Multiobjective clustering algorithm for complex data in learning management systems

Abstract

Learning Management Systems (LMS) is now an emergent technology where massive data are collected and requires handling. This data comes from different sources with multiple features which represents another complex paradigm. However, as part of business intelligence and decision support, this data needs to be classified and analyzed for the management, teachers, as well as students to make the appropriate decisions. Thus, one of the effective data analysis methods is clustering. However, LMS data encompasses multi-features, which are not sufficient to make appropriate decisions. Therefore, single feature clustering algorithms would not help LMS decision-makers. Consequently, multifeatured/multiobjective clustering algorithms could be one of the proposed solutions. Thus, looking at different multiobjective clustering algorithms as compared to the LMS nature of data, those algorithms do not satisfy the clustering purpose. In addition, the LMS data could be huge, complex, and sequential algorithms would not help as well. Thus, this paper is a step forward towards clustering LMS data for better decision making. The paper proposes a new clustering framework based upon distributed systems and a new multiobjective algorithm for the purpose of LMS clustering. The algorithm has been examined experimentally in order to answer some of the questions that help taking decision based upon LMS collected data.[Figure not available: see fulltext.].