在线讨论和其他学习痕迹的自动分类,以检测认知存在
Verena Dornauer1, Michael Netzer1, Éva Kaczkó1,2
1Institute of Medical Informatics, UMIT TIROL - Private University for Health Sciences and Health Technology, Eduard Wallnöfer Zentrum 1, Hall in Tirol, 6060 Austria.
International journal of artificial intelligence in education
|June 3, 2024
概括
研究人员开发了一种德语语言分类器,通过语言分析自动检测学生在在线课程中的认知存在. 这种基于调查社区框架的工具,在不需要额外的学习轨迹的情况下,实现了82%的准确性.
科学领域:
- 教育技术的教育技术
- 在线学习在线学习
- 教育中的人工智能
背景情况:
- 在调查共同体 (CoI) 框架内,认知存在对于有意义的在线学习至关重要.
- 实时仪表板可视化认知存在,可以帮助教师监测和支持学生的进步.
- 目前的分类器通常仅依赖学生帖子的语言分析,对其他学习轨迹的探索有限.
研究的目的:
- 开发和评估在线课程中认知存在的德语语言分类器.
- 调查是否结合额外的学习轨迹 (例如文件附件,标记) 与单独的语言分析相比,提高了分类器的准确性.
- 为未来研究在德国在线学习环境中的认知存在创建基础数据集.
主要方法:
- 使用了来自德国在线大学课程的1,521个手动编码的有意义单位的数据集.
- 使用语言查询和词汇计数 (LIWC) 工具进行语言特征提取.
- 包括额外的学习轨迹,如文件附件,标记和词汇术语使用.
- 应用k-最近邻居,随机森林和多层感知子分类模型.
主要成果:
- 获得了认知存在分类器的最大准确率为82%和0.76的科恩 κ.
- 包括额外的学习轨迹并没有显著提高分类器的预测性能.
- 在基于语言分析的德国在线课程中成功开发了认知存在的自动分类器.
结论:
- 已经开发了一个自动分类器来检测在德国在线课程中的认知存在,主要使用语言分析.
- 开发的分类器代表了创建实时教师仪表板以监控学生参与度的一步.
- 这项研究提供了第一个完全COI编码的德国数据集,对未来的认知存在和在线学习研究有价值.
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