This study explores the role of generative AI (GenAI) in providing formative feedback in children's digital learning experiences, specifically in the context of mathematics education. Using multimodal data, the research compares AI-generated feedback with feedback from human instructors, focusing on its impact on children's learning outcomes. Children engaged with a digital body-scale number line to learn addition and subtraction of positive and negative integers through embodied interaction. The study followed a between-group design, with one group receiving feedback from a human instructor and the other from GenAI. Eye-tracking data and system logs were used to evaluate student's information processing behaviour and cognitive load. The results revealed that while task-based performance did not differ significantly between conditions, the GenAI feedback condition demonstrated lower cognitive load and students show different visual information processing strategies among the two conditions. The findings provide empirical support for the potential of GenAI to complement traditional teaching by providing structured and adaptive feedback that supports efficient learning. The study underscores the importance of hybrid intelligence approaches that integrate human and AI feedback to enhance learning through synergistic feedback. This research offers valuable insights for educators, developers and researchers aiming to design hybrid AI-human educational environments that promote effective learning outcomes.
Practitioner notes
What is already known about this topic?
- Embodied learning approaches have been shown to facilitate deeper cognitive processing by engaging students physically with learning materials, which is especially beneficial in abstract subjects like mathematics.
- GenAI has the potential to enhance educational experiences through personalized feedback, making it crucial for fostering student understanding and engagement.
- Previous research indicates that hybrid intelligence that combines AI with human instructors can contribute to improved educational outcomes.
What this paper adds?
- This study empirically examines the effectiveness of GenAI-generated feedback when compared to human instructor feedback in the context of a multisensory environment (MSE) for math learning.
- Findings from system logs and eye-tracking analysis reveal that
本研究探讨了生成式人工智能（GenAI）在儿童数字学习体验中提供形成性反馈的作用，特别是在数学教育的背景下。该研究使用多模态数据，将人工智能生成的反馈与人类教师的反馈进行比较，重点关注其对儿童学习成果的影响。儿童利用数字体尺数轴，通过具身互动学习正负整数的加减法。这项研究采用了组间设计，一组从人类教练那里得到反馈，另一组从GenAI那里得到反馈。使用眼动追踪数据和系统日志来评估学生的信息处理行为和认知负荷。结果表明，尽管任务型表现在两种条件之间没有显著差异，但GenAI反馈条件表现出较低的认知负荷，并且在两种条件下学生表现出不同的视觉信息加工策略。这些发现为GenAI的潜力提供了实证支持，它可以通过提供支持高效学习的结构化和适应性反馈来补充传统教学。该研究强调了混合智能方法的重要性，这种方法将人类和人工智能的反馈结合起来，通过协同反馈来增强学习。这项研究为旨在设计促进有效学习成果的人工智能-人类混合教育环境的教育工作者、开发人员和研究人员提供了有价值的见解。关于这个主题我们已经知道了什么？具身学习方法已被证明通过让学生接触学习材料来促进更深层次的认知加工，这对数学等抽象学科尤其有益。GenAI有可能通过个性化反馈来增强教育体验，这对于培养学生的理解和参与至关重要。先前的研究表明，将人工智能与人类教师相结合的混合智能有助于改善教育成果。这篇文章补充了什么？本研究实证检验了在多感官环境（MSE）下，基因人工智能生成的反馈与人类教师反馈在数学学习中的有效性。系统日志和眼动追踪分析的结果表明，GenAI反馈可以有效地支持学习，特别是帮助学生管理他们的认知负荷。研究发现，基因人工智能和教师反馈导致不同的信息加工策略。这些发现为反馈模式如何影响认知参与提供了可行的见解。将GenAI整合到教育环境中，为加强传统教学方法提供了机会，实现了利用人工智能和人类反馈优势的适应性学习环境。未来的教育实践应探索结合人工智能和人类反馈的混合模式，以创造包容和有效的学习体验，适应学习者的多样化需求。决策者应该建立指导方针和框架，以促进伦理和公平地采用基因人工智能技术进行学习。这包括解决信任、透明度和可及性问题，以确保GenAI系统有效地支持而不是取代人类教员。
引用次数: 0
The impact of GenAI-enabled coding hints on students' programming performance and cognitive load in an SRL-based Python course
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
IF 8.1
1区 教育学
Q1 EDUCATION & EDUCATIONAL RESEARCH
Pub Date : 2025-04-21
DOI: 10.1111/bjet.13589
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.
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。
引用次数: 0
Development of a generative AI-powered teachable agent for middle school mathematics learning: A design-based research study
面向中学数学学习的生成式人工智能可教代理的开发：基于设计的研究性研究
IF 8.1
1区 教育学
Q1 EDUCATION & EDUCATIONAL RESEARCH
Pub Date : 2025-04-17
DOI: 10.1111/bjet.13586
<div> <section> <p>This paper reports on a design-based research (DBR) study that aims to devise an artificial intelligence (AI)-powered teachable agent that supports secondary school students' learning-by-teaching practices of mathematics learning content. A long-standing pedagogical practice of learning-by-teaching is powered by a recent advancement of generative AI technologies, yielding our teachable agent called <i>ALTER-Math</i>. This study chronicles one usability testing and three cycles of iterative design and implementation process of <i>ALTER-Math</i>. The three empirical studies involved a total of 320 middle school students and six teachers in authentic classroom settings. The first study was exploratory, focusing on the qualitative feedback from the students and teachers through open-ended surveys, interviews and classroom observations. The second study yielded a medium-high (<i>M</i> = 3.26) quantitative survey result on students' perceived engagement and usability on top of the qualitative findings. Finally, the final study included pre- and post-knowledge tests in a quasi-experimental study design as well as student and teacher interviews. The final study revealed a bigger significant knowledge improvement in students who used <i>ALTER-Math</i> compared to the control group, suggesting a positive impact of AI-powered teachable agents on students' learning. The design implications learned from multiple iterations are discussed to inform the future design of AI-powered learning technologies.</p> </section> <section> <div> <div> <h3>Practitioner notes</h3> <p>What is already known about this topic </p><ul> <li>Learning-by-teaching is a long-standing effective pedagogical strategy to enhance students' domain knowledge and feelings of responsibility in learning.</li> <li>Various teachable agents have been developed and have demonstrated benefits in students' learning.</li> <li>Generative AI offers the potential to provide naturalistic, contextualised and adaptive conversations.</li> </ul> <p>What this paper adds </p><ul> <li>Develops a novel generative AI-powered teachable agent for middle school mathematics learning, called <i>ALTER-Math</i>.</li> <li>Reports the iterative design process involving empirical classroom implementations of <i>ALTER-Math</i>.</li> <li>Reveals a bigger significant improvement in the student's mathematical knowledge after using <i>ALTER-Math</i>, compared to the control group.</li>
本文报道了一项基于设计的研究（DBR）研究，该研究旨在设计一个人工智能（AI）驱动的可教代理，以支持中学生在数学学习内容的教学实践中学习。一种长期存在的教学实践是由最近的生成式人工智能技术的进步所推动的，产生了我们称为ALTER-Math的可教代理。本研究记录了ALTER-Math的一个可用性测试和三个迭代设计和实现周期。三个实证研究涉及320名中学生和6名教师在真实的课堂环境中。第一项研究是探索性的，通过开放式调查、访谈和课堂观察，重点关注学生和教师的定性反馈。第二项研究在定性调查结果的基础上，对学生的感知参与和可用性进行了中高（M = 3.26）的定量调查。最后，最后的研究包括准实验研究设计的知识前和知识后测试以及学生和教师访谈。最后的研究显示，与对照组相比，使用ALTER-Math的学生的知识水平有了更大的显著提高，这表明人工智能教学代理对学生的学习产生了积极影响。讨论了从多次迭代中学习到的设计含义，以便为人工智能学习技术的未来设计提供信息。关于这个话题，我们已经知道，教中学习是一种长期有效的教学策略，可以增强学生的领域知识和学习责任感。各种可教代理已被开发出来，并已证明对学生的学习有好处。生成式AI提供了提供自然、情境化和适应性对话的潜力。为中学数学学习开发了一种新的生成式人工智能可教代理，称为ALTER-Math。报告涉及ALTER-Math的经验课堂实施的迭代设计过程。与对照组相比，使用ALTER-Math后，学生的数学知识有了更大的显著提高。对实践和/或政策的启示研究人员可以从这个基于理论的生成式人工智能学习技术的设计示例中得到启发。教育技术设计师可以听到学生和老师对生成式人工智能学习技术的真实声音。研究人员和教育技术设计师可以通过设计影响来指导人工智能学习技术和可教代理的未来设计。
引用次数: 0
“ChatGPT can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of ChatGPT content into academic writing
“ChatGPT可能会犯错误。查看重要信息。”学生将ChatGPT内容整合到学术写作中的认知信念与元认知准确性
IF 8.1
1区 教育学
Q1 EDUCATION & EDUCATIONAL RESEARCH
Pub Date : 2025-04-09
DOI: 10.1111/bjet.13591
Recent studies have conceptualized ChatGPT as an epistemic authority; however, no research has yet examined how epistemic beliefs and metacognitive accuracy affect students' actual use of ChatGPT-generated content, which often contains factual inaccuracies. Therefore, the present experimental study aimed to examine how university students integrate correct and incorrect information from expert-written and ChatGPT-generated articles when writing independently (N = 49) or with ChatGPT assistance (N = 49). Students working with ChatGPT-4o integrated more correct information from both expert-written (d = 0.64) and ChatGPT-generated articles (d = 0.95), but ChatGPT-assisted writing did not affect the amount of incorrect information sourced from the ChatGPT-generated article. Regardless of the condition, hierarchical regressions revealed that lower metacognitive bias was moderately associated with increased inclusion of correct information from the expert-written article (R2 = 12%). Conversely, a higher metacognitive bias (R2 = 10%) and epistemic beliefs (R2 = 12%) were moderately related to the inclusion of incorrect information from ChatGPT-generated articles. These findings suggest that while ChatGPT assistance enhances the integration of correct human- and AI-generated content, metacognitive skills remain essential to mitigate the risks of incorporating incorrect AI-generated information.
Practitioner notes
What is already known about this topic
- Generative AI tools, such as ChatGPT, are increasingly regarded as epistemic authorities due to their authoritative tone and human-like interaction.
- ChatGPT has demonstrated utility in providing correct information and improving productivity in educational and professional contexts, but it is also prone to inaccuracies, hallucinations and misleading content.
- Students' epistemic beliefs and metacognitive skills predict their ability to critically evaluate and integrate conflicting information from multiple resources, particularly when searching for information on the Internet.
What this paper adds
- This study experimentally examines how students integrate correct and incorrect information from expert-written and ChatGPT-generated articles when writing independently or with ChatGPT's assistance.
- The findings show that ChatGPT as
最近的研究将ChatGPT概念化为认知权威；然而，目前还没有研究考察认知信念和元认知准确性如何影响学生对chatgpt生成内容的实际使用，这些内容通常包含事实不准确。因此，本实验研究旨在考察大学生在独立写作（N = 49）或在ChatGPT帮助下写作（N = 49）时，如何整合专家写作和ChatGPT生成的文章中的正确和错误信息。使用chatgpt - 40的学生从专家撰写的文章（d = 0.64）和chatgpt生成的文章（d = 0.95）中整合了更多的正确信息，但chatgpt辅助写作并不影响从chatgpt生成的文章中获取的错误信息的数量。无论在何种情况下，层次回归显示，较低的元认知偏差与专家撰写的文章中正确信息的增加适度相关（R2 = 12%）。相反，较高的元认知偏差（R2 = 10%）和认知信念（R2 = 12%）与chatgpt生成的文章中包含的错误信息中度相关。这些研究结果表明，虽然ChatGPT辅助增强了正确的人类和人工智能生成内容的整合，但元认知技能对于降低整合不正确的人工智能生成信息的风险仍然至关重要。生成式人工智能工具，如ChatGPT，由于其权威的语气和类似人类的交互，越来越多地被视为认知权威。在教育和专业环境中，ChatGPT在提供正确的信息和提高生产力方面已经证明了它的实用性，但它也容易产生不准确、幻觉和误导性的内容。学生的认知信念和元认知技能预测了他们批判性地评估和整合来自多种资源的冲突信息的能力，特别是在互联网上搜索信息时。本研究通过实验检验了学生在独立写作或在ChatGPT的帮助下如何整合来自专家撰写和ChatGPT生成的文章的正确和不正确信息。研究结果表明，ChatGPT辅助提高了正确信息的包含，但并没有显著减少或增加不正确的ChatGPT生成内容的包含。无论学生是独立学习还是使用ChatGPT，元认知准确性和认知信念都是减少错误信息包含的关键因素。对实践和/或政策的影响生成式人工智能工具可以在特定场景中超越人类专家，几乎不需要评估。然而，在这些工具产生误导性或不正确内容的情况下，元认知技能和认知信念的应用对于辨别可靠信息和避免错误整合至关重要。教育干预应包括需要证明知识、评估资源以及反思人类和人工智能生成的文本的活动，以提高学生辨别准确信息和不准确信息的能力。专注于元认知准确性和认知意识的干预措施可以使个人能够批判性地评估和区分可靠和错误的信息，增强他们对错误信息的识别。
引用次数: 0
Analysing nontraditional students' ChatGPT interaction, engagement, self-efficacy and performance: A mixed-methods approach
分析非传统学生的ChatGPT互动、参与、自我效能和表现：一种混合方法的方法
IF 8.1
1区 教育学
Q1 EDUCATION & EDUCATIONAL RESEARCH
Pub Date : 2025-04-09
DOI: 10.1111/bjet.13588
<div> <section> <p>Generative artificial intelligence brings opportunities and unique challenges to nontraditional higher education students, stemming, in part, from the experience of the digital divide. Providing access and practice is critical to bridge this divide and equip students with needed digital competencies. This mixed-methods study investigated how nontraditional higher education students interact with ChatGPT in multiple courses and examined relationships between ChatGPT interactions, engagement, self-efficacy and performance. Data were collected from 73 undergraduate and graduate students through chat logs, course reflections and artefacts, surveys and interviews. ChatGPT interactions were analysed using four metrics: prompt number, depth of knowledge (DoK), prompt relevance and originality. Results showed that ChatGPT prompt numbers (<i>β</i> = 0.256, <i>p</i> < 0.03) and engagement (<i>β</i> = 0.267, <i>p</i> < 0.05) significantly predicted performance, while self-efficacy did not. Students' DoK (<i>r</i> = 0.40, <i>p</i> < 0.01) and prompt relevance (<i>r</i> = 0.42, <i>p</i> < 0.01) were positively correlated with performance. Text mining analysis identified distinct interaction patterns, with ‘strategic inquirers’ demonstrating significantly higher performance than ‘exploratory inquirers’ through more sophisticated follow-up questioning. Qualitative findings revealed that while most students were first-time ChatGPT users who initially showed resistance, they developed growing acceptance. Still, students tended to use ChatGPT sparingly and, even then, as only a starting point for assignments. The study highlights the need for targeted guidance in prompt engineering and AI literacy training to help nontraditional higher education students leverage ChatGPT more effectively for higher-order thinking tasks.</p> </section> <section> <div> <div> <h3>Practitioner notes</h3> <p>What is already known about this topic </p><ul> <li>Nontraditional students face unique challenges in higher education, such as limited technological literacy and digital access.</li> <li>The emergence of generative AI tools presents both opportunities and challenges for addressing educational disparities.</li> <li>Existing studies on AI implementation predominantly focus on traditional students.</li> </ul> <p>What this paper adds </p><ul> <li>Empirical evidence of how nontraditional students interact with ChatGPT through multiple metrics (prompt n
生成式人工智能为非传统高等教育的学生带来了机遇和独特的挑战，部分源于数字鸿沟的经历。提供机会和实践对于弥合这一鸿沟并使学生具备所需的数字能力至关重要。这项混合方法研究调查了非传统高等教育学生如何在多门课程中与ChatGPT互动，并研究了ChatGPT互动、参与度、自我效能和绩效之间的关系。通过聊天记录、课程反思和人工制品、调查和访谈，从73名本科生和研究生中收集了数据。ChatGPT交互使用四个指标进行分析：提示数，知识深度（DoK），提示相关性和独创性。结果显示，ChatGPT提示数（β = 0.256, p < 0.03）和敬业度（β = 0.267, p < 0.05）显著预测绩效，而自我效能感无显著预测。学生的DoK （r = 0.40, p < 0.01）和提示相关性（r = 0.42, p < 0.01）与成绩呈正相关。文本挖掘分析确定了不同的交互模式，通过更复杂的后续问题，“战略询问者”的表现明显高于“探索性询问者”。定性调查结果显示，虽然大多数学生是第一次使用ChatGPT，最初表现出抵触情绪，但他们逐渐接受了。尽管如此，学生们还是倾向于谨慎地使用ChatGPT，即使这样，也只是作为作业的起点。该研究强调，在快速工程和人工智能素养培训中，需要有针对性的指导，以帮助非传统高等教育的学生更有效地利用ChatGPT完成高阶思维任务。非传统学生在高等教育中面临着独特的挑战，例如有限的技术素养和数字访问。生成式人工智能工具的出现为解决教育差距带来了机遇和挑战。现有的人工智能实施研究主要集中在传统学生身上。本文增加的经验证据表明，非传统学生如何通过多个指标（提示数、DoK、相关性和原创性）与ChatGPT互动。不同的交互模式及其与绩效结果的关系。ChatGPT互动、投入、自我效能和绩效的关系。对实践和/或政策的启示需要明确的指导，将快速工程作为高阶思维的关键技能。为非传统学生提供有针对性的技术培训和自主学习资源的重要性。开发全面的人工智能素养培训，解决工具能力和局限性的价值。
引用次数: 0
The impact of generative AI on academic integrity of authentic assessments within a higher education context
生成式人工智能对高等教育背景下真实评估学术完整性的影响
IF 8.1
1区 教育学
Q1 EDUCATION & EDUCATIONAL RESEARCH
Pub Date : 2025-03-31
DOI: 10.1111/bjet.13585
<div> <section> <p>Generative AI (hereinafter GenAI) technology, such as ChatGPT, is already influencing the higher education sector. In this work, we focused on the impact of GenAI on the academic integrity of assessments within higher education institutions, as GenAI can be used to circumvent assessment approaches within the sector, compromising their quality. The purpose of our research was threefold: first, to determine the extent to which the use of GenAI can be detected via the marking and moderation process; second, to understand whether the presence of GenAI affects the marking process; and finally, to establish whether authentic assessments can safeguard academic integrity. We used a series of experiments in the context of two UK-based universities to examine these issues. Our findings indicate that markers, in general, are not able to distinguish assessments that have had GenAI input from assessments that did not, even though the presence of GenAI affects the way markers approach the marking process. Our findings also suggest that the level of authenticity in an assessment has no impact on the ability to safeguard against or detect GenAI usage in assessment creation. In conclusion, we suggest that current approaches to assessments in higher education are susceptible to GenAI manipulation and that the higher education sector cannot rely on authentic assessments alone to control the impact of GenAI on academic integrity. Thus, we recommend giving more critical attention to assessment design and placing more emphasis on assessments that rely on social experiential learning and are performative rather than output-based and asynchronously written.</p> </section> <section> <div> <div> <h3>Practitioner notes</h3> <p>What is already known about this topic </p><ul> <li>GenAI has enabled students to complete higher education assessments quickly and with good quality, leading to challenges in academic integrity.</li> <li>GenAI has transformed the requirements and considerations in assessment design in higher education.</li> <li>Authentic assessments are seen as a prominent way to tackle the GenAI challenge.</li> </ul> <p>What this paper adds </p><ul> <li>We provide quantitative and qualitative experimental evidence suggesting that GenAI can generate authentic assessments that pass the scrutiny of experienced academics.</li> <li>We demonstrate how the use of authentic assessments alone does not pr
生成式人工智能（以下简称GenAI）技术，如ChatGPT，已经在影响高等教育领域。在这项工作中，我们重点关注了GenAI对高等教育机构评估学术诚信的影响，因为GenAI可以被用来规避该部门的评估方法，从而损害其质量。我们研究的目的有三个：首先，确定通过标记和调节过程可以检测到GenAI使用的程度；第二，了解GenAI的存在是否会影响标记过程；最后，确定评估的真实性是否能够维护学术诚信。我们在两所英国大学的背景下进行了一系列实验来研究这些问题。我们的研究结果表明，一般来说，标记者无法区分有GenAI输入的评估和没有GenAI输入的评估，即使GenAI的存在影响了标记者处理标记过程的方式。我们的研究结果还表明，评估中的真实性水平对在评估创建中防范或检测GenAI使用的能力没有影响。总之，我们认为当前的高等教育评估方法容易受到GenAI的操纵，高等教育部门不能仅仅依靠真实的评估来控制GenAI对学术诚信的影响。因此，我们建议对评估设计给予更多的批判性关注，并更加强调依赖于社会体验学习和执行性的评估，而不是基于输出和异步编写的评估。从业者指出，关于这一话题，人们已经知道，GenAI使学生能够快速、高质量地完成高等教育评估，这给学术诚信带来了挑战。GenAI改变了高等教育评估设计的要求和考虑因素。真实的评估被视为解决GenAI挑战的一个突出方法。我们提供了定量和定性的实验证据，表明GenAI可以产生经过经验丰富的学者审查的真实评估。我们证明了仅仅使用真实的评估并不能保护高等教育中学生的学术诚信。我们的定性分析表明，如果标记在评估中怀疑GenAI被篡改，可能会产生假阳性和假阴性结果。因此，学生的学习没有得到正确的评估。对实践和/或政策的影响当大学和国家组织设计有关基因ai的政策时，真实的评估并不是万灵药；重点必须放在评估设计上。学习评估需要从评估产出转向关注过程和与工作场所的相关性。这将意味着从书面评估到同步人际评估的范式转变。如果书面评估不能被信任为学习的可靠指标，那么放弃书面评估将对学院产生深远的影响。
引用次数: 0
Investigating the impact of AR technologies on geometric learning in primary school: A comparison between marker-based and markerless AR
研究AR技术对小学几何学习的影响：基于标记和无标记的AR的比较
IF 8.1
1区 教育学
Q1 EDUCATION & EDUCATIONAL RESEARCH
Pub Date : 2025-03-24
DOI: 10.1111/bjet.13584
<div> <section> <p>Over the past decade, augmented reality (AR) has gained traction in geometric learning for its pedagogical potential. However, research on how learners engage with different AR technologies and when and how to incorporate them has remained largely unexplored. Employing a learning analytics approach, this study investigates the impact of marker-based and markerless AR technologies on geometric learning and student engagement in primary school classrooms. We developed a mobile AR application that integrates both marker-based (ie, using predefined visual markers to trigger content) and markerless (ie, triggering content without predefined markers) AR modes for learning 3D shapes and conducted a quasi-experimental study with 43 sixth-grade students. To comprehensively capture student engagement, we collected pre- and posttests on geometric understanding, along with in-app log and device sensor data. Our findings showed that both AR technologies effectively enhance geometric understanding. However, engagement patterns varied significantly; marker-based AR led to more focused cognitive tasks, while markerless AR facilitated dynamic spatial navigation. The study highlights the distinct technical affordances of each AR technology that can lead to unique pedagogical advantages. Based on these findings, we propose a hybrid AR model for geometric learning that leverages the strengths of both marker-based and markerless AR.</p> </section> <section> <div> <div> <h3>Practitioner notes</h3> <p>What is already known about this topic </p><ul> <li>Augmented reality (AR) is a powerful tool for enhancing geometric learning by providing immersive and interactive learning experiences.</li> <li>Marker-based AR—using predefined visual markers (eg, QR codes or images) to trigger content—has been widely used in education with its ease of use and setup.</li> <li>Markerless AR—using spatial recognition capabilities without predefined visual markers—has recently emerged as a new and accessible technology, offering the potential for more dynamic and immersive learning experiences in classroom settings.</li> </ul> <p>What this paper adds </p><ul> <li>Past studies have predominantly focused on answering <i>whether</i> marker-based AR can be effectively used compared with traditional tools (eg, computers); this paper addresses <i>how and when</i> different AR technologies can be used.</li> <li>Findings show that both marker-based and markerless AR technologies enhance geometric und
在过去的十年中，增强现实（AR）因其教学潜力而在几何学习中获得了牵引力。然而，关于学习者如何使用不同的增强现实技术以及何时以及如何使用这些技术的研究在很大程度上仍未得到探索。采用学习分析方法，本研究调查了基于标记和无标记的AR技术对小学课堂几何学习和学生参与的影响。我们开发了一个移动AR应用程序，集成了基于标记（即使用预定义的视觉标记来触发内容）和无标记（即在没有预定义标记的情况下触发内容）的AR模式，用于学习3D形状，并对43名六年级学生进行了准实验研究。为了全面捕捉学生的参与度，我们收集了几何理解的前后测试，以及应用内日志和设备传感器数据。我们的研究结果表明，这两种增强现实技术都有效地增强了几何理解。然而，用户粘性模式却存在显著差异；基于标记的AR导致更集中的认知任务，而无标记的AR促进了动态空间导航。该研究强调了每种AR技术的独特技术能力，这些技术能力可以带来独特的教学优势。基于这些发现，我们提出了一种用于几何学习的混合增强现实模型，该模型利用了基于标记和无标记的增强现实的优势。从业者注意到，关于这个主题，我们已经知道增强现实（AR）是通过提供沉浸式和交互式学习来增强几何学习的强大工具的经历。基于标记的ar使用预定义的视觉标记（例如QR码或图像）来触发内容，由于其易于使用和设置，已广泛用于教育领域。无标记ar（使用空间识别功能，没有预定义的视觉标记）最近成为一种新的可访问技术，在课堂环境中提供更动态和身临其境的学习体验。过去的研究主要集中在回答基于标记的AR与传统工具（如计算机）相比是否可以有效地使用；本文讨论了如何以及何时使用不同的AR技术。研究结果表明，基于标记和无标记的AR技术都能增强几何理解，但会导致学生的参与模式不同。基于标记的AR促进更集中的认知任务，而无标记的AR鼓励更多动态的空间导航和与学习环境的互动。对实践和/或政策的影响利用学习分析可以更深入地了解学生如何使用数字技术。教育者在设计教学时应该考虑AR技术的独特技术能力。实现混合AR模型，利用基于标记和无标记的AR可以优化几何教育的学习成果。
引用次数: 0
When and how biases seep in: Enhancing debiasing approaches for fair educational predictive analytics
偏见何时以及如何渗入：加强公平教育预测分析的消除偏见方法
IF 8.1
1区 教育学
Q1 EDUCATION & EDUCATIONAL RESEARCH
Pub Date : 2025-03-24
DOI: 10.1111/bjet.13575
<div> <section> <p>The use of predictive analytics powered by machine learning (ML) to model educational data has increasingly been identified to exhibit bias towards marginalized populations, prompting the need for more equitable applications of these techniques. To tackle bias that emerges in training data or models at different stages of the ML modelling pipeline, numerous debiasing approaches have been proposed. Yet, research into state-of-the-art techniques for effectively employing these approaches to enhance fairness in educational predictive scenarios remains limited. Prior studies often focused on mitigating bias from a single source at a specific stage of model construction within narrowly defined scenarios, overlooking the complexities of bias originating from multiple sources across various stages. Moreover, these approaches were often evaluated using typical threshold-dependent fairness metrics, which fail to account for real-world educational scenarios where thresholds are typically unknown before evaluation. To bridge these gaps, this study systematically examined a total of 28 representative debiasing approaches, categorized by the sources of bias and the stage they targeted, for two critical educational predictive tasks, namely forum post classification and student career prediction. Both tasks involve a two-phase modelling process where features learned from upstream models in the first phase are fed into classical ML models for final predictions, which is a common yet under-explored setting for educational data modelling. The study observed that addressing local stereotypical bias, label bias or proxy discrimination in training data, as well as imposing fairness constraints on models, can effectively enhance predictive fairness. But their efficacy was often compromised when features from upstream models were inherently biased. Beyond that, this study proposes two novel strategies, namely Multi-Stage and Multi-Source debiasing to integrate existing approaches. These strategies demonstrated substantial improvements in mitigating unfairness, underscoring the importance of unified approaches capable of addressing biases from various sources across multiple stages.</p> </section> <section> <div> <div> <h3>Practitioner notes</h3> <p>What is already known about this topic </p><ul> <li>Predictive analytics for educational data modelling often exhibit bias against students from certain demographic groups based on sensitive attributes.</li> <li>Bias can emerge in training data or models at different time points of the ML modelling pipeline, resulting in unfair final predictions.</li>
越来越多的人认为，使用机器学习（ML）驱动的预测分析来模拟教育数据，会对边缘人群产生偏见，这促使人们需要更公平地应用这些技术。为了解决在机器学习建模管道的不同阶段训练数据或模型中出现的偏差，已经提出了许多消除偏差的方法。然而，对有效利用这些方法提高教育预测情景公平性的最先进技术的研究仍然有限。先前的研究通常侧重于在狭义的场景中减轻模型构建特定阶段的单一来源的偏差，而忽略了在不同阶段源自多个来源的偏差的复杂性。此外，这些方法通常使用典型的阈值相关公平指标进行评估，这无法解释现实世界的教育场景，因为在评估之前阈值通常是未知的。为了弥补这些差距，本研究系统地检查了28种具有代表性的去偏见方法，根据偏见的来源和他们所针对的阶段进行了分类，用于两个关键的教育预测任务，即论坛帖子分类和学生职业预测。这两项任务都涉及两阶段的建模过程，其中第一阶段从上游模型中学习到的特征被馈送到经典ML模型中进行最终预测，这是教育数据建模的常见但尚未充分探索的设置。研究发现，解决训练数据中的局部刻板偏见、标签偏见或代理歧视，以及对模型施加公平性约束，可以有效提高预测公平性。但是，当来自上游模型的特征存在固有偏见时，它们的功效往往会受到损害。除此之外，本研究提出了两种新的策略，即多阶段和多源去偏，以整合现有的方法。这些战略在减轻不公平方面取得了重大进展，强调了能够在多个阶段解决各种来源的偏见的统一方法的重要性。教育数据建模的预测分析通常会基于敏感属性对某些人口统计群体的学生表现出偏见。在机器学习建模管道的不同时间点，训练数据或模型可能会出现偏差，从而导致不公平的最终预测。为了解决不同阶段的偏差，已经开发了许多消除偏差的方法，包括预处理训练数据、处理中模型和后处理预测结果或训练模型。对28种最先进的去偏方法进行了系统评估，这些方法涵盖了两种不同教育预测情景中的多个偏差来源和多个阶段，确定了导致预测不公平的数据偏差的主要来源。考虑到偏差的多源和多阶段特征，提出了进一步增强预测公平性的去偏策略。揭示专注于单一敏感属性的去偏见的潜在风险。预处理方法，特别是那些解决刻板偏见、标签偏见和代理歧视的方法，通常对提高教育预测的公平性有效。重新加权方法对于较小的数据集特别有用，可以解决刻板偏见。在处理两阶段建模时，上游模型生成的特征中固有编码的偏差可能无法通过应用于下游模型的去偏方法有效地解决。结合消除偏见的方法来解决跨多个阶段的多个偏见来源，显著提高了预测的公平性。
引用次数: 0
Examining students' attitudes and intentions towards using ChatGPT in higher education
调查学生在高等教育中使用ChatGPT的态度和意图
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IF 8.1
1区 教育学
Q1 EDUCATION & EDUCATIONAL RESEARCH
Pub Date : 2025-03-21
DOI: 10.1111/bjet.13582
The release of ChatGPT has sparked an immense academic debate regarding its potential advantages and drawbacks for students. Despite its significance, there is a lack of empirical research on students' attitudes and behavioural intentions towards the utilization of ChatGPT. To fill this gap, we employed the integrated AI acceptance-avoidance model (IAAAM) and used partial least squares structural equation modelling to test the research model by collecting data from 287 university students in the UAE. Our key findings indicate that factors such as performance expectancy and effort expectancy positively influence students' attitudes towards ChatGPT. Additionally, personal development concerns demonstrate a negative association with attitudes and intentions to use ChatGPT, while perceived threat demonstrates a non-significant negative association with both. The findings of this research contribute to the emerging body of literature on ChatGPT's usage both conceptually and empirically. It also offers practical insights for future technology developers, emphasizing the significance of adopting a balanced approach that carefully considers both the benefits and potential drawbacks linked to the utilization of ChatGPT.
Practitioner notes
What is already known about this topic
- ChatGPT represents a significant advancement in natural language processing and has gained immense popularity since its launch, with rapid acceptance and adoption across various sectors.
- In higher education, ChatGPT has seen substantial use among university students and teachers.
- Previous studies have predominantly focused on investigating the attitudes and behavioural intentions of managers in organizational contexts and academic staff towards technology use in universities.
What this paper adds
- This study addresses an empirical gap by focusing on students' behavioural intentions towards employing ChatGPT, offering a student-centric perspective.
- It draws upon established technology acceptance literature and employs the integrated AI acceptance-avoidance model (IAAAM) to explore factors shaping students' attitudes and intentions regarding ChatGPT utilization.
- By adopting a “net valence approach”, the study considers both positive and negative factors associated with ChatGPT use in higher education.
ChatGPT的发布引发了一场关于其对学生的潜在优点和缺点的巨大学术辩论。尽管具有重要意义，但缺乏对学生使用ChatGPT的态度和行为意图的实证研究。为了填补这一空白，我们采用了综合人工智能接受-回避模型（IAAAM），并通过收集阿联酋287名大学生的数据，使用偏最小二乘结构方程模型来测试研究模型。我们的主要研究结果表明，成绩期望和努力期望等因素对学生对ChatGPT的态度有积极的影响。此外，个人发展关注与使用ChatGPT的态度和意图呈负相关，而感知威胁与两者呈非显著负相关。这项研究的发现为ChatGPT在概念上和经验上的使用提供了新的文献。它还为未来的技术开发人员提供了实际的见解，强调了采用一种平衡的方法的重要性，这种方法要仔细考虑与使用ChatGPT相关的好处和潜在的缺点。ChatGPT代表了自然语言处理领域的重大进步，自推出以来获得了巨大的普及，在各个领域得到了迅速的接受和采用。在高等教育中，ChatGPT在大学生和教师中得到了广泛的应用。以前的研究主要集中在调查组织环境中的管理人员和学术人员对大学技术使用的态度和行为意图。本研究通过关注学生对使用ChatGPT的行为意图，提供以学生为中心的视角，解决了经验上的差距。它借鉴了已有的技术接受文献，并采用集成的人工智能接受-回避模型（IAAAM）来探索影响学生对ChatGPT使用态度和意图的因素。通过采用“净价法”，该研究考虑了与高等教育中ChatGPT使用相关的积极因素和消极因素。研究结果通过对影响学生态度和行为意图的多方面因素进行平衡和全面的理解，为教育工作者、学生和政策制定者提供了有价值的见解。对实践和/或政策的启示教育工作者可以利用这些发现来更好地理解学生的观点，并设计有效地将ChatGPT融入学习过程的教育策略。学生可以利用这些见解来做出明智的决定，将ChatGPT纳入他们的学术活动中。决策者可以考虑在高等教育中采用ChatGPT的影响，并制定指导方针和法规，以确保其负责任和合乎道德的使用。
引用次数: 0
Exploring peer facilitation and critical thinking in asynchronous online discussions: A lag sequential analysis approach
探索异步在线讨论中的同伴促进和批判性思维：滞后序列分析方法
IF 8.1
1区 教育学
Q1 EDUCATION & EDUCATIONAL RESEARCH
Pub Date : 2025-03-21
DOI: 10.1111/bjet.13583
<div> <section> <p>Asynchronous online discussions (AODs) are increasingly prevalent in higher education to adapt to educational changes and promote critical thinking among learners. Past research has emphasized instructors' facilitation roles in encouraging learners' critical thinking in AODs, while fewer studies explored peer facilitation and peer participants' critical thinking from the students' perspective as facilitators. This study used a lag sequential analysis approach to examine peer facilitation techniques and critical thinking in a peer-facilitated AOD spanning six tasks over 12 weeks with 40 undergraduate participants. Results highlighted that the most frequently used peer facilitation techniques were <i>giving own opinions or experiences</i> and <i>questioning</i>, with the latter demonstrating the highest number of significant sequential patterns. Peer participants' critical thinking primarily involved <i>analyse</i> and <i>evaluate</i>, with significant sequential patterns observed in lower level and higher order critical thinking stages but not between them. Further investigation revealed the impact of peer facilitation techniques on critical thinking, and a new three-phase model was developed to describe their associations. These findings suggest that dynamic peer facilitation techniques effectively enhance critical thinking, with specific techniques targeting distinct phases of its development in AODs. The study provides actionable insights for educators, offering strategies to optimize facilitation approaches and foster critical thinking skills in higher education settings.</p> </section> <section> <div> <div> <h3>Practitioner notes</h3> <p>What is already known about this topic </p><ul> <li>Asynchronous online discussions are widely used in higher education to encourage learners' critical thinking.</li> <li>Instructors as facilitators play a positive role in encouraging learners' critical thinking in asynchronous online discussions, while the role of peer facilitators is less discussed.</li> <li>In peer-facilitated asynchronous online discussions, the facilitation techniques used by peer facilitators affect the development of critical thinking in peer participants.</li> </ul> <p>What this paper adds </p><ul> <li>Uses lag sequential analysis to examine the sequential patterns of peer facilitation techniques and critical thinking in peer-facilitated asynchronous online discussions.</li> <li>Reports common peer facilitation techniques used by peer facilitators
异步在线讨论（aod）在高等教育中越来越普遍，以适应教育的变化，促进学习者的批判性思维。以往的研究强调教师在aod中对学习者批判性思维的促进作用，而很少有研究从学生作为促进者的角度来探讨同伴促进和同伴参与者的批判性思维。本研究采用滞后序列分析方法，对40名大学生参与者进行了为期12周、跨越6个任务的同伴促进型AOD的同伴促进技术和批判性思维进行了研究。结果强调，最常用的同伴促进技巧是给出自己的意见或经验和提问，后者显示出最多的显著顺序模式。同伴参与者的批判性思维主要涉及分析和评价，在低阶和高阶批判性思维阶段观察到显著的顺序模式，但在两者之间没有显著的顺序模式。进一步的研究揭示了同伴促进技术对批判性思维的影响，并开发了一个新的三阶段模型来描述它们之间的联系。这些研究结果表明，动态同伴促进技术有效地增强了aod的批判性思维，并针对其发展的不同阶段采用了特定的技术。该研究为教育工作者提供了可行的见解，提供了优化促进方法和培养高等教育环境中批判性思维技能的策略。在高等教育中，异步在线讨论被广泛用于鼓励学习者的批判性思维。在异步在线讨论中，教师作为促进者在鼓励学习者批判性思维方面发挥了积极作用，而同伴促进者的作用则较少被讨论。在同伴促进的异步在线讨论中，同伴促进者使用的促进技术影响同伴参与者批判性思维的发展。使用滞后序列分析来检验同伴促进技术和批判性思维在同伴促进异步在线讨论中的顺序模式。报告同伴促进者使用的常见同伴促进技术，并观察到重要的顺序模式。呈现同伴参与者批判性思维的分布和发展顺序模式。研究同伴促进技术和批判性思维之间的联系，并开发了一个新的三阶段模型来描述这种联系。动态同伴促进技术可以有效促进同伴参与者批判性思维的发展，不同阶段的批判性思维与不同类型的同伴促进技术之间存在特定的关系。本研究从学习者作为促进者的角度揭示了异步在线讨论中批判性思维的促进作用，填补了研究空白。研究结果为在高等教育中有效地鼓励大学生批判性思维提供了实践指导。
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