| Citation: | CHEN H，BAI J，YIN C T，et al. Behavior based MOOC user dropout predication framework[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（1）：74-82 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0188
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Though the massive open online courses (MOOC) have greatly changed the way of learning, properly understanding the user’s behavior and then predication of dropout is one of the most challenging tasks. MOOC have significantly altered the way that people learn, yet one of the most difficult challenges is correctly interpreting user behavior and then predicting dropout. In this research, to improve the dropout prediction performance, we firstly analyzed users and courses from the perspective of activities by using the long short term memory mechanism. In this study, we used the long short term memory mechanism to analyze users and courses from the perspective of activities in order to improve the dropout prediction performance. Afterwards we further proposed a multi-attention based multi-perspective feature enhancement method to investigate the correlated activities among users and courses. Finally, we provided a gated mechanism-based feature integration framework for dropout prediction. The experiment study on the public dataset has shown our framework’s promising potential, thereby making it possible to better investigate the reason beneath these phenomena and improve the overall study experience. The experiment study on the open dataset has demonstrated the promising potential of our framework, allowing us to more thoroughly explore the causes of these events and enhance the learning environment as a whole.
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