A hybrid AI approach for predicting academic performance in RBE students
Machine learning has advanced significantly in recent years and is being used in higher education to perform various types of data analysis. While the literature demonstrates the application of machine learning algorithms to predict performance in university education, no such applications are found...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2025
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| _version_ | 1881508815979937792 |
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| author | Gonzales, Willy Cordero, Zindel Abanto Ramirez, Carlos Daniel Susanibar Ramírez, Edgar Tito Iftikhar, Hasnain López Gonzales, Javier Linkolk |
| author_browse | Abanto Ramirez, Carlos Daniel Cordero, Zindel Gonzales, Willy Iftikhar, Hasnain López Gonzales, Javier Linkolk Susanibar Ramírez, Edgar Tito |
| author_facet | Gonzales, Willy Cordero, Zindel Abanto Ramirez, Carlos Daniel Susanibar Ramírez, Edgar Tito Iftikhar, Hasnain López Gonzales, Javier Linkolk |
| author_sort | Gonzales, Willy |
| collection | Producción científica |
| description | Machine learning has advanced significantly in recent years and is being used in higher education to perform various types of data analysis. While the literature demonstrates the application of machine learning algorithms to predict performance in university education, no such applications are found in EBR, let alone in private institutions of a denominational nature, which presents an opportunity to study prediction in these institutions. To address this gap, this research aims to propose a predictive approach as a decision-support tool for regular basic education, using machine learning techniques. Among the techniques utilized, three machine learning models (Logistic Regression, Support Vector Machine, and Random Forest), along with deep learning models (AlexNet, Gated Recurrent Unit, and Bidirectional Gated Recurrent Unit), were analyzed, as well as ensemble models. Nonetheless, the Ensemble model, which combines deep learning and machine learning techniques, is preferred due to its superior accuracy, precision, and sensitivity performance metrics. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/111 |
| institution | UPeU |
| language | English |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/1112026-10-30T16:14:31ZA hybrid AI approach for predicting academic performance in RBE studentsGonzales, WillyCordero, ZindelAbanto Ramirez, Carlos DanielSusanibar Ramírez, Edgar TitoIftikhar, HasnainLópez Gonzales, Javier LinkolkArtificial intelligenceStatisticsHybrid AiPredicting Academic PerformanceRbe StudentsMachine learning has advanced significantly in recent years and is being used in higher education to perform various types of data analysis. While the literature demonstrates the application of machine learning algorithms to predict performance in university education, no such applications are found in EBR, let alone in private institutions of a denominational nature, which presents an opportunity to study prediction in these institutions. To address this gap, this research aims to propose a predictive approach as a decision-support tool for regular basic education, using machine learning techniques. Among the techniques utilized, three machine learning models (Logistic Regression, Support Vector Machine, and Random Forest), along with deep learning models (AlexNet, Gated Recurrent Unit, and Bidirectional Gated Recurrent Unit), were analyzed, as well as ensemble models. Nonetheless, the Ensemble model, which combines deep learning and machine learning techniques, is preferred due to its superior accuracy, precision, and sensitivity performance metrics.Articlehttps://pubmed.ncbi.nlm.nih.gov/41195199https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1651100/pdfhttps://doi.org/10.3389/frai.2025.1651100PMC1258301910.3389/frai.2025.1651100https://hdl.handle.net/20.500.15037/11141195199W4415386646enFrontiers in Artificial Intelligence2624-82122025-10-212026-06-13T02:37:17Z2026-06-13T02:37:17Z |
| spellingShingle | Artificial intelligence Statistics Hybrid Ai Predicting Academic Performance Rbe Students Gonzales, Willy Cordero, Zindel Abanto Ramirez, Carlos Daniel Susanibar Ramírez, Edgar Tito Iftikhar, Hasnain López Gonzales, Javier Linkolk A hybrid AI approach for predicting academic performance in RBE students |
| title | A hybrid AI approach for predicting academic performance in RBE students |
| title_full | A hybrid AI approach for predicting academic performance in RBE students |
| title_fullStr | A hybrid AI approach for predicting academic performance in RBE students |
| title_full_unstemmed | A hybrid AI approach for predicting academic performance in RBE students |
| title_short | A hybrid AI approach for predicting academic performance in RBE students |
| title_sort | hybrid ai approach for predicting academic performance in rbe students |
| topic | Artificial intelligence Statistics Hybrid Ai Predicting Academic Performance Rbe Students |
| topic_facet | Artificial intelligence Statistics Hybrid Ai Predicting Academic Performance Rbe Students |
| url | https://pubmed.ncbi.nlm.nih.gov/41195199 https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1651100/pdf https://doi.org/10.3389/frai.2025.1651100 https://hdl.handle.net/20.500.15037/111 |
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