从证据到洞察力:计算思维研究综合的总体审查
Jin Zhang1, Yaxin Wu1, Yimin Ning2
1Faculty of Education, Henan Normal University, Xinxiang 453007, China.
Journal of Intelligence
|December 24, 2025
概括
这次对33项关于计算思维 (CT) 的研究进行了审查,发现基于项目的学习和编程是有效的. 未来的研究需要标准化的方法,以获得更好的CT干预证据.
科学领域:
- 教育技术的教育技术
- 计算机科学教育计算机科学教育
- 认知科学 认知科学
背景情况:
- 计算思维 (CT) 越来越多地融入教育.
- 需要对现有的元分析和系统审查进行全面审查,以评估研究质量和干预效果.
- 了解调节因素和理论框架对于推进CT研究至关重要.
研究的目的:
- 审查33个关于计算思维 (CT) 的元分析和系统评论.
- 评估所包含的研究质量,干预措施的有效性和CT研究的内容.
- 确定有效的CT干预策略和缓解因素.
主要方法:
- 对33个元分析和CT的系统审查的系统审查.
- 使用AMSTAR 2工具进行包含研究的质量评估.
- 对干预策略,调节因素 (性别,学科,年级) 和理论框架 (生态系统理论) 的分析.
主要成果:
- 分析 (AMSTAR 2平均得分10.9) 和系统审查 (平均得分6.1) 显示质量不同.
- 基于项目的学习,基于文本的编程和基于游戏的学习对CT产生了重大影响.
- 性别和学科背景是稳定的调节因素;干预时间,样本大小和评估方法也影响了结果.
结论:
- 有效的CT干预包括基于项目的学习和编程,以及影响结果的上下文因素.
- 未来的研究应该标准化元分析,统一效果大小指标,并使用纵向研究与认知网络分析.
- 系统性审查需要通过综合的文本和数据驱动合成来提高证据的可信性.
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