Unlocking Potential: Personality-Aware TVET Course Recommendations Revolutionize Skill Development

Personality is a complex amalgamation of ideas, behaviors, and social constructs that shape our self-perception and influence our interactions with others. It tends to remain relatively stable over time. The development of personality-aware recommendation systems is driven by the understanding that human behavior and personality play a significant role in skill acquisition, career progression, and overall success. Technical and Vocational Education and Training (TVET) is crucial in building a skilled workforce, particularly in response to the demands of Industry 5.0. Unlike conventional recommendation systems, personality-aware systems effectively address persistent challenges such as the cold start problem and data sparsity. This paper introduces the Personality-aware TVET Course Recommender System (TCRS), which suggests the top three TVET courses by considering trainees' personality traits, demographic information, and the historical success patterns of previous trainees in similar courses. A standout feature of the TCRS is its Academic System Learner, which continuously incorporates insights from individual trainees' progress in TVET courses, thereby enhancing the accuracy of its machine learning model for predictive analysis. The effectiveness of the TCRS is assessed using seven classifiers, yielding notable prediction accuracies: 99% with Random Forest, 98% with Decision Tree, and 89% with k-Nearest Neighbors (kNN). In real-time testing, the TCRS demonstrated an accuracy rate of 84%.