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

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