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.
Highlights
Profiles Using Machine LearningThe COVID-19 pandemic has posed several challenges to higher education in teaching, learning, research collaboration and institutional governance [1]
Their results show that student learning profiles consisting of four online factors and three traditional factors have the highest predictive power of academic performance
Similar to Cluster 1, the dimension with the lowest mean in this cluster is perception. These results indicate that Cluster 1 is characterised by being more intuitive, verbal, reflective, and global in the learning profile
Summary
Profiles Using Machine LearningThe COVID-19 pandemic has posed several challenges to higher education in teaching, learning, research collaboration and institutional governance [1]. There are countless ways people process information from the environment, and individuals exhibit specific behaviours that allow them to learn efficiently [4]. They prefer interaction, assimilation, and information processing methods. Since there are many learning styles among different individuals, it is a challenging task to determine and predict the learning style of an individual student. According to these ideas, adopting a standard pedagogy method is not appropriate to improve learning for all students. Their results show that student learning profiles consisting of four online factors and three traditional factors have the highest predictive power of academic performance
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