Abstract
In recent years, Massive Online Open Courses (MOOCs) have gained widespread attention. However, the high dropout rate has become an important factor limiting the development of MOOCs. Existing approaches typically utilize time-consuming and laborious feature engineering to select features, which ignore the complex correlation relationships among entities. For solving this issue, in this paper, we propose an approach named structural and temporal learning (STL) for dropout prediction in MOOCs. The multiple entities and the complex correlation relationships among entities are modeled as a heterogeneous information network (HIN). To take full advantage of the rich structural information in the HIN, we present a hierarchical neural network, in which a series of calculations are used to guide and learn the importance of intra-correlation and inter-correlation. Besides, we fully exploit the temporal features of user activities based on activity sequences. Finally, structural and temporal features are fused to predict dropout. The experiments on the MOOCCube dataset demonstrate the effectiveness of STL.
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Acknowledgements
This work is supported by Natural Science Foundation of Zhejiang Province of China under grants No. LR21F020002, and the First class undergraduate course construction project in Zhejiang Province of China.
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Han, T., Hao, P., Bai, C. (2022). Structural and Temporal Learning for Dropout Prediction in MOOCs. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13369. Springer, Cham. https://doi.org/10.1007/978-3-031-10986-7_24
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DOI: https://doi.org/10.1007/978-3-031-10986-7_24
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