Big data analytics using association rules in eLearning
Korn Poonsirivong, Chanintorn Jittawiriaynukoon · 2018
Nowadays most data science researches focus on association rule in order to determine specific patterns and rules from big data. Association rule is built by simple data curation tool such as WEKA which includes classification, clustering, association rules and so on. In general, the association rule can be applicable for large eLearning datasets as they are relevant to unstructured format. In this research, the application of association rule after collecting student's data for years from Learning Management System (LMS) has been investigated. Interestingness metrics and other relevant student preferences are captured after considering several association rule algorithms. Some visualized presentations of rules and their relevant results will be demonstrated in terms of performance metrics and their suitability in eLearning environments.