The impact of GenAI-enabled coding hints on students' programming performance and cognitive load in an SRL-based Python course

IF 8.1 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Anna Y. Q. Huang, Cheng-Yan Lin, Sheng-Yi Su, Stephen J. H. Yang
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引用次数: 0

Abstract

Programming education often imposes a high cognitive burden on novice programmers, requiring them to master syntax, logic, and problem-solving while simultaneously managing debugging tasks. Prior knowledge is a critical factor influencing programming learning performance. A lack of foundational knowledge limits students' self-regulated learning (SRL) abilities, resulting in a performance gap between students with high and low levels of prior knowledge. To address this problem, this study developed CodeFlow Assistant (CFA), a specifically developed generative artificial intelligence (GenAI) tool that provides four levels of scaffolding guidance (flowcharts, cloze coding, basic coding solutions, and advanced coding solutions) to support novice programmers in mastering skills ranging from foundational understanding to advanced application. Through a controlled experiment comparing SRL-based, teaching assistant (TA)-assisted programming (SRLP-TA) and SRL-based, CFA-assisted programming (SRLP-CFA), this study evaluated the effect of CFA on coding performance, cognitive loads, and SRL abilities among novice programming students. The results indicated that compared with the SRLP-TA group, the SRLP-CFA group achieved statistically significantly higher coding scores but showed comparable improvements in understanding programming concepts. Moreover, CFA reduced intrinsic and extraneous cognitive loads while enhancing germane load, fostering deeper knowledge integration and engagement. These findings highlight the role of CFA in enhancing coding performance, particularly in translating conceptual understanding into practice. This tool also statistically significantly improved SRL abilities, such as intrinsic goal orientation, task value, and metacognitive self-regulation.

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在基于srl的Python课程中,支持genai的编码提示对学生编程性能和认知负荷的影响
编程教育通常会给新手程序员带来很高的认知负担,要求他们在管理调试任务的同时掌握语法、逻辑和问题解决。先验知识是影响编程学习绩效的重要因素。基础知识的缺乏限制了学生的自我调节学习能力,导致高水平先验知识和低水平先验知识的学生之间存在成绩差距。为了解决这个问题,本研究开发了CodeFlow Assistant (CFA),这是一个专门开发的生成式人工智能(GenAI)工具,提供了四个级别的脚手架指导(流程图、完形编码、基本编码解决方案和高级编码解决方案),以支持新手程序员掌握从基础理解到高级应用的技能。本研究通过比较基于助教(srlp)辅助编程(SRLP-TA)和基于助教(SRLP-CFA)辅助编程(SRLP-CFA)的对照实验,探讨了基于助教(SRLP-CFA)辅助编程对编程新手编码性能、认知负荷和SRL能力的影响。结果表明,与SRLP-TA组相比,SRLP-CFA组的编码得分有统计学意义上的显著提高,但在理解编程概念方面也有相当的提高。此外,CFA减少了内在和外在的认知负荷,同时增强了相关负荷,促进了更深层次的知识整合和参与。这些发现强调了CFA在提高编码性能方面的作用,特别是在将概念理解转化为实践方面。该工具还显著提高了SRL能力,如内在目标取向、任务价值和元认知自我调节。
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来源期刊
British Journal of Educational Technology
British Journal of Educational Technology EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
15.60
自引率
4.50%
发文量
111
期刊介绍: BJET is a primary source for academics and professionals in the fields of digital educational and training technology throughout the world. The Journal is published by Wiley on behalf of The British Educational Research Association (BERA). It publishes theoretical perspectives, methodological developments and high quality empirical research that demonstrate whether and how applications of instructional/educational technology systems, networks, tools and resources lead to improvements in formal and non-formal education at all levels, from early years through to higher, technical and vocational education, professional development and corporate training.
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