An intelligent distributed environment for active learning

Shang, Yi; Shi, Hongchi; Chen, Su-Shing

doi:10.1145/384055.384059

Cited by 66 publications

(37 citation statements)

References 20 publications

(6 reference statements)

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“…Investigations about learning styles in technology-enhanced learning were conducted and several adaptive systems were developed that aim at incorporating learning styles and providing courses that fit to the individual learning styles of students. Examples of such systems include CS383 (Carver, Howard, & Lane, 1999), IDEAL (Shang, Shi, & Chen, 2001), MAS-PLANG (Peña, Marzo, & de la Rosa, 2002), TANGOW (Paredes & Rodríguez, 2004), and AHA! (Stash, Cristea, & de Bra, 2006).…”

Section: Introductionmentioning

confidence: 99%

In-Depth Analysis of the Felder-Silverman Learning Style Dimensions

Graf¹,

Viola²,

Leo³

et al. 2007

Journal of Research on Technology in Education

206

110

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Section: Introductionmentioning

confidence: 99%

In-Depth Analysis of the Felder-Silverman Learning Style Dimensions

Graf¹,

Viola²,

Leo³

et al. 2007

Journal of Research on Technology in Education

206

110

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“…Shang et al [16] presented a web-based distributed environment for active learning that supports studentcentered, self-paced, and interactive learning approach. Students' learning-related profiles, such as learning styles and background knowledge, were used in selecting, organizing, and presenting learning materials.…”

Section: Literature Reviewmentioning

confidence: 99%

Blended Learning System Performance Evaluation

Zaeri¹

2013

IJCA

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In this paper, the use of the blended learning pedagogy as a teaching environment is studied. This pedagogy is based on a mixture of collaborative learning, problem-based learning and independent learning. Also, it is realized as a combination of a face-to-face environment and online learning, using a proprietary learning management system. More precisely, the methodological approach implemented by the Arab Open University is explored. The Arab Open University has twelve different branches distributed in eight different countries. In addition, the blended learning course design is described and the fulfillment of the corresponding learning outcomes is investigated. The study concludes that the technology provides with more learning choices. Among the choices is the blended learning that has several advantages and strength when compared to its counterpart, the classical learning. However, there are some challenges that need to be considered as well.

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“…IDEAL [7] is an intelligent agent-based environment for active learning. The system consists of a number of specialized agents with different expertise.…”

Section: Intelligent Distributed Environment For Active Learning (Ideal)mentioning

confidence: 99%

Applying Computational Science Techniques to Support Adaptive Learning

Santos¹,

Anido²,

Llamas³

et al. 2003

Lecture Notes in Computer Science

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Abstract. Adaptive Learning Systems offer customized learning experiences according to the actual student needs and capabilities. Effective student modelling, adequate representation of the knowledge domain and proper characterization of learning tools are key issues to provide high quality Adaptive Learning Systems. Most current systems are based on Artificial Intelligence techniques (e.g. fuzzy logic, neural networks, Bayesian networks, etc.) trying to reproduce human teaching behaviours by using a computational representation of expertise. This paper offers a survey on Adaptive Learning showing how Computational Science techniques are applied to instructional systems and identifying forthcoming trends for the future.

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An intelligent distributed environment for active learning

Cited by 66 publications

References 20 publications

In-Depth Analysis of the Felder-Silverman Learning Style Dimensions

In-Depth Analysis of the Felder-Silverman Learning Style Dimensions

Blended Learning System Performance Evaluation

Applying Computational Science Techniques to Support Adaptive Learning

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