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

Abstract

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

Keywords

• learning objects
• multi-label classification (mlc)
• web-based mining techniques
• fuzzy c-means clustering algorithm
• machine learning.