Abstract
Predicting student performance is a very important but yet challenging task in education. In this paper, we propose a Multi-View Network Embedding (MVNE) method for student performance prediction, which effectively fuses multiple data sources. We first construct three networks to model three different types of data sources correlated with student performance, ranging from class performance data, historical grades, to students’ campus social relationships. Then we use joint network embedding to learn the embedding representation of students and questions based on the proposed separated random walk sampling. Student performance is predicted based on both student and question similarities in the low-dimensional representation. Experimental results on the real-world datasets demonstrate the effectiveness of the proposed method.
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Acknowledgments
This work is partially supported by the National Natural Science Foundation of China under grant Nos. 61773331, 61403328 and 61703360. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing any funding agencies.
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Li, J., Yu, Y., Lu, Y., Song, P. (2020). Student Performance Prediction Based on Multi-view Network Embedding. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12307. Springer, Cham. https://doi.org/10.1007/978-3-030-60636-7_11
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DOI: https://doi.org/10.1007/978-3-030-60636-7_11
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