社交参与视角下超图增强的学习趣缘社群群体检测研究

doi:10.3772/j.issn.1000-0135.2024.12.005

情报学报

2024, Vol. 43

Issue (12): 1425-1439 DOI: 10.3772/j.issn.1000-0135.2024.12.005

情报技术与应用

本期目录 | 过刊浏览 | 高级检索

社交参与视角下超图增强的学习趣缘社群群体检测研究

李贺, 刘嘉宇, 沈旺, 时倩如, 解梦凡

吉林大学商学与管理学院，长春 130012

Group Detection in Interest-Based Learning Communities Enhanced by Hypergraph from a Social Engagement Perspective

Li He, Liu Jiayu, Shen Wang, Shi Qianru, Xie Mengfan

School of Business and Management, Jilin University, Changchun130012

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摘要在线学习群体检测是在新一轮科技革命赋能教育创新变革背景下，依据学习者个性化特征优化教育资源分层配置的关键途径。现有学习趣缘社群在线学习群体的检测主要依赖学习者的直接行为记录和互动指标，较少关注学习者潜在的社交参与水平和社群结构。为营造数智环境下学习者画像决策辅助全民自主学习的文化氛围，本文提出一种社交参与视角下超图增强的学习趣缘社群群体检测方法。首先，从影响用户社交参与的维度出发，构建能够体现学习者社交参与水平的特征集。其次，提出超图卷积网络（hypergraph convolutional network，HyperGCN）增强的图聚类算法HG-SDCN（structural deep clustering network based on HyperGCN），解决了利用二分图检测在线学习群体时无法有效捕捉学习者多元交互关系和高阶结构的问题。最后，从真实学习趣缘社群收集数据，验证本文提出方法的检测效果。与基线相比，本文方法在Acc（accuracy）、F1、NMI（normalized mutual information）和ARI（adjusted Rand index）等评价指标上分别提升了16.16、9.77、16.01和22.14个百分点。上述结果不仅证明了HyperGCN在捕捉学习者高阶结构实现在线学习群体检测任务中的有效性，还为未来从社交参与维度制定调整个性化教育资源配置策略提供了方法和理论支撑。

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	李贺
	刘嘉宇
	沈旺
	时倩如
	解梦凡

关键词 ：群体检测, 高阶结构, 社交参与, 超图卷积网络, 学习趣缘社群, 个性化学习

收稿日期: 2024-09-18

基金资助:国家自然科学基金青年科学基金项目“基于复杂系统层次演化的社交媒体群体行为演化机制及特殊行为群体发现研究”（72304111）；吉林省科技发展计划项目“数智赋能下吉林省冰雪旅游大数据资源集成与决策支持平台建设”（20220203176SF）。

作者简介: 李贺，女，1964年生，博士，教授，博士生导师，研究方向为信息行为分析、知识管理；刘嘉宇，男，1995年生，博士，鼎新博士后，助理研究员，研究方向为数据挖掘，E-mail：liujiayu0426@163.com；沈旺，女，1983年生，博士，教授，博士生导师，研究方向为用户信息行为；时倩如，女，1996年生，博士研究生，研究方向为知识管理；解梦凡，女，1993年生，博士研究生，研究方向为个性化推荐；

引用本文:

李贺, 刘嘉宇, 沈旺, 时倩如, 解梦凡. 社交参与视角下超图增强的学习趣缘社群群体检测研究[J]. 情报学报, 2024, 43(12): 1425-1439.
Li He, Liu Jiayu, Shen Wang, Shi Qianru, Xie Mengfan. Group Detection in Interest-Based Learning Communities Enhanced by Hypergraph from a Social Engagement Perspective. 情报学报, 2024, 43(12): 1425-1439.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2024.12.005 或 https://qbxb.istic.ac.cn/CN/Y2024/V43/I12/1425

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