The prediction of academic performance using engineering student’s profiles

The prediction of academic performance using engineering student’s profiles 3024 4032 Eduardo Díaz Medina

Autores: Andrés González Nucamendi, Julieta Noguez, Luis Neri, Víctor Robledo Rella, Rosa María Guadalupe García Castelán y David Escobar Castillejos.

ABSTRACT:

This article describes the determination of student profiles based on the constructs of multiple intelligences and on learning and affective strategies, in order to identify the most important characteristics for ensuring the academic success of engineering students. The two constructs were organized in terms of eight dimensions each: the basis for developing two questionnaires that were completed by 618 undergraduate engineering students, in an attempt to define their student profile. Three alternative measures were designed to determine numerical values for each dimension, according to their capacity to predict academic performance in terms of final grades, using regression analysis. According to the study’s findings, the logical/mathematical dimension plays an important role in student performance, while anxiety has a negative effect on final grades. The definition of appropriate measures to determine students’ cognitive, affective, and self-regulatory profiles can provide instructors with timely information to implement appropriate teaching strategies in their groups.

 

Liga para el artículo:

https://www.sciencedirect.com/science/article/abs/pii/S0045790621002688?via%3Dihub#!

Autor UP: Dr. David Escobar Castillejos
Facultad de Ingeniería