Quantifying variability in predictions of student performance: Examining the impact of bootstrap resampling in data pipelines

Educators seek to develop accurate and timely prediction models to forecast student retention and attrition. Although prior studies have generated single point estimates to quantify predictive efficacy, much less education research has examined variability in student performance predictions using nonparametric bootstrap algorithms in data pipelines. In this study, bootstrapping was applied to examine performance variability among five data mining methods (DMMs) and four filter preprocessing feature selection techniques for forecasting course grades for 3225 students enrolled in an undergraduate biology class. While the median area under the curve (AUC) values obtained from bootstrapping were significantly lower than the AUC point estimates obtained without resampling, DMMs and feature selection techniques impacted variability in different ways. The ensemble technique elastic net regression (GLMNET) significantly outperformed all other DMMs and exhibited the least amount of variability in the AUC. However, all filter feature selection techniques significantly increased variability in student success predictions, compared to when this step was omitted from the data pipeline. We discuss the potential benefits and drawbacks of incorporating bootstrapping into prediction pipelines to track, monitor, and forecast classroom performance, as well as highlight the risks of only examining point estimates.