Quality Managemet In Higher Education: Enhancing Retention And Graduation Rates

Abstract

Quality management and continuous improvement have become increasingly practiced in worldwide industries and organizations. Recently, the higher education sector has been gradually moving towards Quality Management as well. The academic success and retention of university students are major questions for universities worldwide, and many retention programs have been designed to remedy issues of students-at-risk and early dropouts since a university’s academic productivity and efficiency are heavily linked to the institution’s graduation and retention rates. In this way, it important for the university to find ways to identify students needing help and provide them with support. To that end, this research work aims to develop a framework through which the management can identify students-at- risk as early as possible, and to ensure that they are offered appropriate support in a timely manner. In addition, three machine learning-based prediction models have been proposed for predicting course difficulty level (CDL). The accuracy of the proposed prediction models is assessed by using a real dataset collected from the students of the college of engineering in Qatar University, Doha-Qatar