走向自我调节的学习:不同类型的数据驱动反对学生数学词语问题解决表现的影响
Jun Huang1, Yining Cai2, Ziying Lv2
1School of Teacher Education, Central China Normal University, Wuhan, China.
Frontiers in psychology
|October 16, 2024
概括
与基于任务级别的反 (TLF) 相比,基于自我调节水平的反 (SRLF) 显著改善了小学学生的数学词题解决能力. 然而,在短期内,TLF在增强自我调节的学习和认知策略方面略有优势.
科学领域:
- 教育心理学教育心理学
- 认知科学 认知科学
- 数学教育教育 数学教育
背景情况:
- 小学学生因不发达的自我调节学习 (SRL) 而难以解决数学单词问题.
- 可以通过提供定量反或学习提示的有针对性的教学方法来促进SRL.
- 交互式,数据驱动的反技术提供了个性化的策略来增强SRL.
研究的目的:
- 为了比较基于自我调节水平的反 (SRLF) 和基于任务水平的反 (TLF) 对五年级学生数学词语问题解决能力和SRL的有效性.
- 调查不同反模型对学生自我调节学习和认知策略的影响.
主要方法:
- 使用交互式电子书编辑平台创建SRLF和TLF教学模型.
- 两个相似的五年级班级使用这些不同的反模型参加了数学单词解决问题的课程.
- 用ANCOVA和重复ANOVA的统计分析来评估结果.
主要成果:
- 与TLF相比,SRLF对数学单词解决问题的表现产生了显著更大的影响 (部分η2=.107).
- 在SRLF和TLF之间的学习者的SRL中没有观察到任何显著的短期差异;TLF在总的SRL和认知策略中略有优势.
- TLF对SRL和认知策略表现出显著的相互作用效应 (分别为部分η2=0.059和0.056).
结论:
- SRLF是一种更有效的方法,可以提高小学学生在解决数学单词问题的表现.
- 虽然TLF可能为SRL和认知策略提供短期益处,但其长期影响需要进一步研究.
- 这些发现突出了教育技术中定制反机制的潜力,以改善学术表现和学习策略.
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