Internet and Higher Education最新文献_第6页

Teaching via LLM-enhanced simulations: Authenticity and barriers to suspension of disbelief 通过法学硕士增强模拟教学：真实性和暂停怀疑的障碍

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-04-01 Epub Date: 2024-12-10 DOI: 10.1016/j.iheduc.2024.100990

Longwei Zheng , Fei Jiang , Xiaoqing Gu , Yuanyuan Li , Gong Wang , Haomin Zhang

As an innovative method in professional training, simulation-based learning (SBL) has been introduced into teacher education, providing pre-service teacher candidates with experiential learning opportunities. This study explores the efficacy of SBL using large language models (LLMs) to enhance teacher training, focusing on learners' suspension of disbelief (SoD). As a highly advanced form of generative artificial intelligence, LLMs possess robust capabilities in simulating human behavior, which can be harnessed to create simulated students for SBL in teacher training. This instrumental case study examines the experiences of 12 pre-service teachers who participated in a session featuring an LLM-enhanced simulation. The simulation facilitated naturalistic classroom interactions between the participants and simulated students. Our research aimed to understand how pre-service teachers perceive LLM-enhanced SBL, identify factors that influence SoD, and determine the authenticity barriers. Interview data were analyzed using various coding techniques and derived themes from these codes. The findings revealed that LLM-enhanced SBL provided a realistic and engaging environment, significantly benefiting teaching skill development and learning transfer. However, challenges such as lagging responses, weak comprehension of complex contexts, inconsistencies in simulated students' cognition, and incongruent feedback were noted. The primary contribution of this study lies in demonstrating the potential of using LLMs to replace human actors, though significant technical challenges remain. The study also indicates that enhancements in LLM fine-tuning and prompt engineering are needed to improve LLMs' understanding of classroom context and students' cognitive patterns.

本研究利用大型语言模型（large language models, LLMs）来探讨SBL对教师培训的效果，重点关注学习者的暂停怀疑（SoD）。作为一种高度先进的生成式人工智能，法学硕士具有强大的模拟人类行为的能力，可以在教师培训中为SBL创建模拟学生。本工具性案例研究考察了12名职前教师的经历，他们参加了一个以llm增强模拟为特色的会议。模拟促进了参与者与模拟学生之间的自然课堂互动。本研究旨在了解职前教师如何感知llm增强的SBL，识别影响SoD的因素，并确定真实性障碍。使用各种编码技术对访谈数据进行分析，并从这些编码中得出主题。研究结果表明，llm强化的SBL提供了一个真实的、引人入胜的环境，显著有利于教学技能的发展和学习迁移。然而，我们也注意到一些挑战，如反应滞后、对复杂情境的理解不强、模拟学生的认知不一致以及反馈不一致。本研究的主要贡献在于展示了使用法学硕士替代人类参与者的潜力，尽管仍存在重大的技术挑战。该研究还表明，法学硕士需要加强微调和提示工程，以提高法学硕士对课堂情境和学生认知模式的理解。

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引用次数: 0

Investigating perceived fairness of AI prediction system for math learning: A mixed-methods study with college students 调查人工智能预测系统对数学学习的感知公平性：一项针对大学生的混合方法研究

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-04-01 Epub Date: 2025-02-14 DOI: 10.1016/j.iheduc.2025.101000

Yukyeong Song , Chenglu Li , Wanli Xing , Bailing Lyu , Wangda Zhu

Entities such as governments and universities have begun using AI for algorithmic decision-making that impacts people's lives. Despite their known benefits, such as efficiency, the public has raised concerns about the fairness of AI's decision-making. Here, the concept of perceived fairness, defined as people's emotional, cognitive, and behavioral responses toward the justice of the AI system, has been widely discussed as one of the important factors in determining technology acceptance. In the field of AI in education, students are among the biggest stakeholders; thus, it is important to consider students' perceived fairness of AI decision-making systems to gauge technology acceptance. This study adopted an explanatory sequential mixed-method research design involving 428 college students to investigate the factors that impact students' perceived fairness of AI's pass-or-fail prediction decisions in the context of math learning and suggest ways to improve the perceived fairness based on students' voices. The findings suggest that students who received a favorable prediction outcome (i.e., pass), who were presented with a system that had a lower algorithmic bias and higher transparency, who major(ed) in STEM (vs. non-STEM), who have higher math anxiety, and who received the outcome that matches their math knowledge level (i.e., accurate) tend to report a higher level of perceived fairness for the AI's prediction decisions. Interesting interaction effects were also found regarding decision-making, students' math anxiety and knowledge, and the outcome's favorability on students' perceived fairness. Qualitative thematic analysis revealed students' strong desire for transparency with guidance, explainability, and interactive communication with the AI system, as well as constructive feedback and emotional support. This study contributes to the development of a justice theory in the era of AI and suggests practical design implications for AI systems and communication strategies with AI systems in education.

政府和大学等实体已经开始使用人工智能进行影响人们生活的算法决策。尽管人工智能有效率等众所周知的好处，但公众对人工智能决策的公正性表示担忧。在这里，感知公平的概念被定义为人们对人工智能系统的公平性的情感、认知和行为反应，作为决定技术接受度的重要因素之一被广泛讨论。在人工智能教育领域，学生是最大的利益相关者之一；因此，重要的是要考虑学生对人工智能决策系统的公平感，以衡量技术的接受程度。本研究采用解释性顺序混合方法研究设计，涉及428名大学生，探讨影响学生在数学学习情境下对AI预测成败决策感知公平性的因素，并根据学生的声音提出提高感知公平性的方法。研究结果表明，获得有利预测结果（即通过）的学生，获得具有较低算法偏差和较高透明度的系统的学生，主修STEM（相对于非STEM）的学生，具有较高的数学焦虑的学生，以及获得与其数学知识水平相匹配的结果（即准确）的学生倾向于报告更高水平的人工智能预测决策的感知公平性。在决策、学生的数学焦虑和知识以及结果对学生感知公平的好感度方面也发现了有趣的交互效应。定性专题分析显示，学生对透明的指导、可解释性、与AI系统的互动交流，以及建设性的反馈和情感支持有着强烈的渴望。这项研究有助于人工智能时代正义理论的发展，并为人工智能系统和教育中与人工智能系统的沟通策略提供了实际的设计启示。

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引用次数: 0

The paradox of self-efficacy and technological dependence: Unraveling generative AI's impact on university students' task completion 自我效能与技术依赖的悖论：解读生成式人工智能对大学生完成任务的影响

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-04-01 Epub Date: 2024-11-14 DOI: 10.1016/j.iheduc.2024.100978

Ling Zhang , Junzhou Xu

In the era of proliferating artificial intelligence (AI) technology, generative AI is reshaping educational landscapes, prompting a critical examination of its influence on students' learning processes and their self-efficacy amid concerns over growing technological dependence. This study investigates the nuanced relationship between generative AI use and university students' self-efficacy and technological dependence, illuminating the underlying paradoxes and implications for inclusive education practices. Through a survey of 348 university students, with 200 valid responses analyzed, we uncover the direct and indirect impacts of generative AI usage frequency on AI dependence. Our findings reveal a paradoxical effect: enhanced AI usage significantly amplifies students' confidence and efficiency in learning, yet simultaneously intensifies their dependence on AI. This dual impact both supports and complicates the incorporation of AI technologies into educational settings, underscoring the need for a balanced approach to leveraging AI in teaching and learning. Our study underscores the critical importance of a nuanced understanding of AI's role in education. It highlights the necessity of crafting an educational landscape where technology augments learning processes without compromising independent learning capabilities. By navigating the complex interplay between technological advancement and educational inclusivity, our findings guide the development of AI-assisted learning environments that are not only effective but also equitable and accessible.

在人工智能（AI）技术激增的时代，生成式人工智能正在重塑教育景观，促使人们对其对学生学习过程和自我效能的影响进行批判性审视，同时对日益增长的技术依赖性表示担忧。本研究调查了生成式人工智能的使用与大学生的自我效能感和技术依赖性之间的微妙关系，揭示了潜在的悖论和对全纳教育实践的影响。通过对 348 名大学生的调查（分析了 200 份有效回复），我们揭示了生成式人工智能使用频率对人工智能依赖性的直接和间接影响。我们的研究结果揭示了一种矛盾效应：人工智能使用率的提高显著增强了学生的学习信心和效率，但同时也加剧了他们对人工智能的依赖。这种双重影响既支持了将人工智能技术融入教育环境，也使之变得更加复杂，强调了在教学中利用人工智能需要采取一种平衡的方法。我们的研究强调，细致入微地理解人工智能在教育中的作用至关重要。它强调了精心打造教育环境的必要性，在这样的环境中，技术既能增强学习过程，又不会损害自主学习能力。通过驾驭技术进步与教育包容性之间复杂的相互作用，我们的研究结果为人工智能辅助学习环境的发展提供了指导，这种环境不仅有效，而且公平、无障碍。

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引用次数: 0

Investigating the relationship between collaborative design, online learning and educator integrated professional development 探讨协同设计、在线学习与教育者综合专业发展的关系

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-04-01 Epub Date: 2025-02-01 DOI: 10.1016/j.iheduc.2025.100997

Vasiliki Papageorgiou , Edgar Meyer , Iro Ntonia

The global expansion of university-level online programmes has heightened the demand for educators to design and facilitate meaningful learning experiences. However, many educators lack the necessary expertise and experience, highlighting the urgency for contextually relevant professional development opportunities. This paper investigates the collaborative design processes of novice online educators and digital learning professionals when designing online learning and the conditions promoting educators’ development. A multiple case study methodology was employed, recruiting six interdisciplinary design teams from five UK-based universities. Data collection involved two phases of semi-structured interviews and design meeting observations. Findings evidence three key processes: (1) framing the design inquiry, (2) sharing and integrating insider knowledge and expertise, and (3) anticipating the future. Emotional support, skilled facilitation and valuing diverse perspectives acted as enabling conditions. We propose network-enabled and boundary-crossing capabilities as novel dimensions of educators’ development. This paper emphasises the need for purposeful collaborative design initiatives for integrated professional development.

大学级在线课程的全球扩张，提高了对教育工作者设计和促进有意义的学习体验的需求。然而，许多教育工作者缺乏必要的专业知识和经验，这突出了与环境相关的专业发展机会的紧迫性。本文考察了在线教育新手与数字学习专业人员在设计在线学习时的协同设计过程，以及促进教育者发展的条件。采用了多案例研究方法，招募了来自英国五所大学的六个跨学科设计团队。数据收集包括半结构化访谈和设计会议观察两个阶段。研究结果证明了三个关键过程：(1)构建设计探究；(2)分享和整合内部知识和专业知识；(3)预测未来。情感支持、熟练的促进和重视不同的观点是有利条件。我们提出网络能力和跨界能力是教育工作者发展的新维度。本文强调需要有目的的协作设计计划，以实现综合专业发展。

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引用次数: 0

Scaling up online professional development through institution-initiated blended learning programs in higher education 通过高等教育机构发起的混合学习项目，扩大在线职业发展

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-04-01 Epub Date: 2024-11-28 DOI: 10.1016/j.iheduc.2024.100988

Jingjing Zhang , Yicheng Huang , Fati Wu , Wei Kan , Xudong Zhu

The impact of Covid-19 has significantly accelerated the digital transformation in higher education worldwide. This study investigates how digital transformation changes the instructional design and implementation of large-scale blended learning programs for better learner experiences. It emphasizes the significance of diverse stakeholders' engagement in institution-initiated blended programs to promote effective online professional development. Utilizing Research-Practice Partnerships (RPPs), this study adopts a case study approach, employing participatory observation, interviews, log data, and online discourses to analyze how interactions across various sectors and levels affect the transformation to blended programs and the professional development of the involved instructors. The findings reveal that the institution-initiated blended learning approach, resembling an apprenticeship model, effectively integrates teaching practice with online professional development. An in-depth analysis of two university courses shows that key events and task-oriented approaches significantly influence instructors' online engagement, enhancing their digital literacy. Teacher Handbooks, driven by real learner data, guide teaching practices effectively, while collaborative research meetings greatly enhance the use of data analytics in teaching. Although this top-down approach presents challenges, this study demonstrates that instructors can innovatively tackle these issues. Institutional support is more likely to foster collaborative learning communities across various sectors and hierarchies. This study contributes to the field by introducing a pioneering framework in the institution's innovative educational reform, creating online professional development communities for faculty members of all ages, and enabling them to quickly and easily adapt to the digital transformation of higher education.

2019冠状病毒病的影响大大加快了全球高等教育的数字化转型。本研究探讨数字化转型如何改变大规模混合学习计划的教学设计和实施，以获得更好的学习者体验。它强调了不同利益相关者参与机构发起的混合项目以促进有效的在线专业发展的重要性。本研究利用研究-实践伙伴关系（RPPs），采用案例研究方法，采用参与式观察、访谈、日志数据和在线话语来分析不同部门和层次的互动如何影响向混合课程的转变以及参与教师的专业发展。研究结果显示，机构发起的混合学习方法，类似于学徒模式，有效地将教学实践与在线专业发展相结合。对两所大学课程的深入分析表明，关键事件和以任务为导向的方法显著影响了教师的在线参与度，提高了他们的数字素养。教师手册由真实的学习者数据驱动，有效地指导教学实践，而合作研究会议大大提高了数据分析在教学中的应用。尽管这种自上而下的方法存在挑战，但本研究表明，教师可以创新地解决这些问题。机构支持更有可能促进跨不同部门和层次的协作学习社区。该研究通过在该机构的创新教育改革中引入开创性框架，为所有年龄段的教师创建在线专业发展社区，使他们能够快速轻松地适应高等教育的数字化转型，从而为该领域做出了贡献。

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引用次数: 0

Awareness, perception, and adoption of ChatGPT in African HEIs: A multi-dimensional analysis 非洲高校对ChatGPT的认识、认知和采用：一个多维分析

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-04-01 Epub Date: 2025-02-06 DOI: 10.1016/j.iheduc.2025.100999

Olugbenga Ayo Ojubanire , Sunday Adewale Olaleye , Mohamed Amine Marhraoui , Mesiet William Kamihanda , Oluwatosin Ifedayo Oke , Oluwaseun Abigail Ojubanire

The adoption of artificial intelligence (AI), particularly Large Language Models like ChatGPT, has gained significant traction in the education sector, offering numerous benefits for students and educators alike. This study focuses on the triggers and drivers of ChatGPT adoption within African higher education institutions (HEIs). Utilizing the Technology Acceptance Model (TAM) and the Diffusion of Innovations Theory (DIT) as theoretical frameworks, the research proposes a conceptual model to explore the impact of ChatGPT on perceived usefulness and awareness. Additionally, the study examines how ChatGPT awareness influences perceived usefulness through the intermediary role of ChatGPT knowledge. A quantitative methodology was employed, with data collected from higher education institutions in Morocco, Nigeria, and Tanzania. The Partial Least Squares Structural Equation Modelling (PLS-SEM) technique was used to analyze the data. The findings emphasize the positive impact of triggers on both the perceived usefulness and awareness of ChatGPT. Furthermore, the study highlights the significant effect of ChatGPT awareness on perceived usefulness, mediated by knowledge. The research contributes to the existing literature by providing empirical insights into the adoption of ChatGPT in African HEIs and underscores the importance of awareness and knowledge in enhancing perceived usefulness. The study also offers practical recommendations for educators and policymakers to facilitate the effective integration of AI tools in education, considering regional and demographic variations.

人工智能（AI）的采用，特别是像ChatGPT这样的大型语言模型，在教育领域获得了巨大的吸引力，为学生和教育工作者带来了许多好处。本研究的重点是非洲高等教育机构（HEIs）采用ChatGPT的触发因素和驱动因素。本研究以技术接受模型（TAM）和创新扩散理论（DIT）为理论框架，提出了一个概念模型来探讨ChatGPT对感知有用性和认知的影响。此外，本研究考察了ChatGPT意识如何通过ChatGPT知识的中介作用影响感知有用性。采用定量方法，从摩洛哥、尼日利亚和坦桑尼亚的高等教育机构收集数据。采用偏最小二乘结构方程建模（PLS-SEM）技术对数据进行分析。研究结果强调了触发器对ChatGPT感知有用性和意识的积极影响。此外，该研究强调了ChatGPT意识对感知有用性的显著影响，这是由知识介导的。该研究通过提供非洲高等教育机构采用ChatGPT的经验见解，对现有文献做出了贡献，并强调了意识和知识在增强感知有用性方面的重要性。该研究还为教育工作者和政策制定者提供了实用建议，以促进人工智能工具在教育中的有效整合，同时考虑到地区和人口变化。

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引用次数: 0

Humanizing college online instruction: The effects of professional development on faculty perceptions and instructional practices 人性化的大学在线教学：专业发展对教师观念和教学实践的影响

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-04-01 Epub Date: 2025-02-05 DOI: 10.1016/j.iheduc.2025.100998

Di Xu , Yujia Liu , Zhiling Meng Shea , Kimberly Vincent-Layton , Jeffrey White , Michelle Pacansky-Brock

The rapid growth of online learning has raised concerns about quality and equity in virtual education. This study introduces the Humanizing Online STEM Academy, a six-week professional development program designed specifically to promote humanizing and inclusive teaching within STEM college online courses. We document in detail the Academy's design and instructional approach, and examine its impact on the perceptions and instructional practices of 79 faculty participants from eight California institutions, using pre- and post-Academy surveys and in-depth interviews. Results indicate that participants found the humanizing elements covered in the Academy highly beneficial for building trust with students. Post-Academy, instructors reported increased confidence in online teaching, stronger belief in their ability to address equity gaps, and enhanced support for diverse student backgrounds. Their instructional approaches also evolved to prioritize interpersonal interactions and individual student needs. Interviews revealed heightened awareness of student diversity and intentional efforts to accommodate it.

在线学习的快速发展引发了人们对虚拟教育质量和公平性的担忧。本研究介绍了人性化在线STEM学院，这是一个为期六周的专业发展计划，专门用于促进STEM大学在线课程中的人性化和包容性教学。我们详细记录了学院的设计和教学方法，并通过学院前和学院后的调查和深度访谈，研究了它对来自加州八所院校的79名教师参与者的看法和教学实践的影响。结果显示，参与者发现学院所涵盖的人性化元素对与学生建立信任非常有益。学院毕业后，教师们报告说，他们对在线教学的信心增强了，对自己解决公平差距的能力更有信心，并加强了对不同学生背景的支持。他们的教学方法也发展到优先考虑人际交往和学生的个人需求。采访显示，人们对学生多样性的认识有所提高，并有意为之努力。

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引用次数: 0

Transforming online learning research: Leveraging GPT large language models for automated content analysis of cognitive presence 转变在线学习研究：利用GPT大型语言模型对认知存在进行自动内容分析

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-04-01 Epub Date: 2025-02-12 DOI: 10.1016/j.iheduc.2025.101001

Daniela Castellanos-Reyes , Larisa Olesova , Ayesha Sadaf

The last two decades of online learning research vastly flourished by examining discussion board text data through content analysis based on constructs like cognitive presence (CP) with the Practical Inquiry Model (PIM). The PIM sets a footprint for how cognitive development unfolds in collaborative inquiry in online learning experiences. Ironically, content analysis is a resource-intensive endeavor in terms of time and expertise, making researchers look for ways to automate text classification through ensemble machine-learning algorithms. We leveraged large language models (LLMs) through OpenAI's Generative Pre-Trained Transformer (GPT) models in the public API to automate the content analysis of students' text data based on PIM indicators and assess the reliability and efficiency of automated content analysis compared to human analysis. Using the seven steps of the Large Language Model Content Analysis (LACA) approach, we proposed an AI-adapted CP codebook leveraging prompt engineering techniques (i.e., role, chain-of-thought, one-shot, few-shot) for the automated content analysis of CP. We found that a fine-tuned model with a one-shot prompt achieved moderate interrater reliability with researchers. The models were more reliable when classifying students' discussion board text in the Integration phase of the PIM. A cost comparison showed an obvious cost advantage of LACA approaches in online learning research in terms of efficiency. Nevertheless, practitioners still need considerable data literacy skills to deploy LACA at a scale. We offer theoretical suggestions for simplifying the CP codebook and improving the IRR with LLM. Implications for practice are discussed, and future research that includes instructional advice is recommended.

过去二十年来，在线学习研究通过基于认知存在（CP）和实践探究模型（PIM）等结构的内容分析来检查讨论板文本数据，从而取得了巨大的繁荣。PIM为认知发展如何在在线学习体验的协作探究中展开奠定了基础。具有讽刺意味的是，就时间和专业知识而言，内容分析是一项资源密集型的工作，这使得研究人员寻找通过集成机器学习算法自动进行文本分类的方法。我们通过OpenAI的公共API中的生成预训练转换器（GPT）模型利用大型语言模型（llm），基于PIM指标对学生文本数据进行自动化内容分析，并与人工分析相比，评估自动化内容分析的可靠性和效率。使用大型语言模型内容分析（LACA）方法的七个步骤，我们提出了一个人工智能适应的CP代码本，利用提示工程技术（即角色、思维链、一次、几次）进行CP的自动内容分析。我们发现，一个带有一次提示的微调模型与研究人员实现了适度的互解释器可靠性。在PIM整合阶段，对学生讨论版文本进行分类时，模型的可靠性更高。成本比较表明，LACA方法在效率方面具有明显的成本优势。然而，从业者仍然需要相当的数据素养技能来大规模部署LACA。为简化CP码本和利用LLM提高IRR提供了理论建议。讨论了对实践的影响，并建议未来的研究包括教学建议。

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引用次数: 0

Experiential learning through simulations in fully online asynchronous courses: Exploring the role of self-debriefing 在全在线异步课程中通过模拟进行体验式学习：探索自我汇报的作用

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-04-01 Epub Date: 2024-10-26 DOI: 10.1016/j.iheduc.2024.100976

David De Jong , Sara Dexter

Online preparation of professionals is increasing in higher education, which in educational leadership preparation programs raises the need for a means to provide authentic simulations of leadership experiences and help aspirants learn from them. This study presents a content analysis of 826 responses from 59 different school leadership students who, following each of the 14 simulations they experienced, wrote one response in an asynchronous format as a form of self-debriefing. The five themes identified map to the four phases of the experiential learning cycle (Kolb, 1984), suggesting that virtual opportunities to practice leadership in simulations may serve as grounding experiences after which developing professionals can reflect upon, integrate with new understandings, and try out alternate approaches. This study demonstrates the purpose of self-debriefing following online simulations of relevant professional experiences and how self-debriefing may propel students through the full experiential learning cycle, offering a valuable avenue for professional development in higher education.

在高等教育中，专业人士的在线准备工作越来越多，这在教育领导力准备项目中提出了一种提供真实模拟领导力经验的方法，并帮助有志者从中学习。本研究对59名不同学校领导学生的826份回复进行了内容分析，这些学生在经历了14次模拟之后，以异步格式写了一份回复，作为自我汇报的形式。确定的五个主题映射到体验式学习周期的四个阶段（Kolb, 1984），表明在模拟中实践领导力的虚拟机会可以作为基础经验，之后发展中的专业人员可以反思，整合新的理解，并尝试替代方法。本研究展示了在线模拟相关专业经验后进行自我汇报的目的，以及自我汇报如何推动学生完成完整的体验式学习周期，为高等教育的专业发展提供了一条有价值的途径。

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引用次数: 0

Exploring the influential factors of online professional learning completion of college teachers from the Global South in an international training program 探索国际培训项目中全球南部高校教师完成在线专业学习的影响因素

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-01-01 Epub Date: 2024-08-14 DOI: 10.1016/j.iheduc.2024.100963

Chao Wang , Xiao Hu

Online courses emerged as an important mode for large-scale cross-national teachers' professional learning. However, with most previous research on teacher online professional learning (TOPL) focusing on resource-rich and technology-advanced regions, little attention has been paid to the factors influencing the online learning completion of college teachers in Global South contexts. This study aimed to explore the facilitators and inhibitors of this population's online learning completion in a cross-country program. In seven courses, individual, institutional, and country-level data of 3529 teacher-learners from 99 countries were collected. Forty-two learners were further interviewed. We adopted hierarchical linear modeling to analyze the nested relationships among the individual/institutional/country-level factors and course completion. Results revealed several significant associations between individual/institutional/country-level variables and course completion, as well as several moderation effects. Interviews complemented the analytics results. This study uncovers influential factors of TOPL in Global South contexts and provides practical implications for college teachers' online professional learning.

在线课程已成为大规模跨国教师专业学习的重要模式。然而，以往关于教师在线专业学习（TOPL）的研究大多集中在资源丰富、技术先进的地区，很少有人关注影响全球南部地区高校教师完成在线学习的因素。本研究旨在探索在跨国项目中，促进和抑制这类人群完成在线学习的因素。在七门课程中，收集了来自 99 个国家的 3529 名教师学习者的个人、机构和国家层面的数据。我们还进一步采访了 42 名学习者。我们采用分层线性模型分析了个人/机构/国家层面因素与课程完成之间的嵌套关系。结果显示，个人/机构/国家层面的变量与课程完成情况之间存在几种重要的关联以及几种调节效应。访谈对分析结果进行了补充。本研究揭示了在全球南部背景下 TOPL 的影响因素，并为高校教师的在线专业学习提供了实际启示。

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引用次数: 0