Abstract
The research presented here aims at predicting grades by means of a set of relevant student’s variables. To solve this problem, a probabilistic approach was applied in which we assume that the probability of obtaining a certain grade is conditioned on the personal attributes of each student. A Bayesian classifier was the natural choice to include the student’s attributes in the estimation of the likelihood. However, a striking result was observed when the accuracy of the bayesian prediction was lower than the one provided by a baseline predictor based on student’s clustering. A follow-up analysis explains the reason behind this result and provides a guideline for similar classification problems.
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Acknowledgments
This work has received financial support from the Ministry of Science and Innovation of Spain under grant TIN2014-56633-C3-1-R as well as from the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08) and the European Regional Development Fund (ERDF).
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Bahritidinov, B., Sánchez, E. (2017). Probabilistic Classifiers and Statistical Dependency: The Case for Grade Prediction. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(), vol 10338. Springer, Cham. https://doi.org/10.1007/978-3-319-59773-7_40
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DOI: https://doi.org/10.1007/978-3-319-59773-7_40
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