Forecasting Emerging Skill Demands with Machine Learning to Inform Curriculum Development in Uganda’s Higher Education

Abstract

Rapid technological advancement and evolving industry demands have widened the skill gap in Uganda’s labor market. Higher education institutions often struggle to keep pace with these changes, leading to mismatches between graduate competencies and employer expectations. This study uses machine learning techniques to forecast emerging skill demands and inform the development of data-driven curricula in Ugandan universities.

Drawing on more than one million job postings from 2021 to 2023, the research applies natural language processing (NLP), time series forecasting (ARIMA and Holt-Winters), and clustering algorithms to analyze labor market trends. Exploratory Data Analysis (EDA) revealed high-demand skills, while Holt-Winters outperformed ARIMA (MAE: 9.05 vs. 23.87), capturing the seasonal nature of skill fluctuations.

Key findings indicate a growing demand for roles such as interaction designers, network administrators, user experience professionals, and social media managers. In-demand technical skills include Python, Google Analytics, CSS, Tableau, AWS, and Sketch. The increasing emphasis on digital literacy and soft skills underscores the need for more flexible and adaptive curricula.

This study offers actionable recommendations for curriculum reform, including integrating technical skills, developing continuous learning pathways, and enhancing academic-industry collaboration. By applying machine learning to labor market analysis, the research equips universities, policymakers, and stakeholders with the information needed to align higher education with the demands of Uganda’s evolving digital economy.

Keywords: Machine Learning, Labor Market Trends, Skill Forecasting, Higher Education Curriculum, Uganda, ARIMA, Holt-Winters, Data Science, skill mismatch.