Predicting Learning Status in MOOCs using LSTM

摘要：

　　Real-time and open online course resources of MOOCs have attracted a large number of learners in recent years.How-ever,many new questions were emerging about the high dropout rate of learners.For MOOCs platform,predicting the learning status of MOOCs learners in real time with high accuracy is the crucial task,and it also help improve the quality of MOOCs teaching.The prediction task in this paper is inherently a time series prediction problem,and can be treated as time series classification problem,hence this paper proposed a prediction model based on RNN-LSTMs and optimization techniques which can be used to predict learners'learning status.Using datasets provided by Chi-nese University MOOCs as the inputs of model,the average accuracy of model's outputs was about 90%.

关键词： MOOCs Behavior Prediction LSTMs

作者: Feng Xiong Kaifa Zou Zhemin Liu Hongzhi Wang

作者单位: Harbin Institute of Technology

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 93-97

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）