Abstract
Considering the undeniable relevance of education in today’s society, it is of great interest to be able to predict the academic performance of students in order to change teaching methods and create new strategies taking into account the situation of the students and their needs. This study aims to apply data fusion to merge information about several students and predict variations in their Portuguese Language or Math grades from one trimester to another, that is, whether the students improve, worsen or maintain their grade. The possibility to predict changes in a student’s grades brings great opportunities for teachers, because they can get an idea, from the predictions, of possible drops in grades, and can adapt their teaching and try to prevent such drops from happening. After the creation of the models, it is possible to suggest that they are not overfitting, and the metrics indicate that the models are performing well and appear to have high level of performance. For the Portuguese Language prediction, we were able to reach an accuracy of 97.3%, and for the Mathematics prediction we reached 95.8% of accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Prasad, C., Gupta, P.: Educational impact on the society. Int. J. Novel Res. Educ. Learn. 7, 1–7 (2020). ISSN 2394–9686
Romero, C., Ventura, S.: Data Mining in Education. Data Mining and Knowledge Discovery, Wiley Interdisciplinary Reviews (2013)
Lahat, D., Adali, T., Jutten, C.: Multimodal data fusion: an overview of methods, challenges, and prospects. Proc. IEEE 103(9), 1449–1477 (2015)
Asar, S., Jalalpour, S., Ayoubi, F., Rahmani, M., Rezaeian, M.: PRISMA. Preferred Reporting Items for Systematic Reviews and Meta-Analyses. JRUMS (2016)
Qu, Y., Li, F., Li, L., Dou, X., Wang, H.: Can we predict student performance based on tabular and textual data? IEEE Access 10, 86008–86019 (2022)
Zou, W., Li, Y., Shan, X., Wu, X.: Application of data fusion and image video teaching mode in physical education course teaching and evaluation of teaching effect. Secur. Commun. Netw. 2022, 8584350 (2022)
Fernández-García, A.J., Preciado, J.C., Melchor, F., Rodriguez-Echeverria, R., Conejero, J.M., Sánchez-Figueroa, F.: A real-life machine learning experience for predicting university dropout at different stages using academic data. IEEE Access 9, 133076–133090 (2021)
Chango, G., Cerezo, R., Romero, C.: Predicting academic performance of university students from multi-sources data in blended learning. In: DATA 2019: Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems, pp. 1–5 (2019). https://doi.org/10.1145/3368691.3368694
Chango, W., Cerezo, R., Romero, C.: Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses. Comput. Electr. Eng. 89, 106908 (2021). ISSN 0045–7906
Diaz, P., Salas, J.C., Cipriano, A., Nunez, F.: Random forest model predictive control for paste thickening. Miner. Eng. 163, 106760 (2021)
Ogunleye, A., Wang, Q.G.: XGBoost model for chronic kidney disease diagnosis. IEEE/ACM Trans. Comput. Biol. Bioinf. 17(6), 2131–2140 (2019)
Tong, W., Hong, H., Fang, H., Xie, Q., Perkins, R.: Decision forest: combining the predictions of multiple independent decision tree models. J. Chem. Inf. Comput. Sci. 43(2), 525–531 (2003)
Aguiar, E., Ambrose, G.A.A., Chawla, N.V., Goodrich, V., Brockman, J.: Engagement vs performance: using electronic portfolios to predict first semester engineering student persistence. J. Learn. Analyt. 1, 7–33 (2014)
Guo, T., Zhao, W., Alrashoud, M., Tolba, A., Firmin, S., Xia, F.: Multimodal educational data fusion for students’ mental health detection. IEEE Access 10, 70370–70382 (2022)
Moon, J., Ke, F., Sokolikj, Z., Dahlstrom-Hakki, I.: Multimodal data fusion to track students’ distress during educational gameplay. J. Learn. Analyt. 9(3), 75–87 (2022)
Chango, W., Cerezo, R., Sanchez-Santillan, M., et al.: Improving prediction of students’ performance in intelligent tutoring systems using attribute selection and ensembles of different multimodal data sources. J. Comput. High. Educ. 33, 614–634 (2021)
Acknowledgement
This work is supported by: FCT - Fundação para a Ciência e Tecnologia within the RD Units Project Scope: UIDB/00319/2020 and the Northern Regional Operational Programme (NORTE 2020), under Portugal 2020 within the scope of the project “Hello: Plataforma inteligente para o combate ao insucesso escolar”, Ref. NORTE-01-0247-FEDER-047004.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Teixeira, R., Marcondes, F.S., Lima, H., Durães, D., Novais, P. (2023). Data Fusion for Prediction of Variations in Students Grades. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-43078-7_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43077-0
Online ISBN: 978-3-031-43078-7
eBook Packages: Computer ScienceComputer Science (R0)