Predicting Learning Status in MOOCs using LSTM
This work addresses the issue of learner retention for MOOCs platforms, though it is incremental as it applies an existing method (LSTM) to a new domain-specific dataset.
The paper tackled the problem of high dropout rates in MOOCs by predicting learners' learning status in real-time using an LSTM-based model, achieving an average accuracy of about 90% on datasets from Chinese University MOOCs.
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 RNNLSTMs 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%.