MOOC(Massive Open Online Courses)作为一种新的教学模式正发展得如火如荼,但学员退课率一直高居不下,直接影响了MOOC教师以及MOOC平台的发展。本研究以"学堂在线"平台学员的学习行为数据为基础,对影响退课的七种学习行为进...MOOC(Massive Open Online Courses)作为一种新的教学模式正发展得如火如荼,但学员退课率一直高居不下,直接影响了MOOC教师以及MOOC平台的发展。本研究以"学堂在线"平台学员的学习行为数据为基础,对影响退课的七种学习行为进行相关性分析,为了避免多重指标带来的多重共线性问题,根据相关性较小的原则选择其中的五种学习行为。最后采用二元逻辑回归模型进行建模并预测学员的退课情况。实验表明,选取的五种学习行为对退课影响显著,预测准确率较高。本研究为MOOC教师尽早采取教学干预提供了一定的理论依据。展开更多
Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout predictio...Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.展开更多
文摘MOOC(Massive Open Online Courses)作为一种新的教学模式正发展得如火如荼,但学员退课率一直高居不下,直接影响了MOOC教师以及MOOC平台的发展。本研究以"学堂在线"平台学员的学习行为数据为基础,对影响退课的七种学习行为进行相关性分析,为了避免多重指标带来的多重共线性问题,根据相关性较小的原则选择其中的五种学习行为。最后采用二元逻辑回归模型进行建模并预测学员的退课情况。实验表明,选取的五种学习行为对退课影响显著,预测准确率较高。本研究为MOOC教师尽早采取教学干预提供了一定的理论依据。
基金partially supported by the National Natural Science Foundation of China (Nos. 61866007, 61363029, 61662014, 61763007, and U1811264)the Natural Science Foundation of Guangxi District (No. 2018GXNSFDA138006)+2 种基金Guangxi Key Laboratory of Trusted Software (No. KX201721)Humanities and Social Sciences Research Projects of the Ministry of Education (No. 17JDGC022)Chongqing Higher Education Reform Project (No. 183137)
文摘Recently, Massive Open Online Courses(MOOCs) have become a major online learning methodology for millions of people worldwide. However, the dropout rates from several current MOOCs are high. Usually, dropout prediction aims to predict whether a learner will exhibit learning behaviors during several consecutive days in the future. Therefore, the information related to the learning behaviors of a learner in several consecutive days should be considered. After in-depth analysis of the learning behavior patterns of the MOOC learners, this study reports that learners often exhibit similar learning behaviors on several consecutive days, i.e., the learning status of a learner for the subsequent day is likely to be similar to that for the previous day. Based on this characteristic of MOOC learning,this study proposes a new simple feature matrix for keeping information related to the local correlation of learning behaviors and a new Convolutional Neural Network(CNN) model for predicting the dropout. Extensive experimental validations illustrate that the local correlation of learning behaviors should not be neglected. The proposed CNN model considers this characteristic and improves the dropout prediction accuracy. Furthermore, the proposed model can be used to predict dropout temporally and early when sufficient data are collected.
