A Model-Bias Matrix Factorization Approach for Course Score Prediction
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A Model-Bias Matrix Factorization Approach for Course Score Prediction Shi-Ting Zhong1 · Ling Huang2 Guangqiang Xie3 · Yang Li3
· Chang-Dong Wang1 · Jianhuang Lai1 ·
Accepted: 24 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Recommender system · Collaborative filtering · Score prediction · Matrix factorization · Model-bias
1 Introduction The recommender systems in e-commercial platforms can recommend users suitable items according to users’ purchase records or surfing records. In [23], it is shown that there is a need for designing an educational recommender system. The experimental result in [31] shows that students in the group with the online reading recommender system have more enthusiasms in study and have obtained higher scores. Therefore, it is feasible to help university students select suitable courses using recommender system. However, most of the existing educational recommender systems are e-learning systems, in which the systems may collect
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Ling Huang [email protected]
Extended author information available on the last page of the article
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students’ online-learning records to analyze students’ learning abilities as well as their learning interests and then recommend students courses according to their learning performances. And, there are few universities using recommender systems to analyze students’ learning abilities and help students choose suitable courses based on their existing course scores. Due to the restriction of teaching resources, for each course, especially elective course, universities cannot offer the course to all the students [15]. Without recommender systems, students may be confu
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