可解释的练习建议与知识图表
Quanlong Guan1, Xinghe Cheng1, Fang Xiao1
1College of Information Science and Technology, Jinan University, Guangzhou, Guangdong, China; Guangdong Institution of Smart Education, Jinan University, Guangzhou, Guangdong, China.
概括
这项研究介绍了KG4EER,这是一种使用知识图的新可解释的运动推系统. KG4EER有效地将学生与合适的练习相匹配,并提供清晰的解释,提高学习效率.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 知识表示 知识表示
背景情况:
- 向学生推练习对于学习效率至关重要,但由于资源多样性和学生需求的多样性而具有挑战性.
- 现有的方法,如协作过,难以提供合适的建议,而深度学习缺乏可解释性,限制了实际应用.
- 需要一个可解释的练习推系统,可以有效地将学生与适当的练习相匹配,并提供合理的理由.
研究的目的:
- 提出和评估KG4EER,这是一个基于知识图的新系统,用于可解释的炼建议.
- 通过结合可解释性来解决教育环境中现有的推方法的局限性.
- 通过个性化和合理的运动建议来提高学生的学习效率.
主要方法:
- 开发了KG4EER,这是一个可解释的练习推系统,使用知识图.
- 实现了一个特征提取模块来表示学生的学习特征.
- 构建了一个知识图,整合知识概念,学生和练习,以及它们的相互关系.
主要成果:
- 与现有的基线方法相比,KG4EER在三个现实数据集中表现出优异的性能.
- 该系统有效地将各种学生与合适的练习相匹配,为建议提供明确的解释.
- 专家采访证实了该系统的有效性和强大的解释性.
结论:
- 在可解释的运动推系统中,KG4EER提供了显著的进步.
- 知识图的方法有效地弥合了学生需求和炼资源之间的差距.
- 该系统的可解释性提高了用户的信任和在教育环境中的实际实用性.
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