基于虚拟节点策略的自监督对比学业预警模型  

Self-supervised comparative academic early warning model based on virtual node strategy

作　　者：王园淋 欧阳勇[1] 高榕 刘锦行 叶志伟[1] WANG Yuan-lin;OUYANG-Yong;GAO Rong;LIU Jin-hang;YE Zhi-wei(School of Computer Science,Hubei University of Technology,Wuhan 430068,China;State Key Laboratory for Novel Software Technology at Nanjing University,Nanjing 210023,China)

机构地区：[1]湖北工业大学计算机学院,湖北武汉430068 [2]南京大学计算机软件新技术国家重点实验室,江苏南京210023

出　　处：《计算机工程与设计》2024年第7期2195-2202,共8页Computer Engineering and Design

基　　金：南京大学计算机软件新技术国家重点实验室开放课题基金项目(KFKT2021B12);国家自然科学基金项目(62106070);湖北省重点研发计划基金项目(2020BAB012)。

摘　　要：针对大多数学业预警方法忽略了学生社交行为对于学业的影响,且存在数据不平衡问题和长尾分布问题,提出一种基于虚拟节点策略的自监督对比学业预警模型(SCEW)。通过图神经网络对学生成绩、学生社交行为进行融合得到联合图嵌入;利用虚拟节点策略缓解数据不平衡问题的影响;基于自更新策略,采用节点级对比学习将自监督学习集成到网络的训练中,缓解长尾分布问题的影响。在真实数据集上的实验结果表明,SCEW模型的性能优于现有的主流方法。To address the fact that most academic early warning methods ignore the impact of student behavior on academics and suffer from data imbalance and long-tail distribution,a self-supervised comparative academic early warning model based on virtual node strategy(SCEW)was proposed.The student achievement,student social behavior and interest behavior were fused through graph neural networks to obtain joint graph embeddings.The impact of the data imbalance problem was mitigated using the proposed virtual node strategy.A self-renewal based strategy was designed to integrate self-supervised learning into the training of the network using node-level contrastive learning,the impact of the long-tail distribution was mitigated.Experimental results on real datasets show that the SCEW model outperforms existing mainstream methods.

关 键 词：学业预警 学生行为 虚拟节点 数据不平衡 长尾分布 对比学习 自监督学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]