@inproceedings{be2b6af9df52497ca61d87e54b3eecfe,
title = "Predicting learning status in MOOCs using LSTM",
abstract = "Real-time and open online course resources of MOOCs have attracted a large number of learners in recent years. However, 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 Chinese University MOOCs as the inputs of model, the average accuracy of model's outputs was about 90\%.",
keywords = "Behavior Prediction, LSTMs, MOOCs",
author = "Feng Xiong and Kaifa Zou and Zhemin Liu and Hongzhi Wang",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 2019 ACM Turing Celebration Conference - China, ACM TURC 2019 ; Conference date: 17-05-2019 Through 19-05-2019",
year = "2019",
month = may,
day = "17",
doi = "10.1145/3321408.3322855",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery ",
booktitle = "Proceedings of the ACM Turing Celebration Conference - China, ACM TURC 2019",
address = "美国",
}