Artificial intelligence predictive modeling for educational indicators using data profiling techniques - Nai, Soukaina; Elbaghazaoui, Bahaa Eddine; Rifai, Amal; Sadiq, Abdelalim (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
In Morocco, the escalating challenges in the education sector underscore the necessity for precise predictions and informed decision-making. Effective management of the education system depends on robust statistical data, which is crucial for guiding decisions, refining policies, and improving both the quality and accessibility of education. Reliable indicators are vital for ensuring efficiency, equity, and accuracy in educational planning and decision- making. Without dependable data, implementing effective policies, addressing the needs appropriately, and achieving positive outcomes becomes difficult. This paper aims to identify the optimal machine learning model for analyzing educational indicators by comparing a range of advanced models across a comprehensive set of metrics. The objective is to determine the most effective model for profiling relevant information and addressing predictive challenges with high accuracy.