Adaptive vs. planned metacognitive scaffolding for computational thinking: Evidence from generative AI-supported programming in elementary education

IF 13.2 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education Pub Date : 2026-02-01 Epub Date: 2025-10-02 DOI:10.1016/j.compedu.2025.105473

Jinfang Liu , Yi Zhang , Wei Li , Qiyun Wang , Pingxiu Niu , Xue Zhang

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引用次数: 0

Abstract

Metacognitive scaffolding plays a pivotal role in supporting the development of computational thinking (CT) among elementary students. Compared to planned scaffolding, adaptive scaffolding supported by generative AI (GAI) agents offers a promising option by providing real–time, adaptive and personalized response. This study investigated how adaptive metacognitive scaffolding (AMS) and planned metacognitive scaffolding (PMS) influenced elementary students’ CT in a GAI–supported programming environment. 74 students were assigned to AMS, PMS, or control conditions. Using a mixed-methods approach, we examined CT task performance, CT skill use, metacognitive regulation, and cognitive load through standardized assessments, programming artifacts, discourse logs, video-coded behaviors, classroom observation records, cognitive load ratings. Results showed that AMS significantly enhanced contextualized CT performance and promoted more complex, diverse skill use compared to PMS and control groups. AMS also fostered more recursive metacognitive behaviors and reduced mental efforts, while PMS supported higher meaningful engagement. These findings highlight the potential of adaptive scaffolding powered by GAI to support CT development in elementary programming education.

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计算思维的自适应与计划元认知支架：来自基础教育中生成式人工智能支持的编程的证据

元认知支架在小学生计算思维发展中起着举足轻重的作用。与计划脚手架相比，由生成式人工智能（GAI）代理支持的自适应脚手架通过提供实时、自适应和个性化的响应，提供了一个很有前途的选择。本研究探讨了在人工智能支持的编程环境下，适应性元认知支架（AMS）和计划性元认知支架（PMS）对小学生CT的影响。74名学生被分配到AMS， PMS或对照条件。采用混合方法，我们通过标准化评估、编程工件、话语日志、视频编码行为、课堂观察记录、认知负荷评级，检查了CT任务表现、CT技能使用、元认知调节和认知负荷。结果显示，与PMS和对照组相比，AMS显著提高了情境化CT表现，促进了更复杂、更多样化的技能使用。AMS还培养了更多的递归元认知行为，减少了心理努力，而PMS支持更高的有意义的投入。这些发现强调了由GAI驱动的自适应脚手架在支持初级编程教育中的CT开发方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers & Education 工程技术-计算机：跨学科应用

CiteScore

27.10

自引率

5.80%

发文量

204

审稿时长

42 days

期刊介绍： Computers & Education seeks to advance understanding of how digital technology can improve education by publishing high-quality research that expands both theory and practice. The journal welcomes research papers exploring the pedagogical applications of digital technology, with a focus broad enough to appeal to the wider education community.