Title
Analysis and prediction of engineering student behavior and their relation to academic performance using data analytics techniques
Date Issued
02 October 2020
Access level
open access
Resource Type
journal article
Author(s)
de la Fuente-Mella H.
Gutiérrez C.G.
Crawford K.
Foschino G.
Crawford B.
de la Barra C.L.
Caneo F.C.
Monfroy E.
Becerra-Rozas M.
Elórtegui-Gómez C.
Pontificia Universidad Católica de Valparaíso
Publisher(s)
MDPI AG
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.
Start page
1
End page
11
Volume
10
Issue
20
Language
English
OCDE Knowledge area
Psicología (incluye terapias de aprendizaje, habla, visual y otras discapacidades físicas y mentales) Estadísticas, Probabilidad
Scopus EID
2-s2.0-85092764404
Source
Applied Sciences (Switzerland)
ISSN of the container
20763417 DOI 10.3390/app1
Sponsor(s)
Funding: Ricardo Soto is supported by Grant CONICYT/FONDECYT/REGULAR/1190129. Broderick Crawford is supported by Grant CONICYT/FONDECYT/REGULAR/1171243. Hanns de la Fuente-Mella, Ricardo Soto, Broderick Crawford, and Claudio Elórtegui-Gómez are supported by Grant Nucleo de Investigacion en Data Analytics/VRIEA/PUCV/039.432/2020. Felipe Cisternas Caneo and Marcelo Becerra-Rozas are supported by Grant DI Investigación Interdisciplinaria del Pregrado/VRIEA/PUCV/039.324/2020.
Sources of information: Directorio de Producción Científica Scopus