摘要
利用人工智能开展学生学习行为分析与教学效果评价对改变教学方式具有重要的意义。文章提出了一种基于深度学习的互动课堂学生学习行为分析与教学效果评价LBREM方法,该方法首先通过智能学习平台获取学生学习行为视频信息,然后对视频中的学生表情进行检测与学习行为识别,最后以国际汉语课堂教学为例开展学生学习行为统计分析和建立教学效果评价模型并开展实证研究。实证效果表明,LBREM方法能够快速、准确的识别学习者学习行为和开展教学效果评价,在智慧课堂、远程学习、移动学习、MOOC等智能教育教学评价和个性化推荐中具有重要的借鉴意义。
It is of great significance to use artificial intelligence to analyze students'learning behaviors and evaluate teaching effects for changing teaching methods.A deep learning-based LBREM method for analyzing students'learning behaviors and evaluating teaching effects in interactive classroom is proposed.This method firstly obtains video information of students'learning behaviors through intelligent learning platform,and then detects students'facial expressions and recognizes their learning behaviors in the videos.Finally,taking international Chinese classroom teaching as an example,the author conducts statistical analysis of students'learning behaviors and establishes an evaluation model of teaching effects and carries out an empirical study.The empirical results showed that LBREM method can quickly and accurately identify learners'learning behaviors and carry out teaching effects evaluation,which has important reference significance in intelligent education teaching evaluation and personalized recommendations such as smart classroom,distance learning,mobile learning and MOOC.
作者
周楠
周建设
ZHOU Nan;ZHOU Jian-she(School of Literature,Capital Normal University,Beijing,China 100048;Office of Reserch&International Exchange,Beijing Open University,Beijing,China 100081;China Language Intelligence Research Center,Capital Normal University,Beijing,China 100048)
出处
《现代教育技术》
CSSCI
2021年第8期102-111,共10页
Modern Educational Technology
基金
国家自然科学基金“面向视频大数据的人体行为理解关键技术研究”(项目编号:61871028)
北京市教委-自然科学基金重点项目“基于大数据的学生学习行为分析关键技术研究”(项目编号:KZ201951160050)资助。
关键词
学习行为
教学评价
深度学习
智能教育
learning behavior
teaching evaluation
deep learning
intelligence education
