Analysis and prediction of engineering student behavior and their relation to academic performance using data analytics techniques

HANNS ANIBAL DE LA FUENTE MELLA, Claudia Guzmán Gutiérrez, Kathleen Crawford, Giancarla Foschino, BRODERICK CRAWFORD LABRIN, RICARDO JAVIER SOTO DE GIORGIS, CLAUDIO ANTONIO LEON DE LA BARRA SOTO, Felipe Cisternas Caneo, Eric Monfroy, Marcelo Becerra-Rozas, Claudio Elórtegui-Gómez

Research output: Contribution to journalArticlepeer-review

Abstract

This study focuses on identifying personality traits in computer science students and determining whether they are related to academic performance. In addition, the importance of the personality traits based on motivation scale and depression, anxiety, and stress scales were measured. A sample of 188 students from the Computer Engineering Schools of the Pontifical Catholic University of Valparaíso was used. Through econometric two-stage least squares and paired sample correlation analysis, the results obtained indicate that there is a relation between academic performance and the personality traits measured by educational motivation scale and the ranking of university entrance and gender. In addition, these results led to characterization of students based on their personality traits and provided elements that may enhance the development of an effective personality that allows students to successfully face their environment, playing an important role in the educational process.

Original languageEnglish
Article number7114
Pages (from-to)1-11
Number of pages11
JournalApplied Sciences (Switzerland)
Volume10
Issue number20
DOIs
StatePublished - 2 Oct 2020
Externally publishedYes

Keywords

  • Academic performance
  • Econometric analysis
  • Personality traits
  • Process of teaching–learning
  • Two-stage least squares

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