Research Summary: Analysis of learning in Moroccan secondary education: computer model and data-driven pedagogy

Research in Moroccan secondary education highlights the underexploited potential of learning analytics to personalize instruction despite rapid digital transformation. A computational model is proposed to translate student digital interaction data into actionable insights for non-technical educators, focusing on key indicators like engagement and academic achievement, thereby supporting data-driven pedagogical approaches.

Why it matters

This development is crucial for integrating advanced analytical capabilities into educational systems, enabling more personalized and effective learning experiences. By empowering non-technical educators with data-driven insights, institutions can enhance operational efficiency and improve student outcomes in an increasingly digital landscape.

Key insights

• Learning analytics holds significant potential for personalizing instruction and improving teaching practices within educational contexts undergoing digital transformation.
• The potential of learning analytics is largely underutilized in Moroccan pioneer colleges.
• A computational model is designed to convert students' digital traces from educational platforms into interpretable insights.
• The model aims to support data-driven pedagogy, making it accessible to teachers without a technical background.
• The proposed model integrates principles from Learning Analytics, pedagogical visualization, and educational Artificial Intelligence (AI).
• It is structured into five interconnected modules that process learner interaction data.
• The model generates key indicators including student engagement, persistence, and academic achievement.

Source

Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1890197