预测GPcSAGE学生的学习成绩

Xiaochen Lai, Sixuan Zeng, Wenkai Xu, Lu Tong, Jialiu Yang
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引用次数: 0

摘要

教育数据挖掘是数据挖掘中的一个热门研究领域,而学生成绩预测是教育数据挖掘的重要研究课题之一。为了及时准确地预测学生成绩,本文提出了一种基于图神经网络的图皮尔逊相关样本和聚合(GPcSAGE)模型。优化与目标节点相似的相邻节点的采样概率,减弱目标节点属性异常对预测结果的影响,减小采样方差。通过重新配置聚合函数来聚合更重要的信息,提高了算法的效率和预测精度。实验证明了该方法的有效性,有助于预测学生的学习趋势和效果,从而进行精确的教学干预,提高教学质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the academic performance of students with GPcSAGE
Educational data mining is a popular research area in data mining, and predicting student performance is one of the important research topics in educational data mining. In order to predict student performance in a timely and accurate manner, this paper proposes a Graph Pearson correlation Sample and AggreGatE (GPcSAGE) model based on graph neural networks. The sampling probability of neighboring nodes similar to the target node is optimized to weaken the influence of abnormal target node attributes on the prediction results and reduce the sampling variance. The algorithm efficiency and prediction accuracy are improved by reconfiguring the aggregation function to aggregate more important information. The experiments demonstrate the effectiveness of the method, which helps to predict students' learning trends and effects for precise teaching interventions to improve teaching quality.
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