基于动态案例推理方法的自适应学习系统中k近邻方法引导检索

Nihad El Ghouch, E. En-Naimi, Abdelhamid Zouhair, Mohammed Al Achhab
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引用次数: 1

摘要

适应性学习系统的目标是找到适应学习的方法。目前已经有了与展示、内容和导航相关的适应技术,但它们不能动态地创建个性化的路径,也不能通过减少失败和放弃的风险来对每个学习者进行个性化的跟踪。我们提出了一种基于增量动态案例推理的自适应学习系统架构,根据每个学习者的概况和其他学习者的经验提供个性化的实时学习,并基于k近邻方法促进具有相似行为的学习者的研究和分类,并预测未来的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Guided Retrieve through the K-Nearest Neighbors Method in Adaptive Learning System using the Dynamic Case Based Reasoning Approach
The goal of adaptive learning systems is to find ways to adapt learning. There are already adaptation techniques that relate to presentation, content and navigation, but they do not make it possible to dynamically create a personalized path and to carry out an individualized follow-up of each learner by reducing the risk of failure and abandonment. We propose architecture of an adaptive learning system based on Incremental Dynamic Case Based Reasoning to provide a personalized real-time learning according to the profile of each learner and the experiences of other learners and on the K-Nearest Neighbors method to facilitate the research and classification of learners with similar behaviors, as well as to predict future behaviors.
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