Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework.
As education increasingly relies on data-driven methodologies, accurately predicting student performance is essential for implementing timely and effective interventions. The California Student Performance Dataset offers a distinctive basis for analyzing complex elements that affect educational resu...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314823 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832540343114924032 |
|---|---|
| author | Xiaoyi Zhang Yakang Zhang Angelina Lilac Chen Manning Yu Lihao Zhang |
| author_facet | Xiaoyi Zhang Yakang Zhang Angelina Lilac Chen Manning Yu Lihao Zhang |
| author_sort | Xiaoyi Zhang |
| collection | DOAJ |
| description | As education increasingly relies on data-driven methodologies, accurately predicting student performance is essential for implementing timely and effective interventions. The California Student Performance Dataset offers a distinctive basis for analyzing complex elements that affect educational results, such as student demographics, academic behaviours, and emotional health. This study presents the GNN-Transformer-InceptionNet (GNN-TINet) model to overcome the constraints of prior models that fail to effectively capture intricate interactions in multi-label contexts, where students may display numerous performance categories concurrently. The GNN-TINet utilizes InceptionNet, transformer architectures, and graph neural networks (GNN) to improve precision in multi-label student performance forecasting. Advanced preprocessing approaches, such as Contextual Frequency Encoding (CFI) and Contextual Adaptive Imputation (CAI), were used on a dataset of 97,000 occurrences. The model achieved exceptional outcomes, exceeding current standards with a Predictive Consistency Score (PCS) of 0.92 and an accuracy of 98.5%. Exploratory data analysis revealed significant relationships between GPA, homework completion, and parental involvement, emphasizing the complex nature of academic achievement. The results illustrate the GNN-TINet's potential to identify at-risk pupils, providing a robust resource for educators and policymakers to improve learning outcomes. This study enhances educational data mining by enabling focused interventions that promote educational equality, tackling significant challenges in the domain. |
| format | Article |
| id | doaj-art-b0a2af8fc2774d9c820ea3b0ca792490 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b0a2af8fc2774d9c820ea3b0ca7924902025-02-05T05:31:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031482310.1371/journal.pone.0314823Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework.Xiaoyi ZhangYakang ZhangAngelina Lilac ChenManning YuLihao ZhangAs education increasingly relies on data-driven methodologies, accurately predicting student performance is essential for implementing timely and effective interventions. The California Student Performance Dataset offers a distinctive basis for analyzing complex elements that affect educational results, such as student demographics, academic behaviours, and emotional health. This study presents the GNN-Transformer-InceptionNet (GNN-TINet) model to overcome the constraints of prior models that fail to effectively capture intricate interactions in multi-label contexts, where students may display numerous performance categories concurrently. The GNN-TINet utilizes InceptionNet, transformer architectures, and graph neural networks (GNN) to improve precision in multi-label student performance forecasting. Advanced preprocessing approaches, such as Contextual Frequency Encoding (CFI) and Contextual Adaptive Imputation (CAI), were used on a dataset of 97,000 occurrences. The model achieved exceptional outcomes, exceeding current standards with a Predictive Consistency Score (PCS) of 0.92 and an accuracy of 98.5%. Exploratory data analysis revealed significant relationships between GPA, homework completion, and parental involvement, emphasizing the complex nature of academic achievement. The results illustrate the GNN-TINet's potential to identify at-risk pupils, providing a robust resource for educators and policymakers to improve learning outcomes. This study enhances educational data mining by enabling focused interventions that promote educational equality, tackling significant challenges in the domain.https://doi.org/10.1371/journal.pone.0314823 |
| spellingShingle | Xiaoyi Zhang Yakang Zhang Angelina Lilac Chen Manning Yu Lihao Zhang Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework. PLoS ONE |
| title | Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework. |
| title_full | Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework. |
| title_fullStr | Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework. |
| title_full_unstemmed | Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework. |
| title_short | Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework. |
| title_sort | optimizing multi label student performance prediction with gnn tinet a contextual multidimensional deep learning framework |
| url | https://doi.org/10.1371/journal.pone.0314823 |
| work_keys_str_mv | AT xiaoyizhang optimizingmultilabelstudentperformancepredictionwithgnntinetacontextualmultidimensionaldeeplearningframework AT yakangzhang optimizingmultilabelstudentperformancepredictionwithgnntinetacontextualmultidimensionaldeeplearningframework AT angelinalilacchen optimizingmultilabelstudentperformancepredictionwithgnntinetacontextualmultidimensionaldeeplearningframework AT manningyu optimizingmultilabelstudentperformancepredictionwithgnntinetacontextualmultidimensionaldeeplearningframework AT lihaozhang optimizingmultilabelstudentperformancepredictionwithgnntinetacontextualmultidimensionaldeeplearningframework |