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

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...

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Bibliographic Details
Published in:Journal of computer science and technology 2018-07, Vol.33 (4), p.654-667
Main Authors: Lian, De-Fu, Liu, Qi
Format: Article
Language:English
Subjects:
Academic achievement
Analysis
Artificial Intelligence
CAI
Colleges & universities
Computer assisted instruction
Computer Science
Data mining
Data Structures and Information Theory
Distance learning
Information Systems Applications (incl.Internet)
Intelligent tutoring systems
Libraries
Massive open online courses
Mathematical models
Online education
Online instruction
Performance evaluation
Performance prediction
Regular Paper
Software Engineering
Students
Theory of Computation
Undergraduate study
University and college libraries
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Summary: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.
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-018-1847-y