文献类型：期刊文献

英文题名：CLSA: A novel deep learning model for MOOC dropout prediction

作者：Fu, Qian[1];Gao, Zhanghao[2];Zhou, Junyi[1];Zheng, Yafeng[2]

第一作者：Fu, Qian

通讯作者：Zheng, YF[1]

机构：[1]Beijing Normal Univ, Sch Educ Technol, Beijing, Peoples R China;[2]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou, Peoples R China

第一机构：Beijing Normal Univ, Sch Educ Technol, Beijing, Peoples R China

通讯机构：[1]corresponding author), Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou, Peoples R China.|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

年份：2021

卷号：94

外文期刊名：COMPUTERS & ELECTRICAL ENGINEERING

收录：;EI(收录号：20212910646133);Scopus(收录号：2-s2.0-85109995430);WOS:【SCI-EXPANDED(收录号:WOS:000694013100013)】；

基金：This research has been supported by the National Natural Science Foundation of China (Grant Nos. 61907011 and 62077005).

语种：英文

外文关键词：Massive open online courses; Dropout prediction; Convolutional neural network; Long short-term memory network; Attention mechanism

摘要：MOOCs have attracted hundreds of millions of learners with advantages such as being cost-free and having flexible time and space. However, high dropout rates have become the main issue that hinders their further progress. To solve this problem, this research proposes a pipeline model named CLSA to predict the dropout rate based on learners' behavior data. The CLSA model first uses a convolutional neural network to extract local features and builds feature relations using a kernel strategy. Then, it feeds this high-dimensional vector generated by the CNN to a long shortterm memory network to obtain a time-series incorporated vector representation. After that, it employs a static attention mechanism on the vector to obtain an attention weight on each dimension. When tested on the KDD CUP 2015 dataset, our model reached 87.6% accuracy, which was higher than the previous best model (over 2.8%). The Fl-score of our model reached 86.9%, which was 1.6% higher than the previous state-of-the-art result.