研究热点和在教育中应用的特征技术的前沿 - - 基于CiteSpace的可视化分析
Lijing Zhang1, Yu Wang2, Jia Li3
1Marxism School, China Pharmaceutical University, Nanjing, China.
Frontiers in psychology
|February 2, 2026
概括
这项研究分析了教育应用型技术 (PTAE) 的演变,揭示了向智能,适应性学习生态系统的转变. 关键领域包括学习者分析,教育数据挖掘和混合学习治理,以改善教育环境.
科学领域:
- 教育技术的教育技术
- 学习分析学习分析
- 教育中的人工智能
背景情况:
- 在过去的十年中,教育应用型号技术 (PTAE) 的领域迅速发展.
- 现有研究强调了数据驱动方法在教育环境中的日益集成.
研究的目的:
- 分析从2014年到2024年在教育中应用的个人资料技术 (PTAE) 的演变轨迹.
- 确定核心研究集群和PTAE内部的转型路径.
- 探索挑战,并提出构建适应性智能教育环境的战略.
主要方法:
- 从科学网络核心集合 (SSCI和SCI-E) 中对300篇文献进行了图书统计分析.
- 使用CiteSpace 6.3.R1软件进行数据分析和可视化.
- 确定了核心研究集群和主题演变.
主要成果:
- 三个核心集群出现了:学习者分析和推,智能技术驱动的教育数据挖掘 (EDM) 和混合学习生态系统治理.
- 该领域正在从基本特征表征转变为数据驱动的预测和全景智能生态系统.
- 研究模式正在从静态描述转变为使用学习分析框架的动态,精确的干预.
结论:
- 现代适应性教育系统的基础是动态和精确的干预机制.
- 算法公平性和道德考虑是智能教育环境中的关键挑战.
- 提出了开发生态适应性和智能教育环境的实际策略.
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