在教育环境中实施联合学习以保护隐私的情绪检测.
Rommel Gutiérrez1, William Villegas-Ch1, Sergio Luján-Mora2
1Escuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, Ecuador.
Frontiers in artificial intelligence
|November 24, 2025
概括
这项研究引入了一种保护隐私的联合学习模型,用于在教育中检测情绪. 该系统提高了学生的参与度和学业成绩,同时通过本地处理保护敏感数据.
科学领域:
- 教育技术的教育技术
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 情绪检测对于学生的成功至关重要,但在传统模型中面临隐私和可扩展性问题.
- 目前的系统经常传输敏感的学生数据,损害了机密性,限制了广泛采用.
研究的目的:
- 为教育环境开发和评估一个基于学习的联合情绪检测模型.
- 为解决集中式情绪检测系统固有的数据隐私和可扩展性限制.
主要方法:
- 开发了一个联合学习模型,以在学生设备上本地处理情绪数据.
- 该模型被集成到Moodle平台中,并使用先进的匿名化和预处理技术进行评估.
主要成果:
- 该模型实现了高性能指标:87%的精度,85%的回忆,86%的F1得分.
- 在低光和环境噪音等具有挑战性的条件下,性能保持稳健.
- 观察到学术参与率增加了15%,平均学业成绩提高了12%.
结论:
- 联合学习为教育中的情绪检测提供了一种保护隐私,可扩展和有效的解决方案.
- 拟议的系统对教育动态产生了积极的影响,提高了学生的参与度和学术成果.
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