A Comparative Analysis of Student Performance Prediction: Evaluating Optimized Deep Learning Ensembles Against Semi-Supervised Feature Selection-Based Models
Advancements in modern technology have significantly increased the availability of educational data, presenting researchers with new challenges in extracting meaningful insights. Educational Data Mining offers analytical methods to support the prediction of student outcomes, development of intellige...
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MDPI AG
2025-04-01
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| author | Jose Antonio Lagares Rodríguez Norberto Díaz-Díaz Carlos David Barranco González |
| author_facet | Jose Antonio Lagares Rodríguez Norberto Díaz-Díaz Carlos David Barranco González |
| author_sort | Jose Antonio Lagares Rodríguez |
| collection | DOAJ |
| description | Advancements in modern technology have significantly increased the availability of educational data, presenting researchers with new challenges in extracting meaningful insights. Educational Data Mining offers analytical methods to support the prediction of student outcomes, development of intelligent tutoring systems, and curriculum optimization. Prior studies have highlighted the potential of semi-supervised approaches that incorporate feature selection to identify factors influencing academic success, particularly for improving model interpretability and predictive performance. Many feature selection methods tend to exclude variables that may not be individually powerful predictors but can collectively provide significant information, thereby constraining a model’s capabilities in learning environments. In contrast, Deep Learning (DL) models paired with Automated Machine Learning techniques can decrease the reliance on manual feature engineering, thereby enabling automatic fine-tuning of numerous model configurations. In this study, we propose a reproducible methodology that integrates DL with AutoML to evaluate student performance. We compared the proposed DL methodology to a semi-supervised approach originally introduced by Yu et al. under the same evaluation criteria. Our results indicate that DL-based models can provide a flexible, data-driven approach for examining student outcomes, in addition to preserving the importance of feature selection for interpretability. This proposal is available for replication and additional research. |
| format | Article |
| id | doaj-art-eb93c134ba414de39b0d9cc193dcdf10 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-eb93c134ba414de39b0d9cc193dcdf102025-08-20T02:30:45ZengMDPI AGApplied Sciences2076-34172025-04-01159481810.3390/app15094818A Comparative Analysis of Student Performance Prediction: Evaluating Optimized Deep Learning Ensembles Against Semi-Supervised Feature Selection-Based ModelsJose Antonio Lagares Rodríguez0Norberto Díaz-Díaz1Carlos David Barranco González2Department of Computer Science, Intelligent Data Analysis Group (DATAi), Universidad Pablo de Olavide, 41013 Seville, SpainDepartment of Computer Science, Intelligent Data Analysis Group (DATAi), Universidad Pablo de Olavide, 41013 Seville, SpainDepartment of Computer Science, Intelligent Data Analysis Group (DATAi), Universidad Pablo de Olavide, 41013 Seville, SpainAdvancements in modern technology have significantly increased the availability of educational data, presenting researchers with new challenges in extracting meaningful insights. Educational Data Mining offers analytical methods to support the prediction of student outcomes, development of intelligent tutoring systems, and curriculum optimization. Prior studies have highlighted the potential of semi-supervised approaches that incorporate feature selection to identify factors influencing academic success, particularly for improving model interpretability and predictive performance. Many feature selection methods tend to exclude variables that may not be individually powerful predictors but can collectively provide significant information, thereby constraining a model’s capabilities in learning environments. In contrast, Deep Learning (DL) models paired with Automated Machine Learning techniques can decrease the reliance on manual feature engineering, thereby enabling automatic fine-tuning of numerous model configurations. In this study, we propose a reproducible methodology that integrates DL with AutoML to evaluate student performance. We compared the proposed DL methodology to a semi-supervised approach originally introduced by Yu et al. under the same evaluation criteria. Our results indicate that DL-based models can provide a flexible, data-driven approach for examining student outcomes, in addition to preserving the importance of feature selection for interpretability. This proposal is available for replication and additional research.https://www.mdpi.com/2076-3417/15/9/4818deep learning (DL)machine learning (ML)feature selection (FS)educational data mining (EDM)artificial neuronal networks (ANNs)AutoML |
| spellingShingle | Jose Antonio Lagares Rodríguez Norberto Díaz-Díaz Carlos David Barranco González A Comparative Analysis of Student Performance Prediction: Evaluating Optimized Deep Learning Ensembles Against Semi-Supervised Feature Selection-Based Models Applied Sciences deep learning (DL) machine learning (ML) feature selection (FS) educational data mining (EDM) artificial neuronal networks (ANNs) AutoML |
| title | A Comparative Analysis of Student Performance Prediction: Evaluating Optimized Deep Learning Ensembles Against Semi-Supervised Feature Selection-Based Models |
| title_full | A Comparative Analysis of Student Performance Prediction: Evaluating Optimized Deep Learning Ensembles Against Semi-Supervised Feature Selection-Based Models |
| title_fullStr | A Comparative Analysis of Student Performance Prediction: Evaluating Optimized Deep Learning Ensembles Against Semi-Supervised Feature Selection-Based Models |
| title_full_unstemmed | A Comparative Analysis of Student Performance Prediction: Evaluating Optimized Deep Learning Ensembles Against Semi-Supervised Feature Selection-Based Models |
| title_short | A Comparative Analysis of Student Performance Prediction: Evaluating Optimized Deep Learning Ensembles Against Semi-Supervised Feature Selection-Based Models |
| title_sort | comparative analysis of student performance prediction evaluating optimized deep learning ensembles against semi supervised feature selection based models |
| topic | deep learning (DL) machine learning (ML) feature selection (FS) educational data mining (EDM) artificial neuronal networks (ANNs) AutoML |
| url | https://www.mdpi.com/2076-3417/15/9/4818 |
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