Abstract
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.
Funder
Catholic University of the North
Agencia Nacional de Investigación y Desarrollo
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
6 articles.
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