A Data-Driven NLP Skills Gap Analysis of Uganda’s TVET Curriculum and its Effects on Graduate Employability

Abstract

This thesis evaluates the outcomes of the revisions to Uganda’s Technical and Vocational Education and Training (TVET) curriculum, focusing on graduate employability. The study applies data science methodologies, particularly Natural Language Processing (NLP), to assess how well the current curriculum aligns with industry needs. Data was collected from 350 TVET graduates, feedback from 50 employers who assessed over 1,250 graduates, and 30 stakeholders analyzed the curriculum. An NLP-based recommendation system was developed using TF-IDF and cosine similarity to quantify alignment between skills taught and those required in the workforce. Findings reveal significant gaps in digital skills, technical preparedness, and alignment with evolving industry expectations. Employers reported a 68% deficiency in digital competencies, with a mean curriculum-employer similarity score of 0.42. The NLP system achieved an F1-score of 0.87, outperforming manual reviews in skill-gap identification. The study provides actionable recommendations for curriculum reform, including the integration of digital tools, periodic review mechanisms, and the use of real-time feedback loops from the industry. These insights contribute to national development goals such as Uganda Vision 2040 by enhancing TVET effectiveness and workforce readiness.