Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method
Predictions of student performance are important to the education system as a whole, helping students to know how their learning is changing and adjusting teachers' and school policymakers' plans for their future growth. However, selecting meaningful features from the huge amount of educat...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2024.1518939/full |
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| author | Rencheng Fang Tao Zhou Baohua Yu Zhigang Li Long Ma Tao Luo Yongcai Zhang Xinqi Liu |
| author_facet | Rencheng Fang Tao Zhou Baohua Yu Zhigang Li Long Ma Tao Luo Yongcai Zhang Xinqi Liu |
| author_sort | Rencheng Fang |
| collection | DOAJ |
| description | Predictions of student performance are important to the education system as a whole, helping students to know how their learning is changing and adjusting teachers' and school policymakers' plans for their future growth. However, selecting meaningful features from the huge amount of educational data is challenging, so the dimensionality of student achievement features needs to be reduced. Based on this motivation, this paper proposes an improved Binary Snake Optimizer (MBSO) as a wrapped feature selection model, taking the Mat and Por student achievement data in the UCI database as an example, and comparing the MBSO feature selection model with other feature methods, the MBSO is able to select features with strong correlation to the students and the average number of student features selected reaches a minimum of 7.90 and 7.10, which greatly reduces the complexity of student achievement prediction. In addition, we propose the MDBO-BP-Adaboost model to predict students' performance. Firstly, the model incorporates the good point set initialization, triangle wandering strategy and adaptive t-distribution strategy to obtain the Modified Dung Beetle Optimization Algorithm (MDBO), secondly, it uses MDBO to optimize the weights and thresholds of the BP neural network, and lastly, the optimized BP neural network is used as a weak learner for Adaboost. MDBO-BP-Adaboost After comparing with XGBoost, BP, BP-Adaboost, and DBO-BP-Adaboost models, the experimental results show that the R2 on the student achievement dataset is 0.930 and 0.903, respectively, which proves that the proposed MDBO-BP-Adaboost model has a better effect than the other models in the prediction of students' achievement with better results than other models. |
| format | Article |
| id | doaj-art-00f59c4bacc8427da13cc9c6a5dd392a |
| institution | Kabale University |
| issn | 2624-909X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj-art-00f59c4bacc8427da13cc9c6a5dd392a2025-01-14T06:10:42ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2025-01-01710.3389/fdata.2024.15189391518939Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost methodRencheng FangTao ZhouBaohua YuZhigang LiLong MaTao LuoYongcai ZhangXinqi LiuPredictions of student performance are important to the education system as a whole, helping students to know how their learning is changing and adjusting teachers' and school policymakers' plans for their future growth. However, selecting meaningful features from the huge amount of educational data is challenging, so the dimensionality of student achievement features needs to be reduced. Based on this motivation, this paper proposes an improved Binary Snake Optimizer (MBSO) as a wrapped feature selection model, taking the Mat and Por student achievement data in the UCI database as an example, and comparing the MBSO feature selection model with other feature methods, the MBSO is able to select features with strong correlation to the students and the average number of student features selected reaches a minimum of 7.90 and 7.10, which greatly reduces the complexity of student achievement prediction. In addition, we propose the MDBO-BP-Adaboost model to predict students' performance. Firstly, the model incorporates the good point set initialization, triangle wandering strategy and adaptive t-distribution strategy to obtain the Modified Dung Beetle Optimization Algorithm (MDBO), secondly, it uses MDBO to optimize the weights and thresholds of the BP neural network, and lastly, the optimized BP neural network is used as a weak learner for Adaboost. MDBO-BP-Adaboost After comparing with XGBoost, BP, BP-Adaboost, and DBO-BP-Adaboost models, the experimental results show that the R2 on the student achievement dataset is 0.930 and 0.903, respectively, which proves that the proposed MDBO-BP-Adaboost model has a better effect than the other models in the prediction of students' achievement with better results than other models.https://www.frontiersin.org/articles/10.3389/fdata.2024.1518939/fullfeature selectionMBSOMDBOAdabooststudent performance prediction |
| spellingShingle | Rencheng Fang Tao Zhou Baohua Yu Zhigang Li Long Ma Tao Luo Yongcai Zhang Xinqi Liu Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method Frontiers in Big Data feature selection MBSO MDBO Adaboost student performance prediction |
| title | Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method |
| title_full | Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method |
| title_fullStr | Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method |
| title_full_unstemmed | Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method |
| title_short | Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method |
| title_sort | prediction model of middle school student performance based on mbso and mdbo bp adaboost method |
| topic | feature selection MBSO MDBO Adaboost student performance prediction |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2024.1518939/full |
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