Enhancing Learning Object Analysis through Fuzzy C-Means Clustering and Web Mining Methods

The development of learning objects (LO) and e-pedagogical practices has significantly influenced changed the performance e-learning systems. This promotes a genuine sharing resources creates new opportunities for learners to explore them easily. Therefore, need system categorization these becomes mandatory. In this vein, classification theories combined with web mining techniques can highlight LOs make very useful learners. study consists two main phases. First, we extract metadata from objects, using algorithm Web exploration such as feature selection techniques, which are mainly implemented find best set features that allow us build models. key role in object is identify pertinent eliminate redundant an excessively dimensional dataset. Second, according particular form similarity Multi-Label Classification (MLC) based on Fuzzy C-Means (FCM) algorithms. As clustering algorithm, used perform accuracy Euclidean distance metrics measurement. Finally, assess effectiveness FCM, series experimental studies real-world dataset were conducted. findings indicate proposed approach exceeds traditional leads viable results. Doi: 10.28991/ESJ-2023-07-03-010 Full Text: PDF