Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction Confidence
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 p...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of New Mexico
2025-05-01
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| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | https://fs.unm.edu/NSS/40.%20RandomForestWord.pdf |
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| _version_ | 1849225467399241728 |
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| author | Franklin Parrales-Bravo Roberto Tolozano-Benites Alexander Castro-Mora Leonel Vasquez-Cevallos Elsy Rodríguez-Revelo |
| author_facet | Franklin Parrales-Bravo Roberto Tolozano-Benites Alexander Castro-Mora Leonel Vasquez-Cevallos Elsy Rodríguez-Revelo |
| author_sort | Franklin Parrales-Bravo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-40d75d987dba4ff48395f0c9bbf45d02 |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-40d75d987dba4ff48395f0c9bbf45d022025-08-24T17:52:10ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-05-0184504512Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction ConfidenceFranklin Parrales-BravoRoberto Tolozano-BenitesAlexander Castro-MoraLeonel Vasquez-CevallosElsy Rodríguez-ReveloThis 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. https://fs.unm.edu/NSS/40.%20RandomForestWord.pdfrandom forestneutrosophic logicacademic performance predictioneducational data miningprediction confidencesupervised learning |
| spellingShingle | Franklin Parrales-Bravo Roberto Tolozano-Benites Alexander Castro-Mora Leonel Vasquez-Cevallos Elsy Rodríguez-Revelo Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction Confidence Neutrosophic Sets and Systems random forest neutrosophic logic academic performance prediction educational data mining prediction confidence supervised learning |
| title | Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction Confidence |
| title_full | Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction Confidence |
| title_fullStr | Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction Confidence |
| title_full_unstemmed | Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction Confidence |
| title_short | Integrating Random Forest with Neutrosophic Logic for Predicting Student Academic Performance and Assessing Prediction Confidence |
| title_sort | integrating random forest with neutrosophic logic for predicting student academic performance and assessing prediction confidence |
| topic | random forest neutrosophic logic academic performance prediction educational data mining prediction confidence supervised learning |
| url | https://fs.unm.edu/NSS/40.%20RandomForestWord.pdf |
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