Navigating the Future of Education: Harnessing Data Mining to Illuminate Pathways to Success

Abstract

This study aims to enhance educational assessment tools in Algerian middle schools by utilizing Educational Data Mining (EDM) techniques to predict student performance in the Middle School Certificate Examination. Recognizing a gap in the Algerian education system’s use of abundant student data for improving learning outcomes and decision-making, this research applies advanced data analytics to overcome the limitations of traditional evaluation methods. Using a dataset of 541,760 student records from the Ministry of National Education, the study employs regression models including CatBoost, Ridge, and linear regression, to analyze and forecast student examination performance. Findings indicate that EDM can effectively predict student outcomes, offering educators and policymakers a valuable resource for crafting targeted educational interventions and enhancing student success rates. This research underscores the significance of bridging the gap between available data and data-driven decision-making, advocating for the integration of EDM to modernize and improve educational efficacy in Algeria.