Internet and Higher Education最新文献

Behavior patterns characterize students' choices and relate to cognitive load and performance in learner-controlled environments 行为模式表征学生的选择，并与学习者控制环境中的认知负荷和表现有关

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2026-04-01 Epub Date: 2025-12-20 DOI: 10.1016/j.iheduc.2025.101073

Anna Gorbunova , Kseniia A. Adamovich , Alexander Savelyev , Jamie Costley

Asynchronous online learning environments offer flexibility for students to navigate learning at their own pace, resulting in diverse behavioral patterns that can significantly impact cognitive load and academic performance. However, limited research has explored how learner-controlled environments shape these patterns and their relationship to learning outcomes and levels of cognitive load. This study investigates behavioral patterns in an online asynchronous graduate law class (n = 90) at a large university, analyzing data from a learning management system to categorize students into clusters based on their interactions with instructional components (e.g., video lectures, video-based examples, and problem-solving tasks). Cluster analysis revealed three distinct patterns: balanced learners, who achieved the highest performance; practice-oriented learners, who exhibited lower intrinsic cognitive load; and classic learners, characterized by comparatively lower extraneous cognitive load. However, these differences in extraneous load were not statistically significant between clusters, suggesting that the additional cognitive demands of learner control may have imposed similar baseline levels of extraneous load regardless of behavior pattern. Contrary to expectations, learner behavior patterns extended beyond example-based and problem-solving-first approaches, highlighting greater variability in learner strategies. These findings underscore the importance of understanding instructional paths in learner-controlled environments and how behavioral patterns can interact with both cognitive load and learning outcomes.

异步在线学习环境为学生提供了以自己的节奏进行学习的灵活性，从而产生了不同的行为模式，可以显著影响认知负荷和学习成绩。然而，有限的研究探讨了学习者控制的环境如何塑造这些模式，以及它们与学习结果和认知负荷水平的关系。本研究调查了一所大型大学的在线异步研究生法律课程（n = 90）的行为模式，分析了来自学习管理系统的数据，根据学生与教学组件（例如，视频讲座，视频示例和解决问题的任务）的互动将学生分类为集群。聚类分析揭示了三种不同的模式：平衡的学习者，他们取得了最高的成绩；实践型学习者表现出较低的内在认知负荷；而经典学习者的特点是相对较低的外部认知负荷。然而，这些额外负荷的差异在集群之间没有统计学意义，这表明学习者控制的额外认知需求可能会施加相似的额外负荷基线水平，而不管行为模式如何。与预期相反，学习者的行为模式超越了以实例为基础和问题解决为先的方法，突出了学习者策略的更大可变性。这些发现强调了理解学习者控制环境中的教学路径以及行为模式如何与认知负荷和学习结果相互作用的重要性。

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引用次数: 0

Co-regulated learning in online private student chat groups 共同管理的在线私人学生小组学习

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2026-04-01 Epub Date: 2025-12-13 DOI: 10.1016/j.iheduc.2025.101070

Elizabeth Cooke , Suijing Yang , Daniel Taylor-Griffiths , Jason M. Lodge

Peer interactions in higher education settings frequently occur in informal, private student chat groups. However, little is known about how co-regulatory learning interactions in these groups influence students' self-regulated learning, a process that is positively associated with academic success. The aim of this qualitative study was to explore the nature of the relationship between co- and self-regulated learning within the context of student-initiated online chat groups. Data were collected through semi-structured interviews and analysed utilising a reflexive thematic analysis framework. Five overarching themes were identified in the data: (a) flexible peer connection, (b) knowledge sharing, (c) monitoring progress, (d) support and reassurance, and (e) motivation. The findings suggest that students regularly engage in co-regulatory learning interactions with their peers, motivated by convenient, flexible access to timely support. Group chats were perceived as valuable environments where social interactions helped students achieve their individual learning goals. The novel results of this study help refine and advance theoretical understandings of co-regulated learning and its relationship with self-regulated learning. They offer initial insight into the mechanisms by which co-regulated learning may facilitate the emergence of self-regulated learning and highlight the important role social context plays in shaping regulatory learning interactions. The findings suggest productive areas for further research to corroborate, expand and quantify these exploratory insights.

高等教育环境中的同伴互动经常发生在非正式的、私人的学生聊天群中。然而，关于这些群体中的共同调节学习互动如何影响学生的自我调节学习，这一过程与学业成功呈正相关，我们知之甚少。本质性研究的目的是探讨在学生发起的在线聊天群背景下，共同学习和自我调节学习之间关系的本质。通过半结构化访谈收集数据，并利用反身性主题分析框架进行分析。数据中确定了五个总体主题：(a)灵活的同伴联系，(b)知识共享，(c)监测进展，(d)支持和保证，以及(e)激励。研究结果表明，学生经常与同龄人进行共同监管学习互动，以方便、灵活地获得及时的支持。群聊被认为是社会互动帮助学生实现个人学习目标的有价值的环境。本研究的新结果有助于完善和推进对共同调节学习及其与自我调节学习关系的理论理解。他们对共同调节学习促进自我调节学习的机制提供了初步的见解，并强调了社会环境在形成调节学习互动中的重要作用。这些发现为进一步的研究提供了富有成效的领域，以证实、扩展和量化这些探索性的见解。

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引用次数: 0

The effects of AI-enhanced adaptive socially shared regulation strategies on science pre-service teachers' instructional design skills and socially shared regulation in CSCL 人工智能增强的适应性社会共享调节策略对CSCL科学职前教师教学设计技能和社会共享调节的影响

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2026-04-01 Epub Date: 2025-12-16 DOI: 10.1016/j.iheduc.2025.101072

Jun-Qi Wu , Meng-meng Zhang , Fei-yan Wu , Jun Huang , Ya-juan Han , Yu Liu

Socially shared regulation of learning(SSRL) is closely related to the quality of learning. However, it is difficult for SSRL in CSCL to occur autonomously, as it often requires the support of certain strategies and tools. This study aimed to introduce Artificial intelligence(AI)-Enhanced Group Awareness tools(GATs) and Adaptive Prompts(APs) as adaptive SSRL strategies into CSCL.In this study, a 2 × 2 quasi-experiment was conducted with 64 science pre-service teachers, equally divided into three experimental groups and one control group. The results found that: (1)The combination of the two strategies was able to comprehensively affect the SSRL process. (2) In terms of improving the level of SSRL and instructional design skills, the combination of the two strategies was more effective than AI-Enhanced GATs on their own, which was more effective than AI-enhanced APs on their own.

社会共享学习调节（social shared regulation of learning， SSRL）与学习质量密切相关。然而，CSCL中的SSRL很难自主发生，因为它通常需要某些策略和工具的支持。本研究旨在将人工智能(AI)-增强群体意识工具（GATs）和自适应提示（APs）作为自适应SSRL策略引入CSCL。本研究采用2 × 2准实验方法，将64名科学职前教师平均分为3个实验组和1个对照组。结果发现：(1)两种策略的结合能够全面地影响SSRL过程。(2)在提高SSRL水平和教学设计技能方面，两种策略的组合比单独使用ai增强型GATs更有效，而单独使用ai增强型GATs比单独使用ai增强型ap更有效。

引用次数: 0

Students' perceptions of metaverse-enhanced self-regulated learning: An AI virtual assistant can be key 学生对超空间增强自我调节学习的看法：人工智能虚拟助手可能是关键

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2026-04-01 Epub Date: 2025-11-27 DOI: 10.1016/j.iheduc.2025.101060

Nguyen Thi Huyen , Nguyen Yen Chi , Pham Xuan Lam , Le Hieu Hoc

In the evolving landscape of higher education, metaverse is emerging as a transformative force. This study explores the experiences of 388 university students across two “Soft skills” courses, one using a traditional blended learning model and the other integrating the metaverse platform GatherTown. Results indicate that GatherTown was associated with better Time Management and Task Strategies and provided contextual features that interacted with learners' self-regulated learning to support engagement, rather than directly improving test performance. Qualitative feedback emphasized peer collaboration as a key benefit of the metaverse environment and outlined students' expectations for intuitive, user-friendly AI virtual agents capable of providing personalized feedback and adaptive support. Together, these findings highlight the potential of metaverse platforms in higher education and offer initial insights into designing effective AI agents that effectively enhance self-regulated learning.

在不断发展的高等教育格局中，虚拟世界正在成为一股变革力量。本研究探讨了388名大学生在两门“软技能”课程上的经验，一门课程使用传统的混合学习模式，另一门课程集成了虚拟世界平台GatherTown。结果表明，GatherTown与更好的时间管理和任务策略相关，并提供与学习者自我调节学习互动的上下文功能，以支持参与度，而不是直接提高测试成绩。定性反馈强调同伴协作是虚拟环境的一个关键优势，并概述了学生对能够提供个性化反馈和自适应支持的直观、用户友好的人工智能虚拟代理的期望。总之，这些发现突出了虚拟世界平台在高等教育中的潜力，并为设计有效增强自我调节学习的有效人工智能代理提供了初步见解。

引用次数: 0

Exploring AI-driven learning assistance in Chinese higher education: A multidisciplinary and regional analysis of professional coursework 探索中国高等教育中人工智能驱动的学习辅助：专业课程的多学科和区域分析

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2026-04-01 Epub Date: 2026-02-03 DOI: 10.1016/j.iheduc.2026.101075

Yicheng Sun , Hanbo Yang , Hi Kuen Yu , Richard Suen

Recent advances in generative AI (GenAI) technologies have reshaped student learning in higher education. However, most studies treat AI usage as a generalized construct, overlooking differences across various disciplines, educational levels, and regions. This gap is particularly relevant in China, where regional disparities in access and readiness may influence student engagement with AI tools. This study examines how university students in China perceive, utilize, and are influenced by generative AI tools across various disciplines and regions, while also assessing the accuracy and disciplinary relevance of AI-generated responses in professional coursework. The study includes four mixed-methods investigations: a national survey of students’ AI usage patterns and attitudes; an evaluation of ChatGPT’s responses to subject-specific questions based on semantic similarity and expert scoring; behavioral clustering and analysis of one-month AI usage logs to identify distinct user profiles; and an exploration of AI tools’ impact on academic performance and long-term outcomes. Findings show significant variation in AI engagement across disciplines and regions. Students in economically developed areas report higher usage and broader application, while those in central and western regions have more limited access. Students’ academic performance can be improved to varying degrees through AI-driven learning, with engineering and natural sciences students primarily using AI for technical tasks, while humanities and arts students employ it for linguistic and creative support. Evaluation of ChatGPT’s responses reveals moderate accuracy, with disciplinary variations. Despite generally positive attitudes, both students and experts express concerns about content reliability, over-reliance on AI, and its potential to hinder independent thinking and creativity.

生成人工智能（GenAI）技术的最新进展重塑了高等教育中学生的学习方式。然而，大多数研究将人工智能的使用视为一个广义的结构，忽视了不同学科、教育水平和地区之间的差异。这一差距在中国尤为重要，中国在获取和准备方面的地区差异可能会影响学生对人工智能工具的参与。本研究考察了中国大学生如何感知、利用不同学科和地区的生成式人工智能工具，并受其影响，同时还评估了专业课程中人工智能生成反应的准确性和学科相关性。该研究包括四项混合方法调查：一项关于学生人工智能使用模式和态度的全国调查；基于语义相似度和专家评分对ChatGPT对特定主题问题的回答进行评估；行为聚类和分析一个月的人工智能使用日志，以识别不同的用户配置；探索人工智能工具对学习成绩和长期成果的影响。研究结果显示，不同学科和地区的人工智能参与度存在显著差异。经济发达地区的学生使用率更高，应用范围更广，而中西部地区的学生使用率更有限。通过人工智能驱动的学习，学生的学习成绩可以在不同程度上得到提高，工程和自然科学专业的学生主要将人工智能用于技术任务，而人文和艺术专业的学生则将其用于语言和创意支持。对ChatGPT回答的评估显示出适度的准确性，但存在学科差异。尽管普遍持积极态度，但学生和专家都对内容的可靠性、对人工智能的过度依赖以及它可能阻碍独立思考和创造力表示担忧。

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引用次数: 0

Do we worry about the use of artificial intelligence and plagiarism? Students' AI-giarism behaviour through the fraud triangle 我们担心人工智能的使用和剽窃吗？通过欺诈三角分析学生的人工智能捐赠行为

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2026-04-01 Epub Date: 2025-12-13 DOI: 10.1016/j.iheduc.2025.101071

Miraç Yücel Başer , Metin Kozak , İbrahim Halil Erdoğan

The combination of AI and plagiarism is an emerging issue following the coining of the term AI-giarism. However, there has been little research that investigated the factors that lead students to engage in AI-giarism. In response to this gap, the present study adopts the fraud triangle framework to examine students' intentions toward AI-giarism and identify the underlying factors contributing to it. Data were collected from 312 students enrolled in 25 universities and analyzed using structural equation modelling. The results indicate that AI capacity, Justification of plagiarism, unawareness of AI deception, and academic pressure increase AI-giarism behaviour among students. In contrast to previous research, the study found no significant relationship between AI-giarism and either lax enforcement or a lack of understanding of AI. By offering empirical insights into the antecedents of AI-giarism, the present study advances the current body of literature, which has been more conceptual or student perception-centric.

人工智能与剽窃的结合是继人工智能剽窃（AI-giarism）一词出现后出现的一个新问题。然而，很少有研究调查导致学生参与人工智能的因素。针对这一差距，本研究采用欺诈三角框架来考察学生对人工智能的意图，并确定其潜在因素。收集了来自25所大学的312名学生的数据，并使用结构方程模型进行了分析。研究结果表明，人工智能的能力、剽窃的正当性、对人工智能欺骗的不了解以及学术压力增加了学生的人工智能剽窃行为。与之前的研究相反，该研究发现，人工智能滥用与执法不严或对人工智能缺乏了解之间没有显著关系。通过对人工智能主义的前因后果提供实证见解，本研究推动了目前更多以概念或学生感知为中心的文献。

引用次数: 0

Can theory-driven learning analytics dashboard enhance human-AI collaboration in writing learning? Insights from an empirical experiment 理论驱动的学习分析仪表板能否增强人类与人工智能在写作学习中的协作？来自经验实验的见解

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2026-01-01 Epub Date: 2025-09-20 DOI: 10.1016/j.iheduc.2025.101054

Angxuan Chen , Jingjing Lian , Xinran Kuang , Jiyou Jia

The integration of Generative AI (GenAI) into education has raised concerns about over-reliance and superficial learning, particularly in writing tasks in higher education. This study explores whether a theory-driven learning analytics dashboard (LAD) can enhance human-AI collaboration in the academic writing task by improving writing knowledge gains, fostering self-regulated learning (SRL) skills and shaping different human-AI dialogue characteristics. Grounded in Zimmerman's SRL framework, the LAD provided real-time feedback on learners' goal-setting, writing processes and reflection, while monitoring the quality of learner-AI interactions. A quasi-experiment was conducted involving 52 postgraduate students in a human-AI collaborative writing task. The students were divided into an experimental group (EG) that used the LAD and a control group (CG) that did not. Pre- and post- knowledge tests, questionnaires measuring SRL and cognitive load, and students' dialogue data with GenAI were collected and analyzed. Results showed that the EG achieved significantly higher writing knowledge gains and improved SRL skills, particularly in self-efficacy and cognitive strategies. However, the EG also reported increased test anxiety and cognitive load, possibly due to heightened metacognitive awareness. Epistemic Network Analysis revealed that the EG engaged in more reflective, evaluative interactions with GenAI, while the CG focused on more transactional and information-seeking exchanges. These findings contribute to the growing body of literature on the educational use of GenAI and highlight the importance of designing interventions that complement GenAI tools, ensuring that technology enhances rather than undermines the learning process.

将生成式人工智能（GenAI）整合到教育中，引起了人们对过度依赖和肤浅学习的担忧，特别是在高等教育的写作任务中。本研究探讨了理论驱动的学习分析仪表板（LAD）是否可以通过提高写作知识获取、培养自我调节学习（SRL）技能和塑造不同的人类与人工智能对话特征，来增强学术写作任务中的人类与人工智能协作。基于Zimmerman的SRL框架，LAD对学习者的目标设定、写作过程和反思提供实时反馈，同时监控学习者与人工智能互动的质量。我们对52名研究生进行了一项准实验，让他们参与人类与人工智能的协作写作任务。学生被分为实验组（EG）使用LAD和对照组（CG）不使用。收集和分析了知识前后测试、测量SRL和认知负荷的问卷以及学生与GenAI的对话数据。结果表明，EG显著提高了学生的写作知识增益，提高了学生的SRL技能，尤其是在自我效能感和认知策略方面。然而，EG也报告了考试焦虑和认知负荷的增加，可能是由于元认知意识的增强。认知网络分析显示，EG与GenAI进行更多的反思性、评价性互动，而CG则侧重于更多的交易性和信息寻求性交流。这些发现促进了越来越多关于GenAI在教育中的应用的文献，并强调了设计干预措施以补充GenAI工具的重要性，确保技术增强而不是破坏学习过程。

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引用次数: 0

Learning presence in the revised community of inquiry framework: A systematic review of empirical research (2010–2024) 修订后的探究共同体框架中的学习存在：实证研究的系统回顾（2010-2024）

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2026-01-01 Epub Date: 2025-11-04 DOI: 10.1016/j.iheduc.2025.101058

Chi-Cheng Chang , Wan-Hsuan Yen , Shu-Chuan Liu , Cheng-Te Kuo

The Community of Inquiry (CoI) framework explains effective online and blended learning through teaching, social, and cognitive presence. To better capture learner agency, learning presence (LP) has been proposed as a fourth dimension. This study presents the first systematic review focused exclusively on LP within the Revised CoI (RCoI), synthesizing 26 quantitative studies published between 2010 and 2024. Following PRISMA guidelines, we analyzed bibliometric trends, conceptual definitions, measurement approaches, and LP's role in relation to other presences and outcomes. The findings indicate that LP is reliably measurable and often mediates the effects of teaching and social presence on cognitive presence. LP is positively associated with learner engagement, satisfaction, and achievement across many contexts, though the strength of these relationships varies. Cross-cultural validations demonstrate broad applicability but also reveal variations requiring further refinement. Overall, LP enhances the explanatory power of the CoI framework by foregrounding learner self-regulation and motivation, though its theoretical boundaries remain contested. This review provides a foundation for advancing theory, measurement, and design practices related to learner agency in online learning.

探究社区（CoI）框架通过教学、社交和认知存在解释了有效的在线和混合学习。为了更好地捕捉学习者代理，学习在场（LP）被提出作为第四个维度。本研究首次对修订后的CoI （RCoI）中的LP进行了系统评价，综合了2010年至2024年间发表的26项定量研究。根据PRISMA的指导方针，我们分析了文献计量学趋势、概念定义、测量方法以及LP与其他存在和结果的关系。研究结果表明，LP是可靠的可测量的，并且经常中介教学和社会在场对认知在场的影响。在许多情况下，LP与学习者的投入、满意度和成就呈正相关，尽管这些关系的强度各不相同。跨文化验证展示了广泛的适用性，但也揭示了需要进一步改进的差异。总体而言，LP通过强调学习者自我调节和动机，增强了CoI框架的解释力，尽管其理论界限仍存在争议。本文综述为在线学习中学习者代理相关的理论、测量和设计实践提供了基础。

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引用次数: 0

An active instructional approach based on the SAMR framework: Integrating AIGC into undergraduate freshmen learning 基于SAMR框架的主动教学方法：将AIGC融入本科新生学习

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2026-01-01 Epub Date: 2025-10-18 DOI: 10.1016/j.iheduc.2025.101056

Juan Wu , Jingwen Pan , Yaoyuan Zhou , Mengyu Liu , Yanling Li , Ronghuai Huang

The question of how to use artificial intelligence generated content (AIGC) properly to enhance learning among college students is a key concern for contemporary educators. Although previous studies have discussed the influence of AIGC on college teaching and student learning and its functions in this context, there remains a lack of discussions regarding ways of guiding students' use of AIGC and studies on the specific topic of helping college freshmen use AIGC properly. Based on the substitution, augmentation, modification and redefinition (SAMR) model, this study develops a progressively active teaching framework that integrates AIGC into learning. This framework is used to design learning activities for general education courses targeting freshmen. This exploratory study was conducted in the context of a 16-week course. During the teaching process, AIGC interaction log data and AIGC experience records were collected from students, following which data processing was conducted using the discourse analysis, quantitative statistical analysis, and epistemic network analysis (ENA) methods to obtain the ultimate results of this study: (1) A combination of active teaching with the SAMR model can improve the quality of interactions between students and AIGC; (2) teaching strategies rooted in active learning can enhance students' ability to use AIGC; and (3) improvements in students' technical skills strengthen the quality of their interactions with AIGC. This study makes novel contributions to the literature on active learning strategies for teachers and curriculum designers, and it offers practical guidance for educational practitioners and college students regarding the integration of AI technology into both teaching and learning.

如何正确地利用人工智能生成内容（AIGC）来促进大学生的学习，是当代教育工作者关注的一个关键问题。虽然已有研究探讨了AIGC对大学教学和学生学习的影响及其在这一背景下的作用，但对于如何引导学生使用AIGC，以及如何帮助大学新生正确使用AIGC这一具体课题的研究仍然缺乏。本研究以替代、增强、修正和重新定义（SAMR）模式为基础，开发了一个将AIGC融入学习的渐进式主动教学框架。该框架用于设计面向新生的通识教育课程的学习活动。这项探索性研究是在为期16周的课程背景下进行的。在教学过程中，收集学生的AIGC互动日志数据和AIGC体验记录，运用语篇分析、定量统计分析和认知网络分析（ENA）等方法对数据进行处理，得到本研究的最终结果：(1)主动教学与SAMR模型相结合，可以提高学生与AIGC互动的质量；(2)基于主动学习的教学策略可以提高学生使用AIGC的能力；(3)学生技术技能的提高加强了他们与AIGC互动的质量。本研究为教师和课程设计者的主动学习策略文献做出了新颖的贡献，并为教育从业者和大学生提供了将人工智能技术融入教与学的实践指导。

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引用次数: 0

The impact of a telepresence robot on group conditions and student engagement: A mixed-method study in higher education 远程呈现机器人对群体条件和学生参与的影响：高等教育中的混合方法研究

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2026-01-01 Epub Date: 2025-10-17 DOI: 10.1016/j.iheduc.2025.101055

Mark Steins , Kars Mennens , Simon Beausaert , Dominik Mahr , Gaby Odekerken-Schröder , Alexandru Mariş , Frank Mathmann

Drawing on team learning theory, this paper examines whether and how a telepresence robot impacts group conditions and student engagement among graduate students collaborating within a hybrid classroom. Hybrid classrooms face challenges, such as an asymmetry of presence, compromising collaborative learning between remote and on-site students. A field experiment was conducted with 17 hybrid classrooms across two Master of Science courses. In the eight experimental groups, one remote student joined via a telepresence robot, while any additional remote students in the same group participated via the smart screen. In the nine control groups, all remote students participated solely via the smart screen. Analysis of short-term longitudinal survey data from 156 students indicated that students in experimental groups with a telepresence robot reported higher levels of social cohesion, psychological safety and group potency, especially in the early course stages. These group conditions positively influenced student engagement. Interviews with ten on-site students reveal that the telepresence robot enhanced remote students' presence through physical embodiment and fostered empowerment via autonomous mobility. This reduced presence asymmetry facilitated more natural interactions, reinforcing group conditions: social cohesion through interpersonal connections, psychological safety through reduced participation barriers, and group potency through increased knowledge sharing. The telepresence robot also fostered inclusive behavior among on-site students, driven by their reciprocation of remote students' engagement and recognition of them as valuable contributors to collaborative learning. These findings advance understanding of team learning and the role of telepresence robots in hybrid classroom settings, promoting more effective virtual inclusion for remote students.

利用团队学习理论，本文研究了远程呈现机器人是否以及如何影响混合教室中研究生合作的小组条件和学生参与度。混合教室面临着挑战，例如存在的不对称，影响远程和现场学生之间的协作学习。在两个理学硕士课程的17个混合教室中进行了实地实验。在八个实验组中，一个远程学生通过远程呈现机器人加入，而同一组的其他远程学生通过智能屏幕参与。在九个对照组中，所有远程学生都只通过智能屏幕参与。对156名学生的短期纵向调查数据的分析表明，使用远程呈现机器人的实验组的学生报告了更高水平的社会凝聚力、心理安全感和群体效力，特别是在课程的早期阶段。这些群体条件对学生的参与有积极影响。对10名现场学生的采访表明，远程呈现机器人通过物理体现增强了远程学生的存在感，并通过自主移动培养了赋权。这种减少的在场不对称促进了更多的自然互动，加强了群体条件：通过人际关系产生的社会凝聚力，通过减少参与障碍产生的心理安全感，以及通过增加知识共享产生的群体效力。远程呈现机器人还培养了现场学生的包容行为，通过他们对远程学生参与的回报和对他们作为协作学习的有价值贡献者的认可来驱动他们。这些发现促进了对团队学习和远程呈现机器人在混合教室环境中的作用的理解，促进了远程学生更有效的虚拟包容。

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引用次数: 0