Abstract
In universities using the academic credit system, choosing elective courses is a crucial task that significantly affects student performance. Because of poor performances, numerous students have been receiving formal warnings and expulsions from universities. Certainly, a good study plan from course recommendation methods plays an important role in obtaining a good study performance. In addition, early warnings that release on challenging courses enable students to prepare better for such courses. Predicting student learning performance is a vital factor in the courses recommendation system and is an essential task of an academic advisor. Many research methods solved this problem with diverse approaches such as association rules, deep learning, and recommender systems (RS). It recently built the courses recommendation system, which is used for personalized recommendation, especially the matrix factorization (MF) technique; But, the prediction accuracy of the MF still need to be improved. So, many studies try to integrate more information (e.g., social networks, course relationships) into the model. Besides, deep learning addresses the student performance prediction, which currently is state of the art, but it usually is general rules (not a personalized prediction). Indeed, deep learning and matrix factorization have advantages and disadvantages, so they need to compound together to get better. This paper proposes an approach to predict student performance that utilizes the deep learning architecture to carry out the MF method to enhance prediction accuracy, called deep matrix factorization. Experimental results of the proposed approach are positive when we perform on the published educational dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, L., Chen, P., Lin, Z.: Artificial intelligence in education: a review. IEEE Access 8, 75264–75278 (2020). https://doi.org/10.1109/ACCESS.2020.2988510
Guo, L., Wang, D., Gu, F., Li, Y., Wang, Y., Zhou, R.: Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view. Asia Pac. Educ. Rev. 22(3), 441–461 (2021). https://doi.org/10.1007/s12564-021-09697-7
Khodeir, N.: Student modeling using educational data mining techniques. In: ACCS/PEIT 2019 - 2019 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & Information Technology, pp. 7–14 (2019). https://doi.org/10.1109/ACCS-PEIT48329.2019.9062874
Bogarín, A., Cerezo, R., Romero, C.: A survey on educational process mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8, e1230 (2018). https://doi.org/10.1002/widm.1230
Elbadrawy, A., Polyzou, A., Ren, Z., Sweeney, M., Karypis, G., Rangwala, H.: Predicting student performance using personalized analytics. Computer (Long. Beach. Calif). 49, 61–69 (2016). https://doi.org/10.1109/MC.2016.119
Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K., Sattar, M.: Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Appl. Sci. 10(11), 3894 (2020). https://doi.org/10.3390/app10113894
Patikorn, T., Baker, R.S., Heffernan, N.T.: ASSISTments longitudinal data mining competition special issue: a preface. J. Educ. Data Min. 12, i–xi (2020). https://doi.org/10.5281/ZENODO.4008048
Alzubaidi, L., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 1–74 (2021). https://doi.org/10.1186/s40537-021-00444-8
Lara-Cabrera, R., González-Prieto, Ángel., Ortega, F.: Deep matrix factorization approach for collaborative filtering recommender systems. Appl. Sci. 10(14), 4926 (2020). https://doi.org/10.3390/app10144926
Namoun, A., Alshanqiti, A.: Predicting student performance using data mining and learning analytics techniques: a systematic literature review. Appl. Sci. 11(1), 237 (2020). https://doi.org/10.3390/app11010237
Ayyappan, G.: Ensemble classifications for student academics performance data set. Indian J. Comput. Sci. Eng. 10, 31–34 (2019). https://doi.org/10.21817/INDJCSE/2019/V10I1/191001009
Ghorbani, R., Ghousi, R.: Comparing different resampling methods in predicting students’ performance using machine learning techniques. IEEE Access 8, 67899–67911 (2020). https://doi.org/10.1109/ACCESS.2020.2986809
Nguyen, H.Q., Pham, T.T., Vo, V., Vo, B., Quan, T.T.: The predictive modeling for learning student results based on sequential rules. Int. J. Innov. Comput. Inf. Control. 14, 2129–2140 (2018). https://doi.org/10.24507/IJICIC.14.06.2129
Hasnawi, M., Kurniati, N., Mansyur, S.H., Irawati, Hasanuddin, T.: Combination of case based reasoning with nearest neighbor and decision tree for early warning system of student achievement. In: Proceedings of 2nd East Indonesia Conference on Computer and Information Technology Internet Things Ind. EIConCIT 2018, pp. 78–81 (2018). https://doi.org/10.1109/EICONCIT.2018.8878512
Zia, A., Usman, M.: Elective learning objects group recommendation using non-cooperative game theory. In: Proceedings of 2018 International Conference on Frontiers of Information Technology, FIT 2018, pp. 194–199 (2019). https://doi.org/10.1109/FIT.2018.00041
Esteban, A., Zafra, A., Romero, C.: Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization. Knowl.-Based Syst. 194, 105385 (2020). https://doi.org/10.1016/J.KNOSYS.2019.105385
Rivera, A.C., Tapia-Leon, M., Lujan-Mora, S.: Recommendation systems in education: a systematic mapping study. In: Rocha, Á., Guarda, T. (eds.) ICITS 2018. AISC, vol. 721, pp. 937–947. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73450-7_89
Thanh-Nhan, H.L., Nguyen, H.H., Thai-Nghe, N.: Methods for building course recommendation systems. Proc. - 2016 8th International Conference on Frontiers of Information Technology, KSE 2016, pp. 163–168 (2016). https://doi.org/10.1109/KSE.2016.7758047
Chen, R., et al.: A novel social recommendation method fusing user’s social status and homophily based on matrix factorization techniques. IEEE Access 7, 18783–18798 (2019). https://doi.org/10.1109/ACCESS.2019.2893024
Thanh-Nhan, H.L., Huy-Thap, L., Thai-Nghe, N.: Toward integrating social networks into intelligent tutoring systems. In: Proceedings of 2017 9th International Conference on Knowledge and Systems Engineering, KSE 2017, 2017-January, pp. 112–117 (2017). https://doi.org/10.1109/KSE.2017.8119444
Huynh-Ly, T.N., Le, H.T., Nguyen, T.N.: Integrating courses’ relationship into predicting student performance. Int. J. Adv. Trends Comput. Sci. Eng. 9, 6375–6383 (2020). https://doi.org/10.30534/IJATCSE/2020/320942020
Thai-Nghe, N., Schmidt-Thieme, L.: Multi-relational factorization models for student modeling in intelligent tutoring systems. In: Proceedings - 2015 IEEE International Conference on Knowledge and Systems Engineering, KSE 2015, pp. 61–66. Institute of Electrical and Electronics Engineers Inc. (2015). https://doi.org/10.1109/KSE.2015.9
Assielou, K., Théodore, C., Tra, B., Lambert, T., Daniel, K.: Emotional impact for predicting student performance in intelligent tutoring systems (ITS). Int. J. Adv. Comput. Sci. Appl. 11(7), 219–225 (2020). https://doi.org/10.14569/IJACSA.2020.0110728
Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., Ragos, O.: Transfer learning from deep neural networks for predicting student performance. Appl. Sci. 10(6), 2145 (2020). https://doi.org/10.3390/app10062145
Dien, T., Hoai, S., Thanh-Hai, N., Thai-Nghe, N.: Deep learning with data transformation and factor analysis for student performance prediction. Int. J. Adv. Comput. Sci. Appl. 11(8), 711–721 (2020). https://doi.org/10.14569/IJACSA.2020.0110886
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Huynh-Ly, TN., Le, HT., Thai-Nghe, N. (2021). Integrating Deep Learning Architecture into Matrix Factorization for Student Performance Prediction. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2021. Lecture Notes in Computer Science(), vol 13076. Springer, Cham. https://doi.org/10.1007/978-3-030-91387-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-91387-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91386-1
Online ISBN: 978-3-030-91387-8
eBook Packages: Computer ScienceComputer Science (R0)