Supplementary file 2_Learning behavior, student engagement, and academic performance in learning analytics.docx

Academic performance was represented by examination score, while final grade was excluded because its calculation procedure was not documented. We used ordinary least squares regression to examine the statistical associations. We also included an observed variable path model and machine learning models as alternative specifications.

We used an 80% training and 20% held-out test split for predictive validation.</p>Results<p>Learning behavior showed a small and nonsignificant association with student engagement (β₁ = 0.0017, p = 0.8112). Student engagement showed a negative association with academic performance (γ₁ = −1.2485, p = 0.0014), whereas learning behavior showed a positive association (γ₂ = 1.0075, p = 0.0009). The baseline model showed limited explanatory power (R² = 0.004).

On the held-out test set, the random forest model provided the strongest predictive performance (R² = 0.564, RMSE = 11.719, MAE = 8.896, and MAPE = 14.013%). The robustness analysis showed similar coefficient magnitudes across the alternative static specifications.</p>Discussion<p>The findings indicate statistical associations among learning behavior, student engagement, and academic performance within the analytical dataset.

They do not establish causal relationships or temporal relationships because the dataset is cross-sectional and contains no documented temporal ordering or repeated observations. The results support transparent construct definition, comparative model evaluation, and out-of-sample validation in learning analytics research.</p>