Indicators of Good Student Performance in Moodle Activity Data
This work addresses the problem of predicting student performance for educators using learning management systems, but it is incremental as it applies existing analysis methods to new data without introducing novel techniques.
The paper tackled the problem of identifying early predictors of good student performance by analyzing Moodle activity data, finding that early submission, high activity levels, and evening activity correlate with better results, with some exceptions where high activity leads to poor outcomes.
In this paper we conduct an analysis of Moodle activity data focused on identifying early predictors of good student performance. The analysis shows that three relevant hypotheses are largely supported by the data. These hypotheses are: early submission is a good sign, a high level of activity is predictive of good results and evening activity is even better than daytime activity. We highlight some pathological examples where high levels of activity correlates with bad results.