文献类型：期刊文献

中文题名：基于图神经网络的学业表现预测方法研究综述

英文题名：Survey on Graph Neural Network-based Methods for Academic Performance Prediction

作者：翟洁[1];陈乐旋[1];庞智玉[1]

机构：[1]华东理工大学信息科学与工程学院,上海200030

年份：2026

卷号：53

期号：2

起止页码：16

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：;北大核心:【北大核心2023】；

基金：上海高校市级一流课程建设项目(沪教委高[2025]5号);2024年度教育部产学合作协同育人项目;2024年度教育部-华为“智能基座”产教融合协同育人基地一流课程项目。

语种：中文

中文关键词：图神经网络;学业表现预测;静态特征;动态特征;大模型;教育数据挖掘

外文关键词：Graph neural network;Academic performance prediction;Static features;Dynamic features;Large models;Educational data mining

摘要：目前,学业表现预测作为个性化教育支持系统的核心环节,已成为教育数据挖掘领域的研究热点,在教学决策优化与学生发展指导中发挥重要作用。然而,传统预测方法难以有效应对教育场景中多源异构数据的复杂关联、时序演化及群体依赖等挑战,导致预测精度与泛化能力受限。图神经网络凭借强大的关系建模与表示学习能力,为应对上述问题提供了新范式。因此,许多学者致力于将图神经网络应用于学业表现预测的研究中。针对当前基于图神经网络的学生学业表现预测任务的研究工作进行系统性综述,首先从问题定义出发,解析学业表现预测的核心挑战;接着梳理了图神经网络的基础知识和常用模型;然后分类综述了静态特征建模、融合静态和动态特征建模以及新兴大模型技术赋能等学业表现预测方法的代表性模型及应用场景,在此基础上,系统性总结和分析了基于图神经网络的学业表现预测方法的评估相关数据集及指标;最后从模型的可扩展性、可解释性、多模态语义信息融合以及动态图预训练等维度展望未来研究方向。
Currently,academic performance prediction,as a core component of personalized educational support systems,has become a focal point of research in the field of educational data mining,playing a significant role in optimizing teaching decisions and guiding student development.However,traditional prediction methods struggle to effectively address the challenges posed by the complex correlations,temporal evolution,and group dependencies inherent in multi-source heterogeneous data within educational contexts,resulting in limitations in prediction accuracy and generalization capabilities.Graph Neural Networks(GNNs),leveraging their powerful relational modeling and representation learning abilities,provide a novel paradigm for addressing these challenges.Consequently,numerous researchers are dedicated to applying GNNs to academic performance prediction research.This paper presents a systematic review of current research efforts on GNN-based academic performance prediction tasks.Starting from the problem definition,it analyzes the core challenges of academic performance prediction.It then outlines the foundational knowledge and common models of GNNs.Subsequently,it categorizes and reviews the representative models and their application scenarios for academic performance prediction,including static feature modeling,combined static and dynamic feature modeling,and techniques empowered by emerging large model technologies.Building on this,the paper systematically summarizes and analyzes the evaluation-related datasets and metrics used for GNN-based academic performance prediction methods.Finally,it prospects future research directions from perspectives such as model scalability,interpretability,multimodal semantic information fusion,and dynamic graph pre-training.