Self-Evaluation Model for Graduate Programs: A Comprehensive Approach to Quality Assurance

Abstract

The implementation of the cluster analysis technique in the evaluation of graduate programs to improve the quality of education in higher education institutions was addressed. In addition, graduate programs were identified and grouped based on key indicators, such as graduation and retention rates, to optimize self-evaluation and accreditation processes. An exploratory quantitative design was used, selecting random samples of students from various HEI graduate programs using data obtained from the institutional Power BI dashboard. The relevance of incorporating advanced analytical techniques in the external evaluation of educational programs to foster informed decision making was established. In addition, innovative techniques that facilitate the efficient and effective opening of graduate programs were examined. The successful implementation of cluster analysis contributes significantly to the continuous improvement of educational quality, enhances institutional effectiveness and enriches the educational experience of students.

Received: 14 June 2024 / Accepted: 26 August 2024 / Published: 05 September 2024