一个时空图形扩散和联合的对比学习框架,用于跨机构的教育评估.
1School of Marxism, Anhui Vocational and Technical College, HeFei, 233030, AnHui, China.
Scientific reports
|December 31, 2025
概括
这项研究提出了使用时空图形扩散和联合对比学习进行跨校教育评估的新框架. 它提高了预测准确度,并减少了教育数据分析中的隐私风险.
科学领域:
- 教育技术的教育技术
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 传统的教育评估方法与数据孤岛作斗争,导致局部过拟合和失败的动态关联建模.
- 跨校教育数据存在挑战,原因是分布异质和隐私问题.
研究的目的:
- 引入一种新的评估框架,将时空图谱扩散模型 (STG-DM) 和联合对比学习 (FedCL) 结合起来,用于跨校教育中的协作优化.
- 开发一个系统,准确地建模多式模式教育行为的时空演变,同时克服数据库的局限性.
主要方法:
- 开发了一个热力学驱动的时空扩散方程和一个自适应的图形卷积机制.
- 集成STG-DM与FedCL,以实现协作优化和解决隐私泄露风险.
- 实现了一个多式联网实时分析引擎,用于空间-时间热图染和协作决策.
主要成果:
- 与传统的时空模型相比,在跨区域教育战略预测中,平均绝对误差 (MAE) 降低了18.7%.
- 在30所大学的异质数据分布中,成功地将隐私泄露风险 (ε) 降低到1.5以下.
- 实现了校际模型通用化性能的平衡优化,对于10万级节点的系统响应延迟低于两秒.
结论:
- 新的框架有效地克服了跨校教育评估中传统方法的局限性.
- 该系统为教育经理提供高效可靠的数据智能工具,增强协作决策.
- STG-DM和FedCL的整合为保护隐私,准确和可扩展的教育数据分析提供了强大的解决方案.
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