Conference Agenda

The shift toward hybrid education models in engineering programs has created the need to develop intelligent systems capable of personalizing learning experiences. This study presents a methodological framework for implementing learning analytics aimed at the early identification of students at academic risk and the design of personalized interventions in engineering faculties. A predictive model was developed based on data from learning management systems (LMS), formative assessments, and student activity patterns. The methodology included Monte Carlo simulation with 1,000 iterations for model validation, applied to a simulated cohort of 847 students over four academic semesters. The predictive model achieved an area under the ROC curve of 0.803 (95% CI: 0.744–0.845), with a sensitivity of 70.9% and a specificity of 77.7%. The personalized interventions implemented demonstrated statistically significant improvements in the academic performance of the treatment group (GPA: 2.68 ± 0.59) compared to the control group (GPA: 2.38 ± 0.64), with a medium effect size (Cohen’s d = 0.49, p < 0.001). The retention rate increased from 73.1% to 88.6% among students who received the intervention (χ² = 12.08, p < 0.001). The results suggest that integrating learning analytics into hybrid education environments constitutes an effective strategy for curriculum personalization and for reducing the risk of dropout in engineering programs.