A Predictive Model to Support Decision-Making for the Accreditation of Learning Programs Using Data Mining and Machine Learning

Terabytes of data are produced by higher education institutions every year, and this data is crucial for determining how countries will develop. There are significant amounts of this educational data in a variety of relatively recent formats. We suggest a model for gathering, securing, and analyzing this substantial amount of data. A quality assurance body sets standards, and the analysis of the data is used to assess the institution in relation to those standards for the accreditation of higher education programs. Therefore, the model supports the decision-making process in accreditation evaluation. The research in this paper takes academic staff appropriateness and adequacy into consideration when proposing a model for data mining and machine learning-based accreditation criteria prediction.