基于语义链接网络的多面学习路径推荐

Juan Yang, Zhixing Huang, Hongtao Liu
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引用次数: 1

摘要

认知过载是当前自主学习智能学习系统存在的主要问题之一。为学习者提供个性化的学习路径可以有效地消除用户的学习迷失。在本文中,我们提出了一个多方面的推荐框架,根据学习者的不同学习风格,为学习者提供个性化的学习路径。构建推荐系统主要包括以下三个步骤:(1)分析学习过程中不同维度的学习风格的影响;(2)通过自组织规则将学习对象(LOs)自动组织成一个多面语义链接网络(Semantic Linked Network, SLN);(3)通过基于构建的语义链接网络的推理机向学习者推荐学习路径。实验验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-faceted Learning Paths Recommendation Via Semantic Linked Network
Cognition overload is one of the major problems in current self-learning intelligent learning systems. Providing learners with the personalized learning path can effectively smooth over users’ learning disorientation. In this paper, we propose a multi-faceted recommendation framework that provides learners with personalized learning paths based on their different learning styles. Building the recommendation system mainly involves the following three steps: (1) analyze the influences of the learning style in different dimensions during the learning process, (2) automatically organize the Learning Objects (LOs) into a multi-faceted Semantic Linked Network (SLN) via self-organized rules, (3) recommend the learning path to the learner through a reasoning machine based on the constructed SLN. The experiments verify the efficiency of the proposed method.
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