Multi-Label Classification of Learning Objects Using Clustering Algorithms Based on Feature Selection

Abstract

In the field of online learning, the development of learning objects (LOs) has increased. LOs promote reusing and referencing educational content in various learning environments. However, despite this progress, the lack of a conceptual model for sharing suitable LOs between learners makes multiple challenges. In this regard, multi-label classification plays a significant role to make high-quality LOs, which can be accessible and reusable. This article highlights a new way of using learning objects based on Multi-Label Classification (MLC) and clustering algorithms with feature selection techniques. It suggests a new system that makes the most suitable choice among many alternative sources based on the Sharable Content Object Reference Model (SCORM). The proposed algorithm has been tested on a real-world application dataset related to the data analysis service for the learning science community. The experimental results show that our algorithm outperforms the traditional approach and produces good results.

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

“…The classification of learning objects, as described by the authors in Churchill [1], is a process of machine learning tasks that is used to structure a group of learning objects by means of different methods, including Bayesian classification, and Support Vector Machine, etc. The term Learning Objects (LO) as a concept in an e-learning system was mentioned by Wayne Hodgins in 1994, and since that time, several definitions have been proposed with different terminology like "Pedagogical Objects", "Content Objects", and "Knowledge Objects" to enrich this definition [2]. In 2002, the Learning Technology Standards Committee of the Institute of Electrical and Electronics Engineers (known as IEEE) defined a learning object as "any entity, whether digital or not, that can be used for learning, education, or training" [3].…”