人工智能对高等教育改革和在数字智能时代培养人才的影响
Limin Qian1, Weiran Cao2, Lifeng Chen3
1Principal's office, Hangzhou City University, Hangzhou, 310015, China.
Scientific reports
|February 19, 2025
概括
本研究介绍了一个智能教育优化模型 (SEOM),使用自适应随机森林和图形神经网络算法来改善高等教育中的教学资源分配和学习路径建议. 在各种教学场景中,SEOM表现出高准确性和稳定性.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 机器学习用于学习分析.
背景情况:
- 教学资源的低效分配和不准确的学习路径建议阻碍了有效的高等教育.
- 现有的模式缺乏适应复杂的教学模式和多维的教育资源的适应性.
研究的目的:
- 提出一种新的智能教育优化模型 (SEOM),以提高教育资源分配和个性化的学习路径建议.
- 将改进的随机森林算法 (RFA) 与图形神经网络 (GNN) 结合起来,以获得强大的优化解决方案.
主要方法:
- 开发了集成自适应RFA和GNN算法的SEOM.
- 使用来自国家高等教育智能教育平台的公共数据进行培训和验证的SEOM.
- 评估了SEOM在线混合,个性化和基于项目的教学场景中的表现.
主要成果:
- SEOM实现了高准确度 (85-97%) 和概括能力与低错误率 (RMSE 0.2-0.5,MAE 0.1-0.5).
- 在复杂的教学模式和多维资源环境中表现出强大的稳定性.
- 在捕获知识点依赖性 (适配指数>0.85,比较适配指数>0.95) 和在异常网络条件下 (资源分配效率>60%) 的适应性方面表现出高准确性.
结论:
- SEOM有效地优化了教育资源的分配,并在复杂和动态的环境中推个性化的学习路径.
- 该模型显著提高了管理员和开发人员教学决策的智能和效率.
- SEOM强的表现表明其在推进智能教育系统方面的潜力.
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