Psychological evaluation of university students: A data mining point of view

Hugo Alatrista-Salas, Juan Lazo-Lazo, Miguel Nunez-Del-Prado, Fiorella Otiniano-Campos, Jorge Perez-Reyes-De-La-Flor

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaEDUNINE 2019 - 3rd IEEE World Engineering Education Conference
Subtítulo de la publicación alojadaModern Educational Paradigms for Computer and Engineering Career, Proceedings
EditoresClaudio da Rocha Brito, Melany M. Ciampi
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728116662
DOI
EstadoPublicada - mar. 2019
Publicado de forma externa
Evento3rd IEEE World Engineering Education Conference, EDUNINE 2019 - Lima, Perú
Duración: 17 mar. 201920 mar. 2019

Serie de la publicación

NombreEDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings

Conferencia

Conferencia3rd IEEE World Engineering Education Conference, EDUNINE 2019
País/TerritorioPerú
CiudadLima
Período17/03/1920/03/19

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