Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction Confidence

Authors

Franklin Parrales-Bravo, Centro de Estudios en Tecnologías Aplicadas, Universidad Bolivariana del Ecuador, Guayaquil, EcuadorFollow
Roberto Tolozano-Benites, Centro de Estudios en Tecnologías Aplicadas, Universidad Bolivariana del Ecuador, Guayaquil, EcuadorFollow
Alexander Castro-Mora, Grupo de Investigación en Inteligencia Artificial, Universidad de Guayaquil, Guayaquil, EcuadorFollow
Leonel Vasquez-Cevallos, SIMUEES Simulation Clinic, Universidad Espíritu Santo, Samborondón, EcuadorFollow
Elsy Rodríguez-Revelo, Centro de Estudios en Tecnologías Aplicadas, Universidad Bolivariana del Ecuador, Guayaquil, EcuadorFollow

Abstract

This study proposes a hybrid approach to predict students’ final academic performance in a mathematics course by integrating Random Forest, a supervised machine learning model, with neutrosophic logic to assess prediction reliability. The objective is to improve educational forecasting by not only predicting grades but also quantifying the confidence of each prediction through neutrosophic components—truth (T), indeterminacy (I), and falsity (F). The model was trained on a dataset of demographic, academic, and social attributes from Portuguese schools, achieving robust performance (MAE = 1.54, R2 = 0.61). Key contributions include: (1) a framework for transparent AI-assisted decision-making in education, (2) actionable insights for identifying at-risk students, and (3) a novel application of neutrosophic logic to interpret prediction uncertainties. The results demonstrate the potential of combining machine learning with neutrosophic analysis to improve academic interventions.

Recommended Citation

Parrales-Bravo, Franklin; Roberto Tolozano-Benites; Alexander Castro-Mora; Leonel Vasquez-Cevallos; and Elsy Rodríguez-Revelo. "Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction Confidence." Neutrosophic Sets and Systems 84, 1 (2025). https://digitalrepository.unm.edu/nss_journal/vol84/iss1/40