A Decision Support System for Personalized Learning in Higher Education

Abstract

This research presents a Decision Support System (DSS) designed to facilitate personalized learning in higher education. Utilizing the Best Worst Method (BWM), a popular Multi-Criteria Decision-Making (MCDM) technique, the study evaluated various learning strategies against set criteria based on the preferences and priorities of educators at University X. The results revealed One-on-One Tutoring as the most preferred method for personalized learning, followed by technology-enabled strategies such as Online Self-paced Courses and Adaptive Learning Software. These findings provide critical insights into the relative importance of different learning strategies, contributing to the development of a DSS capable of recommending the most suitable approaches for personalized learning. The implementation of such a system has been proposed as a means to augment decision-making processes within educational environments and potentially yield positive impacts on academic achievement. It is advisable to conduct additional research to authenticate these findings in various educational settings and investigate prospects for integrating empirical data into the process of decision-making.