Predicting Student Performance with Graph Structure Density-Based Graph Neural Networks

Lai Xiaochen, Hao Wentao, Zhang Zheng, Bai Xiaohan
Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:25-32, 2024.

Abstract

Educational data mining is a key area in data mining, focusing on predicting student performance. This research offers a new perspective for EDM, aiding educators in timely interventions based on performance predictions to reduce student failure and improve teaching quality. A novel approach: Graph Structure Density-based Sampling-Aggregation (GSDSA), is presented. GSDSA effectively reduces computational costs and improves training efficiency by limiting the number of neighbor nodes and using the Jaccard coefficient to measure node similarity. Moreover, it also considers relationships among students, employing the Pearson correlation coefficient to analyze none-graph-structured data, enhancing its processing capabilities. Experimental results demonstrate that GSDSA surpasses various graph neural networks and machine learning algorithms in classification accuracy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v245-xiaochen24a, title = {Predicting Student Performance with Graph Structure Density-Based Graph Neural Networks}, author = {Xiaochen, Lai and Wentao, Hao and Zheng, Zhang and Xiaohan, Bai}, booktitle = {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing}, pages = {25--32}, year = {2024}, editor = {Nianyin, Zeng and Pachori, Ram Bilas}, volume = {245}, series = {Proceedings of Machine Learning Research}, month = {26--28 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v245/main/assets/xiaochen24a/xiaochen24a.pdf}, url = {https://proceedings.mlr.press/v245/xiaochen24a.html}, abstract = {Educational data mining is a key area in data mining, focusing on predicting student performance. This research offers a new perspective for EDM, aiding educators in timely interventions based on performance predictions to reduce student failure and improve teaching quality. A novel approach: Graph Structure Density-based Sampling-Aggregation (GSDSA), is presented. GSDSA effectively reduces computational costs and improves training efficiency by limiting the number of neighbor nodes and using the Jaccard coefficient to measure node similarity. Moreover, it also considers relationships among students, employing the Pearson correlation coefficient to analyze none-graph-structured data, enhancing its processing capabilities. Experimental results demonstrate that GSDSA surpasses various graph neural networks and machine learning algorithms in classification accuracy.} }
Endnote
%0 Conference Paper %T Predicting Student Performance with Graph Structure Density-Based Graph Neural Networks %A Lai Xiaochen %A Hao Wentao %A Zhang Zheng %A Bai Xiaohan %B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2024 %E Zeng Nianyin %E Ram Bilas Pachori %F pmlr-v245-xiaochen24a %I PMLR %P 25--32 %U https://proceedings.mlr.press/v245/xiaochen24a.html %V 245 %X Educational data mining is a key area in data mining, focusing on predicting student performance. This research offers a new perspective for EDM, aiding educators in timely interventions based on performance predictions to reduce student failure and improve teaching quality. A novel approach: Graph Structure Density-based Sampling-Aggregation (GSDSA), is presented. GSDSA effectively reduces computational costs and improves training efficiency by limiting the number of neighbor nodes and using the Jaccard coefficient to measure node similarity. Moreover, it also considers relationships among students, employing the Pearson correlation coefficient to analyze none-graph-structured data, enhancing its processing capabilities. Experimental results demonstrate that GSDSA surpasses various graph neural networks and machine learning algorithms in classification accuracy.
APA
Xiaochen, L., Wentao, H., Zheng, Z. & Xiaohan, B.. (2024). Predicting Student Performance with Graph Structure Density-Based Graph Neural Networks. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:25-32 Available from https://proceedings.mlr.press/v245/xiaochen24a.html.

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