通过启发式挖矿算法 (HMA) 和诱导式挖矿算法 (IMA) 在小组协作话语中探索认知和情绪的相互作用
Wei Xu1, Ye-Feng Lou1, Hang Chen2
1College of Educational Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang China.
概括
在混合学习中得分高的小组表现出更多的反思和自我调节. 他们也有更多的认知无关的情感事件,这表明认知和情感之间的复杂相互作用是有效的学习.
科学领域:
- 教育心理学教育心理学
- 认知科学 认知科学
- 信息技术教育信息技术教育
背景情况:
- 混合协作学习结合了在线和离线的教育方法.
- 了解认知和情绪因素之间的相互作用对于有效的学习至关重要.
- 之前的研究还没有完全探索混合学习环境中的这些相互作用.
研究的目的:
- 在混合协作学习中研究认知和情感互动之间的关系.
- 在混合学习小组中识别与高和低任务性能相关的行为模式.
- 为设计有效的混合学习课程提供见解.
主要方法:
- 利用启发式挖矿算法和感应式挖矿算法来分析参与者的行为.
- 在16周的信息技术教学课程中观察了30名本科生.
- 根据任务绩效将参与者分为高得分和低得分的组.
主要成果:
- 获得高分的小组表现出更频繁的反思和自我调节阶段和周期.
- 与认知无关的情绪事件的频率在高得分组中更高.
- 行为分析揭示了成功和不那么成功的学习群体之间的不同交互模式.
结论:
- 反思和自我调节是成功的混合协作学习的关键组成部分.
- 与认知无关的情绪事件可能在高绩效方面发挥重要作用.
- 结果为优化混合课程设计和实施提供了实际建议.
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