在沙特阿拉伯的中学学生中开发和验证由人工智能支持的自我调节的科学和数学学习量表 (AI-SSRSML)
Ataallh Aodh Alatoai1, Ali Saleh Alshahri2
1Science Education, Curriculum & Instruction Department, College of Education and Arts, University of Tabuk, Tabuk, Saudi Arabia. aalatoai@ut.edu.sa.
BMC psychology
|November 27, 2025
概括
人工智能支持的自我调节的科学和数学学习量表 (AI-SSRSML量表) 是一种新的,经过验证的工具,用于评估学生在技术增强环境中的学习. 它表现出强大的可靠性和性别不变性,支持教育研究和政策.
科学领域:
- 教育技术的教育技术
- 心理测量 心理测量 心理测量
- 学习科学 学习科学
背景情况:
- 教育中的技术整合正在迅速发展.
- 为人工智能支持的学习制定强有力的措施至关重要.
- 现有的尺度可能无法捕捉人工智能增强的自我调节学习的细微差别.
研究的目的:
- 开发和验证人工智能支持的自我调节的科学和数学学习量表 (AI-SSRSML量表).
- 在技术增强的教育环境中评估尺度的心理特征.
- 为在沙特阿拉伯提供可靠的仪器来测量人工智能支持的自我调节学习.
主要方法:
- 探索性顺序混合方法设计,采用沙特阿拉伯塔布克省920名中学生的数据.
- 规模的开发涉及文献审查,采访和心理测试 (EFA,CFA,EGA).
- 评估了可靠性 (克朗巴赫的alpha,麦当劳的omega,CR,ICC) 和测量不变性根据性别.
主要成果:
- 一个由六个因素组成的解决方案出现了:超认知规划,成长思维认可,自我监测和反思,战略AI利用,动机弹性和适应性寻求帮助.
- 最终的48项AI-SSRSML尺度显示了可接受的适应指数和出色的可靠性.
- 超认知规划项目对整体AI-SSRSML得分最具预测性.
结论:
- 人工智能-SSRSML尺度是一个有效的,可靠的,性别不变的工具.
- 它可以有效地测量人工智能支持的在中学科学和数学中的自我调节学习.
- 该尺度为技术增强教育的研究,干预和政策提供了心理测量上健全的基础.
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