Sequence compulsive incremental updating of knowledge in learning management systems

Abstract

Growing popularity of Learning Management Systems (LMS) coupled with setting up of variety of rubrics to evaluate methods of Learning, Teaching and Assessment Strategies (LTAS) by various accreditation boards has compelled many establishments/universities to run all their courses through one or the other forms of LMS. This has paved way to gather large amount of data on a day to day basis in an incremental way, making LMS data suitable for incremental learning through data mining techniques. The data mining technique which is employed in this research is clustering. This paper focuses on challenges involved in the instantaneous knowledge extraction from such an environment where streams of heterogeneous log records are generated every moment. In obtaining the overall knowledge from such LMS data, we have proposed a novel idea in which instead of reprocessing the entire data from the beginning, we processed only the recent chunk of data (incremental part) and append the obtained knowledge to the knowledge extracted from previous chunk(s). Obtained results when compared with teachers handling the modules/subjects match exactly with the expected results. © 2013 Springer-Verlag.