Intelligent Agent-Based e-Learning System For Adaptive Learning

H. Lai, Minhong Wang, Huaiqing Wang
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引用次数: 23

Abstract

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.
基于智能agent的自适应学习电子学习系统
适应性学习方法支持学习者通过个性化的方式实现预期的学习成果。以往的研究错误地将适应性e-Learning视为个性化学习材料的呈现方式,这是不完全正确的。适应性学习的主要思想是使学习内容个性化,以适应个体的能力差异。本研究基于能力-治疗互动理论和建构性结盟模型,设计了一个自适应学习模型。该模式旨在通过增强学生的内在学习动机来提高学生的学习效果。该模型使用多智能体框架进行操作,并在受控的实验室环境下进行验证。结果很有希望。学生的个体差异,特别是实验组的个体差异已经明显缩小。有学习困难的学生在测试后表现出显著的进步。但本研究未对该模型的纵向效应进行检验,后续将进行研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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