Improving SMART learning: Course completion via AI-driven hybrid system integration in big data - Abdellah Bakhouyi; Amine Dehbi; Lahcen Amhaimar; Said Broumi; Mohamed Talea; Abderrahim Khalidi (Telematics and Informatics Reports)
Rapid data growth in various sectors, like education, is driving the need for robust systems capable of enhancing the learning experience and at the same time guaranteeing course completion. Integrating artificial intelligence in hybrid systems for big data management and processing is highly pertinent within smart-learning. This dissertation is about the development of an AI-enabled hybrid system that smartly leverages big data with the objective of optimizing course completion. The hybrid system implements state-of-the-art machine learning modeling techniques, including Decision Trees, Support Vector Machines, and Naïve Bayesian Classifiers for analyzing the performance of the students' data as well as for predicting successful completion of the course. In essence, when these systems are integrated, the hybrid system can accommodate the complexity and variability of educational data. Experimental analysis illustrates that the proposed model is competitive in the domain of course completion and provides a high number of accuracies, thereby enhancing its ability to boost the learning experience and academic outcomes in smart educational environments.