Deep Biased Matrix Factorization for Student Performance Prediction


  • Thanh-Nhan Huynh-Ly Lac Hong University image/svg+xml
  • Huy-Thap Le Lac Hong University image/svg+xml
  • Nguyen Thai-Nghe Can Tho University, Can Tho City, Vietnam



Educational data mining, deep matrix factorization, courses recommendation, student performance prediction


In universities that use the academic credit system, selecting elective courses is a crucial task that can have a significant impact on a student's academic performance. Students who perform poorly in their courses may receive formal warnings or even face expulsion from the university. Thus, a well-designed study plan from a course recommendation system can play an essential role in achieving good academic performance. Additionally, early warnings regarding challenging courses can help students better prepare and improve their chances of success. Therefore, predicting student performance is a vital component of both the course recommendation system and the academic advisor's role. To this end, numerous studies have addressed the prediction of student performance using various approaches such as association rules, machine learning, and recommender systems. More recently, personalized machine learning approaches, particularly the matrix factorization technique, have been used in the course recommendation system. However, the accuracy of these approaches in predicting student performance still needs improvement. To address this issue, this study proposes an approach called Deep Biased Matrix Factorization, which carries out deep factorization via multi-layer to enhance prediction accuracy. Experimental results on an educational dataset have demonstrated that the proposed approach can significantly improve the accuracy of student performance prediction. By using this approach, universities can better recommend elective courses to their students as well as predict student performance, which can help them make informed decisions and achieve better academic outcomes.


B. Albreiki, N. Zaki, and H. Alashwal, “A systematic literature review of student’ performance prediction using machine learning techniques,” Educ. Sci., vol. 11, no. 9, 2021, doi: 10.3390/educsci11090552.

R. a. Sottilare, A. C. Graesser, X. Hu, and K. Brawner, “Design Recommendations for Intelligent Tutoring Systems Volume 3: Authoring Tools & Expert Modeling Techniques,” Book, vol. 3, no. JUNE, pp. 1–388, 2015.

Q. Li and J. Kim, “A deep learning-based course recommender system for sustainable development in education,” Appl. Sci., vol. 11, no. 19, 2021, doi: 10.3390/app11198993.

K. Fahd, S. J. Miah, and K. Ahmed, “Predicting student performance in a blended learning environment using learning management system interaction data,” Appl. Comput. Informatics, 2021, doi: 10.1108/ACI-06-2021-0150.

X. Tan, J. She, S. Chen, S. Ohno, and H. Kameda, “Analysis of Student Learning Behavior based on Moodle Log Data,” in International Conference on Human System Interaction, HSI, Jul. 2021, vol. 2021-July, doi: 10.1109/HSI52170.2021.9538680.

R. Hasan, S. Palaniappan, S. Mahmood, A. Abbas, K. U. Sarker, and M. U. Sattar, “Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques,” Appl. Sci. 2020, Vol. 10, Page 3894, vol. 10, no. 11, p. 3894, Jun. 2020, doi: 10.3390/APP10113894.

S. U. Masruroh, D. Rosyada, Zulkifli, Sururin, and N. A. R. Vitalaya, “Adaptive Recommendation System in Education Data Mining using Knowledge Discovery for Academic Predictive Analysis: Systematic Literature Review,” 2021, doi: 10.1109/CITSM52892.2021.9588895.

T. Patikorn, R. S. Baker, and N. T. Heffernan, “ASSISTments Longitudinal Data Mining Competition Special Issue: A Preface,” J. Educ. Data Min., vol. 12, no. 2, pp. i–xi, Aug. 2020, doi: 10.5281/ZENODO.4008048.

L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J. Big Data 2021 81, vol. 8, no. 1, pp. 1–74, Mar. 2021, doi: 10.1186/S40537-021-00444-8.

J. Bobadilla, S. Alonso, and A. Hernando, “Deep learning architecture for collaborative filtering recommender systems,” Appl. Sci., vol. 10, no. 7, 2020, doi: 10.3390/app10072441.

T. N. Huynh-Ly, H. T. Le, and N. Thai-Nghe, “Integrating Deep Learning Architecture into Matrix Factorization for Student Performance Prediction,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, vol. 13076 LNCS, doi: 10.1007/978-3-030-91387-8_26.

H. Nawang, M. Makhtar, and W. M. A. F. W. Hamzah, “A systematic literature review on student performance predictions,” International Journal of Advanced Technology and Engineering Exploration, vol. 8, no. 84. Accent Social and Welfare Society, pp. 1441–1453, Nov. 01, 2021, doi: 10.19101/IJATEE.2021.874521.

M. I. Hoque, A. kalam Azad, M. Abu Hurayra Tuhin, and Z. U. Salehin, “University students result analysis and prediction system by decision tree algorithm,” Adv. Sci. Technol. Eng. Syst., vol. 5, no. 3, 2020, doi: 10.25046/aj050315.

R. Ghorbani and R. Ghousi, “Comparing Different Resampling Methods in Predicting Students’ Performance Using Machine Learning Techniques,” IEEE Access, vol. 8, pp. 67899–67911, 2020, doi: 10.1109/ACCESS.2020.2986809.

C. Romero, S. Ventura, M. Pechenizkiy, and R. SJd Baker, “Handbook of Educational Data Mining.”

L. M. Crivei, G. Czibula, and A. Mihai, “A Study on Applying Relational Association Rule Mining Based Classification for Predicting the Academic Performance of Students,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11775 LNAI, doi: 10.1007/978-3-030-29551-6_25.

P. Dixit, H. Nagar, and S. Dixit, “Student performance prediction using case based reasoning knowledge base system (CBR-KBS) based data mining,” Int. J. Inf. Educ. Technol., vol. 12, no. 1, 2022, doi: 10.18178/ijiet.2022.12.1.1583.

A. Zia and M. Usman, “Elective learning objects group recommendation using non-cooperative game theory,” Proc. - 2018 Int. Conf. Front. Inf. Technol. FIT 2018, pp. 194–199, Jan. 2019, doi: 10.1109/FIT.2018.00041.

A. Esteban, A. Zafra, and C. Romero, “Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization,” Knowledge-Based Syst., vol. 194, p. 105385, Apr. 2020, doi: 10.1016/J.KNOSYS.2019.105385.

A. C. Rivera, M. Tapia-Leon, and S. Lujan-Mora, “Recommendation Systems in Education: A Systematic Mapping Study,” Adv. Intell. Syst. Comput., vol. 721, pp. 937–947, Jan. 2018, doi: 10.1007/978-3-319-73450-7_89.

H. L. Thanh-Nhan, H. H. Nguyen, and N. Thai-Nghe, “Methods for building course recommendation systems,” Proc. - 2016 8th Int. Conf. Knowl. Syst. Eng. KSE 2016, pp. 163–168, Nov. 2016, doi: 10.1109/KSE.2016.7758047.

R. Chen, Y. S. Chang, Q. Hua, Q. Gao, X. Ji, and B. Wang, “An enhanced social matrix factorization model for recommendation based on social networks using social interaction factors,” Multimed. Tools Appl., vol. 79, no. 19–20, pp. 14147–14177, May 2020, doi: 10.1007/s11042-020-08620-3.

H. L. Thanh-Nhan, L. Huy-Thap, and N. Thai-Nghe, “Toward integrating social networks into intelligent tutoring systems,” Proc. - 2017 9th Int. Conf. Knowl. Syst. Eng. KSE 2017, vol. 2017-Janua, pp. 112–117, Nov. 2017, doi: 10.1109/KSE.2017.8119444.

T. N. Huynh-Ly, H. T. Le, and T. N. Nguyen, “Integrating courses’ relationship into predicting student performance,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 4, pp. 6375–6383, Jul. 2020, doi: 10.30534/IJATCSE/2020/320942020.

M. Tsiakmaki, G. Kostopoulos, S. Kotsiantis, and O. Ragos, “Transfer Learning from Deep Neural Networks for Predicting Student Performance,” Appl. Sci. 2020, Vol. 10, Page 2145, vol. 10, no. 6, p. 2145, Mar. 2020, doi: 10.3390/APP10062145.

T. T. Dien, S. H. Luu, N. Thanh-Hai, and N. Thai-Nghe, “Deep Learning with Data Transformation and Factor Analysis for Student Performance Prediction,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 8, pp. 711–721, 2020, doi: 10.14569/IJACSA.2020.0110886.

R. Lara-Cabrera, Á. González-Prieto, and F. Ortega, “Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems,” Appl. Sci. 2020, Vol. 10, Page 4926, vol. 10, no. 14, p. 4926, Jul. 2020, doi: 10.3390/APP10144926.

F. Ricci, L. Rokach, and B. Shapira, “Introduction to Recommender Systems Handbook,” in Recommender Systems Handbook, Springer US, 2011, pp. 1–35.




How to Cite

Huynh-Ly T-N, Le H-T, Thai-Nghe N. Deep Biased Matrix Factorization for Student Performance Prediction. EAI Endorsed Trans Context Aware Syst App [Internet]. 2023 Apr. 20 [cited 2023 Jun. 2];9(1):e4. Available from: