learning: Possibilities of Applying Problem-Based Learning from Primary School to Higher Education

Directory of Open Access Journals (Sweden)

Czékmán Balázs

2016-06-01

Full Text Available Problem Based Learning was originally created for medical students to better diagnose new illnesses; this methodology can be used in almost all the fields of education. Teachers can teach by appealing to students’ natural instincts to create, and they can improve the students’ performance in different disciplines. So, we can say that it is an easy way of the acquisition and integration of new knowledge. While the content and structure of PBL courses may differ, the general goals and learning objectives tend to be similar. It begins with the assumption that learning is an active, integrated, and constructive process influenced by social and contextual factors. The task of our paper is to show how Problem-Based Learning can be used from primary to university level education in teaching different subjects.

Blended Learning Based on Schoology: Effort of Improvement Learning Outcome and Practicum Chance in Vocational High School

Science.gov (United States)

Irawan, Vincentius Tjandra; Sutadji, Eddy; Widiyanti

2017-01-01

The aims of this study were to determine: (1) the differences in learning outcome between Blended Learning based on Schoology and Problem-Based Learning, (2) the differences in learning outcome between students with prior knowledge of high, medium, and low, and (3) the interaction between Blended Learning based on Schoology and prior knowledge to…

USING PCU-CAMEL, A WEB-BASED LEARNING ENVIRONMENT, IN EVALUATING TEACHING-LEARNING PROCESS

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Arlinah Imam Rahardjo

2008-01-01

Full Text Available PCU-CAMEL (Petra Christian University-Computer Aided Mechanical Engineering Department Learning Environment has been developed to integrate the use of this web-based learning environment into the traditional, face-to-face setting of class activities. This integrated learning method is designed as an effort to enrich and improve the teaching-learning process at Petra Christian University. A study was conducted to introduce the use of PCU-CAMEL as a tool in evaluating teaching learning process. The study on this method of evaluation was conducted by using a case analysis on the integration of PCU-CAMEL to the traditional face-to-face meetings of LIS (Library Information System class at the Informatics Engineering Department of Petra Christian University. Students’ responses documented in some features of PCU-CAMEL were measured and analyzed to evaluate the effectiveness of this integrated system in developing intrinsic motivation of the LIS students of the first and second semester of 2004/2005 to learn. It is believed that intrinsic motivation can drive students to learn more. From the study conducted, it is concluded that besides its capability in developing intrinsic motivation, PCU-CAMEL as a web-based learning environment, can also serve as an effective tool for both students and instructors to evaluate the teaching-learning process. However, some weaknesses did exist in using this method of evaluating teaching-learning process. The free style and unstructured form of the documentation features of this web-based learning environment can lead to ineffective evaluation results

System-on-Chip Environment: A SpecC-Based Framework for Heterogeneous MPSoC Design

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Daniel D. Gajski

2008-07-01

Full Text Available The constantly growing complexity of embedded systems is a challenge that drives the development of novel design automation techniques. C-based system-level design addresses the complexity challenge by raising the level of abstraction and integrating the design processes for the heterogeneous system components. In this article, we present a comprehensive design framework, the system-on-chip environment (SCE which is based on the influential SpecC language and methodology. SCE implements a top-down system design flow based on a specify-explore-refine paradigm with support for heterogeneous target platforms consisting of custom hardware components, embedded software processors, dedicated IP blocks, and complex communication bus architectures. Starting from an abstract specification of the desired system, models at various levels of abstraction are automatically generated through successive step-wise refinement, resulting in a pin-and cycle-accurate system implementation. The seamless integration of automatic model generation, estimation, and verification tools enables rapid design space exploration and efficient MPSoC implementation. Using a large set of industrial-strength examples with a wide range of target architectures, our experimental results demonstrate the effectiveness of our framework and show significant productivity gains in design time.

PENGEMBANGAN CASE BASE LEARNING PADA MATA KULIAH PEREKONOMIAN INDONESIA

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Hastarini Dwi Atmani

2011-05-01

Full Text Available In this time, teacher centered learning is a methods in part of higher education in Indonsia. This method, students passively receive information.Case base learning is an instructional design model that is a variant of project oriented learning. Cases are factually-based, complex problems written to stimulate classroom discussion and collaborative analysis. This one, students construct knowledge through gathering and synthesizing information and integrating it with the general skills of inquiry, communication, critical thinking, and problem solving. Key words : active learning, case base learning.

PERANCANGAN WEB BASED LEARNING SEBAGAI MEDIA PEMBELAJARAN BERBASIS ICT

OpenAIRE

Ricky Firmansyah; Iis Saidah

2016-01-01

ABSTRACT The media is very important component of communication process. The effectiveness of media is very influential on extent to which a communication role will be accepted by the audience with fast and precise, or vice versa. E-Learning is present as ICT based learning media that allows students and teachers interact in different places. Web Based Learning (WBL) is used as one part of the E-Learning. This study focuses on designing web-based ICT as a learning medium that is used for ...

Does individual learning styles influence the choice to use a web-based ECG learning programme in a blended learning setting?

Science.gov (United States)

Nilsson, Mikael; Östergren, Jan; Fors, Uno; Rickenlund, Anette; Jorfeldt, Lennart; Caidahl, Kenneth; Bolinder, Gunilla

2012-01-16

The compressed curriculum in modern knowledge-intensive medicine demands useful tools to achieve approved learning aims in a limited space of time. Web-based learning can be used in different ways to enhance learning. Little is however known regarding its optimal utilisation. Our aim was to investigate if the individual learning styles of medical students influence the choice to use a web-based ECG learning programme in a blended learning setting. The programme, with three types of modules (learning content, self-assessment questions and interactive ECG interpretation training), was offered on a voluntary basis during a face to face ECG learning course for undergraduate medical students. The Index of Learning Styles (ILS) and a general questionnaire including questions about computer and Internet usage, preferred future speciality and prior experience of E-learning were used to explore different factors related to the choice of using the programme or not. 93 (76%) out of 123 students answered the ILS instrument and 91 the general questionnaire. 55 students (59%) were defined as users of the web-based ECG-interpretation programme. Cronbach's alpha was analysed with coefficients above 0.7 in all of the four dimensions of ILS. There were no significant differences with regard to learning styles, as assessed by ILS, between the user and non-user groups; Active/Reflective; Visual/Verbal; Sensing/Intuitive; and Sequential/Global (p = 0.56-0.96). Neither did gender, prior experience of E-learning or preference for future speciality differ between groups. Among medical students, neither learning styles according to ILS, nor a number of other characteristics seem to influence the choice to use a web-based ECG programme. This finding was consistent also when the usage of the different modules in the programme were considered. Thus, the findings suggest that web-based learning may attract a broad variety of medical students.

Heterogeneous Agent Model with Memory and Asset Price Behaviour

Czech Academy of Sciences Publication Activity Database

Vošvrda, Miloslav; Vácha, Lukáš

2003-01-01

Roč. 12, č. 2 (2003), s. 155-168 ISSN 1210-0455 R&D Projects: GA ČR GA402/00/0439; GA ČR GA402/01/0034 Institutional research plan: CEZ:AV0Z1075907 Keywords : efficient markets hypothesis * technical trading rules * heterogeneous agent model with memory and learning Subject RIV: AH - Economics

Evaluation of a Learning Object Based Learning Environment in Different Dimensions

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Ünal Çakıroğlu

2009-11-01

Full Text Available Learning Objects (LOs are web based learning resources presented by Learning Object Repositories (LOR. For recent years LOs have begun to take place on web and it is suggested that appropriate design of LOs can make positive impact on learning. In order to support learning, research studies recommends LOs should have been evaluated pedagogically and technologically, and the content design created by using LOs should have been designed through appropriate instructional models. Since the use of LOs have recently begun, an exact pedagogical model about efficient use of LOs has not been developed. In this study a LOR is designed in order to be used in mathematics education. The LOs in this LOR have been evaluated pedagogically and technologically by mathematics teachers and field experts. In order to evaluate the designed LO based environment, two different questionnaires have been used. These questionnaires are developed by using the related literature about web based learning environments evaluation criteria and also the items are discussed with the field experts for providing the validity. The reliability of the questionnaires is calculated cronbach alpha = 0.715 for the design properties evaluation survey and cronbach alpha =0.726 for pedagogic evaluation. Both of two questionnaires are five point Likert type. The first questionnaire has the items about “Learning Support of LOs, Competency of LOR, The importance of LOs in mathematics education, the usability of LOs by students”. “The activities on LOs are related to outcomes of subjects, there are activities for students have different learning styles. There are activities for wondering students.” are examples for items about learning support of LOs. “System helps for exploration of mathematical relations”, “I think teaching mathematics with this system will be enjoyable.” are example items for importance of LOs in mathematics education. In the competency of LOR title, �

The E-Learning Setting Circle: First Steps toward Theory Development in E-Learning Research

Science.gov (United States)

Rüth, Marco; Kaspar, Kai

2017-01-01

E-learning projects and related research generate an increasing amount of evidence within and across various disciplines and contexts. The field is very heterogeneous as e-learning approaches are often characterized by rather unique combinations of situational factors that guide the design and realization of e-learning in a bottom-up fashion.…

Contextual Approach with Guided Discovery Learning and Brain Based Learning in Geometry Learning

Science.gov (United States)

Kartikaningtyas, V.; Kusmayadi, T. A.; Riyadi

2017-09-01

The aim of this study was to combine the contextual approach with Guided Discovery Learning (GDL) and Brain Based Learning (BBL) in geometry learning of junior high school. Furthermore, this study analysed the effect of contextual approach with GDL and BBL in geometry learning. GDL-contextual and BBL-contextual was built from the steps of GDL and BBL that combined with the principles of contextual approach. To validate the models, it uses quasi experiment which used two experiment groups. The sample had been chosen by stratified cluster random sampling. The sample was 150 students of grade 8th in junior high school. The data were collected through the student’s mathematics achievement test that given after the treatment of each group. The data analysed by using one way ANOVA with different cell. The result shows that GDL-contextual has not different effect than BBL-contextual on mathematics achievement in geometry learning. It means both the two models could be used in mathematics learning as the innovative way in geometry learning.

Joint sparsity based heterogeneous data-level fusion for target detection and estimation

Science.gov (United States)

Niu, Ruixin; Zulch, Peter; Distasio, Marcello; Blasch, Erik; Shen, Dan; Chen, Genshe

2017-05-01

Typical surveillance systems employ decision- or feature-level fusion approaches to integrate heterogeneous sensor data, which are sub-optimal and incur information loss. In this paper, we investigate data-level heterogeneous sensor fusion. Since the sensors monitor the common targets of interest, whose states can be determined by only a few parameters, it is reasonable to assume that the measurement domain has a low intrinsic dimensionality. For heterogeneous sensor data, we develop a joint-sparse data-level fusion (JSDLF) approach based on the emerging joint sparse signal recovery techniques by discretizing the target state space. This approach is applied to fuse signals from multiple distributed radio frequency (RF) signal sensors and a video camera for joint target detection and state estimation. The JSDLF approach is data-driven and requires minimum prior information, since there is no need to know the time-varying RF signal amplitudes, or the image intensity of the targets. It can handle non-linearity in the sensor data due to state space discretization and the use of frequency/pixel selection matrices. Furthermore, for a multi-target case with J targets, the JSDLF approach only requires discretization in a single-target state space, instead of discretization in a J-target state space, as in the case of the generalized likelihood ratio test (GLRT) or the maximum likelihood estimator (MLE). Numerical examples are provided to demonstrate that the proposed JSDLF approach achieves excellent performance with near real-time accurate target position and velocity estimates.

Problem-Based Learning: Exploiting Knowledge of How People Learn to Promote Effective Learning

Science.gov (United States)

Wood, E. J.

2004-01-01

There is much information from educational psychology studies on how people learn. The thesis of this paper is that we should use this information to guide the ways in which we teach rather than blindly using our traditional methods. In this context, problem-based learning (PBL), as a method of teaching widely used in medical schools but…

The Effectiveness of Project Based Learning in Trigonometry

Science.gov (United States)

Gerhana, M. T. C.; Mardiyana, M.; Pramudya, I.

2017-09-01

This research aimed to explore the effectiveness of Project-Based Learning (PjBL) with scientific approach viewed from interpersonal intelligence toward students’ achievement learning in mathematics. This research employed quasi experimental research. The subjects of this research were grade X MIPA students in Sleman Yogyakarta. The result of the research showed that project-based learning model is more effective to generate students’ mathematics learning achievement that classical model with scientific approach. This is because in PjBL model students are more able to think actively and creatively. Students are faced with a pleasant atmosphere to solve a problem in everyday life. The use of project-based learning model is expected to be the choice of teachers to improve mathematics education.

The LEONARDO-DA-VINCI pilot project "e-learning-assistant" - Situation-based learning in nursing education.

Science.gov (United States)

Pfefferle, Petra Ina; Van den Stock, Etienne; Nauerth, Annette

2010-07-01

E-learning will play an important role in the training portfolio of students in higher and vocational education. Within the LEONARDO-DA-VINCI action programme transnational pilot projects were funded by the European Union, which aimed to improve the usage and quality of e-learning tools in education and professional training. The overall aim of the LEONARDO-DA-VINCI pilot project "e-learning-assistant" was to create new didactical and technical e-learning tools for Europe-wide use in nursing education. Based on a new situation-oriented learning approach, nursing teachers enrolled in the project were instructed to adapt, develop and implement e- and blended learning units. According to the training contents nursing modules were developed by teachers from partner institutions, implemented in the project centers and evaluated by students. The user-package "e-learning-assistant" as a product of the project includes two teacher training units, the authoring tool "synapse" to create situation-based e-learning units, a student's learning platform containing blended learning modules in nursing and an open sourced web-based communication centre. Copyright 2009 Elsevier Ltd. All rights reserved.

Game-based versus traditional case-based learning: comparing effectiveness in stroke continuing medical education.

Science.gov (United States)

Telner, Deanna; Bujas-Bobanovic, Maja; Chan, David; Chester, Bob; Marlow, Bernard; Meuser, James; Rothman, Arthur; Harvey, Bart

2010-09-01

To evaluate family physicians' enjoyment of and knowledge gained from game-based learning, compared with traditional case-based learning, in a continuing medical education (CME) event on stroke prevention and management. An equivalence trial to determine if game-based learning was as effective as case-based learning in terms of attained knowledge levels. Game questions and small group cases were developed. Participants were randomized to either a game-based or a case-based group and took part in the event. Ontario provincial family medicine conference. Thirty-two family physicians and 3 senior family medicine residents attending the conference. Participation in either a game-based or a case-based CME learning group. Scores on 40-item immediate and 3-month posttests of knowledge and a satisfaction survey. Results from knowledge testing immediately after the event and 3 months later showed no significant difference in scoring between groups. Participants in the game-based group reported higher levels of satisfaction with the learning experience. Games provide a novel way of organizing CME events. They might provide more group interaction and discussion, as well as improve recruitment to CME events. They might also provide a forum for interdisciplinary CME. Using games in future CME events appears to be a promising approach to facilitate participant learning.

The effects of case-based team learning on students' learning, self regulation and self direction.

Science.gov (United States)

Rezaee, Rita; Mosalanejad, Leili

2015-01-26

The application of the best approaches to teach adults in medical education is important in the process of training learners to become and remain effective health care providers. This research aims at designing and integrating two approaches, namely team teaching and case study and tries to examine the consequences of these approaches on learning, self regulation and self direction of nursing students. This is a quasi experimental study of 40 students who were taking a course on mental health. The lessons were designed by using two educational techniques: short case based study and team based learning. Data gathering was based on two valid and reliable questionnaires: Self-Directed Readiness Scale (SDLRS) and the self-regulating questionnaire. Open ended questions were also designed for the evaluation of students' with points of view on educational methods. The Results showed an increase in the students' self directed learning based on their performance on the post-test. The results showed that the students' self-directed learning increased after the intervention. The mean difference before and after intervention self management was statistically significant (p=0.0001). Also, self-regulated learning increased with the mean difference after intervention (p=0.001). Other results suggested that case based team learning can have significant effects on increasing students' learning (p=0.003). This article may be of value to medical educators who wish to replace traditional learning with informal learning (student-centered-active learning), so as to enhance not only the students' knowledge, but also the advancement of long- life learning skills.

The Effects of Case-Based Team Learning on Students’ Learning, Self Regulation and Self Direction

Science.gov (United States)

Rezaee, Rita; Mosalanejad, Leili

2015-01-01

Introduction: The application of the best approaches to teach adults in medical education is important in the process of training learners to become and remain effective health care providers. This research aims at designing and integrating two approaches, namely team teaching and case study and tries to examine the consequences of these approaches on learning, self regulation and self direction of nursing students. Material & Methods: This is aquasi experimental study of 40 students who were taking a course on mental health. The lessons were designed by using two educational techniques: short case based study and team based learning. Data gathering was based on two valid and reliablequestionnaires: Self-Directed Readiness Scale (SDLRS) and the self-regulating questionnaire. Open ended questions were also designed for the evaluation of students’with points of view on educational methods. Results: The Results showed an increase in the students’ self directed learning based on their performance on the post-test. The results showed that the students’ self-directed learning increased after the intervention. The mean difference before and after intervention self management was statistically significant (p=0.0001). Also, self-regulated learning increased with the mean difference after intervention (p=0.001). Other results suggested that case based team learning can have significant effects on increasing students’ learning (p=0.003). Conclusion: This article may be of value to medical educators who wish to replace traditional learning with informal learning (student-centered-active learning), so as to enhance not only the students’ ’knowledge, but also the advancement of long- life learning skills. PMID:25946918

Reflective learning in community-based dental education.

Science.gov (United States)

Deogade, Suryakant C; Naitam, Dinesh

2016-01-01

Community-based dental education (CBDE) is the implementation of dental education in a specific social context, which shifts a substantial part of dental clinical education from dental teaching institutional clinics to mainly public health settings. Dental students gain additional value from CBDE when they are guided through a reflective process of learning. We propose some key elements to the existing CBDE program that support meaningful personal learning experiences. Dental rotations of 'externships' in community-based clinical settings (CBCS) are year-long community-based placements and have proven to be strong learning environments where students develop good communication skills and better clinical reasoning and management skills. We look at the characteristics of CBDE and how the social and personal context provided in communities enhances dental education. Meaningfulness is created by the authentic context, which develops over a period of time. Structured reflection assignments and methods are suggested as key elements in the existing CBDE program. Strategies to enrich community-based learning experiences for dental students include: Photographic documentation; written narratives; critical incident reports; and mentored post-experiential small group discussions. A directed process of reflection is suggested as a way to increase the impact of the community learning experiences. We suggest key elements to the existing CBDE module so that the context-rich environment of CBDE allows for meaningful relations and experiences for dental students and enhanced learning.

Beyond Problem-Based Learning: Using Dynamic PBL in Chemistry

Science.gov (United States)

Overton, Tina L.; Randles, Christopher A.

2015-01-01

This paper describes the development and implementation of a novel pedagogy, dynamic problem-based learning. The pedagogy utilises real-world problems that evolve throughout the problem-based learning activity and provide students with choice and different data sets. This new dynamic problem-based learning approach was utilised to teach…

Students' Satisfaction and Perceived Learning with a Web-based Course

Directory of Open Access Journals (Sweden)

Derek Holton

2003-01-01

Full Text Available This paper describes a study, which explored students' responses and reactions to a Web-based tertiary statistics course supporting problem-based learning. The study was undertaken among postgraduate students in a Malaysian university. The findings revealed that the majority of the students were satisfied with their learning experience and achieved comparable learning outcomes to students in the face-to-face version of the course. Students appreciated the flexibility of anytime, anywhere learning. The majority of the students was motivated to learn and had adequate technical support to complete the course. Improvement in computer skills was an incidental learning outcome from the course. The student-student and student-teacher communication was satisfactory but a few students felt isolated learning in the Web environment. These students expressed a need for some face-to-face lectures. While the majority of the students saw value in learning in a problem-based setting, around a third of the students expressed no opinion on, or were dissatisfied with, the problem-based environment. They were satisfied with the group facilitators and learning materials but were unhappy with the group dynamics. Some of the students felt unable to contribute to or learn from the asynchronous Web-based conferences using problem-based approach. Some of the students were not punctual and were not prepared to take part in the Web-based conferences. The findings have suggested a need to explicitly design an organising strategy in the asynchronous Web-based conferences using problem-based approach to aid students in completing the problem-based learning process.

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Science.gov (United States)

Drzewiecki, Wojciech

2016-12-01

In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

Teaching Chinese in heterogeneous classrooms: strategies and practices

Directory of Open Access Journals (Sweden)

Rong Zhang Fernandez

2014-12-01

Full Text Available The heterogeneous nature of the Chinese classroom is a reality in the teaching of Chinese in France, both in secondary and higher education. This heterogeneity is due to several reasons: different levels of language knowledge, different origins and backgrounds of the students, different teaching/learning objectives, different cultural and family background, and social factors. Our research has been conducted in  a final-year LIE college class (langue inter-établissement; in a French secondary school. In our study, the following questions have been posed: How to best adapt the teaching of Chinese to fit the needs of all students? Would differentiated instruction be a solution? What would be the best strategies and practices, in view of the CEFR requirements related to teaching content, to tasks and to assessment? Taking into account a detailed analysis of the class in question in terms of the type of students, the differences in their knowledge of language, and their learning goals, , we adopt  the theory of differentiated instruction –  its main ideas strategies, its overall methodology and practical techniques to address the difficulties ensuing from classroom heterogeneity. The differentiation is implemented at the level of content, task selection, course structure and evaluation. Are there any limitations to differentiated instruction? Strong discrepancies in the levels of students’ knowledge is potentially a problem, and differences in their work pace as well as the teachers’ increased workload can also present difficulties. New ways of organizing language classes such as grouping students on the basis of their various language skills could help solve these issues.

Effects of case-based learning on communication skills, problem-solving ability, and learning motivation in nursing students.

Science.gov (United States)

Yoo, Moon-Sook; Park, Hyung-Ran

2015-06-01

The purpose of this study was to explore the effects of case-based learning on communication skills, problem-solving ability, and learning motivation in sophomore nursing students. In this prospective, quasi-experimental study, we compared the pretest and post-test scores of an experimental group and a nonequivalent, nonsynchronized control group. Both groups were selected using convenience sampling, and consisted of students enrolled in a health communication course in the fall semesters of 2011 (control group) and 2012 (experimental group) at a nursing college in Suwon, South Korea. The two courses covered the same material, but in 2011 the course was lecture-based, while in 2012, lectures were replaced by case-based learning comprising five authentic cases of patient-nurse communication. At post-test, the case-based learning group showed significantly greater communication skills, problem-solving ability, and learning motivation than the lecture-based learning group. This finding suggests that case-based learning is an effective learning and teaching method. © 2014 Wiley Publishing Asia Pty Ltd.

Comparing Problem-Based Learning Students to Students in a Lecture-Based Curriculum: Learning Strategies and the Relation with Self-Study Time

Science.gov (United States)

Wijnen, Marit; Loyens, Sofie M. M.; Smeets, Guus; Kroeze, Maarten; van der Molen, Henk

2017-01-01

In educational theory, deep processing (i.e., connecting different study topics together) and self-regulation (i.e., taking control over one's own learning process) are considered effective learning strategies. These learning strategies can be influenced by the learning environment. Problem-based learning (PBL), a student-centered educational…

Project-Based Learning Not Just for STEM Anymore

Science.gov (United States)

Duke, Nell K.; Halvorsen, Anne-Lise; Strachan, Stephanie L.

2016-01-01

The popularity of project-based learning has been driven in part by a growing number of STEM schools and programs. But STEM subjects are not the only fertile ground for project-based learning (PBL). Social studies and literacy content, too, can be adapted into PBL units to benefit teaching and learning, the authors argue. They review key studies…

Development and Evaluation of a Computer-Based Learning Environment for Teachers: Assessment of Learning Strategies in Learning Journals

Directory of Open Access Journals (Sweden)

Inga Glogger

2013-01-01

Full Text Available Training teachers to assess important components of self-regulated learning such as learning strategies is an important, yet somewhat neglected, aspect of the integration of self-regulated learning at school. Learning journals can be used to assess learning strategies in line with cyclical process models of self-regulated learning, allowing for rich formative feedback. Against this background, we developed a computer-based learning environment (CBLE that trains teachers to assess learning strategies with learning journals. The contents of the CBLE and its instructional design were derived from theory. The CBLE was further shaped by research in a design-based manner. Finally, in two evaluation studies, student teachers (N1=44; N2=89 worked with the CBLE. We analyzed satisfaction, interest, usability, and assessment skills. Additionally, in evaluation study 2, effects of an experimental variation on motivation and assessment skills were tested. We found high satisfaction, interest, and good usability, as well as satisfying assessment skills, after working with the CBLE. Results show that teachers can be trained to assess learning strategies in learning journals. The developed CBLE offers new perspectives on how to support teachers in fostering learning strategies as central component of effective self-regulated learning at school.

The Effects of Brain Based Learning Approach on Motivation and Students Achievement in Mathematics Learning

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Mekarina, M.; Ningsih, Y. P.

2017-09-01

This classroom action research is based by the facts that the students motivation and achievement mathematics learning is less. One of the factors causing is learning that does not provide flexibility to students to empower the potential of the brain optimally. The aim of this research was to improve the student motivation and achievement in mathematics learning by implementing brain based learning approach. The subject of this research was student of grade XI in senior high school. The research consisted of two cycles. Data of student achievement from test, and the student motivation through questionnaire. Furthermore, the finding of this research showed the result of the analysis was the implementation of brain based learning approach can improve student’s achievement and motivation in mathematics learning.

The Effect of Learning Based on Technology Model and Assessment Technique toward Thermodynamic Learning Achievement

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Makahinda, T.

2018-02-01

The purpose of this research is to find out the effect of learning model based on technology and assessment technique toward thermodynamic achievement by controlling students intelligence. This research is an experimental research. The sample is taken through cluster random sampling with the total respondent of 80 students. The result of the research shows that the result of learning of thermodynamics of students who taught the learning model of environmental utilization is higher than the learning result of student thermodynamics taught by simulation animation, after controlling student intelligence. There is influence of student interaction, and the subject between models of technology-based learning with assessment technique to student learning result of Thermodynamics, after controlling student intelligence. Based on the finding in the lecture then should be used a thermodynamic model of the learning environment with the use of project assessment technique.

Deep and surface learning in problem-based learning: a review of the literature

NARCIS (Netherlands)

D.H.J.M. Dolmans (Diana); S.M.M. Loyens (Sofie); Marcq, H. (Hélène); D. Gijbels (David)

2016-01-01

textabstractIn problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested

Deep and surface learning in problem-based learning: a review of the literature

NARCIS (Netherlands)

D.H.J.M. Dolmans (Diana); S.M.M. Loyens (Sofie); H. Marcq (Hélène); D. Gijbels (David)

2015-01-01

textabstractIn problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested

Learning styles: individualizing computer-based learning environments

Directory of Open Access Journals (Sweden)

Tim Musson

1995-12-01

Full Text Available While the need to adapt teaching to the needs of a student is generally acknowledged (see Corno and Snow, 1986, for a wide review of the literature, little is known about the impact of individual learner-differences on the quality of learning attained within computer-based learning environments (CBLEs. What evidence there is appears to support the notion that individual differences have implications for the degree of success or failure experienced by students (Ford and Ford, 1992 and by trainee end-users of software packages (Bostrom et al, 1990. The problem is to identify the way in which specific individual characteristics of a student interact with particular features of a CBLE, and how the interaction affects the quality of the resultant learning. Teaching in a CBLE is likely to require a subset of teaching strategies different from that subset appropriate to more traditional environments, and the use of a machine may elicit different behaviours from those normally arising in a classroom context.

Problem-Based Learning to Foster Deep Learning in Preservice Geography Teacher Education

Science.gov (United States)

Golightly, Aubrey; Raath, Schalk

2015-01-01

In South Africa, geography education students' approach to deep learning has received little attention. Therefore the purpose of this one-shot experimental case study was to evaluate the extent to which first-year geography education students used deep or surface learning in an embedded problem-based learning (PBL) format. The researchers measured…

Adaptive heterogeneous multi-robot teams

Energy Technology Data Exchange (ETDEWEB)

Parker, L.E.

1998-11-01

This research addresses the problem of achieving fault tolerant cooperation within small- to medium-sized teams of heterogeneous mobile robots. The author describes a novel behavior-based, fully distributed architecture, called ALLIANCE, that utilizes adaptive action selection to achieve fault tolerant cooperative control in robot missions involving loosely coupled, largely independent tasks. The robots in this architecture possess a variety of high-level functions that they can perform during a mission, and must at all times select an appropriate action based on the requirements of the mission, the activities of other robots, the current environmental conditions, and their own internal states. Since such cooperative teams often work in dynamic and unpredictable environments, the software architecture allows the team members to respond robustly and reliably to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. After presenting ALLIANCE, the author describes in detail the experimental results of an implementation of this architecture on a team of physical mobile robots performing a cooperative box pushing demonstration. These experiments illustrate the ability of ALLIANCE to achieve adaptive, fault-tolerant cooperative control amidst dynamic changes in the capabilities of the robot team.

Heutagogy: An alternative practice based learning approach.

Science.gov (United States)

Bhoyrub, John; Hurley, John; Neilson, Gavin R; Ramsay, Mike; Smith, Margaret

2010-11-01

Education has explored and utilised multiple approaches in attempts to enhance the learning and teaching opportunities available to adult learners. Traditional pedagogy has been both directly and indirectly affected by andragogy and transformational learning, consequently widening our understandings and approaches toward view teaching and learning. Within the context of nurse education, a major challenge has been to effectively apply these educational approaches to the complex, unpredictable and challenging environment of practice based learning. While not offered as a panacea to such challenges, heutagogy is offered in this discussion paper as an emerging and potentially highly congruent educational framework to place around practice based learning. Being an emergent theory its known conceptual underpinnings and possible applications to nurse education need to be explored and theoretically applied. Through placing the adult learner at the foreground of grasping learning opportunities as they unpredictability emerge from a sometimes chaotic environment, heutagogy can be argued as offering the potential to minimise many of the well published difficulties of coordinating practice with faculty teaching and learning. Copyright © 2010 Elsevier Ltd. All rights reserved.

Developing a Blended Learning-Based Method for Problem-Solving in Capability Learning

Science.gov (United States)

Dwiyogo, Wasis D.

2018-01-01

The main objectives of the study were to develop and investigate the implementation of blended learning based method for problem-solving. Three experts were involved in the study and all three had stated that the model was ready to be applied in the classroom. The implementation of the blended learning-based design for problem-solving was…

Collective Machine Learning: Team Learning and Classification in Multi-Agent Systems

Science.gov (United States)

Gifford, Christopher M.

2009-01-01

This dissertation focuses on the collaboration of multiple heterogeneous, intelligent agents (hardware or software) which collaborate to learn a task and are capable of sharing knowledge. The concept of collaborative learning in multi-agent and multi-robot systems is largely under studied, and represents an area where further research is needed to…

Work-Based Learning: A New Higher Education?

Science.gov (United States)

Boud, David, Ed.; Solomon, Nicky, Ed.

This three-part book contains 16 chapters exploring work-based learning from a theoretical and case-study perspective in the United Kingdom. Part 1, Framing Work-based Learning, contains the following four chapters: "New Practices for New Times" (David Boud, Nicky Solomon, and Colin Symes); "Repositioning Universities and Work"…

Scaffolding Problem-Based Learning with CSCL Tools

Science.gov (United States)

Lu, Jingyan; Lajoie, Susanne P.; Wiseman, Jeffrey

2010-01-01

Small-group medical problem-based learning (PBL) was a pioneering form of collaborative learning at the university level. It has traditionally been delivered in face-to-face text-based format. With the advancement of computer technology and progress in CSCL, educational researchers are now exploring how to design digitally-implemented scaffolding…

Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.

Science.gov (United States)

Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Gerjets, Peter; Spüler, Martin

2017-01-01

In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.

The role of differentiation and standards-based grading in the science learning of struggling and advanced learners in a detracked high school honors biology classroom

Science.gov (United States)

MacDonald, Michelina Ruth Carter

and advanced learners. My fourth finding reflects what I learned about heterogeneous grouping: (4) Heterogeneously grouping students for argumentation through engagement in science inquiry serves both to reinforce proficiency of learning goals for struggling learners and simultaneously push all learners towards advanced proficiency. These findings indicate how planning for and implementing a differentiated, standards-based instructional unit can support the learning needs of both struggling and advanced learners in a detracked, honors biology classroom.

The quality and impact of computer supported collaborative learning (CSCL) in radiology case-based learning

International Nuclear Information System (INIS)

Kourdioukova, Elena V.; Verstraete, Koenraad L.; Valcke, Martin

2011-01-01

Objective: The aim of this research was to explore (1) clinical years students' perceptions about radiology case-based learning within a computer supported collaborative learning (CSCL) setting, (2) an analysis of the collaborative learning process, and (3) the learning impact of collaborative work on the radiology cases. Methods: The first part of this study focuses on a more detailed analysis of a survey study about CSCL based case-based learning, set up in the context of a broader radiology curriculum innovation. The second part centers on a qualitative and quantitative analysis of 52 online collaborative learning discussions from 5th year and nearly graduating medical students. The collaborative work was based on 26 radiology cases regarding musculoskeletal radiology. Results: The analysis of perceptions about collaborative learning on radiology cases reflects a rather neutral attitude that also does not differ significantly in students of different grade levels. Less advanced students are more positive about CSCL as compared to last year students. Outcome evaluation shows a significantly higher level of accuracy in identification of radiology key structures and in radiology diagnosis as well as in linking the radiological signs with available clinical information in nearly graduated students. No significant differences between different grade levels were found in accuracy of using medical terminology. Conclusion: Students appreciate computer supported collaborative learning settings when tackling radiology case-based learning. Scripted computer supported collaborative learning groups proved to be useful for both 5th and 7th year students in view of developing components of their radiology diagnostic approaches.

The quality and impact of computer supported collaborative learning (CSCL) in radiology case-based learning.

Science.gov (United States)

Kourdioukova, Elena V; Verstraete, Koenraad L; Valcke, Martin

2011-06-01

The aim of this research was to explore (1) clinical years students' perceptions about radiology case-based learning within a computer supported collaborative learning (CSCL) setting, (2) an analysis of the collaborative learning process, and (3) the learning impact of collaborative work on the radiology cases. The first part of this study focuses on a more detailed analysis of a survey study about CSCL based case-based learning, set up in the context of a broader radiology curriculum innovation. The second part centers on a qualitative and quantitative analysis of 52 online collaborative learning discussions from 5th year and nearly graduating medical students. The collaborative work was based on 26 radiology cases regarding musculoskeletal radiology. The analysis of perceptions about collaborative learning on radiology cases reflects a rather neutral attitude that also does not differ significantly in students of different grade levels. Less advanced students are more positive about CSCL as compared to last year students. Outcome evaluation shows a significantly higher level of accuracy in identification of radiology key structures and in radiology diagnosis as well as in linking the radiological signs with available clinical information in nearly graduated students. No significant differences between different grade levels were found in accuracy of using medical terminology. Students appreciate computer supported collaborative learning settings when tackling radiology case-based learning. Scripted computer supported collaborative learning groups proved to be useful for both 5th and 7th year students in view of developing components of their radiology diagnostic approaches. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Adaptive Landmark-Based Navigation System Using Learning Techniques

DEFF Research Database (Denmark)

Zeidan, Bassel; Dasgupta, Sakyasingha; Wörgötter, Florentin

2014-01-01

The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal. In...... hexapod robots. As a result, it allows the robots to successfully learn to navigate to distal goals in complex environments.......The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal....... Inspired by this, we develop an adaptive landmark-based navigation system based on sequential reinforcement learning. In addition, correlation-based learning is also integrated into the system to improve learning performance. The proposed system has been applied to simulated simple wheeled and more complex...

Creation of Exercises for Team-Based Learning in Business

Science.gov (United States)

Timmerman, John E.; Morris, R. Franklin, Jr.

2015-01-01

Team-based learning (TBL) is an approach that builds on both the case method and problem-based learning and has been widely adopted in the sciences and healthcare disciplines. In recent years business disciplines have also discovered the value of this approach. One of the key characteristics of the team-based learning approach consists of…

Student Accountability in Team-Based Learning Classes

Science.gov (United States)

Stein, Rachel E.; Colyer, Corey J.; Manning, Jason

2016-01-01

Team-based learning (TBL) is a form of small-group learning that assumes stable teams promote accountability. Teamwork promotes communication among members; application exercises promote active learning. Students must prepare for each class; failure to do so harms their team's performance. Therefore, TBL promotes accountability. As part of the…

Problem-Based Learning in Social Work Education