Fab: content-based, collaborative recommendation
Communications of the ACM
E-Learning: Strategies for Delivering Knowledge in the Digital Age
E-Learning: Strategies for Delivering Knowledge in the Digital Age
Instructional Design for Web-Based Training
Instructional Design for Web-Based Training
Intelligent agent supported personalization for virtual learning environments
Decision Support Systems
SIETTE: A Web-Based Tool for Adaptive Testing
International Journal of Artificial Intelligence in Education
E-Learning in the 21st Century: A Framework for Research and Practice, 2nd edition
E-Learning in the 21st Century: A Framework for Research and Practice, 2nd edition
Multiagent Based Selection of Tutor-Subject-Student Paradigm in an Intelligent Tutoring System
International Journal of Intelligent Information Technologies
A New Behavior Management Architecture for Language Faculty of an Agent for Task Delegation
International Journal of Intelligent Information Technologies
Sociomateriality Implications of Multi-Agent Supported Collaborative Work Systems
International Journal of Intelligent Information Technologies
Building Norms-Adaptable Agents from Potential Norms Detection Technique PNDT
International Journal of Intelligent Information Technologies
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Adaptive learning approaches support learners to achieve the intended learning outcomes through a personalized way. Previous studies mistakenly treat adaptive e-Learning as personalizing the presentation style of the learning materials, which is not completely correct. The main idea of adaptive learning is to personalize the earning content in a way that can cope with individual differences in aptitude. In this study, an adaptive learning model is designed based on the Aptitude-Treatment Interaction theory and Constructive Alignment Model. The model aims at improving students' learning outcomes through enhancing their intrinsic motivation to learn. This model is operationalized with a multi-agent framework and is validated under a controlled laboratory setting. The result is quite promising. The individual differences of students, especially in the experimental group, have been narrowed significantly. Students who have difficulties in learning show significant improvement after the test. However, the longitudinal effect of this model is not tested in this study and will be studied in the future.