多模态学习分析实证研究的发展与展望

被引:22
作者
刘清堂 [1 ]
李小娟 [2 ]
谢魁 [3 ]
常瑀倍 [2 ]
郑欣欣 [2 ]
机构
[1] 华中师范大学湖北省教育信息化研究中心
[2] 华中师范大学人工智能教育学部
[3] 俄亥俄州立大学数字化学习研究实验室
关键词
多模态学习分析; 学习行为; 学习标签; 正式学习情境; 实证研究;
D O I
10.13811/j.cnki.eer.2022.01.009
中图分类号
G434 [计算机化教学];
学科分类号
040110 ;
摘要
多模态学习分析(MMLA)是智能化探究有效学习发生机理的关键技术。研究对国外37篇实证文献的任务情境设计和MMLA的四个过程进行系统综述,梳理出多模态数据集的生成场域多以发展认知为主,少关注情感价值的培育;学习标签注释以计算科学指导为主,缺乏不同时间尺度行为关联的理论指导;预测结果多关注学习行为表现,轻心智发展的过程解释;多模态数据分析反馈聚焦个性化学习支持,忽视决策支持。未来实证研究发展应聚焦有效学习与情感体验,融合计算科学和认知带理论,协同人机优势提供反馈支持,开展MMLA系统开发者和利益相关者的深度对话,不断迭代设计与优化分析系统和应用模式,有效促进"人工智能+教育"的发展。
引用
收藏
页码:71 / 78+85 +85
页数:9
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