J48-Based Decision Tree Algorithm in Detecting Kolb’s Learning Style Preferences of Information Technology Students

Abstract

Teacher education institutions developed interventions to augment both the Teacher’s teaching and the Learner’s learning process. Previous studies focused on detecting learning styles in e-learning using learning management systems and adaptive learning in the online learning process, specifically using the Felder-Silverman learning style model. On the contrary, this study aimed to develop a decision tree-based model to detect the Learner’s learning style inspired by Kolb’s learning style in a face-to-face learning environment. Knowledge discovery in databases through data mining was utilized using the J48 algorithm to develop a decision tree-based model. This study was participated by 408 out of 462 information technology students in a state university in the Philippines. The study’s result was able to develop four J48-based decision trees with conditional rule models for activist, reflector, theorist and pragmatist learners. The evaluation of the decision tree models using confusion matrix and receiver operating curve showed a very high accuracy detection of every learning style (weighted average of 88-96%). This result recommends applying this in an actual system or computer application for easy and fast learning style detection based on the characteristics of the learners.