International Journal of
Information and Education Technology

Abstract—The research addresses the complex relationship between education and job outcomes in Africa, examining it through a deep learning approach using South Africa’s most recent Quarterly Labour Force Survey (2024). In this study, we developed a Multilayer Perceptron (MLP) model to predict employment and long-term employment status, achieving an accuracy of 99.71% for employment prediction and 91% for long-term unemployed predictions. The use of Local Interpretable Model-Agnostic Explanations (LIME) also helped interpret the model, revealing education level, industry type, job-seeking behavior, and work experience as important predictors. We observed a discrepancy between educational attainment and job-market demands, noting that technical and vocational training plays a crucial role in addressing labor shortages. These findings have important implications for AI-generated employment predictions, supporting the use of data-driven research to inform labor-policy development and workforce planning. Key recommendations include expanding vocational training, aligning educational curricula with current labor-market demands, and developing upskilling programs for workers in transitional careers. Additionally, integrating Artificial Intelligence (AI) tools can improve national labor-market forecasting. This study contributes to promoting a more inclusive and data-driven transition from education to employment across Africa.

Keywords—education-to-employment, deep learning, employment prediction, Africa, long-term unemployment, technical and vocational education, artificial intelligence

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Cite: Delali Kwasi Dake, Elijah Ofori, and Selorm Adablanu, "From Education to Employment: A Deep Learning Approach to Understanding Job Market Trends in Africa," International Journal of Information and Education Technology, vol. 16, no. 4, pp. 1007-1019, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).