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The paper presents a Random forest model in the task of predicting student success (grade) on the base of input predictors (lectures, quizzes, labs and videos) extracted from Moodle activity logs. Since 2010. University of Rijeka is using Moodle based Learning Management Systems (LMS) to complement traditional teaching. LMS is used for documents sharing, quizzes, assessments, video lecturing, tracking student progress and much more. When student access an LMS using his personal account, a digital profile is created that is saved in LMS log files. These logs were used to create a dataset with couple of hundreds of observations. However, building a prediction model using Random forest algorithm is relatively easy comparing to explaining the results. Interpreting Random forest and other machine learning black box models is a challenge regarding to complexity of their decision-making mechanisms. There are a number of new techniques allowing us to interpret such models, and couple of them is used in this paper for that purpose. Another problem a researcher is facing using black box algorithms is GDPR. General Data Protection Regulation has a significant impact on many aspects of EU citizen’s data collection and processing. This paper will highlight most challenging GDPR restrictions on data mining including GDPR’s "right to explanation".

Using LMS activity logs to predict student failure with random forest algorithm