An Ensemble Classifier for TVET Course prediction using Big Five Personality Traits

Technical and Vocational Education and Training (TVET) is an academic program that facilitates skilled workforce development for the industry nationally and internationally. Personality-aware recommendation systems are proven to be more accurate as compared to traditional recommendation systems. This paper evaluates an ensemble classifier for TVET Trade prediction based on Big Five Personality (BFI) Traits. Data collection for this research was conducted by prominent TVET training providers in the public sector of Punjab - Pakistan. After data collection, data cleaning, and preparation, using machine learning, an ensemble method using K Nearest Neighbor, Decision Tree, and Logistic Regression classifiers to predict the TVET course based on BFI personality traits, age, gender, and exam score. The result discovered that the ensemble classifier has attained a prediction accuracy of 83.95% as well as K Nearest Neighbor algorithm has achieved the utmost accuracy of 97.32%. This good perdition accuracy supports the argument that in the future, TVET courses can be predicted based on BFI personality Traits and other variables.