TOWARD DIGITAL GOVERNANCEIN HIGHER EDUCATION: DESIGNING AN ANALYTICAL DECISION-MAKING FRAMEWORK FOR THE SELECTION OF MASTERS AND DOCTORAL STUDENTS IN GUINEA

Abstract

The digital transformation of higher education has become a key driver for improving university governance, particularly in graduate admission processes. In Guinea, these processes remain largely manual, lack transparency, and are increasingly inefficient in handling the growing number of applications. This paper proposes an analytical decision-making framework that integrates Business Intelligence technologies and machine learning techniques to optimize the selection of Master's and Doctoral students. The proposed methodology is based on the design of a data warehouse, the implementation of an Extract, Transform, and Load (ETL) process, and the development of predictive models to assess applicants' likelihood of academic success. The expected results indicate improved decision quality, reduced selection bias, and enhanced governance within higher education institutions. This study contributes to the modernization of Guinea's higher education system by promoting data-driven governance and evidence-based decision-making.