Identifying low-performing regions in Moroccan education: A deep learning approach using the PISA dataset

Abstract

This study highlights the ongoing nature of the school reform movement, emphasizing the need for continuous attention and action. Despite this effort, academic performance has exhibited relative stability in recent years, while significant regional performance disparities persist. Addressing these inequalities requires novel approaches to enhance educational quality. Past research has explored clustering algorithms in developed countries, providing insights into personalized teaching strategies based on students' learning style preferences. In response, our research aims to identify underperforming regions in Morocco, necessitating attention and intervention. We employ an unsupervised deep learning method called "deep embedding clustering" to group Moroccan students based on their performance. The results are subsequently visualized on a choropleth map, revealing intricate patterns and trends in educational performance that might not be immediately apparent. The analysis employs the comprehensive program for international student assessment (PISA) dataset, encompassing individual students' responses and plausible values reflecting cognitive abilities. The findings indicate that the "Guelmim-Oued Noun" region exhibits the highest performance level among all regions, while "Dakhla-Oued Eddahab," "Béni Mellal-Khénifra," and "Oriental" regions display lower performance levels. As a result, this study urges policymakers to incorporate tailored measures into regional policies to improve students' educational outcomes.