Data-Driven Interventions in Higher Education: Enhancing Student Performance through Educational Data Mining
Year: 2026
Type: conference-paper
Abstract: This paper aims to explore how data mining can enhance performance in higher education while taking into account external factors such as socioeconomic status, family background, and access to different resources. The study analyzes student performance records for the Computer Science Department for two semesters, Fall and Spring term. Descriptive statistics, data visualization, and hypothesis testing techniques were applied to assess the overall performance of students in response to the interventions. The results indicate an increase in performance during Spring term due to changes made in the methods of pedagogy, framing of the curriculum, and system supports provided in the semester, guided by data. This study finds that data mining has the ability to assist in revealing students who may not perform well and provide the needed assistance to help attain increased participation in higher education. It also establishes that data mining can significantly improve academic performance, lessen performance discrepancies, and assist in building a more favorable academic environment.
Institution: Bani-Waleed University
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203484355