PhD

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Browsing PhD by Author "Rana Hammad Hassan"

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    An Intelligent Technical and Vocational Education and Training (TVET) Course Recommendation System based on the Trainee’s Aptitude
    (UMT,Lahore, 2025-01-20) Rana Hammad Hassan
    Personality encompasses the distinct patterns of thoughts, emotions, and behaviors that differentiate individuals and typically remain stable throughout one's life. Aligning these individual traits with learning aptitudes holds promise for improving course outcomes, maximizing returns on investment, and reducing dropout rates significantly. This interdisciplinary research bridges insights from Computer Science (CS) and Human Psychology by analyzing data from Technical and Vocational Education and Training (TVET) programs, focusing on the Big Five Personality traits (BFI). This study marks a pioneering effort in both the Pakistani and global TVET sectors, linking TVET learning skills with individual personalities. We have addressed important ethical considerations, including data privacy and informed consent, ensuring the responsible use of human subjects in this study.
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    An intelligent technical and vocational education and training (TVET) course recommendation system based on the trainee’s aptitude
    (UMT, Lahore, 2024) Rana Hammad Hassan
    Personality encompasses the distinct patterns of thoughts, emotions, and behaviors that differentiate individuals and typically remain stable throughout one's life. Aligning these individual traits with learning aptitudes holds promise for improving course outcomes, maximizing returns on investment, and reducing dropout rates significantly. This interdisciplinary research bridges insights from Computer Science (CS) and Human Psychology by analyzing data from Technical and Vocational Education and Training (TVET) programs, focusing on the Big Five Personality traits (BFI). This study marks a pioneering effort in both the Pakistani and global TVET sectors, linking TVET learning skills with individual personalities. We have addressed important ethical considerations, including data privacy and informed consent, ensuring the responsible use of human subjects in this study. The study introduces the deep learning-based Personality-aware TVET Course Recommendation System (TVET-CRS). Over four years, data collection, analysis, and evaluation were conducted in collaboration with one of Punjab, Pakistan's largest TVET training providers. To test the hypothesis, machine learning techniques such as Chi-Square analysis, 5-fold cross-validation, and ensemble classifiers were employed. These methodologies laid the foundation for the development of a robust personality-aware TVET course recommendation system. Notably, the research's original contributions include the development of TVET-CRS and the creation of the first personality dataset specific to Pakistan's TVET sector. TVET-CRS has achieved an impressive accuracy rate of 91%, surpassing benchmarks established in existing literature across all evaluation metrics. Particularly noteworthy is its Cohen’s Kappa score of 0.84, indicating substantial agreement between predicted and actual trades, alongside its lowest error rate of NMAE at 0.04 and highest ranking NDCG of 0.96. These findings carry significant implications across various stages of the TVET training cycle, including dropout prediction, career guidance, on-the-job training assessments, exam evaluations, and course recommendations. The adaptable methodology employed in this research holds potential for application within the TVET sectors of developing countries. The dissemination of these findings is anticipated to attract interest from TVET training providers, policymakers, international funding agencies, researchers, and academics alike, aiming to enrich knowledge, enhance the effectiveness of TVET programs, and maximize returns on investment.