设计一个集成的多模式机器学习框架,用于实时评估学生参与度和学习结果优化
Deepali Kayande1, Swetta Kukreja1
1Amity School of Engineering and Technology, Amity University, Mumbai, India.
MethodsX
|September 22, 2025
概括
这项研究引入了一个多模式框架,用于实时预测学生参与度和自适应性学习. 它显著提高了学习成果,并降低了远程教育中学率.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 人与计算机的交互
背景情况:
- 有效的远程学习需要实时监控学生的参与度和认知负载.
- 在当前的电子学习环境中,高退学率和低于最佳的学习成果仍然存在.
- 整合不同的数据模式可以提供一个更全面的学生学习状态的视图.
研究的目的:
- 开发和评估一个多模式的框架,准确的实时预测学生的参与度,学风险和认知负载.
- 评估框架产生的适应性学习途径对远程学习成果的影响.
- 证明拟议系统的计算效率和实际可用性.
主要方法:
- 开发了一个新的多模式框架,整合了基于行为,生理和交互的特征.
- 采用了五种相互连接的模型,利用现实世界的数据集进行培训和验证.
- 一个基于强化学习的自适应学习路径 (RL-ALP) 组件被整合到个性化学习优化中.
主要成果:
- 该框架实现了高准确性:94.2%的参与预测,92.3%的学风险,91.5%的认知负载估计.
- 适应式学习途径提高了远程学习成绩14.3%,并减少了大约20.3%的学率.
- 该框架证明了计算效率,推断时间为21.3毫秒,比以前的方法快50%.
结论:
- 拟议的多模式框架准确地实时监测学生的参与度和认知负载.
- 适应式学习优化显著提高远程学习的性能和留学生.
- 该系统为教育工作者提供了有价值的数据驱动的见解,以改进教学策略.
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