RT conference output
T1 Early Prediction of Academic Performance in a Database Course Using Moodle Interaction Logs
A1 Balderas Alberico, Antonio
A1 Caballero Hernández, Juan Antonio
A1 Palomo Duarte, Manuel
A1 Muñoz Ortega, Andrés
A1 Dodero Beardo, Juan Manuel
A2 Ingeniería Informática
K1 Learning Analytics
K1 Academic Performance Prediction
K1 Engagement Patterns
K1 Moodle
K1 Classification
K1 Early Warning Systems
AB Academic failure and high dropout rates remain persistent challenges in STEM education, particularly in core disciplines like Computer Science. Using Moodle interaction logs for a compulsory course on Database Systems (183,917 log records from 131 students), we engineered 16 behavioral features that capture engagement, regularity, resource diversity, and temporal activity patterns. Four classification models were trained and evaluated using 5-fold cross-validation and an independent test set: Support Vector Machine, Random Forest, k-Nearest Neighbors, and Logistic Regression. The best-performing models reached an AUC of up to 0.812 (Logistic Regression) and 0.809 (Random Forest) on the independent test set. Notably, Random Forest demonstrated a recall of 81.2% for at-risk students, suggesting its potential suitability as the basis for an Early Warning System (EWS). Feature importance analysis reveals that resource access, total engagement, and formative quiz interaction are the strongest predictors of the final outcome. Furthermore, activity within the first four weeks ranks among the top six predictors, providing empirical evidence that early behavioral signals carry significant predictive power. These results suggest that identification of at-risk students is highly feasible and actionable within the first month of instruction, offering a robust foundation for automated pedagogical interventions.
PB CEUR-WS
YR 2026
FD 2026-05-08
LK http://hdl.handle.net/10498/40203
UL http://hdl.handle.net/10498/40203
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026