基于多模态数据分析的在线学习智能评估反馈
李睿;刘子韬;
摘要(Abstract)：
基于互联网新一代信息技术的飞速发展,在线学习在我国基础教育中蓬勃发展,已经成为推动教育创新的重要力量。在线学习为学生提供了时空灵活性,丰富的学习资源共享以及全场景的行为数据化记录。然而,由于在线课堂发生在虚拟的网络空间中,其教学质量参差不齐的问题也日渐暴露。因此,我们提出一套合理的在线课堂智能质量评估反馈体系。该评估体系基于真实的多模态在线课堂数据,覆盖不同的在线教学形态,能够从老师视角和学生视角,全面考虑和衡量质量评估核心要素,进而实现在线教学的精准评估反馈。
关键词(KeyWords)： 在线学习;智能评估;多模态数据分析
基金项目(Foundation): 科技创新2030—“新一代人工智能”重大项目—“智慧教育人工智能开放创新平台”项目（2020AAA0104500）;; 北京市科技新星项目（Z201100006820068）
作者(Authors): 李睿;刘子韬;
DOI: 10.16453/j.cnki.ISSN2096-5036.2022.02.008
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