Data Mining and Machine Learning-Based Predictive Model to Support Decision-Making for the Accreditation of Learning Programmes at the Higher Education Authority

Accreditation of learning programmes is a critical process for ensuring the quality and standards of higher education institutions. This paper presents a predictive model leveraging data mining and machine learning techniques to enhance decision-making in the accreditation of learning programmes at the Higher Education Authority (HEA). The proposed model utilizes historical data, including institutional, programme-specific, and performance-related features, to predict the likelihood of accreditation success. We demonstrate how this predictive model can assist HEA in allocating resources efficiently and making informed decisions about accreditation, ultimately improving the quality and accountability of higher education.