Personality-Aware Course Recommender System Using Deep Learning for Technical and Vocational Education and Training
Rana Hammad Hassan, Malik Tahir Hassan, M. Shujah Islam Sameem, Muhammad Aasim Rafique
Abstract
Personality represents enduring patterns, providing insights into an individual’s aptitude and behavior. Integrating these insights with learning tendencies shows promise in enhancing learning outcomes, optimizing returns on investment, and reducing dropout rates. This interdisciplinary study integrates techniques in advanced artificial intelligence (AI) with human psychology by analyzing data from the trades of Technical and Vocational Education and Training (TVET) education, by combining them with individual personality traits. This research aims to address dropout rates by providing personalized trade recommendations for TVET, with the goal of optimizing outcome-based personalized learning. The study leverages advanced AI techniques and data from a nationwide TVET program, including information on trades, trainees’ records, and the Big Five personality traits, to develop a Personality-Aware TVET Course Recommendation System (TVET-CRS). The proposed framework demonstrates an accuracy rate of 91%, and a Cohen’s Kappa score of 0.84, with an NMAE at 0.04 and an NDCG at 0.96. TVET-CRS can be effectively integrated into various aspects of the TVET cycle, including dropout prediction, career guidance, on-the-job training assessments, exam evaluations, and personalized course recommendations.