MoodleMiner: Data Mining Analysis Tool for Moodle Learning Management System

Gökhan Akçapınar, Alper Bayazıt

Abstract


The purpose of this study is to develop a tool through which non-experts can carry out basic data mining analyses on logs they obtained via Moodle Learning Management System. The study also includes the findings obtained by applying the developed tool on a data set from a real course. The developed tool automatically extracts the features regarding student interactions with the learning system by using their click-stream data, and analyzes this data by using the data mining libraries available in the R programming language. The tool has enabled the 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. In the future versions of the tool, it is planned to add different analyzes such as association rule mining, sequential pattern mining etc.

Keywords


Educational data mining; learning analytics; Moodle; R; learning management system; log analysis tool

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ISSN: 1305-3515