Personalized Guidance for Moroccan Students: An Approach Based on Machine Learning and Big Data

Abstract

Helping Moroccan students choose their high school presents significant challenges influenced by a variety of factors, including academic achievement, potential, and environmental influences. This study addresses these complexities using advanced data analytics and intelligent algorithms. We collected and examined authentic data from various secondary schools across Morocco, using the MASSAR system, a centralized education platform. To ensure robust model evaluation and optimized performance, we implemented 5-fold cross-validation and extensive hyper-parameter tuning for both support vector machine (SVM) and neural network models. Advanced classification algorithms, including hybrid learning techniques with neural networks and SVM algorithms, were applied, resulting in outstanding precision measures: 99.17% accuracy, 99.20% precision, 99.37% recall, 99.28% F1 score, and 0.99 area under the curve (AUC). By integrating this hybrid learning approach, powered by big data technologies such as Hadoop and Hadoop Distributed File System (HDFS), we accurately predict student choices and offer valuable academic advice. The use of a Hadoop cluster accelerated execution time by 40%. This pioneering merger underlines the adaptability and effectiveness of our approach to meeting the real-world educational challenges specific to the Moroccan context.