Jointly Recommending Library Books and Predicting Academic Performance: A Mutual Reinforcement Perspective
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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.
Keywordsbook-borrowing record educational data mining matrix factorization multi-task learning student performance prediction transfer learning
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- Reye J. Student modelling based on belief networks. International Journal of Artificial Intelligence in Education, 2004, 14(1): 63-96.Google Scholar
- Piech C, Bassen J, Huang J, Ganguli S, Sahami M, Guibas L, Sohl-Dickstein J. Deep knowledge tracing. In Proc. Annual Conference on Neural Information Processing Systems, December 2015, pp.505-513.Google Scholar
- Rasch G. On general laws and the meaning of measurement in psychology. In Proc. the 4th Berkeley Symposium on Mathematical Statistics and Probability, July 1960, Volume 4, pp.321-333.Google Scholar
- Embretson S E, Reise S P. Item Response Theory. Psychology Press, 2013.Google Scholar
- Thai-Nghe N, Horváth T, Schmidt-Thieme L. Factorization models for forecasting student performance. In Proc. the 4th International Conference on Educational Data Mining, July 2011, pp.11-20.Google Scholar
- Sun Y, Ye S W, Inoue S Y, Sun Y. Alternating recursive method for Q-matrix learning. In Proc. the 7th International Conference on Educational Data Mining, July 2014, pp.14-20.Google Scholar
- Töscher A, Jahrer M. Collaborative filtering applied to educational data mining. In Proc. the KDD Cup 2010 Workshop, July 2010. http://pslcdatashop.org/KDDCup/workshop/papers/KDDCup2010_Toescher_Jahrer.pdf, June 2018.
- Barnes T. The Q-matrix method: Mining student response data for knowledge. In Proc. AAAI Educational Data Mining Workshop, July 2005.Google Scholar
- Taylor C, Veeramachaneni K, O’Reilly U M. Likely to stop? Predicting stopout in massive open online courses. arXiv:1408.3382, 2014. https://arxiv.org/abs/1408.3382, May 2018.
- Halawa S, Greene D, Mitchell J. Dropout prediction in MOOCs using learner activity features. Experiences and Best Practices in and Around MOOCs, 2014, 37: 1-10.Google Scholar
- Qiu J Z, Tang J, Liu T X, Gong J, Zhang C H, Zhang Q, Xue Y F. Modeling and predicting learning behavior in MOOCs. In Proc. the 9th ACM International Conference on Web Search and Data Mining, February 2016, pp.93-102.Google Scholar
- Anderson A, Huttenlocher D, Kleinberg J, Leskovec J. Engaging with massive online courses. In Proc. the 23rd International Conference on World Wide Web, April 2014, pp.687-698.Google Scholar
- Ramesh A, Goldwasser D, Huang B, Daume III H, Getoo L. Learning latent engagement patterns of students in online courses. In Proc. the 28th AAAI Conference on Artificial Intelligence, July 2014, pp.1272-1278.Google Scholar
- Tamhane A, Ikbal S, Sengupta B, Duggirala M, Appleton J. Predicting student risks through longitudinal analysis. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2014, pp.1544-1552.Google Scholar
- Lakkaraju H, Aguiar E, Shan C, Miller D, Bhanpuri N, Ghani R, Addison K L. A machine learning framework to identify students at risk of adverse academic outcomes. In Proc. the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2015, pp.1909-1918.Google Scholar
- Mann P H. Students and Books. Routledge and Kegan Paul, 1974.Google Scholar
- de Jager K. Impacts and outcomes: Searching for the most elusive indicators of academic library performance. In Proc. the 4th Northumbria International Conference on Performance Measurement in Libraries and Information Services, August 2001, pp.291-297.Google Scholar
- Lian D F, Ye Y Y, Zhu W Y, Liu Q, Xie X, Xiong H. Mutual reinforcement of academic performance prediction and library book recommendation. In Proc. the 16th International Conference on Data Mining, December 2016, pp.1023-1028.Google Scholar
- Martinez D. Predicting student outcomes using discriminant function analysis. In Proc. the 39th Annual Meeting of the Research and Planning Group, May 2001. https://files.eric.ed.gov/fulltext/ED462116.pdf, May 2018.
- Thai-Nghe N, Drumond L, Horváth T, Schmidt-Thieme L. Multi-relational factorization models for predicting student performance. In Proc. KDD Workshop on Knowledge Discovery in Educational Data, August 2011.Google Scholar
- Wu R Z, Liu Q, Liu Y P, Chen E H, Su Y, Chen Z G, Hu G P. Cognitive modelling for predicting examinee performance. In Proc. the 24th International Joint Conference on Artificial Intelligence, July 2015, pp.1017-1024.Google Scholar
- González-Brenes J P, Mostow J. Dynamic cognitive tracing: Towards unified discovery of student and cognitive models. In Proc. the 5th International Conference on Educational Data Mining, Jun 2012, pp.49-56.Google Scholar
- He J Z, Bailey J, Rubinstein B I P, Zhang R. Identifying at-risk students in massive open online courses. In Proc. the 29th AAAI Conference on Artificial Intelligence, January 2015, pp.1749-1755.Google Scholar
- Balakrishnan G. Predicting student retention in massive open online courses using hidden Markov models. Technical Report, University of California, 2013. https://www2.eecs. berkeley.edu/Pubs/TechRpts/2013/EECS-2013-109.pdf, May 2018.
- Zhong Y, Yuan N J, Zhong W, Zhang F Z, Xie X. You are where you go: Inferring demographic attributes from location check-ins. In Proc. the 8th ACM International Conference on Web Search and Data Mining, February 2015, pp.295-304.Google Scholar
- Zhu Y,Wang X, Zhong E H, Liu N N, Li H, Yang Q. Discovering spammers in social networks. In Proc. the 26th AAAI Conference on Artificial Intelligence, July 2012, pp.171-177.Google Scholar
- Culotta A, Ravi N K, Cutler J. Predicting the demographics of Twitter users from website traffic data. In Proc. the 29th AAAI Conference on Artificial Intelligence and the 27th Innovative Applications of Artificial Intelligence Conference, January 2015, pp.72-78.Google Scholar
- Hu J, Zeng H J, Li H, Niu C, Chen Z. Demographic prediction based on user’s browsing behavior. In Proc. the 16th International Conference on World Wide Web, May 2007, pp.151-160.Google Scholar
- Mooney R J, Roy L. Content-based book recommending using learning for text categorization. In Proc. the 5th ACM Conference on Digital Libraries, June 2000, pp.195-204.Google Scholar
- Huang Z, Chung W Y, Ong T H, Chen H. A graph-based recommender system for digital library. In Proc. the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries, July 2002, pp.65-73.Google Scholar
- Noia T D, Cantador I, Ostuni V C. Linked open data-enabled recommender systems: ESWC 2014 challenge on book recommendation. In Semantic Web Evaluation Challenge, Presutti V, Stankovic M, Cambria E, Cantador I, Iorio A D, Noia T D, Lange C, Recupero D R, Tordai A (eds.), Springer, 2014, pp.129-143.Google Scholar
- Wang C, Blei D M. Collaborative topic modeling for recommending scientific articles. In Proc. the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2011, pp.448-456.Google Scholar
- Wang H, Wang N Y, Yeung D Y. Collaborative deep learning for recommender systems. In Proc. the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2015, pp.1235-1244.Google Scholar
- Lian D F, Ge Y, Zhang F Z, Yuan N J, Xie X, Zhou T, Rui Y. Content-aware collaborative filtering for location recommendation based on human mobility data. In Proc. IEEE International Conference on Data Mining, November 2015, pp.261-270.Google Scholar
- Zhang Y, Yin H Z, Huang Z, Du X Z, Yang G W, Lian D F. Discrete deep learning for fast content aware recommendation. In Proc. the 11th ACM International Conference on Web Search and Data Mining, February 2018, pp.717-726.Google Scholar
- Xie M, Yin H Z, Wang H, Xu F J, Chen W T, Wang S. Learning graph-based POI embedding for location-based recommendation. In Proc. the 25th ACM International Conference on Information and Knowledge Management, October 2016, pp.15-24.Google Scholar
- Lian D F, Zheng K, Ge Y, Cao L B, Chen E H, Xie X. GeoMF++: Scalable location recommendation via joint geographical modeling and matrix factorization. ACM Transactions on Information Systems, 2018, 36(3): Article No. 33.Google Scholar
- Yin H Z, Chen H X, Sun X S, Wang H, Wang Y, Nguyen Q V H. SPTF: A scalable probabilistic tensor factorization model for semantic-aware behavior prediction. In Proc. IEEE International Conference on Data Mining, November 2017, pp.585-594.Google Scholar
- Lian D F, Zhang Z Y, Ge Y, Zhang F Z, Yuan N J, Xie X. Regularized content-aware tensor factorization meets temporal-aware location recommendation. In Proc. the 16th International Conference on Data Mining, December 2016, pp.1029-1034.Google Scholar
- Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In Proc. the 8th IEEE International Conference on Data Mining, December 2008, pp.263-272.Google Scholar
- Besag J. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society. Series B (Methodological), 1986, B-48(5/6): 259-302.Google Scholar
- Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In Proc. the 25th Conference on Uncertainty in Artificial Intelligence, June 2009, pp.452-461.Google Scholar