开放教育资源的跨模式适应性重建
Tang Shengju1, Feng Li2, Zhan Wang2
1Engineering Education Innovation Research Center, The Open University of Sichuan, Chengdu, 610073, China. tangshengjutsj@163.com.
Scientific reports
|August 22, 2025
概括
我们开发了一个动态知识图增强的跨模式推模型 (DKG-CMR), 这种人工智能方法减少了心理工作量,改善了资源的发现,提高了教育的可访问性.
科学领域:
- 人工智能
- 认知科学
- 教育技术
背景情况:
- 个人化在线学习资源对不同类型的成年学习者来说是一项挑战.
- 现有的推系统难以提供跨模式数据和实时相关性.
- 优化推系统的认知效率对于用户体验至关重要.
研究的目的:
- 提出和评估一个增强动态知识图的跨模式推模型 (DKG-CMR).
- 改善不同数据类型 (文本,视频,用户行为) 的意义对齐.
- 减少认知负担,提高成年学习者的推效率.
主要方法:
- 根据学习者的行为和课程目标不断更新的动态知识图.
- 采用对比式学习来增强多式模式理解和相似性学习.
- 通过使用NASA-TLX对1520名成年学习者进行了DKG-CMR模型的评估.
主要成果:
- 在多模式理解方面取得了高精度 (F1分数=0.912),表现比基线高33.7%.
- 动态知识图提高了35.5%的推准确度,且系统的延迟时间较低.
- 成年学习者报告认为他们的心理工作量减少了40.5%,资源选时间减少了56.8%.
结论:
- 在开放式教育中,DKG-CMR模型有效地提高了资源的可访问性和个性化.
- 减少认知负载是提高用户体验和效率的关键因素.
- 建立了认知友好性推 (CFR) 标准,平衡技术认知优化的准确性和操作效率.
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