Abstract
Machine learning for education is an emerging discipline where a model is developed based on training data to make predictions on students’ performance. The main aim is to identify students who would have difficulty in their learning and to take precautionary measures to help them. In this paper, we conduct a comparative analysis of the most used machine learning classification models in the literature. We evaluate the performance of the models in terms of accuracy, F-measure, and execution time using two real-life education datasets. The performance of the models is data-driven. We give insights into the models’ performance and advise on the best model to use accordingly. We believe the results of this paper will be widely used by education professionals for accurate predictions.
A. Hennebelle—Independent Scientist Engineer.
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References
Romero, C., Ventura, S.: Educational data mining and learning analytics: an updated survey. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 10, e1355 (2020)
Cortez, P., Silva, A.M.G.: Using data mining to predict secondary school student performance (2008)
Amrieh, E.A., Hamtini, T., Aljarah, I.: Mining educational data to predict student’s academic performance using ensemble methods. Int. J. Database Theory Appl. 9, 119–136 (2016)
Amrieh, E.A., Hamtini, T., Aljarah, I.: Preprocessing and analyzing educational data set using X-API for improving student’s performance. In: 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (2015)
Rimadana, M.R., Kusumawardani, S.S., Santosa, P.I., Erwianda, M.S.F.: Predicting student academic performance using machine learning and time management skill data. In: 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 511–515 (2019)
López, M.I., Luna, J.M., Romero, C., Ventura, S.: Classification via clustering for predicting final marks based on student participation in forums. In: Proceedings of the 5th International Conference on Educational Data Mining, of EDM 2012, Chania, Greece, pp. 148–151 (2012)
Wati, M., Indrawan, W., Widians, J.A., Puspitasari, N.: Data mining for predicting students’ learning result. In: 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT). IEEE, Kuta Bali (2017)
Mehboob, B., Muzamal Liaqat, R., Abbas, N.: Student performance prediction and risk analysis by using data mining approach. J. Intell. Comput. 8, 49 (2017)
Tekin, A.: Early prediction of students’ grade point averages at graduation: a data mining approach. Eur. J. Educ. Res. 54, 207–226 (2014)
Almutairi, S., Shaiba, H., Bezbradica, M.: Predicting students’ academic performance and main behavioral features using data mining techniques. In: International Conference on Computing, pp. 245–259. Springer, Cham (2019)
Mueen, A., Zafar, B., Manzoor, U.: Modeling and predicting students’ academic performance using data mining techniques. Int. J. Mod. Educ. Comput. Sci. 8, 36–42 (2016)
Hussain, S., Atallah, R., Kamsin, A., Hazarika, J.: Classification, clustering and association rule mining in educational datasets using data mining tools: a case study. In: CSOC2018 2018: Cybernetics and Algorithms in Intelligent Systems, pp. 196–211 (2018)
Daud, A., Aljohani, N.R., Abbasi, R.A., et al.: Predicting student performance using advanced learning analytics. In: Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, pp. 415–421 (2017)
Rivas, A., Gonzalez-Briones, A., Hernandez, G., et al.: Artificial neural network analysis of the academic performance of students in virtual learning environments. Neurocomputing 423, 713–720 (2020)
Ajibade, S.-S.M., Ahmad, N.B.B., Shamsuddin, S.M.: Educational data mining: enhancement of student performance model using ensemble methods. In: IOP Conference Series: Materials Science and Engineering (2019)
Costa, E.B., Fonseca, B., Santana, M.A., et al.: Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Comput. Hum. Behav. 73, 247–256 (2017)
Kostopoulos, G., Lipitakis, A.-D., Kotsiantis, S., Gravvanis, G.: Predicting student performance in distance higher education using active learning. In: International Conference on Engineering Applications of Neural Networks, pp. 75–86. Springer, Cham (2017)
Kiu, C.-C.: Data mining analysis on student’s academic performance through exploration of student’s background and social activities. In: 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA). Subang Jaya, Malaysia (2018)
Migueis, V.L., Freitas, A., Garcia, P.J., Silva, A.: Early segmentation of students according to their academic performance: a predictive modelling approach. Decis. Support Syst. 115, 36–51 (2018)
EMC Education Services: Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. Wiley (2015)
Hassoun, M.H.: Fundamentals of artificial neural networks (1995)
Ramchoun, H., Idrissi, M.A.J., Ghanou, Y., Ettaouil, M.: Multilayer perceptron: architecture optimization and training. Int. J. Interact. Multimed. Artif. Intell. 4, 26–30 (2016)
Liaw, A., Wiener, M.: Classification and regression by random forest. R news 2, 18–22 (2002)
Ramaswami, M., Bhaskaran, R.: A study on feature selection techniques in educational data mining. J. Comput. 1, 7–11 (2009)
Hall, M., Frank, E., Holmes, G., et al.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11, 10–18 (2009)
Erasmus Programme. https://en.wikipedia.org/wiki/Erasmus_Programme. Accessed 18 Dec 2020
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This work was funded by the National Water and Energy Center.
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Ismail, L., Materwala, H., Hennebelle, A. (2021). Comparative Analysis of Machine Learning Models for Students’ Performance Prediction. In: Antipova, T. (eds) Advances in Digital Science. ICADS 2021. Advances in Intelligent Systems and Computing, vol 1352. Springer, Cham. https://doi.org/10.1007/978-3-030-71782-7_14
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DOI: https://doi.org/10.1007/978-3-030-71782-7_14
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