大规模开放在线课程(Massive open online courses,MOOCs)在世界各国的发展如火如荼,它引发了教育革命,教育相关行业也都重新定位,为MOOCs的发展出一份力。主要从:提供互联网链接和资源访问通道;加入MOOCs联盟,和高校及社区合作推广MOO...大规模开放在线课程(Massive open online courses,MOOCs)在世界各国的发展如火如荼,它引发了教育革命,教育相关行业也都重新定位,为MOOCs的发展出一份力。主要从:提供互联网链接和资源访问通道;加入MOOCs联盟,和高校及社区合作推广MOOCs;指导MOOCs学员利用社交网络,完成MOOCs学习;公共图书馆创建自己的MOOCs课程4个方面,论述了公共图书馆对MOOCs的作用。展开更多
近年来大规模开放在线课程获得了较为广泛的关注。由于学习者学习方式不合理使得学习兴趣下降,学习效果不佳,MOOCs辍学率很高,针对这一问题,从学习者学习活动日志中自动抽取一段时间内连续特征,以学习者行为特征为自变量,建立MOOCs辍学...近年来大规模开放在线课程获得了较为广泛的关注。由于学习者学习方式不合理使得学习兴趣下降,学习效果不佳,MOOCs辍学率很高,针对这一问题,从学习者学习活动日志中自动抽取一段时间内连续特征,以学习者行为特征为自变量,建立MOOCs辍学预测模型。在KDD Cup 2015数据集上的实验表明,使用基于卷积神经网络的长短期记忆CNN_LSTM辍学预测模型,能够帮助MOOCs课程教师和设计者追踪课程学习者在不同时间步长的学习状态,从而动态监控不同阶段的辍学行为,模型的预测准确率高,这将为教师改进教学方法提供更合理的指导和建议。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
文摘大规模开放在线课程(Massive open online courses,MOOCs)在世界各国的发展如火如荼,它引发了教育革命,教育相关行业也都重新定位,为MOOCs的发展出一份力。主要从:提供互联网链接和资源访问通道;加入MOOCs联盟,和高校及社区合作推广MOOCs;指导MOOCs学员利用社交网络,完成MOOCs学习;公共图书馆创建自己的MOOCs课程4个方面,论述了公共图书馆对MOOCs的作用。
文摘近年来大规模开放在线课程获得了较为广泛的关注。由于学习者学习方式不合理使得学习兴趣下降,学习效果不佳,MOOCs辍学率很高,针对这一问题,从学习者学习活动日志中自动抽取一段时间内连续特征,以学习者行为特征为自变量,建立MOOCs辍学预测模型。在KDD Cup 2015数据集上的实验表明,使用基于卷积神经网络的长短期记忆CNN_LSTM辍学预测模型,能够帮助MOOCs课程教师和设计者追踪课程学习者在不同时间步长的学习状态,从而动态监控不同阶段的辍学行为,模型的预测准确率高,这将为教师改进教学方法提供更合理的指导和建议。
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.
