混合学习中的AI绩效评估:对学生持续学习动机的机制和影响
Hao Ji1, Lingling Suo1, Hua Chen1
1Management College, Beijing Union University, Beijing, China.
Frontiers in psychology
|December 31, 2024
概括
在混合学习中,人工智能绩效评估通过预期确认,感知有用性和满意度间接增强学生的动机. 这项研究提供了优化AI评估持续学习参与的策略.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 高等教育教学的教学.
背景情况:
- 混合学习整合了在线和离线的方法,提供了优势,但在维持学生的动力方面提出了挑战.
- 保持持续学习的动机对于学生在高等教育不断变化的教学环境中的成功至关重要.
研究的目的:
- 调查人工智能 (AI) 绩效评估对混合学习环境中的学生动机的影响.
- 探索人工智能绩效评估影响持续学习动机的调解因素.
主要方法:
- 利用问卷调查收集混合学习环境中学生的数据.
- 采用结构方程建模 (SEM) 来分析AI绩效评估,调解因素和持续学习动机之间的关系.
主要成果:
- 人工智能绩效评估通过预期确认,感知有用性和学习满意度间接地对持续学习动机产生积极影响.
- 人工智能绩效评估和持续学习动机之间没有发现直接关系.
结论:
- 人工智能绩效评估可以成为增强学生在混合学习中的动力的一个有价值的工具,但它的有效性是由其他因素介导的.
- 建议包括优化人工智能系统,使用多种指标,个性化反和改进的可用性,以最大限度地影响学生的参与.
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