DIGITIZING TVET EDUCATION THROUGH UNIFIED ANALYSIS OF PERSONALITY AND LEARNING SKILLS

Personality exerts a pivotal influence on Technical and Vocational Education and Training (TVET) by shaping individuals' learning approaches, social interactions, and practical skill applications. Customizing educational strategies and curriculum designs to suit diverse personality profiles cultivates a more conducive learning environment, enabling each student to optimize their abilities and contribute effectively to their chosen vocational fields. Incorporating personality considerations into TVET programs not only enhances skill acquisition but also fosters personal growth and professional success among learners. This research aims to test our hypothesis that can we predict TVET courses based on personality traits and whether is there any role of demographics (age and gender), and examination performance scores in TVET course prediction. Data for this research were collected from one of the largest TVET training providers and a five-fold cross-validation technique with MDS analysis and Decision Tree methods were used. The result discovered that TVET courses can be predicted based on personality traits and demographics and Examination Scores have a significant role in TVET course prediction. Prediction accuracy of 78% is achieved by SVC, 76% by Naïve Bayes, and 74% by Random Forest classifies.