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 : 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

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 : 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

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 : 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

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 : 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

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 : 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

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 : 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

Students' interaction patterns of online dialogic peer feedback from a perspective of community of inquiry: An Epistemic network analysis approach 探究共同体视角下的学生在线对话同伴反馈互动模式：认知网络分析方法

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-11-04 DOI: 10.1016/j.iheduc.2025.101059

Ying Zhan , Lingyun Huang , Shen Ba

Dialogic peer feedback has been regarded as a powerful approach to improving peer feedback quality and student learning. However, few studies have examined how students interact with each other when negotiating the meaning of peer feedback. To fill in this research gap, this study explored the dialogues between feedback givers and receivers, which were automatically recorded by an online platform in two assessment courses at a Hong Kong university. It used the framework of Community of Inquiry to code the collected dialogues and analyzed them through epistemic network analysis. The results showed a strong connection between triggering event and exploration in the aspect of cognitive presence and the significant involvement of social presence in these connections. Additionally, students' interaction patterns significantly varied by age, gender and feedback dispositions. The findings shed light on how to scaffold online dialogic peer feedback to maximize its learning effects.

对话式同伴反馈被认为是提高同伴反馈质量和学生学习的有力途径。然而，很少有研究调查学生在讨论同伴反馈的意义时是如何相互作用的。为了填补这一研究空白，本研究探索了反馈者和接受者之间的对话，这些对话由一个在线平台自动记录在香港一所大学的两门评估课程中。利用探究共同体的框架对收集到的对话进行编码，并通过认知网络分析对其进行分析。结果表明，触发事件与探索在认知在场方面存在较强的联系，而社会在场在这些联系中起着重要的作用。此外，学生的互动模式因年龄、性别和反馈倾向而有显著差异。研究结果揭示了如何构建在线对话同伴反馈，以最大限度地提高其学习效果。

引用次数: 0

Incorporating an LMS learning analytic into proactive advising: Validity and use in a randomized experiment 将LMS学习分析纳入前瞻性建议：在随机实验中的有效性和使用

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-10-22 DOI: 10.1016/j.iheduc.2025.101057

Matthew M. Rust , Benjamin A. Motz

This paper presents two studies examining the effectiveness of using learning analytics to inform targeted, proactive advising interventions aimed at improving student success. Study 1 validates a simple learning analytic as predictive of end-of-term outcomes and persistence. Results suggest that this analytic measure, based on students' activity in the learning management system (LMS), has predictive utility for identifying students who might benefit from a proactive advising intervention. In Study 2, we test whether an LMS analytic-informed proactive advising intervention improves end-of-term outcomes and persistence for a sample of pre-major college students. Students with low LMS activity were randomly assigned to receive enhanced, proactive advising messages, or business-as-usual advising. On average, treatment students earned nearly one-third of a grade point higher term GPAs, accumulated fewer grades of D,F, or W, and exhibited 79 % higher odds of persisting compared to the control group. These findings provide strong evidence for the effectiveness of proactive advising interventions, where advisors' efforts are targeted using learning analytics. By providing advisors with comprehensible insights, institutions might improve student outcomes and promote the use of data-informed interventions in academic advising.

本文介绍了两项研究，研究了使用学习分析来通知有针对性的、积极主动的建议干预措施的有效性，旨在提高学生的成功。研究1验证了一个简单的学习分析作为期末结果和持久性的预测。结果表明，这种基于学生在学习管理系统（LMS）中的活动的分析措施，对于识别可能从主动建议干预中受益的学生具有预测效用。在研究2中，我们测试了LMS分析知情的主动建议干预是否改善了大学预科学生的期末结果和持久性。LMS活动较低的学生被随机分配接收增强的、主动的建议信息，或照常工作的建议。平均而言，接受治疗的学生的学期平均绩点比对照组高出近三分之一，累积的D、F或W成绩更少，并且与对照组相比，坚持治疗的几率高出79%。这些发现为主动咨询干预的有效性提供了强有力的证据，其中顾问的努力是有针对性的，使用学习分析。通过向顾问提供可理解的见解，机构可以改善学生的成绩，并促进在学术建议中使用数据知情干预措施。

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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 : 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 : 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