Learning behavior, student engagement, and academic performance in learning analytics

Learning behavior and student engagement are important educational constructs that are associated with academic performance. Learning analytics provides opportunities to examine these relationships using large educational datasets. However, clear construct separation, appropriate statistical validation, and comparison with alternative predictive models remain necessary.

We analyzed a publicly available educational analytics dataset containing 14,003 records. After removing 1,534 exact duplicate records, we retained 12,469 unique records. Learning behavior was represented by study hours, online course participation, and assignment completion. Student engagement was represented by attendance, discussion participation, and extracurricular involvement. 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.

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.

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.