Abstract
Students starting university have different characteristics, which can impact their performance in the classroom. In this study, 743 freshmen were surveyed. The collected variables are grouped into five categories: demographic data, learning approach, personality, emotional intelligence, and perceived social support. These characteristics provide a profile of the student that will impact their behavior and academic performance during their university life. Based on these data, we have applied data mining techniques in order to build patterns of behavior that represent correlations between the characteristics of the students. Our results highlight the importance of using pattern mining techniques on data associated with the psychological evaluation of new university students.
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Alatrista-Salas, H., Lazo-Lazo, J., Nunez-Del-Prado, M., Otiniano-Campos, F., & Perez-Reyes-De-La-Flor, J. (2019). Psychological evaluation of university students: A data mining point of view. In EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/EDUNINE.2019.8875829
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