A Predictive Model to Support Decision Making for the Accreditation of Learning Programmes using Data Mining and Machine Learning

Abstract

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. The analysis of the data is used to evaluate the institution against a standard set by a Quality Assurance body, for the accreditation of higher education learning programmes. Therefore, the model supports the decision-making process in accreditation evaluation. The paper provides a proposed model using data mining and machine learning for the prediction of accreditation criteria, in the case of this paper the research considers academic staff appropriateness and adequacy.