Computers and Education Open最新文献_第5页

User perception and anatomical understanding from use of 3D anatomy technology by exercise science students: A pilot study 运动科学学生使用3D解剖技术的用户感知和解剖理解：一项试点研究

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-10-01 DOI: 10.1016/j.caeo.2025.100295

Leesa Anne Grier , Anthony Leicht

Understanding anatomy poses a significant challenge for non-medical university students, particularly those without prior science knowledge, such as exercise science students. Technology-Enhanced Learning (TEL) resources, including 3D anatomy platforms, have been explored to support student engagement and improve academic outcomes with variable results.

Objectives: This pilot study investigated the potential of the Complete Anatomy (CA) 3DTEL resource, combined with traditional blended learning methods, to enhance anatomy knowledge of first-year, undergraduate exercise science students.

Design: Cohort observational study.

Methods: The study followed 36 participants (46 % female, 54 % male) across two 13-week units. Guided implementation of CA was introduced during unit 1, while students engaged with the resource independently during unit 2. User satisfaction was assessed via surveys, and academic performance evaluated by comparing final unit grades with a 2022 cohort that did not use the resource.

Results: Results indicated stable student satisfaction and a significantly different academic performance for the 2023 cohort, with median grades increasing from a Pass (50.0–64.9 %) to a Credit (65.0–74.9 %).

Conclusion: These findings suggest that integrating 3DTEL resources with blended learning can positively support anatomy learning for science-naïve students.

理解解剖学对非医科大学的学生来说是一个巨大的挑战，特别是那些没有科学知识的学生，比如运动科学的学生。包括3D解剖平台在内的技术增强学习（TEL）资源已被探索，以支持学生的参与，并以不同的结果改善学术成果。目的：本初步研究探讨了完整解剖学（CA） 3DTEL资源与传统混合式学习方法相结合的潜力，以提高运动科学本科一年级学生的解剖学知识。设计：队列观察研究。方法：该研究对36名参与者（46%女性，54%男性）进行了为期13周的随访。第一单元引入了CA的指导实施，而学生在第二单元独立使用资源。通过调查来评估用户满意度，通过将最终单元成绩与未使用该资源的2022年队列进行比较来评估学习成绩。结果：结果表明，2023年的学生满意度稳定，学习成绩显著不同，成绩中位数从及格（50.0% - 64.9%）增加到学分（65.0% - 74.9%）。结论：将3DTEL资源与混合学习相结合，对science-naïve学生的解剖学学习具有积极的支持作用。

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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 : 2025-10-01 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

Social media discussions on educators: Selecting and appraisal of recent research using TF-IDF 社会媒体对教育者的讨论：使用TF-IDF选择和评价最近的研究

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-09-30 DOI: 10.1016/j.caeo.2025.100293

Mateo R. Borbon Jr. , Ryan A. Ebardo

This systematic literature review, analyzing 36 peer-reviewed publications from 2019 to February of 2025, addresses a critical gap by examining the use of social media analytics (SMA) for faculty evaluation. Employing a novel methodological approach that combines machine learning-assisted screening (ASReview) with TF-IDF, the study finds that platforms like Twitter and Facebook are increasingly analyzed using sentiment analysis, machine learning, and text mining. These techniques provide real-time, unfiltered student feedback on teaching effectiveness, complementing traditional evaluation instruments and helping to monitor institutional reputation. While SMA offers valuable insights, the review highlights significant challenges, including data quality and credibility, algorithmic bias, ethical concerns, and generalizability. Effectively leveraging SMA's potential requires addressing these issues through robust theoretical frameworks, balanced institutional policies, and enhanced digital literacy to improve teaching practices while safeguarding academic integrity.

这篇系统的文献综述分析了2019年至2025年2月的36篇同行评审的出版物，通过研究社交媒体分析（SMA）在教师评估中的使用，解决了一个关键的差距。该研究采用了一种将机器学习辅助筛选（ASReview）与TF-IDF相结合的新方法，发现Twitter和Facebook等平台越来越多地使用情感分析、机器学习和文本挖掘进行分析。这些技术提供了实时的、未经过滤的学生对教学效果的反馈，补充了传统的评估工具，并有助于监测机构的声誉。虽然SMA提供了有价值的见解，但该综述强调了重大挑战，包括数据质量和可信度、算法偏见、伦理问题和可泛化性。有效利用SMA的潜力需要通过健全的理论框架、平衡的制度政策和增强数字素养来解决这些问题，以改善教学实践，同时维护学术诚信。

引用次数: 0

Extended executive cognition, a learning outcome for the AI age 扩展执行认知，人工智能时代的学习成果

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-09-30 DOI: 10.1016/j.caeo.2025.100294

Alexander M. Sidorkin

Artificial intelligence fundamentally transforms professional expertise across disciplines, creating an expanding gap between higher education curricula and emerging workplace practices. This paper introduces "Extended Executive Cognition" as a critical learning outcome for the AI age, the ability to strategically allocate cognitive effort, coordinate AI-assisted tasks, and manage hybrid intelligence problem-solving. Drawing on curriculum theory, executive function psychology, and distributed cognition research, we argue that post-educational success increasingly depends on metacognitive skills for human-AI collaboration rather than traditional academic competencies. Extended Executive Cognition requires developing accurate mental models of AI capabilities and limitations to enable effective task delegation. The framework presented offers concrete curriculum integration strategies across general education and discipline-specific contexts, including assessment approaches that capture metacognitive development rather than mere product evaluation. By reconceptualizing learning outcomes around cognitive orchestration rather than content production, universities can prepare graduates for continuous technological evolution while preserving distinctly human capacity as education's central value.

人工智能从根本上改变了跨学科的专业知识，在高等教育课程和新兴工作场所实践之间造成了越来越大的差距。本文介绍了“扩展执行认知”作为人工智能时代的关键学习成果，战略分配认知努力，协调人工智能辅助任务和管理混合智能解决问题的能力。根据课程理论、执行功能心理学和分布式认知研究，我们认为，教育后的成功越来越依赖于人类与人工智能合作的元认知技能，而不是传统的学术能力。扩展执行认知需要开发准确的人工智能能力和局限性的心智模型，以实现有效的任务授权。所提出的框架提供了跨通识教育和学科特定背景的具体课程整合策略，包括捕捉元认知发展而不仅仅是产品评估的评估方法。通过围绕认知编排而不是内容生产重新定义学习成果，大学可以让毕业生为持续的技术进化做好准备，同时保留明显的人类能力作为教育的核心价值。

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引用次数: 0

Artificial intelligence as a pedagogical communicator: mixed-methods insights from Raffles International College Bangkok 人工智能作为教学传播者：来自曼谷莱佛士国际学院的混合方法见解

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-09-30 DOI: 10.1016/j.caeo.2025.100297

Qinjie Shen

Artificial intelligence (AI) is increasingly employed as a pedagogical assistant in higher education, but its role as a communicator in diverse classrooms is not fully understood. This mixed-methods study investigates AI as a pedagogical communicator at a private international college in Bangkok. The study extends the Technology Acceptance Model by integrating social presence and trust constructs to examine factors influencing student engagement and satisfaction with AI-mediated instruction. In an explanatory sequential design, a survey of 300 students was analyzed using structural equation modeling, followed by 10 in-depth interviews. Results indicated that student satisfaction is driven by two mediated pathways: perceived social presence in AI communication builds trust, which enhances satisfaction, and perceived usefulness of AI feedback promotes engagement, which likewise increases satisfaction. Ease of interacting with the AI increased perceived presence and usefulness and thus indirectly boosted satisfaction; however, it also raised expectations that sometimes dampened satisfaction. Interview themes clarified these patterns. Students valued the AI’s clear explanations, responsiveness, and round-the-clock support, which improved efficiency and engagement, but also noted its lack of human warmth and increased pressure from constant availability. These findings suggest design principles for AI communicators, such as using friendly, context-aware language and clear rationales to build trust, and providing stepwise, practical feedback to sustain engagement. In sum, culturally responsive AI tutors that balance human-like connection with instructional efficiency can enhance student engagement and satisfaction in multicultural educational settings.

人工智能（AI）越来越多地被用作高等教育的教学助理，但它在不同课堂中作为沟通者的作用尚未得到充分认识。这项混合方法的研究调查了人工智能作为曼谷一所私立国际学院的教学传播者。该研究通过整合社会存在和信任结构来扩展技术接受模型，以检查影响学生参与和对人工智能介导的教学满意度的因素。在解释性序列设计中，使用结构方程模型对300名学生的调查进行了分析，随后进行了10次深度访谈。结果表明，学生满意度由两种中介途径驱动：人工智能交流中感知到的社交存在建立信任，从而提高满意度；人工智能反馈的感知有用性促进了参与度，同样也提高了满意度。与AI互动的便利性增加了存在感和有用性，从而间接提高了满意度；然而，它也提高了期望值，有时会降低满意度。访谈主题阐明了这些模式。学生们重视人工智能的清晰解释、响应能力和24小时支持，这提高了效率和参与度，但也注意到它缺乏人性的温暖，并且增加了持续可用性的压力。这些发现为人工智能传播者提供了设计原则，例如使用友好的、情境感知的语言和清晰的理由来建立信任，并提供逐步的、实用的反馈来维持参与。总之，在多元文化教育环境中，具有文化响应能力的人工智能导师能够平衡与人类的联系和教学效率，从而提高学生的参与度和满意度。

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引用次数: 0

Privacy, identity, and fairness: Unpacking ethical influences on metaverse adoption in university learning 隐私、身份与公平：揭示大学学习中伦理对虚拟世界采用的影响

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-09-24 DOI: 10.1016/j.caeo.2025.100292

Mousa Al-kfairy , Meera Alalawi , Saed Alrabaee , Omar Alfandi

As immersive technologies like the Metaverse continue to reshape higher education, it becomes increasingly vital to examine the ethical dimensions shaping student engagement with these platforms. This study investigates how university students perceive privacy, digital identity, informed consent, and algorithmic fairness in Metaverse-based classrooms, and how these perceptions influence their trust and behavioral intention to adopt the technology. A quantitative survey was conducted with 310 university students, all of whom had prior exposure to virtual learning platforms. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0, the study found that Metaverse Ethical Dimensions (MED) significantly influence both Trusting Intention (TRI) (

β

= 0.740, p < 0.001) and Intention to Use (IU) (

β

= 0.573, p < 0.001). Additionally, TRI partially mediates the relationship between MED and IU (

β

= 0.206, p = 0.001). These results highlight the central role of ethical design and user trust in promoting the adoption of Metaverse-based classrooms.

随着像Metaverse这样的沉浸式技术继续重塑高等教育，审视影响学生与这些平台互动的道德维度变得越来越重要。本研究调查了大学生在基于metaverse的课堂中如何感知隐私、数字身份、知情同意和算法公平性，以及这些感知如何影响他们的信任和采用该技术的行为意愿。对310名大学生进行了一项定量调查，他们之前都接触过虚拟学习平台。通过SmartPLS 4.0使用偏最小二乘结构方程模型（PLS-SEM），研究发现，meta伦理维度（MED）显著影响信任意图（TRI）（β = 0.740, p < 0.001）和使用意图（IU）（β = 0.573, p < 0.001）。此外，TRI部分介导了MED和IU之间的关系（β = 0.206, p = 0.001）。这些结果突出了道德设计和用户信任在促进采用基于metaverse的教室中的核心作用。

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引用次数: 0

The community of inquiry framework and learning analytics: A systematic review of previous research 探究框架与学习分析的共同体：对以往研究的系统回顾

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-09-19 DOI: 10.1016/j.caeo.2025.100289

Secil Caskurlu , Daniela Castellanos-Reyes , Jieun Lim , Kadir Kozan

The purpose of the current systematic review is to provide a comprehensive overview of how the Community of Inquiry framework and learning analytics have been informing each other, thus providing suggestions for how to enhance future research and practice in online education. Overall results revealed that (a) research was primarily conducted in MOOCs and traditional online courses; (b) text and log data were the primary sources analyzed using various statistical, computational, and/or machine learning methods through various tools and software; and (c) descriptive and/or predictive analytics were the most common learning analytics methodology thus describing and/or predicting student and instructor outcomes including teaching, social and cognitive presence. Even though few studies have explicitly named it as the guiding framework, the Community of Inquiry framework has significantly influenced learning analytics research design and some studies have offered new theoretical insights. As for research quality or characteristics, fewer studies fully reported such important details as participant characteristics and data preprocessing procedures. All these findings led to the conclusion that the Community of Inquiry framework and learning analytics have been mutually beneficial so far, and similar future research needs to pay more attention to reporting quality thereby providing richer insights into both the theory and practice of online education.

当前系统综述的目的是全面概述探究共同体框架和学习分析是如何相互影响的，从而为如何加强在线教育的未来研究和实践提供建议。总体结果显示：(a)研究主要在mooc和传统在线课程中进行；(b)文本和日志数据是通过各种工具和软件使用各种统计、计算和/或机器学习方法分析的主要来源；(c)描述性和/或预测性分析是最常见的学习分析方法，因此描述和/或预测学生和教师的结果，包括教学、社会和认知存在。尽管很少有研究明确地将其命名为指导框架，但探究共同体框架对学习分析研究设计产生了重大影响，一些研究提供了新的理论见解。在研究质量或特征方面，充分报道参与者特征、数据预处理过程等重要细节的研究较少。所有这些发现得出的结论是，到目前为止，探究社区框架和学习分析是互惠互利的，未来类似的研究需要更多地关注报告质量，从而为在线教育的理论和实践提供更丰富的见解。

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引用次数: 0

Contextual knowledge and TPACK: Evidence from a global south setting 背景知识和TPACK：来自全球南方背景的证据

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-09-18 DOI: 10.1016/j.caeo.2025.100290

Van Loi Nguyen , Chung Thi Thanh Hang , Nguyen Trong Nguyen , Huynh Truong Sang

Extensive research on Technological Pedagogical Content Knowledge (TPACK) has revealed how contextual layers influence teachers’ perceptions and practice of technology integration, but the extent to which contextual knowledge (XK) contributes to the TPACK framework remains underexplored. This study, adopting the transformative view of TPACK in context, aimed to explore the XK-TPACK relationship. Data was collected from a survey on 148 English as a foreign language teachers working across various educational settings in the lower Mekong Delta of Vietnam. Partial Least Squares Factor analysis and Structural Equation Modeling (SEM) were used to explore three hypothesized models. Validation results supported the substantial direct contribution of XK to TPACK variation. This finding extends the literature and suggests that the interplay of TPACK domains becomes more complex, considering contextual knowledge. Future research should be conducted to corroborate this model.

对技术教学内容知识（TPACK）的广泛研究揭示了情境层如何影响教师对技术整合的认知和实践，但情境知识（XK）对TPACK框架的贡献程度仍未得到充分探讨。本研究采用语境中TPACK的变革性观点，旨在探讨XK-TPACK的关系。数据收集自对148名英语作为外语教师的调查，这些教师在越南湄公河三角洲下游的不同教育机构工作。利用偏最小二乘因子分析和结构方程模型（SEM）探讨了三个假设模型。验证结果支持XK对TPACK变异的实质性直接贡献。这一发现扩展了文献，并表明考虑到上下文知识，TPACK域的相互作用变得更加复杂。未来的研究应进一步证实这一模型。

引用次数: 0

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 : 2025-09-18 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

Gamifying cybersecurity: A narrative-driven approach to teaching steganography 游戏化网络安全：一种叙事驱动的隐写术教学方法

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-09-15 DOI: 10.1016/j.caeo.2025.100288

Femke G.J. Weijsenfeld, Dipti K. Sarmah

Serious games are increasingly used in educational settings to enhance student engagement and support deeper learning. While research shows that such games can improve holistic understanding and knowledge retention, their application in specialised cybersecurity topics such as steganography, the art of concealing information to avoid detection, remains limited. Current teaching approaches for steganography often rely on traditional methods, such as lectures and textbooks, offering little interactivity or immersion. This study addresses this gap by designing and evaluating StegAdventure, a narrative-based serious game designed to improve student engagement with steganography concepts. To assess the game’s effectiveness, we conducted a controlled study with 54 higher education students in The Netherlands, randomly divided into an experimental group (n = 27) who played the game and a control group (n = 27) who studied the same content through a traditional text-based resource. Participants in both groups completed the User Engagement Scale - Short Form (UES-SF) to assess perceived engagement, and a knowledge test to measure learning outcomes. Our analysis revealed a significant difference in engagement levels, favouring the game-based approach, while no significant difference was observed in knowledge test scores. These findings suggest that StegAdventure can serve as a valuable teaching tool, particularly for increasing student engagement in complex cybersecurity topics, with the potential to support long-term knowledge retention.

严肃游戏越来越多地用于教育环境，以提高学生的参与度和支持更深入的学习。虽然研究表明，这类游戏可以提高整体理解和知识保留，但它们在专业网络安全主题（如隐写术）中的应用仍然有限。隐写术是一种隐藏信息以避免被发现的艺术。目前的隐写术教学方法往往依赖于传统的方法，如讲座和教科书，提供很少的互动性或沉浸。本研究通过设计和评估《StegAdventure》来解决这一问题，这是一款基于叙述的严肃游戏，旨在提高学生对隐写术概念的参与度。为了评估游戏的有效性，我们对荷兰的54名高等教育学生进行了一项对照研究，将他们随机分为实验组（n = 27）和对照组（n = 27），实验组玩游戏，对照组通过传统的基于文本的资源学习相同的内容。两组的参与者都完成了用户参与量表-简短形式（UES-SF）来评估感知参与，以及知识测试来衡量学习成果。我们的分析揭示了在参与水平上的显著差异，倾向于基于游戏的方法，而在知识测试分数上没有观察到显著差异。这些发现表明，StegAdventure可以作为一种有价值的教学工具，特别是在增加学生对复杂网络安全主题的参与方面，具有支持长期知识保留的潜力。

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引用次数: 0