Abstract
Recommender algorithms are widely used in e-commercial platforms to recommend users suitable items according to users’ preferences. In recent years, an increasing amount of attention has been paid to the application of recommender system in education. There are many online learning systems that can recommend students suitable courses according to students’ learning performances. However, there are few universities using recommender system to recommend students suitable elective courses. It is generally known that students in higher grade take the courses earlier than those in lower grade. Therefore, the elective course scores of sophomores can be predicted by using the course score information from students of higher grades. However, the unbalanced distribution of course-enrollment data makes it hard to predict the score of the courses that are in a low selection rate. Therefore, we propose a model-bias matrix factorization algorithm to predict sophomores’ elective course scores, which takes into account the score prediction deviation caused by the course selection rate so as to make more accurate prediction than the traditional matrix factorization approaches. The experimental results show that our proposed model outperforms the state-of-the-art methods in the task of university students’ course score prediction.
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Funding
This work was supported by National Key Research and Development Program of China (2018YFC0809700), NSFC (61876193), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), and the Fundamental Research Funds for the Central Universities (19lgjc10).
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Zhong, ST., Huang, L., Wang, CD. et al. A Model-Bias Matrix Factorization Approach for Course Score Prediction. Neural Process Lett 54, 3583–3600 (2022). https://doi.org/10.1007/s11063-020-10385-7
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DOI: https://doi.org/10.1007/s11063-020-10385-7