Development of Machine Learning Models using Study Behavior Predictors of Students’ Academic Performance Through Moodle
Edmund D. Evangelista

Edmund D. Evangelista, DIT Student, St. Paul University Philippines, Cagayan, Philippines,  Southeast Asia.

Manuscript received on 05 April 2019 | Revised Manuscript received on 14 April 2019 | Manuscript Published on 24 May 2019 | PP: 22-27 | Volume-8 Issue-6S3 April 2019 | Retrieval Number: F10050486S319/19©BEIESP

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Abstract: Applying data mining and machine learning techniques on Moodle logs is an emerging trend that can help track student’s performance and decrease the failure rate. Due to Moodle’s limitation to provide these features, this study was conceptualized. The study made use of historical data from Moodle logs of past academic years to pre-process and develop machine learning models using an open source data mining tool named Weka. This study made use of predictor attributes related to study behavior of students such as Course Viewing Time, Resource Views, Quiz Taken, Replied in Discussions, and Viewed at Weekends. However, it was found out that predictor attributes such as Activities Completed, Course Views and Assignment Passed are the ones which are strongly correlated to students’ performance. Moreover, the predictive accuracy of a model improves depending on the machine learning algorithm being used. Algorithms such as J48, Random Forest, JRip, and OneR have been consistently performing well regardless of the model it is being trained into; and, achieved a predictive accuracy as high as 96.42%. The study was able to reflect the predicted results of Weka back to Moodle through an integrator and developed block using Moodle API. Finally, the developed application was evaluated by IT Experts using the ISO 25010 criteria.

Keywords: Data Mining, Machine Learning, Predictive Analytics, Predict Students’ Performance, Moodle Logs.
Scope of the Article: Machine Learning