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

Hanns de la Fuente-Mella; Claudia Guzmán Gutiérrez; Kathleen Crawford; Giancarla Foschino; Broderick Crawford; Ricardo Soto; Claudio León de la Barra; Felipe Cisternas Caneo; Eric Monfroy; Marcelo Becerra-Rozas; Claudio Elórtegui-Gómez

doi:10.3390/app10207114

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

Hanns de la Fuente-Mella, Claudia Guzmán Gutiérrez, Kathleen Crawford, Giancarla Foschino, Broderick Crawford, Ricardo Soto, Claudio León de la Barra, Felipe Cisternas Caneo, Eric Monfroy, Marcelo Becerra-Rozas, Claudio Elórtegui-Gómez

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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 language	English
Article number	7114
Pages (from-to)	1-11
Number of pages	11
Journal	Applied Sciences (Switzerland)
Volume	10
Issue number	20
DOIs	https://doi.org/10.3390/app10207114
State	Published - 2 Oct 2020

Keywords

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

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10.3390/app10207114

Cite this

de la Fuente-Mella, H., Gutiérrez, C. G., Crawford, K., Foschino, G., Crawford, B., Soto, R., de la Barra, C. L., Caneo, F. C., Monfroy, E., Becerra-Rozas, M., & Elórtegui-Gómez, C. (2020). Analysis and prediction of engineering student behavior and their relation to academic performance using data analytics techniques. Applied Sciences (Switzerland), 10(20), 1-11. Article 7114. https://doi.org/10.3390/app10207114

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

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 Valpara{\'i}so 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.",

keywords = "Academic performance, Econometric analysis, Personality traits, Process of teaching–learning, Two-stage least squares",

author = "{de la Fuente-Mella}, Hanns and Guti{\'e}rrez, {Claudia Guzm{\'a}n} and Kathleen Crawford and Giancarla Foschino and Broderick Crawford and Ricardo Soto and {de la Barra}, {Claudio Le{\'o}n} and Caneo, {Felipe Cisternas} and Eric Monfroy and Marcelo Becerra-Rozas and Claudio El{\'o}rtegui-G{\'o}mez",

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de la Fuente-Mella, H, Gutiérrez, CG, Crawford, K, Foschino, G, Crawford, B , Soto, R , de la Barra, CL, Caneo, FC, Monfroy, E, Becerra-Rozas, M & Elórtegui-Gómez, C 2020, 'Analysis and prediction of engineering student behavior and their relation to academic performance using data analytics techniques', Applied Sciences (Switzerland), vol. 10, no. 20, 7114, pp. 1-11. https://doi.org/10.3390/app10207114

TY - JOUR

T1 - Analysis and prediction of engineering student behavior and their relation to academic performance using data analytics techniques

AU - de la Fuente-Mella, Hanns

AU - Gutiérrez, Claudia Guzmán

AU - Crawford, Kathleen

AU - Foschino, Giancarla

AU - Crawford, Broderick

AU - Soto, Ricardo

AU - de la Barra, Claudio León

AU - Caneo, Felipe Cisternas

AU - Monfroy, Eric

AU - Becerra-Rozas, Marcelo

AU - Elórtegui-Gómez, Claudio

PY - 2020/10/2

Y1 - 2020/10/2

N2 - 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.

AB - 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.

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KW - Personality traits

KW - Process of teaching–learning

KW - Two-stage least squares

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

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