Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (1): 92-98.DOI: 10.3778/j.issn.1002-8331.2001-0305

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Method and Application of Massive Online Collaborative Learning Grouping

CHEN Tiantian, HE Xiuqing, GE Wenshuang, HE Juhou   

  1. 1.Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an 710062, China
    2.School of Computer Science, Shaanxi Normal University, Xi’an 710062, China
  • Online:2021-01-01 Published:2020-12-31



  1. 1.陕西师范大学 现代教学技术教育部重点实验室,西安 710062
    2.陕西师范大学 计算机科学学院,西安 710062


The problem of learning loneliness is one of the reasons for the low completion rate of MOOC. Constructing a collaborative learning group that adapts to the learning characteristics of learners can solve this problem. This method uses auto-encoder to extract the key features of learners, and then uses the fuzzy C-means algorithm to iteratively group learners based on the principle of homogeneous grouping, so that online learners change from learning alone to collaborative learning. Thereby it improves online learning experience and reduces learning loneliness. Grouped online collaborative learning with 19,846 online learners who chose the introductory courses in computer science and programming on edX as experimental subjects. The results show that based on this method, learners in each group have high homogeneity, which can solve the problem of learning loneliness well.

Key words: learning loneliness, online collaborative learning group, auto-encoder, fuzzy C-means, MOOC



关键词: 学习孤独感, 在线协作学习分组, 自编码神经网络, 模糊C均值, MOOC课程