Exploring predictors affecting quality control in higher education using AHP and Random forest

Abstract

The purpose of this study is to explore predictive factors that affect quality control in higher education using random forest and AHP based on Higher Education Information Disclosure discosure data. Three research questions were investigated: 1) What is AHP model derived from the analysis of previous studies and literature review? 2) What is relative weight of each variable calculated through AHP? 3) What is a predictive model of quality management in Higher Education derived through random forest? To answer these questions, this study conducted literature review, AHP survey, and random forest analyses. The results of this study showed that the input AHP model constructed 5 factors, 31 indicators and the output AHP model constructed 2 factors, 11 indicators. Secondly, the AHP analysis indicated that the most important input variables were ‘educational restitution rate’, ‘cost of education per student’, and ‘retention rate of full-time faculty’. and the output variables with the highest global importance were ‘employment rate’ and ‘recruitment rate’. Finally, the results of random forest based on the AHP results showed that all target variables, excluding ‘employment rates’, were influenced by the ‘amount of governments financial program’, ‘educational restitution rate’, and ‘cost of education per student’. ‘Employment rate’ was related to ‘percentage of industry full-time faculty’, ‘filed training curriculum’, and ‘percentage of full-time faculty’.