Bayesian Network Model Based Classifiers Are Used in an Intelligent E-learning System

The use of information and communication technology for educational purposes has increased recently, and the development of network technologies has had a significant influence on the methods employed in electronic learning (E-learning). Most popular trends in education are e-learning, which shows how teaching and learning approaches are evolving in tandem with technological advancements. Technologies that facilitate education's scalability, automation, customization, and innovation offer enormous promise. The cost of e-learning has greatly decreased, and the benefits of its rapid, inexpensive, and time-saving instruction are substantial. Education technology (Edtech) solution providers helps to e-learning and guaranteeing that each student has a smooth and customized learning experience implies a significant impact on learners all across the world during the past COVID-19 epidemic. Thus, e-learning becomes popular teaching method in many educational institutions. However, online learning programs demand a physical examination by a real professor. Therefore, an automated evaluation system for learning prototype utilizing an excellent e-learning system is suggested in the current study. The Baye’s Theorem-based Bayesian Network (BN) concept can be best fit to construct intelligent e-learning systems. Groups of questions serve as the nodes and directed arcs serve as the edges of the directed acyclic graph (DAG) known as BN. This network is employed for ambiguous reasoning. The Baye’s network employing K2, KNN, and J48 was used to compare the BN model against AI classification techniques. Hence discovered that the performance of suggested smart e-learning approach utilizing BN outperforms that of the other two approaches, J48 and K-nearest.