The purpose of this study is to develop a tool by which non-experts can carry out basic data mining analyses on logs they obtained via the Moodle learning management system. The study also includes findings obtained by applying the developed tool on a data set from a real course. The developed tool automatically extracts features regarding student interactions with the learning system by using their click-stream data, and analyzes these data by using the data mining libraries available in the R programming language. The tool has enabled users who do not have any expertise in data mining or programming to automatically carry out data mining analyses. The information generated by the tool will help researchers and educators alike in grouping students by their interaction levels, determining at-risk students, monitoring students' interaction levels, and identifying important features that impact students academic performances. The data processed by the tool can also be exported to be used in various other analyses.
Key words: Educational data mining, learning analytics, Moodle, R, learning management system, log analysis tool
|
