Abstract
Massive open online courses (MOOCs) have become one of the most popular ways of learning in recent years due to their flexibility and convenience. However, high dropout rate has become a prominent problem that hinders the further development of MOOCs. Therefore, the prediction of student dropouts is the key to further enhance the MOOCs platform. The traditional dropout prediction models based on machine learning are difficult to guarantee the prediction effect due to the shortcomings such as insufficient mining of feature information and not considering the influence of time series. To address this problem, in this paper, we propose the learning behavior feature fused deep learning network model (LBDL) for MOOC dropout prediction. The core of the model lies in modeling different types of information separately and incorporating them into an overall framework. In the data processing stage, the LBDL model divides the data features into video learning behavior features containing time series information and general information features. For video learning behavior features, the model uses Bi-LSTM and attention mechanisms to mine time series information, and for general information features, it uses embedding layer and fully connected layer for processing. A hidden vector containing both types of feature information can be obtained by two different modeling approaches. Then the original feature information is combined to train the gradient boosting framework LightGBM. Experiments on the MOOCCube video dataset show that the AUC and F1-Score of our model can reach 82.39% and 74.89%, respectively, which are higher than other baseline models. It indicates that the proposed LBDL model has better performance in the dropout rate prediction problem.
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Data availability
Data sets used during the current study are available from the appropriate authors upon reasonable request.
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Acknowledgements
All authors would like to thank all the participants who devoted their time and energy in taking part in the experiments. We thank the associate editor and the reviewers for their useful feedback that improved this paper.
Funding
This work is supported by the National Natural Science Foundation of China (Grant Nos. 62071379, 62071378, 61901365 and 62106196), the Natural Science Basic Research Plan in Shaanxi Province of China (Grant Nos. 2021JM-461 and 2020JM-299), the Fundamental Research Funds for the Central Universities (Grant No. GK202103085), and New Star Team of Xi'an University of Posts & Telecommunications (Grant No. xyt2016-01).
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Xiao Chen: designer of the paper model, responsible for data processing, experiment and writing the original draft. Hanqiang Liu: put forward his own suggestions and ideas on the paper model, participated in the revision of the manuscript and approved the final manuscript. Feng Zhao: provided suggestions on the experimental part of the model and participated in the revision of the manuscript.
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Liu, H., Chen, X. & Zhao, F. Learning behavior feature fused deep learning network model for MOOC dropout prediction. Educ Inf Technol 29, 3257–3278 (2024). https://doi.org/10.1007/s10639-023-11960-w
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DOI: https://doi.org/10.1007/s10639-023-11960-w