Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (22): 258-264.DOI: 10.3778/j.issn.1002-8331.1812-0312

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Prediction of Loss and Teaching?Intervention for Learners in MOOC from Perspective of Deep Learning

LIN Pengfei, HE Xiuqing, CHEN Tiantian, WU Huajun, HE Juhou   

  1. 1.Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an 710062, China
    2.School of Computer Science, Shaanxi Normal University, Xi’an 710062, China
  • Online:2019-11-15 Published:2019-11-13



  1. 1.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

Abstract: MOOC(Massive Open Online Courses) provides learners with high-quality courses, but its low completion rate has become an important factor keeping it out of the learners. With the analysis of open dataset from edX, it can be found that the gradual loss of learners is a factor of low completion rate, and there is a complex correlation between learning behavior and achievement. With predicting the grade with linear regression and DNN(Deep Neural Network), the experiment shows that the DNN can fit the complex relationship between learning behavior and achievement better, and predict grade more accurate to forewarn the loss of learners. With the iteration of individualized teaching intervention on potential lost learners, the completion rate in MOOC can be improved well.

Key words: MOOC, learning behavior analysis, linear regression, Deep Neural Network(DNN), performance prediction, personalized learning

摘要: MOOC(Massive Open Online Courses)在为学习者提供优质课程的同时,低完成率成为影响其有效推广的重要因素。通过对edX开放数据集分析发现,学习者的逐渐流失是导致MOOC课程低完成率的因素之一,且学习行为与成绩之间存在复杂的相关性;基于线性回归和深度神经网络(Deep Neural Network,DNN)预测学习者的成绩,实验证明,DNN能够更好地拟合学习行为与成绩之间复杂的相关性,实现对成绩更加精准的预测,预警学习者流失;对预测的潜在流失学习者迭代进行个性化的教学干预,提高MOOC课程的完成率。

关键词: MOOC, 学习行为分析, 线性回归, 深度神经网络, 成绩预测, 个性化教学