Computers and Education Open最新文献_第6页

Rethinking artificial-intelligence literacy through the lens of teacher educators: The adaptive AI model 通过教师教育者的视角重新思考人工智能素养：自适应人工智能模型

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-09-18 DOI: 10.1016/j.caeo.2025.100291

Liat Eyal

As artificial intelligence becomes increasingly integrated into educational settings, models for measuring related literacy among both teachers and students are rapidly emerging. Yet despite their strengths and benefits, many impose fixed competency levels or overlook contextual factors. Using design-based research, and with the participation of 22 higher-education teacher educators, this study critiques existing models and introduces the novel Adaptive Artificial-Intelligence-Literacy Model (AALM), grounded in case-study analysis. This evaluation framework highlights the dynamic, multi-dimensional nature of artificial-intelligence literacy, organized around three inter-related core axes: contextual fitness, professional needs, and dynamic development. A reflective self-assessment tool is also presented, enabling teachers to evaluate their own artificial-intelligence literacy. This framework offers practical guidance for educational policy and teacher development, advocating for assessment approaches that consider social and cultural contexts, professional needs, and the evolving nature of skills amid rapid technological change. Finally, the case studies illustrate the model's relevance across diverse educational settings.

随着人工智能越来越多地融入教育环境，衡量教师和学生相关素养的模型正在迅速出现。然而，尽管他们有优势和好处，许多人强加了固定的能力水平，或者忽视了环境因素。本研究采用基于设计的研究方法，在22名高等教育教师教育者的参与下，对现有模型进行了批判，并引入了基于案例研究分析的新型自适应人工智能识字模型（AALM）。该评估框架突出了人工智能素养的动态、多维性，围绕三个相互关联的核心轴进行组织：环境适应性、专业需求和动态发展。还提出了一种反思性自我评估工具，使教师能够评估自己的人工智能素养。该框架为教育政策和教师发展提供了实用指导，倡导采用考虑社会和文化背景、专业需求以及快速技术变革中技能演变性质的评估方法。最后，案例研究说明了该模型在不同教育环境中的相关性。

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引用次数: 0

Location-based augmented reality in education: A systematic literature review 基于位置的增强现实在教育中的应用：系统的文献综述

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-08-05 DOI: 10.1016/j.caeo.2025.100277

Xavier Fonseca , Pia Spangenberger , Manuel Baer , Rene Schmidt , Heinrich Söbke

The domain of Location-based Augmented Reality (LBAR) in education is growing rapidly with the increasing ubiquity of mobile devices and their advanced capabilities. LBAR enables the superimposition of digital content onto physical environments and has been recognized for its transformative potential in educational contexts. Despite its advantages, the adoption of LBAR in education remains limited, hindered by several challenges. This paper presents a systematic review of peer-reviewed literature from 2000 to 2023 to assess the current state of LBAR in education. It explores 1) the state of the art in LBAR research, 2) its use cases and reported impacts across educational levels, and 3) the challenges and limitations identified in existing studies. Among the 50 studies included in the review, 38 % of investigated studies indicate a positive acceptance of LBAR and evident enthusiasm regarding LBAR’s application for learning purposes. However, multidimensional complexities are revealed at the methodological, technological, pedagogical, and at content and organisational levels. While LBAR tools present strong potential to enhance learning experiences, that potential is not yet fully realized. Their long-term value depends on how well they are integrated into established pedagogical practices, learning content, and educational contexts.

随着移动设备及其先进功能的日益普及，基于位置的增强现实（LBAR）在教育领域的发展迅速。LBAR能够将数字内容叠加到物理环境中，并因其在教育环境中的变革潜力而得到认可。尽管有其优势，LBAR在教育中的应用仍然有限，受到一些挑战的阻碍。本文对2000年至2023年的同行评议文献进行了系统回顾，以评估LBAR在教育中的现状。它探讨了1)LBAR研究的现状，2)它的用例和报告的跨教育水平的影响，以及3)现有研究中确定的挑战和限制。在本次综述的50项研究中，有38%的研究对LBAR表示积极接受，并对LBAR应用于学习有明显的热情。然而，在方法、技术、教学、内容和组织层面上揭示了多维复杂性。虽然LBAR工具具有增强学习经验的强大潜力，但这种潜力尚未得到充分实现。它们的长期价值取决于它们与已建立的教学实践、学习内容和教育环境的整合程度。

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引用次数: 0

Relationship between pre-service teachers’ perceived competencies, affective dispositions, and readiness to use artificial intelligence: A study informed by the intelligent-TPACK 职前教师感知能力、情感倾向和使用人工智能的准备程度之间的关系：一项由智能tpack提供信息的研究

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-11-08 DOI: 10.1016/j.caeo.2025.100305

Ayesha Sadaf , Daniel Maxwell , Can Küplüce , Heiko Holz

As artificial intelligence (AI) continues to reshape educational contexts, understanding pre-service teachers’ (PSTs) readiness to integrate AI into their teaching is increasingly critical. This study explored the relationship among PSTs’ perceived AI competencies, affective dispositions, and intentions to use AI in teaching. Eighty PSTs from a southeastern U.S. university completed an online survey assessing their technological, pedagogical, content, and ethical knowledge related to AI, as well as affective dispositions such as interest, self-efficacy, attitudes, and behavioral intentions. Findings revealed that PSTs reported low-to-moderate AI competencies, with the highest confidence in TPACK and the lowest in technological knowledge and AI ethics.

Affective responses were mixed, with moderate interest and perceived relevance of AI, but low self-efficacy and intention to use. Correlational analyses showed strong relationships between AI competencies and positive affective dispositions, particularly self-efficacy and interest, which significantly predicted intention to use AI. Regression analyses further showed that ethical awareness and integrated pedagogical-technological knowledge predicted perceptions of teacher preparation, while ethics also reinforced technological content knowledge. This study highlights the interconnected roles of competency, affect, and ethics in shaping PSTs’ readiness for AI integration in teaching.

随着人工智能（AI）继续重塑教育环境，了解职前教师（pst）是否愿意将人工智能整合到他们的教学中变得越来越重要。本研究探讨了教师感知的人工智能能力、情感倾向和在教学中使用人工智能的意图之间的关系。来自美国东南部一所大学的80名pst完成了一项在线调查，评估了他们与人工智能相关的技术、教学、内容和伦理知识，以及兴趣、自我效能、态度和行为意图等情感倾向。调查结果显示，pst报告了低至中等的人工智能能力，对TPACK的信心最高，对技术知识和人工智能伦理的信心最低。情感反应是混合的，对人工智能的兴趣和感知相关性中等，但自我效能和使用意图较低。相关分析显示，人工智能能力与积极情感倾向之间存在很强的关系，尤其是自我效能感和兴趣，它们显著地预测了使用人工智能的意愿。回归分析进一步表明，伦理意识和综合教学技术知识预测教师准备的感知，而伦理也增强了技术内容知识。本研究强调了能力、情感和道德在塑造教师将人工智能融入教学的准备方面的相互关联的作用。

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引用次数: 0

Enhancing enthusiasm for STEM education with AI: Domain-specific chatbot as personalized learning assistant 用人工智能提高STEM教育的热情：特定领域的聊天机器人作为个性化学习助手

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-11-24 DOI: 10.1016/j.caeo.2025.100315

Cilia Ricarda Rücker, Sebastian Becker-Genschow

Generative AI has been increasingly integrated into educational settings for its potential to facilitate personalized learning. However, evidence-based implementation concepts remain essential to realize its pedagogical value. Therefore, this study investigates the educational efficacy of ADA, a domain-specific AI chatbot customized for secondary mathematics education, in enhancing learning outcomes and student engagement. A cluster-randomized controlled study was conducted with 195 ninth-grade students from German secondary schools in authentic classroom settings. The objective of the study was to compare ADA's personalized support against conventional differentiation materials. The intervention targeted the Heron method for estimating square roots in a single-lesson intervention with a pre-post assessment. As one of the few cluster-randomized controlled studies on custom AI chatbots in secondary mathematics education, this investigation assessed both affective and cognitive aspects of learning simultaneously to provide a comprehensive research picture. The findings indicated a high degree of student acceptance across the dimensions of the Technology Acceptance Model. Additionally, situational interest significantly improved in the chatbot condition. The study found that trends in performance and emotional responses were favorable, while cognitive load increased slightly. The findings emphasize the evident potential of customized AI chatbots to supplement differentiated instruction in the domain of mathematics education. They provide a critical foundation for future research investigating the potential of AI chatbots to enhance student learning through careful integration and customization.

生成式人工智能因其促进个性化学习的潜力而越来越多地融入教育环境。然而，基于证据的实施概念仍然是实现其教学价值的必要条件。因此，本研究调查了ADA（一种为中学数学教育定制的特定领域的AI聊天机器人）在提高学习成果和学生参与度方面的教育功效。本研究以195名德国中学九年级学生为研究对象，在真实的课堂环境中进行整群随机对照研究。该研究的目的是比较ADA的个性化支持与传统分化材料。干预的目标是在单课干预中估计平方根的Heron方法，并进行前后评估。作为为数不多的针对中学数学教育中定制AI聊天机器人的集群随机对照研究之一，本研究同时评估了学习的情感和认知方面，以提供全面的研究图景。研究结果表明，在技术接受模型的各个维度上，学生的接受程度都很高。此外，在聊天机器人条件下，情境兴趣显著提高。研究发现，表现和情绪反应的趋势是有利的，而认知负荷略有增加。研究结果强调了定制人工智能聊天机器人在数学教育领域补充差异化教学方面的明显潜力。它们为未来研究人工智能聊天机器人的潜力提供了重要的基础，通过仔细整合和定制来增强学生的学习。

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引用次数: 0

Authentic open data inquiry in schools 学校的真实开放数据查询

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-09-09 DOI: 10.1016/j.caeo.2025.100287

Alejandra Celis Vargas , Rikke Magnussen , Ingrid Mulder , Birger Larsen

Open data offers the potential to enhance citizen participation, transparency, and accountability in society. However, a lack of competencies that enable citizens to engage in open data ecosystems remains a barrier. Although authentic open data inquiry has been identified as a promising approach to develop open data learning designs in schools, its connection to open data competencies is not yet well understood. To advance the understanding of open data competencies and learning designs, this study focuses on the inductive analysis of two design-based research cycles and four interventions in Danish schools. A cohort of 7th to 9th grade pupils (n = 78) and their teachers (n = 4) engaged with The Open Data Newsroom, an open data learning design that situates pupils in the role of data journalists to solve an environmental mystery. Following a thematic analysis approach, we examined qualitative data from observations, surveys, and interviews to identify four categories that encompass pupils’ practices for (1) navigating open data: find and assess relevant information and data to identify a problem; (2) developing authentic open data analysis: analyse and interpret data in connection to real-world problems and local contexts; (3) building authentic data arguments and stories: explain a problem with data from different sources and domains to lay audiences; and (4) creating open data representations: build tools to support inquiry and communication. We argue that these practices, grounded in data literacy and real-world problem solving, contribute to defining open data competencies in schools, and we present a model to illustrate this connection.

开放数据提供了增强公民参与、透明度和社会问责制的潜力。然而，缺乏使公民参与开放数据生态系统的能力仍然是一个障碍。虽然真实的开放数据调查已被确定为在学校开发开放数据学习设计的有前途的方法，但其与开放数据能力的联系尚未得到很好的理解。为了促进对开放数据能力和学习设计的理解，本研究侧重于对丹麦学校中两个基于设计的研究周期和四个干预措施的归纳分析。一组七年级到九年级的学生（n = 78）和他们的老师（n = 4）参与了开放数据新闻编辑室，这是一种开放数据学习设计，让学生扮演数据记者的角色，解决一个环境之谜。遵循主题分析方法，我们检查了来自观察、调查和访谈的定性数据，以确定包含学生实践的四个类别：(1)浏览开放数据：查找和评估相关信息和数据以识别问题；(2)发展真正的开放数据分析：分析和解释与现实世界问题和当地背景相关的数据；(3)构建真实的数据论证和故事：用不同来源和领域的数据向外行观众解释问题；(4)创建开放的数据表示：构建支持查询和交流的工具。我们认为，这些以数据素养和解决现实问题为基础的实践有助于定义学校的开放数据能力，我们提出了一个模型来说明这种联系。

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引用次数: 0

Computing education using generative artificial intelligence tools: A systematic literature review 使用生成式人工智能工具的计算教育：系统的文献综述

IF 4.1 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-06-21 DOI: 10.1016/j.caeo.2025.100266

Friday Joseph Agbo , Chris Olivia , Godsalvation Oguibe , Ismaila Temitayo Sanusi , Godwin Sani

Recent advances in generative artificial intelligence (GenAI) are revolutionizing computing education, causing paradigm shifts from the traditional teaching and learning technique. Studies are exploring GenAI tools in computing classes from intro to advanced topics with the aim to showcase how to reshape computing education in this new era of GenAI. This study examined the computing education research landscape to unravel how GenAI tools have been used in that domain, what are the characteristics of those studies in terms of computing topics, context, and tools, offering insights into the pros and cons for integrating GenAI in computing education based on the performance indicators reported in the literature. This study employed a systematic literature review approach to identify and analyze 78 relevant articles. The findings of this study show that educators are exploring GenAI tools in computer sciences classes from K-12 through graduate levels. Beyond programming education, GenAI has also been explored in college upper-level computer science courses such as Computer Graphics and Human-Computer Interaction. The performance analysis of these tools are presented in this study, indicating a progressive advancement from when the technology was introduced. This study also discusses learning outcomes, good practices, and potential risks to avoid when exploring GenAI, as reported in the studies, which could guide how computing educators design their instructional strategies using GenAI. This study contributes to broadening the understanding of exploring, adapting, or using GenAI in computer science education and may spark interest among educators who are unwilling to explore GenAI or may not understand how or what strategies to adopt.

生成式人工智能（GenAI）的最新进展正在彻底改变计算机教育，导致传统教学技术的范式转变。研究人员正在探索GenAI工具在计算机课程中的应用，从入门到高级主题，目的是展示如何在GenAI的新时代重塑计算机教育。本研究考察了计算机教育研究的前景，以揭示GenAI工具如何在该领域中使用，这些研究在计算主题、背景和工具方面的特点是什么，并根据文献中报告的绩效指标，提供了将GenAI整合到计算机教育中的利弊。本研究采用系统文献回顾的方法，对78篇相关文献进行识别和分析。这项研究的结果表明，教育工作者正在从K-12到研究生阶段的计算机科学课程中探索GenAI工具。除了编程教育，GenAI也被用于大学高级计算机科学课程，如计算机图形学和人机交互。本研究提出了这些工具的性能分析，表明该技术自引入以来一直在进步。本研究还讨论了在探索GenAI时要避免的学习结果、良好实践和潜在风险，这些研究报告可以指导计算机教育者如何使用GenAI设计他们的教学策略。本研究有助于拓宽对在计算机科学教育中探索、适应或使用GenAI的理解，并可能激发那些不愿探索GenAI或可能不知道如何或采用何种策略的教育者的兴趣。

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引用次数: 0

Direct vs. video observation of skill performance: effects on peer feedback dynamics in motor learning 技能表现的直接观察与视频观察：同伴反馈动态对运动学习的影响

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-10-01 DOI: 10.1016/j.caeo.2025.100296

Omar Trabelsi , Mohamed Yaakoubi , Ahmed Ghorbel , Amir Romdhani , Mustapha Bouchiba , Mohamed Abdelkader Souissi , Okba Selmi , Katja Weiss , Thomas Rosemann , Adnene Gharbi , Beat Knechtle

Video technology facilitates feedback delivery in motor learning, benefiting teacher/coach and peer feedback. Most studies focus on attributing motor learning improvements to video-based feedback but never examine how peer feedback dynamics change to mediate the effect of video observation (VO) on learning outcomes. Therefore, this study compares the frequency, type, and accuracy of peer feedback based on direct observation (DO) and VO in a long jump learning context. Forty-one sports science students (M^age: 20.13±0.71) participated in a four-session long jump learning unit. Students were then randomly paired for experimental procedures: one performed a jump while the other observed. Observers first provided 30 seconds of verbal feedback based on DO and then after viewing a video recording of the jump (VO). Roles were then switched. Audio recordings were transcribed and analyzed for overall feedback frequency. Feedback instances were then classified as implicit or explicit, with the latter assessed for accuracy. The main results showed that VO significantly increased the median frequency of overall feedback and explicit feedback compared to DO, with no significant difference in implicit feedback. The median accuracy of explicit feedback was also significantly higher based on VO compared to DO. These findings help explain the previously documented beneficial effects of video-based peer feedback in motor learning. They demonstrate that VO enables peers to provide more feedback, particularly explicit, more than implicit, with higher accuracy.

视频技术有助于在运动学习中提供反馈，有利于教师/教练和同伴反馈。大多数研究将运动学习的改善归因于基于视频的反馈，但从未研究同伴反馈动态变化如何调节视频观察（VO）对学习结果的影响。因此，本研究比较了跳远学习情境下基于直接观察的同伴反馈（DO）和基于直接观察的同伴反馈（VO）的频率、类型和准确性。41名体育理科生（年龄：20.13±0.71）参加了四节跳远学习单元。然后，学生们被随机配对，进行实验程序：一个人跳，另一个人观察。观察者首先根据DO提供30秒的口头反馈，然后观看跳跃的视频记录（VO）。然后角色互换了。录音被转录并分析总体反馈频率。然后将反馈实例分类为隐式或显式，并评估后者的准确性。主要结果表明，VO显著提高了整体反馈和显式反馈的中位数频率，而内隐反馈的中位数频率差异不显著。基于VO的显式反馈的中位数准确性也显著高于基于DO的中位数准确性。这些发现有助于解释先前记录的基于视频的同伴反馈对运动学习的有益影响。他们证明，VO使同行能够提供更多的反馈，特别是明确的，而不是隐含的，具有更高的准确性。

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引用次数: 0

Room for collaboration: Analyzing group learning in spatial digital learning environments 协作空间：分析空间数字学习环境中的小组学习

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-11-17 DOI: 10.1016/j.caeo.2025.100308

Michael Montag , Heinrich Söbke , Mario Wolf , Florian Wehking , Eckhard Kraft

Collaboration within learning activities, i.e., solving tasks together, is one of the effective elements for designing learning activities. Collaboration is also seen as conducive to learning in spatial digital learning environments (SDLE), such as virtual reality environments. However, less is known about the detailed design of collaboration, such as learning requirements and specific interaction between learners. Thus, this evaluation study examines the groupwise exploration of SDLEs using the example of a 360°-based digital representation of a waterworks. The study combines ecological authenticity with a multidimensional assessment approach, examining motivation, emotions, cognitive load, and social presence in authentic group learning contexts. We used standardized instruments to collect data regarding motivation and emotion as learning requirements, social presence and cognitive load. A pre- and post-test supplemented the data, as did semi-structured interviews. T-tests show that learning in groups can be more stressful for learners but leads to a more positive affect overall (Collaborative Load–Motivation Trade-off Principle). Further, we found that pairs achieve better learning outcomes than triads. A side finding revealed that learning in face-to-face settings appears to be more effective than online learning. The study contributes to an informed instructional design of learning in SDLEs and thus might advance this valuable learning technology.

学习活动中的协作，即共同解决任务，是设计学习活动的有效要素之一。协作也被视为有助于在空间数字学习环境（SDLE）中学习，如虚拟现实环境。然而，对协作的详细设计知之甚少，例如学习需求和学习者之间的具体交互。因此，本评估研究以自来水厂的360°数字表示为例，考察了SDLEs的分组探索。本研究将生态真实性与多维评估方法相结合，考察了真实群体学习情境中的动机、情绪、认知负荷和社会存在。我们使用标准化的工具来收集动机和情绪作为学习要求、社会存在和认知负荷的数据。前测和后测补充了数据，半结构化访谈也是如此。t检验表明，小组学习对学习者来说压力更大，但总体上产生了更积极的影响（协作负载-动机权衡原则）。此外，我们发现结对学习比三合学习取得更好的学习效果。另一项研究发现，面对面学习似乎比在线学习更有效。该研究有助于在特殊语言学习环境中进行明智的教学设计，从而可能推进这一有价值的学习技术。

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引用次数: 0

Research on the coupling of talent cultivation and reform practice of higher education in architecture 建筑专业人才培养与高等教育改革实践的耦合研究

IF 4.1 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-06-16 DOI: 10.1016/j.caeo.2025.100268

Zhiwu Zhou , Qiong Tian , Julián Alcalá , Víctor Yepes

With the sustainable development of the global construction industry and the rapid development of the intelligent building industry, colleges and universities worldwide are stepping up the training of comprehensive talents in intelligent application in the construction profession. Therefore, establishing a cross-regional and perfect construction talent training system has become a critical research and practice topic for university researchers. This study uses computational procedures to fit mathematical theories, coupled model analysis, and other innovative training framework systems. It also analyzes the problems and shortcomings in talent training from three aspects: the current status of industrialization of higher education construction at home and abroad. Combined with the current status of talent training in higher education and the different needs of various countries for architectural talents, a Chinese-style "six-stage multi-module innovation optimization" training framework and a "construction talent training model" are proposed to improve the innovation system. Through in-depth research, a talent training goal system and improvement strategy adapted to the development of China's construction industrialization have been established. The research results effectively compensate for the shortcomings of higher vocational education in this field. The framework of this study can serve as a beacon for deepening education in the construction profession in various countries around the world, guiding higher vocational education to move towards the training of high-tech and high-skilled talents.

随着全球建筑业的持续发展和智能建筑产业的快速发展，世界各地的高校都在加大对建筑专业智能应用复合型人才的培养力度。因此，建立跨区域、完善建设的人才培养体系已成为高校科研工作者的重要研究和实践课题。本研究使用计算程序拟合数学理论、耦合模型分析及其他创新训练框架系统。并从国内外高等教育建设产业化现状三个方面分析了人才培养中存在的问题和不足。结合高等教育人才培养的现状和各国对建筑人才的不同需求，提出了中国式的“六阶段多模块创新优化”培养框架和“建筑人才培养模式”，以完善创新体系。通过深入研究，建立了适应中国建筑工业化发展的人才培养目标体系和提升策略。研究成果有效地弥补了高职教育在这方面的不足。本研究的框架可以作为世界各国深化建筑专业教育的灯塔，引导高等职业教育向培养高技术、高技能人才的方向发展。

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引用次数: 0

The Why and How prospective mathematics teachers want to employ digital mathematics tools in classrooms 未来的数学教师为什么以及如何在课堂上使用数字数学工具

IF 4.1 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 Epub Date: 2025-06-16 DOI: 10.1016/j.caeo.2025.100269

Robert Weinhandl , Branko Anđić , Viktoria Riegler , Valentina Bleckenwegner , Selina Baldinger , Jonas Mayrhofer , Christoph Helm

Digital mathematics tools range from simple pocket calculators to computer algebra systems and dynamic geometry software up to AI applications for confirming mathematical proofs. Our study aims to identify what motivates prospective mathematics teachers to employ digital mathematics tools in their prospective teaching and how their behavioral intentions affect different forms of technology use. To achieve our research aim, we conducted a digital questionnaire study in which 489 prospective mathematics teachers participated. In terms of completeness, the data of 409 participants could be used for further analysis. The structural equation model analyses conducted in our study indicate that especially Performance Expectancy (β = 0.359) and Facilitating Conditions (β = 0.424) significantly influence the behavioral intentions of prospective mathematics teachers to use digital mathematics tools. In turn, behavioral intentions significantly influence how prospective mathematics teachers want to use digital mathematics tools in the classroom. Our findings highlight that Behavioral Intentions act as a key link between technology acceptance and Cognitive Engagement in prospective mathematics teachers. Additionally, demographic factors such as age, gender, and study progress moderate these relationships. These insights can inform teacher training programs by tailoring support to different groups to ensure more effective integration of digital tools in mathematics classrooms.

数字数学工具的范围从简单的袖珍计算器到计算机代数系统和动态几何软件，再到用于确认数学证明的人工智能应用程序。我们的研究旨在确定是什么激励未来的数学教师在他们的未来教学中使用数字数学工具，以及他们的行为意图如何影响不同形式的技术使用。为了达到我们的研究目的，我们进行了一项有489名准数学教师参与的数字问卷研究。在完整性方面，409名参与者的数据可用于进一步分析。本研究的结构方程模型分析表明，绩效期望（β = 0.359）和促进条件（β = 0.424）显著影响准数学教师使用数字数学工具的行为意向。反过来，行为意向显著影响未来数学教师在课堂上如何使用数字数学工具。我们的研究结果强调，行为意向在未来数学教师的技术接受和认知参与之间起着关键的作用。此外，年龄、性别和学习进度等人口因素会缓和这些关系。这些见解可以为教师培训计划提供信息，为不同群体提供量身定制的支持，以确保更有效地将数字工具整合到数学课堂中。

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引用次数: 0