分析学生学习人工智能的动机的网络结构:一个自决理论的视角
Jiajing Li1, Jianhua Zhang2, Ching Sing Chai2
1College of Education for the Future, Beijing Normal University, Zhuhai, China.
学生经常学习人工智能是因为内或羞耻感,而不是享受. 培养能力是提高AI学习动机和参与度的关键.
科学领域:
- 教育心理学教育心理学
- 人工智能教育 人工智能教育
背景情况:
- 学生的动机对于学习至关重要,但在人工智能环境中未被充分研究.
- 自决理论 (SDT) 为理解通过心理需求的动机提供了一个框架.
研究的目的:
- 为了调查学生学习AI的动机的结构.
- 应用SDT来分析高等教育中的AI学习动机.
主要方法:
- 网络分析被用来探索AI的动机系统.
- 数据来自47所机构的1465名大学生.
主要成果:
- 内置调节 (内,羞耻) 是人工智能动机的核心,而内在的动机是外围的.
- 在AI学习环境中,能力满意度比自主性或相关性更有影响力.
结论:
- 人工智能学习的动机往往是由外部压力驱动的,而不是固有的兴趣.
- 提高人工智能教育需要专注于明确的目标,标准,并通过人工智能工具建立学生的竞争力.
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