An intelligent rule-oriented framework for extracting key factors for grants scholarships in higher education

Abstract

Education is a fundamental sector in all countries, where in some countries students compete to get an educational grant due to its high cost. The incorporation of artificial intelligence in education holds great promise for the advancement of educational systems and processes. Educational data mining involves the analysis of data generated within educational environments to extract valuable insights into student performance and other factors that enhance teaching and learning. This paper aims to analyze the factors influencing students' performance and consequently, assist granting organizations in selecting suitable students in the Arab region (Jordan as a use case). The problem was addressed using a rule-based technique to facilitate the utilization and implementation of a decision support system. To this end, three classical rule induction algorithms, namely PART, JRip, and RIDOR, were employed. The data utilized in this study was collected from un-dergraduate students at the University of Jordan from 2010 to 2020. The constructed models were evaluated based on metrics such as accuracy, recall, precision, and f1-score. The findings indicate that the JRip algorithm outperformed PART and RIDOR in most of the datasets based on f1-score metric. The interpreted decision rules of the best models reveal that both features; the average study years and high school averages play vital roles in deciding which students should receive scholarships. The paper concludes with several suggested implications to support and enhance the decision-making process of granting agencies in the realm of higher education.