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Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 654–667 | Cite as

Jointly Recommending Library Books and Predicting Academic Performance: A Mutual Reinforcement Perspective

  • De-Fu Lian
  • Qi Liu
Regular Paper
  • 67 Downloads

Abstract

The prediction of academic performance is one of the most important tasks in educational data mining, and has been widely studied in massive open online courses (MOOCs) and intelligent tutoring systems. Academic performance can be affected by factors like personality, skills, social environment, and the use of library books. However, it is still less investigated about how the use of library books can affect the academic performance of college students and even leverage book-loan history for predicting academic performance. To this end, we propose a supervised content-aware matrix factorization for mutual reinforcement of academic performance prediction and library book recommendation. This model not only addresses the sparsity challenge by explainable dimension reduction techniques, but also quantifies the importance of library books in predicting academic performance. Finally, we evaluate the proposed model on three consecutive years of book-loan history and cumulative grade point average of 13 047 undergraduate students in one university. The results show that the proposed model outperforms the competing baselines on both tasks, and that academic performance not only is predictable from the book-loan history but also improves the recommendation of library books for students.

Keywords

book-borrowing record educational data mining matrix factorization multi-task learning student performance prediction transfer learning 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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