Abstract
The growing application of information and communication technologies (ICTs) in teaching and learning processes has generated an overload of valuable information for all those involved in education field. Historical information from students’ academic records has become a valuable source of data that has been used for different purposes. Unfortunately, a high percentage of research has been developed from the perspective and the need of teachers and educational administrators. This perspective has left the student in the background. This paper proposes the application of a hybrid machine learning approach, with the aim of laying the groundwork for a future implementation of a recommendation system that allows students to make decisions related to their learning process. The work has been executed on the historical academic information of students of computer engineering degree. The results obtained in this article show the effectiveness of applying a hybrid machine learning approach. This architecture is composed of, on the one hand, techniques of supervised learning applied with the objective of classifying the data in clusters, and on the other hand, having this initial classification, unsupervised learning techniques applied with the objective of carrying out a predictive analysis of students’ historical grade records.
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References
Bogarín, A., Cerezo, R., Romero, C.: A survey on educational process mining. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 8, 1230 (2018)
Park, K., Ali, A., Kim, D., An, Y., Kim, M., Shin, H.: Robust predictive model for evaluating breast cancer survivability. Eng. Appl. Artif. Intell. 26, 2194–2205 (2013)
Gil, D., Fernández-Alemán, J., Trujillo, J., García-Mateos, G., Luján-Mora, S., Toval, A.: The effect of green software: a study of impact factors on the correctness of software. Sustainability 10, 3471 (2018)
Sweeney, M., Lester, J., Rangwala, H.: Next-term student grade prediction. In: Proceedings—IEEE International Conference on Big Data, pp. 970–975 (2015)
Rechkoski, L., Ajanovski, V. V., Mihova, M.: Evaluation of grade prediction using model-based collaborative filtering methods. In: IEEE Global Engineering Education Conference, pp. 1096–1103. EDUCON (2018)
Hwang, C.S., Su, Y.C.: Unified clustering locality preserving matrix factorization for student performance prediction. IAENG Int. J. Comput. Science. 42, 1–9 (2015)
Hershkovitz, A., Nachmias, R.: Learning about online learning processes and students’ motivation through web usage mining. Interdiscip. J. E-Learn. Learn. Objects. 5, 197–214 (2009)
Mashiloane, L., Mchunu, M.: Mining for marks: a comparison of classification algorithms when predicting academic performance to identify “students at risk.” In: Proceedings of: International Conference, Mining Intelligence and Knowledge Exploration, pp. 541–552. MIKE (2013)
Huete, J.F., Fernández-luna, J.M., Campos, L.M.De, Rueda-morales, M.A.: Using past-prediction accuracy in recommender systems. Inf. Sci. 199, 78–92 (2012)
Bydžovská, H.: Are collaborative filtering methods suitable for student performance prediction? In: Proceedings of 17th Portuguese Conference on Artificial Intelligence, pp. 425–430. EPIA (2015)
Georgios, K., Sotiris, K., Panagiotis, P.: Predicting student performance in distance higher education using semi-supervised techniques. In: Proceedings of 5th International Conference, pp. 259–270. MEDI (2015)
Polyzou, A., Karypis, G.: Grade prediction with models specific to students and courses. Int. J. Data Sci. Analytics. 2, 159–171 (2016)
Rendle, S.: Factorization machines. In: Proceedings—IEEE International Conference on Data Mining, pp. 995–1000. ICDM (2010)
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Buenaño-Fernandez, D., Luján-Mora, S., Gil, D. (2019). A Hybrid Machine Learning Approach for the Prediction of Grades in Computer Engineering Students. In: Visvizi, A., Lytras, M. (eds) Research & Innovation Forum 2019. RIIFORUM 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-30809-4_13
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DOI: https://doi.org/10.1007/978-3-030-30809-4_13
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