This study presents the design and implementation of a single-institution intelligent diagnostic system to identify low mid-period academic performance, aimed at activating proactive and preventive tutoring before a final assessment. The system features an integrated analytical architecture comprising an inferential framework, a predictive framework, an explainability framework, a validation framework, and a Streamlit-based web prototype. The sample uses 18,604 longitudinal academic records from 1264 unique students enrolled across 7 consecutive academic periods (2017–2020) at an Ecuadorian university. Results indicate that curricular level is the structural predictor with the greatest independent contribution (semi-partial R2 = 0.044), followed by academic period (semi-partial R2 = 0.026). Random Forest achieved the best overall performance (MAE = 1.267 ± 0.04; RMSE = 1.714 ± 0.05; R2 = 0.551 ± 0.02), outperforming other algorithms. SHAP explainability confirms the primacy of curricular level and academic period as individual-level risk-associated factors, enabling the generation of interpretable alerts for tutors. The equity analysis revealed that students aged 30–50 years (ratio = 1.375) and the province with code 18 (ratio = 1.395) constitute priority subgroups for data enrichment prior to institutional deployment. External validation with real users is identified as the next research stage.
Guaman et al. (Thu,) studied this question.