Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques

Ramírez, Patricio; Alfaro, Jorge; Gallardo, Mauricio

Keywords: learning styles, machine learning, hybrid university teaching


In a hybrid university learning environment, the rapid identification of students’ learning styles seems to be essential to achieve complementarity between conventional face-to-face pedagogical strategies and the application of new strategies using virtual technologies. In this context, this research aims to generate a predictive model to detect undergraduates’ learning style profiles quickly. The methodological design consists of applying a k-means clustering algorithm to identify the students’ learning style profiles and a decision tree C4.5 algorithm to predict the student’s membership to the previously identified groups. A cluster sample design was used with Chilean engineering students. The research result is a predictive model that, with few questions, detects students’ profiles with an accuracy of 82.93%; this prediction enables a rapid adjustment of teaching methods in a hybrid learning environment.

Más información

Título de la Revista: APPLIED SCIENCES-BASEL
Volumen: 11
Editorial: MDPI
Fecha de publicación: 2021
Página de inicio: 1
Página final: 11
Idioma: Inglés