British Journal of Educational Technology最新文献_第2页

Co-creating an equality diversity and inclusion learning analytics dashboard for addressing awarding gaps in higher education 共同创建平等、多样性和包容性学习分析仪表板，以解决高等教育中的奖励差距问题

IF 6.7 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

British Journal of Educational Technology

Pub Date : 2024-07-12 DOI: 10.1111/bjet.13509

Vaclav Bayer, Paul Mulholland, Martin Hlosta, Tracie Farrell, Christothea Herodotou, Miriam Fernandez

<div> <section> <p>Educational outcomes from traditionally underrepresented groups are generally worse than for their more advantaged peers. This problem is typically known as the awarding gap (we use the term awarding gap over ‘attainment gap’ as attainment places the responsibility on students to attain at equal levels) and continues to pose a challenge for educational systems across the world. While Learning Analytics (LA) dashboards help identify patterns contributing to the awarding gap, they often lack stakeholder involvement, offering very little support to institutional Equality, Diversity and Inclusion (EDI) leads or educators to pinpoint and address these gaps. This paper introduces an innovative EDI LA dashboard, co-created with diverse stakeholders. Rigorously evaluated, the dashboard provides fine-grained insights and course-level analysis, empowering institutions to effectively address awarding gaps and contribute to a diverse and inclusive higher education landscape.</p> </section> <section> <div> <div> <h3>Practitioners notes</h3> <p>What is already known about this topic </p><ul> <li>Traditionally underrepresented groups face educational disparities, commonly known as the awarding gap.</li> <li>Underachievement is a complex multi-dimensional problem and cannot be solely attributable to individual student deficiencies.</li> <li>LA dashboards targeting this specific problem are often not public, there is little research about them, and are frequently designed with little involvement of educational stakeholders.</li> </ul> <p>What this paper adds </p><ul> <li>Pioneers the introduction of a dashboard specifically designed to address the awarding gap problem.</li> <li>Emphasises the significant data needs of educational stakeholders in tackling awarding gaps.</li> <li>Expands the design dimensions of Learning Analytics (LA) by introducing a specific design approach rooted in established user experience (UX) design methods.</li> </ul> <p>Implications for practice and/or policy </p><ul> <li>Insights from this study will guide practitioners, designers, and developers in creating AI-based educational systems to effectively target the awarding gap problem.</li> </ul> </

传统上代表性不足的群体的教育成果通常不如条件较好的同龄人。这一问题通常被称为 "获奖差距"（我们使用 "获奖差距 "一词，而不是 "成绩差距"，因为 "成绩差距 "要求学生承担达到同等水平的责任），并继续对世界各地的教育系统构成挑战。虽然学习分析（LA）仪表板有助于识别造成奖励差距的模式，但它们往往缺乏利益相关者的参与，对机构的平等、多样性和包容性（EDI）领导或教育工作者提供的支持很少，无法准确定位和解决这些差距。本文介绍了与不同利益相关者共同创建的创新性 EDI LA 仪表盘。经过严格评估，该仪表板提供了精细的洞察力和课程层面的分析，使各机构能够有效地解决奖励差距问题，为实现多元化和全纳高等教育做出贡献。成绩不佳是一个复杂的多维问题，不能完全归咎于个别学生的不足。针对这一具体问题的洛杉矶仪表板往往不公开，相关研究也很少，而且在设计时往往很少有教育利益相关者的参与。本文的新增内容率先推出了专门用于解决奖励差距问题的仪表板。强调教育利益相关者在解决获奖差距问题时对数据的重大需求。通过引入植根于成熟的用户体验 (UX) 设计方法的特定设计方法，扩展了学习分析 (LA) 的设计维度。对实践和/或政策的启示本研究的启示将指导实践者、设计者和开发者创建基于人工智能的教育系统，以有效解决获奖差距问题。

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引用次数: 0

The life cycle of large language models in education: A framework for understanding sources of bias 教育领域大型语言模型的生命周期：了解偏见来源的框架

IF 6.7 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

British Journal of Educational Technology

Pub Date : 2024-07-12 DOI: 10.1111/bjet.13505

Jinsook Lee, Yann Hicke, Renzhe Yu, Christopher Brooks, René F. Kizilcec

<div> <section> <p>Large language models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM-based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education technology has renewed concerns over algorithmic bias, which may exacerbate educational inequalities. Building on prior work that mapped the traditional machine learning life cycle, we provide a framework of the LLM life cycle from the initial development of LLMs to customizing pre-trained models for various applications in educational settings. We explain each step in the LLM life cycle and identify potential sources of bias that may arise in the context of education. We discuss why current measures of bias from traditional machine learning fail to transfer to LLM-generated text (eg, tutoring conversations) because text encodings are high-dimensional, there can be multiple correct responses, and tailoring responses may be pedagogically desirable rather than unfair. The proposed framework clarifies the complex nature of bias in LLM applications and provides practical guidance for their evaluation to promote educational equity.</p> </section> <section> <div> <div> <h3>Practitioner notes</h3> <p>What is already known about this topic </p><ul> <li>The life cycle of traditional machine learning (ML) applications which focus on predicting labels is well understood.</li> <li>Biases are known to enter in traditional ML applications at various points in the life cycle, and methods to measure and mitigate these biases have been developed and tested.</li> <li>Large language models (LLMs) and other forms of generative artificial intelligence (GenAI) are increasingly adopted in education technologies (EdTech), but current evaluation approaches are not specific to the domain of education.</li> </ul> <p>What this paper adds </p><ul> <li>A holistic perspective of the LLM life cycle with domain-specific examples in education to highlight opportunities and challenges for incorporating natural language understanding (NLU) and natural language generation (NLG) into EdTech.</li> <li>Potential sources of bias are identified in each step of the LLM life cycle and discussed in the context of education.</li> <li>A framework for understanding where to expect potential harms of LLMs for

大语言模型（LLM）越来越多地被应用于教育领域，为学生和教师提供个性化支持。基于 LLM 的应用程序具有前所未有的理解和生成自然语言的能力，有可能提高教学效果和学习成果，但将 LLM 整合到教育技术中再次引发了对算法偏见的担忧，因为这可能会加剧教育不平等。在之前绘制传统机器学习生命周期图的工作基础上，我们提供了一个 LLM 生命周期框架，从 LLM 的初始开发到为教育环境中的各种应用定制预训练模型。我们解释了 LLM 生命周期中的每个步骤，并确定了教育背景下可能出现的潜在偏差来源。我们讨论了为什么目前传统机器学习的偏差测量方法无法应用于 LLM 生成的文本（如辅导对话），因为文本编码是高维的，可能存在多个正确的回答，而且定制回答可能在教学上是可取的，而不是不公平的。所提出的框架澄清了 LLM 应用程序中偏见的复杂性质，并为其评估提供了实用指导，以促进教育公平。众所周知，传统的机器学习应用在生命周期的不同阶段会出现偏差，而测量和减轻这些偏差的方法已经开发出来并经过了测试。大型语言模型（LLM）和其他形式的生成式人工智能（GenAI）越来越多地被教育技术（EdTech）所采用，但目前的评估方法并不是专门针对教育领域的。本文的补充内容从整体上透视 LLM 的生命周期，并结合教育领域的具体实例，强调将自然语言理解（NLU）和自然语言生成（NLG）纳入教育技术的机遇和挑战。在 LLM 生命周期的每个步骤中确定潜在的偏差来源，并结合教育进行讨论。了解 LLM 对学生、教师和 GenAI 技术在教育领域的其他用户的潜在危害的框架，该框架可指导偏差测量和缓解方法。对实践和/或政策的启示教育从业者和政策制定者应该意识到，偏差可能源于LLM生命周期中的多个步骤，而生命周期视角为他们提供了一种启发式方法，可以要求技术开发者解释每个步骤，以评估偏差风险。与传统的 ML 相比，测量教育领域中使用 LLM 的系统的偏差更为复杂，这在很大程度上是因为对自然语言生成的评估高度依赖于上下文（例如，什么算作作业的良好反馈各不相同）。教育技术开发人员可以在收集和整理数据集方面发挥重要作用，以便对 LLM 应用程序进行评估和基准测试。

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引用次数: 0

Advancing equity and inclusion in educational practices with AI-powered educational decision support systems (AI-EDSS) 利用人工智能驱动的教育决策支持系统（AI-EDSS）促进教育实践中的公平与全纳

IF 6.7 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

British Journal of Educational Technology

Pub Date : 2024-07-10 DOI: 10.1111/bjet.13507

Olga Viberg, René F. Kizilcec, Alyssa Friend Wise, Ioana Jivet, Nia Nixon

A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches to accommodate diverse global values and promote learning opportunities that best meet the needs and goals of all learners as individuals and members of different communities. Advances in learning analytics (LA), natural language processes (NLP), and artificial intelligence (AI), especially generative AI technologies, offer potential to aid educational decision making by supporting analytic insights and personalized recommendations. However, these technologies also raise serious risks for reinforcing or exacerbating existing inequalities; these dangers arise from multiple factors including biases represented in training datasets, the technologies' abilities to take autonomous decisions, and processes for tool development that do not centre the needs and concerns of historically marginalized groups. To ensure that Educational Decision Support Systems (EDSS), particularly AI-powered ones, are equipped to promote equity, they must be created and evaluated holistically, considering their potential for both targeted and systemic impacts on all learners, especially members of historically marginalized groups. Adopting a socio-technical and cultural perspective is crucial for designing, deploying, and evaluating AI-EDSS that truly advance educational equity and inclusion. This editorial introduces the contributions of five papers for the special section on advancing equity and inclusion in educational practices with AI-EDSS. These papers focus on (i) a review of biases in large language models (LLMs) applications offers practical guidelines for their evaluation to promote educational equity, (ii) techniques to mitigate disparities across countries and languages in LLMs representation of educationally relevant knowledge, (iii) implementing equitable and intersectionality-aware machine learning applications in education, (iv) introducing a LA dashboard that aims to promote institutional equality, diversity, and inclusion, and (v) vulnerable student digital well-being in AI-EDSS. Together, these contributions underscore the importance of an interdisciplinary approach in developing and utilizing AI-EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio-technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized in this process.

世界各地教育机构的一个主要目标是为所有学习者提供全纳、公平的优质教育和终身学习机会。要实现这一目标，就必须采取因地制宜的方法，以适应全球不同的价值观，并提供最能满足所有学习者作为个人和不同社区成员的需求和目标的学习机会。学习分析（LA）、自然语言处理（NLP）和人工智能（AI），特别是生成式人工智能技术的进步，通过支持分析洞察力和个性化建议，为辅助教育决策提供了潜力。然而，这些技术也带来了强化或加剧现有不平等现象的严重风险；这些危险来自多个因素，包括训练数据集中的偏见、技术自主决策的能力，以及工具开发过程没有将历史上被边缘化群体的需求和关切作为中心。为了确保教育决策支持系统（EDSS），特别是人工智能驱动的系统，能够促进公平，必须全面地创建和评估这些系统，考虑其对所有学习者，特别是历史上被边缘化的群体成员产生针对性和系统性影响的潜力。采用社会技术和文化视角对于设计、部署和评估真正促进教育公平和全纳的人工智能教育与发展系统至关重要。这篇社论介绍了五篇论文为 "在教育实践中利用人工智能教育与数据采集系统促进公平与全纳 "专题部分所做的贡献。这些论文的重点是：(i) 评述大型语言模型（LLMs）应用中的偏差，为其评估提供实用指南，以促进教育公平；(ii) 在 LLMs 代表教育相关知识时，减少不同国家和语言之间差异的技术；(iii) 在教育中实施公平和具有交叉意识的机器学习应用；(iv) 引入旨在促进机构平等、多样性和包容性的洛杉矶仪表板；(v) AI-EDSS 中脆弱的学生数字福祉。这些贡献共同强调了跨学科方法在开发和利用 AI-EDSS 方面的重要性，这不仅能在全球范围内促进更具包容性和公平性的教育景观，而且还揭示了对更广泛的公平背景的迫切需要，这种公平背景包括以下社会技术问题：AI 被用于支持何种决策、出于何种目的，以及在此过程中谁的目标被优先考虑。

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引用次数: 0

Evidence-based learning analytics: Reusing and reapplying successful methods and techniques in real learning settings 循证学习分析：在实际学习环境中重复使用和重新应用成功的方法和技术

IF 6.7 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

British Journal of Educational Technology

Pub Date : 2024-07-10 DOI: 10.1111/bjet.13506

Cristian Cechinel, Jorge Maldonado-Mahauad, Roberto Munoz, Xavier Ochoa

引用次数: 0

Examining cognitive processes of spatial thinking in university students: Insights from a web‐based geographic information systems study 考察大学生空间思维的认知过程：基于网络的地理信息系统研究的启示

IF 6.6 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

British Journal of Educational Technology

Pub Date : 2024-07-06 DOI: 10.1111/bjet.13502

Xi Xiang, Di Xi

Spatial thinking is essential for nurturing spatially literate graduates in tertiary education. However, there is limited research on individual differences in cognitive processes and their impact on spatial problem solving in disciplinary contexts. This study aimed to investigate cognitive processes involved in spatial thinking in geography majors using a web‐based geographic information systems (GIS) mapping tool. The results revealed three clusters characterised by distinctive cognitive processes: spatial analytic, spatial diagrammatic and alternative. Each cluster adopted unique spatial strategies to solve problems with web‐based GIS. Notably, spatial analytic learners demonstrated the most optimal profile, resulting in high spatial task performance. These findings have implications for maximising students' learning potential in spatial thinking in the tertiary classroom, optimising performance outcomes in spatial problem solving and building intelligent tutoring systems for adaptive learning.Practitioner notesWhat is already known about this topic There are individual differences in spatial reasoning. The processes of spatial thinking may have an impact on learners' spatial performance outcomes. What this paper adds Three clusters characterised by distinctive processes of spatial thinking were identified: spatial analytic, spatial diagrammatic and alternative. Each cluster adopted unique spatial strategies to solve problems with web‐based GIS. Spatial analytic learners demonstrated the optimal profile, resulting in high‐level spatial performance, whereas alternative learners exhibited the maladaptive profile, which was associated with low task outcomes. Implications for practice and/or policy Web‐based GIS mapping tools make it possible to track the processes of spatial thinking that have remained largely unexplored. Cluster analysis and lag sequential analysis reveal differences in spatial reasoning, aiding educators in maximising the potential for university students to learn spatial thinking and optimising performance outcomes in spatial problem solving. Our findings could inform learning technology designers to build adaptive learning applications in which students receive automatic feedback and tailored support while completing spatial tasks at th

空间思维对于培养具有空间素养的高等教育毕业生至关重要。然而，关于认知过程中的个体差异及其对学科背景下空间问题解决的影响的研究十分有限。本研究旨在利用基于网络的地理信息系统（GIS）绘图工具，调查地理专业学生空间思维的认知过程。研究结果显示了三个具有独特认知过程的集群：空间分析型、空间图示型和替代型。每个群组都采用独特的空间策略来解决基于网络的 GIS 问题。值得注意的是，空间分析型学习者表现出了最理想的特征，从而获得了较高的空间任务绩效。这些发现对于在高等教育课堂上最大限度地挖掘学生的空间思维学习潜能、优化空间问题解决的成绩结果以及为适应性学习构建智能辅导系统都有意义。空间思维过程可能会对学习者的空间表现结果产生影响。本文新增内容确定了三个以独特的空间思维过程为特征的集群：空间分析型、空间图解型和替代型。每个群组都采用独特的空间策略来解决基于网络的 GIS 问题。空间分析型学习者表现出最佳特征，从而获得高水平的空间表现，而替代型学习者则表现出不适应特征，与低任务成果相关。对实践和/或政策的启示基于网络的 GIS 制图工具使得追踪空间思维过程成为可能，而这种思维过程在很大程度上还没有被探索过。聚类分析和滞后序列分析揭示了空间推理的差异，有助于教育工作者最大限度地挖掘大学生学习空间思维的潜力，优化空间问题解决的绩效成果。我们的研究结果可以为学习技术设计者提供信息，帮助他们构建自适应学习应用程序，让学生在按照自己的节奏完成空间任务的过程中获得自动反馈和量身定制的支持。

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引用次数: 0

Exploring the impact of VoiceBots on multimedia programming education among Ghanaian university students 探索语音机器人对加纳大学生多媒体编程教育的影响

IF 6.6 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

British Journal of Educational Technology

Pub Date : 2024-07-04 DOI: 10.1111/bjet.13504

Harry Barton Essel, Dimitrios Vlachopoulos, Henry Nunoo‐Mensah, John Opuni Amankwa

Conversational user interfaces (CUI), including voice interfaces, which allow users to converse with computers via voice, are gaining wide popularity. VoiceBots allow users to receive a response in real‐time, regardless of the communication device. VoiceBots have been explored in fields such as customer service to automate repetitive queries and help reduce redundant tasks; however, they have not been widely applied in the classroom. This study aimed to explore the effects of VoiceBot implementation on student learning. A pre‐test–post‐test design was implemented with 65 participating undergraduate students in multimedia programming who were randomly allocated to scenarios representing a 2 × 2 design (experimental and control cohorts). Data were collected using an academic achievement test and semi‐structured interviews, which allowed for a more in‐depth analysis of the students' experiences with the VoiceBot. The results showed that how the VoiceBot was applied positively influenced student learning in the experimental cohort. Moreover, the focus group data demonstrated that the VoiceBot can be a valuable assistant for students and could be easily replicated in other courses. To the best of our knowledge, this study was the first to use VoiceBot to engage undergraduate students in Ghana, thus contributing to the growing literature stream on the development of VoiceBots to improve student learning experiences. This study elucidates the design process using a zero‐coding technique, which is considered a suitable approach for educational institutions with limited resources.Practitioner notesWhat is already known about this topic Conversational user interfaces (CUIs), including voice interfaces, have gained popularity and are used to interact with computers through natural language. VoiceBots have been utilised in various fields such as customer service to automate tasks and reduce redundancy. Instant messaging systems such as WhatsApp and Telegram have been used for communication in educational contexts. Advances in artificial intelligence (AI) and natural language processing (NLP) have led to significant improvements in voice‐enabled CUIs (VoiceBots). Existing studies indicate that chatbots affect students' motivation, learning experiences, and achievements; however, research on using VoiceBots for learning improvement is limited. What this paper adds A VoiceBot was introduced as an assistant to facilitate learning in a multimedia programming course. The study used an experimental design with an experimental cohort using a WhatsApp group platform equipped with a zero‐coding VoiceBot and a c

对话式用户界面（CUI），包括允许用户通过语音与计算机对话的语音界面，正在受到广泛欢迎。语音机器人可以让用户实时收到回复，而不受通信设备的限制。语音机器人已在客户服务等领域进行了探索，以实现重复性查询的自动化，并帮助减少冗余任务；然而，它们尚未广泛应用于课堂。本研究旨在探索语音机器人的实施对学生学习的影响。研究采用了前测-后测设计，65 名参与研究的多媒体编程本科生被随机分配到代表 2 × 2 设计（实验组和对照组）的情景中。数据收集采用了学业成绩测试和半结构式访谈，以便更深入地分析学生使用 VoiceBot 的体验。结果显示，如何应用 VoiceBot 对实验组学生的学习产生了积极影响。此外，焦点小组的数据还表明，语音机器人对学生来说是一个有价值的助手，很容易在其他课程中推广。据我们所知，这项研究是加纳第一项使用语音机器人吸引本科生参与的研究，从而为越来越多的关于开发语音机器人以改善学生学习体验的文献流做出了贡献。本研究阐明了使用零编码技术的设计过程，该技术被认为是适合资源有限的教育机构的一种方法。实践者注释关于本主题的已知内容对话式用户界面（CUI），包括语音界面，已经得到普及，并被用于通过自然语言与计算机进行交互。语音机器人已被用于客户服务等多个领域，以实现任务自动化并减少冗余。WhatsApp 和 Telegram 等即时通讯系统已被用于教育领域的通信。人工智能（AI）和自然语言处理（NLP）技术的进步极大地改善了支持语音的 CUI（语音机器人）。现有研究表明，聊天机器人会影响学生的学习动机、学习体验和学习成绩；然而，利用语音机器人提高学习效率的研究还很有限。本文新增内容在多媒体编程课程中引入语音机器人作为辅助工具，以促进学习。研究采用了实验设计，实验组使用配备了零编码 VoiceBot 的 WhatsApp 群组平台，对照组则没有配备 VoiceBot。研究发现，与 VoiceBot 互动的学生的学习成绩优于对照组。研究还就如何将语音机器人融入教育机构提出了明确的建议。对实践和/或政策的启示研究结果表明，语音机器人可以在提高学生学习成绩方面发挥重要作用，尤其是在多媒体编程等科目上。教育机构可以建立由学科专家组成的学习设计和技术中心，将 VoiceBots 有效地融入学习过程。教师必须具备足够的技术能力，才能让学生使用语音机器人，因此有必要开展有针对性的在职培训。未来的研究可以探索语音机器人在不同学科领域和教育水平中的使用情况，分析使用模式对学习成果的影响，并评估其对学生参与和学习动机的长期影响。

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引用次数: 0

Robot NAO integrated lesson vs. traditional lesson: Measuring learning outcomes on the topic of “societal change” and the mediating effect of students' attitudes 机器人 NAO 综合课与传统课：衡量 "社会变革 "主题的学习成果以及学生态度的中介效应

IF 6.6 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

British Journal of Educational Technology

Pub Date : 2024-07-04 DOI: 10.1111/bjet.13501

Violeta Rosanda, Ivan Bratko, Mateja Gačnik, Vid Podpečan, Andreja Istenič

Our research aims to examine the effectiveness of introducing social robots as educational technology within authentic classroom activities without modifying them to be designed for a robot. We chose as test subject the fifth‐grade curricular topic “The role of technology and its impact on society”, meeting the critical stage of moral development students aged of 11–12. The study, with both experimental (EG) and control groups (CG), will be conducted over 6 weeks. This study will examine the impact of robot‐supported lessons with post‐participation testing on learning outcomes and examine students' perception of the robot in the classroom as a potential correlation with academic performance. The form of the study will be a between‐group non‐randomised controlled experiment. Control and experimental groups will be matched concerning gender, mastery of technology and previous knowledge and understanding of the curricular topic in focus. The instructional design of process‐outcome strategies will incorporate all of Bloom's taxonomic levels. In the review of related studies, we identified gaps in social robot‐supported lessons within the regular curriculum between‐group experiment. Based on a review of related research showing more focus on robot performance in the classroom from technical‐interaction aspects we want to convey from pedagogical starting point. The robot's placement in the pedagogical process will be considered an integral part of the teacher's technical environment. We will use the pre‐participation test to establish whether there is the initial equivalence between EG and CG in terms of gender, mastery of technology, and previous knowledge and understanding of the curricular topic under examination.

我们的研究旨在考察在真实的课堂活动中引入社交机器人作为教育技术的有效性，而不对这些活动进行为机器人而设计的修改。我们选择了五年级的课程主题 "技术的作用及其对社会的影响 "作为测试对象，以满足 11-12 岁学生道德发展的关键阶段。这项研究将分实验组（EG）和对照组（CG）进行，为期 6 周。本研究将通过参与后测试，考察机器人辅助课程对学习成果的影响，并考察学生对课堂上机器人的看法，因为这可能与学习成绩相关。研究形式为组间非随机对照实验。对照组和实验组将在性别、技术掌握程度以及先前对重点课程主题的知识和理解方面进行匹配。过程-结果策略的教学设计将包含布卢姆分类学的所有层次。在对相关研究的回顾中，我们发现了在常规课程组间实验中社交机器人辅助课程的不足。在回顾相关研究的基础上，我们希望从教学的角度出发，从技术互动的角度出发，更多地关注机器人在课堂上的表现。机器人在教学过程中的位置将被视为教师技术环境的一个组成部分。我们将通过参与前测试来确定 EG 和 CG 在性别、技术掌握程度、先前知识以及对所考察课程主题的理解方面是否存在初始等同性。

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引用次数: 0

Investigating teacher orchestration load in scripted CSCL: A multimodal data analysis perspective 调查脚本化 CSCL 中的教师协调负荷：多模态数据分析视角

IF 6.7 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

British Journal of Educational Technology

Pub Date : 2024-06-26 DOI: 10.1111/bjet.13500

Lubna Hakami, Davinia Hernández-Leo, Ishari Amarasinghe, Batuhan Sayis

Despite the growing interest in using multimodal data to analyse students' actions in Computers-Supported Collaborative Learning (CSCL) settings, studying teacher's orchestration load in such settings remains overlooked. The notion of classroom orchestration, and orchestration load, offer a lens to study the implications of increasingly complex technology-supported learning environments on teacher performance. A combination of multimodal data may aid in understanding teachers' orchestration actions and, as a result, gain insights regarding the orchestration load teachers perceive in scripted CSCL situations. Studying teacher orchestration load in CSCL helps understand the workload teachers experience while facilitating student collaboration and assists in informing design decisions for teacher supporting tools. In this paper, we collect and analyse data from different modalities (i.e. electrodermal activity, observation notes, log data, dashboard screen recordings and responses to self-reported questionnaires) to study teachers' orchestration load in scripted CSCL. A tool called PyramidApp was used to deploy CSCL activities and a teacher-facing dashboard was used to facilitate teachers in managing collaboration in real time. The findings of the study show the potential of multimodal data analysis in investigating and estimating the orchestration load experienced by teachers in scripted CSCL activities. Study findings further demonstrate factors emerging from multimodal data such as task type, activity duration, and number of students influenced teachers' orchestration load.

尽管人们对使用多模态数据分析学生在计算机支持的协作学习（CSCL）环境中的行为越来越感兴趣，但对教师在这种环境中的协调负荷的研究仍然被忽视。课堂协调和协调负荷的概念为研究日益复杂的技术支持学习环境对教师绩效的影响提供了一个视角。多模态数据的结合可能有助于理解教师的协调行为，从而深入了解教师在脚本化 CSCL 情境中感知到的协调负荷。研究教师在 CSCL 中的协调负荷有助于了解教师在促进学生合作时的工作量，并有助于为教师辅助工具的设计决策提供信息。在本文中，我们收集并分析了来自不同模式的数据（即电皮活动、观察记录、日志数据、仪表盘屏幕记录和对自我报告问卷的答复），以研究教师在脚本化 CSCL 中的协调负荷。一个名为 X 的工具被用来部署 CSCL 活动，一个面向教师的仪表板被用来帮助教师实时管理协作。研究结果表明，多模态数据分析在调查和估计教师在脚本化 CSCL 活动中的协调负荷方面具有潜力。研究结果进一步表明，任务类型、活动持续时间和学生人数等多模态数据中出现的因素影响了教师的协调负荷。

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引用次数: 0

What factors influence scientific concept learning? A study based on the fuzzy‐set qualitative comparative analysis 影响科学概念学习的因素有哪些？基于模糊集定性比较分析的研究

IF 6.6 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

British Journal of Educational Technology

Pub Date : 2024-06-25 DOI: 10.1111/bjet.13499

Jingjing Ma, Qingtang Liu, Shufan Yu, Jindian Liu, Xiaojuan Li, Chunhua Wang

This research employs the fuzzy‐set qualitative comparative analysis (fsQCA) method to investigate the configurations of multiple factors influencing scientific concept learning, including augmented reality (AR) technology, the concept map (CM) strategy and individual differences (eg, prior knowledge, experience and attitudes). A quasi‐experiment was conducted with 194 seventh‐grade students divided into four groups: AR and CM (N = 52), AR and non‐CM (N = 51), non‐AR and CM (N = 40), non‐AR and non‐CM (N = 51). These students participated in a science lesson on ‘The structure of peach blossom’. This study represents students' science learning outcomes by measuring their academic performance and cognitive load. The fsQCA results reveal that: (1) factors influencing students' academic performance and cognitive load are interdependent, and a single factor cannot constitute a necessary condition for learning outcomes; (2) multiple pathways can lead to the same learning outcome, challenging the notion of a singular best path derived from traditional analysis methods; (3) the configurations of good and poor learning outcomes exhibit asymmetry. For example, high prior knowledge exists in both configurations leading to good and poor learning outcomes, depending on how other conditions are combined.Practitioner notesWhat is already known about this topic Augmented reality proves to be a useful technological tool for improving science learning. The concept map can guide students to describe the relationships between concepts and make a connection between new knowledge and existing knowledge structures. Individual differences have been emphasized as essential external factors in controlling the effectiveness of learning. What this paper adds This study innovatively employed the fsQCA analysis method to reveal the complex phenomenon of the scientific concept learning process at a fine‐grained level. This study discussed how individual differences interact with AR and concept map strategy to influence scientific concept learning. Implications for practice and/or policy No single factor present or absent is necessary for learning outcomes, but the combinations of AR and concept map strategy always obtain satisfactory learning outcomes. There are multiple pathways to achieving good learning outcomes rather than a single optimal solution. The implementation of educational interventions should fully consid

本研究采用模糊集定性比较分析（fsQCA）方法，研究影响科学概念学习的多种因素的配置，包括增强现实（AR）技术、概念图（CM）策略和个体差异（如已有知识、经验和态度）。我们对 194 名七年级学生进行了一项准实验，分为四组：AR和CM组（52人）、AR和非CM组（51人）、非AR和CM组（40人）、非AR和非CM组（51人）。这些学生参加了 "桃花的结构 "科学课。本研究通过测量学生的学习成绩和认知负荷来反映学生的科学学习成果。研究结果表明(1) 影响学生学业成绩和认知负荷的因素是相互依存的，单一因素不能构成学习结果的必要条件；(2) 多种路径可以导致相同的学习结果，这对传统分析方法得出的单一最佳路径的概念提出了挑战；(3) 好的和差的学习结果的配置表现出不对称性。例如，高先验知识存在于导致好的和差的学习结果的两种配置中，这取决于其他条件是如何组合的。概念图可以引导学生描述概念之间的关系，并在新知识和现有知识结构之间建立联系。个体差异被强调为控制学习效果的重要外部因素。本文的补充本研究创新性地采用了fsQCA分析方法，从细微处揭示了科学概念学习过程的复杂现象。本研究探讨了个体差异如何与 AR 和概念图策略相互作用，从而影响科学概念学习。对实践和/或政策的启示任何单一因素的存在或不存在都不是学习成果的必要条件，但AR和概念图策略的组合总能获得令人满意的学习成果。取得良好学习效果有多种途径，而不是单一的最佳解决方案。教育干预措施的实施应充分考虑学生的个体差异，如先前的知识、经验和态度。

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引用次数: 0

Evidence-based multimodal learning analytics for feedback and reflection in collaborative learning 以证据为基础的多模态学习分析，用于协作学习中的反馈和反思

IF 6.7 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

British Journal of Educational Technology

Pub Date : 2024-06-22 DOI: 10.1111/bjet.13498

Lixiang Yan, Vanessa Echeverria, Yueqiao Jin, Gloria Fernandez-Nieto, Linxuan Zhao, Xinyu Li, Riordan Alfredo, Zachari Swiecki, Dragan Gašević, Roberto Martinez-Maldonado

<div> <section> <p>Multimodal learning analytics (MMLA) offers the potential to provide evidence-based insights into complex learning phenomena such as collaborative learning. Yet, few MMLA applications have closed the learning analytics loop by being evaluated in real-world educational settings. This study evaluates the effectiveness of an MMLA solution in enhancing feedback and reflection within a complex and highly dynamic collaborative learning environment. A two-year longitudinal study was conducted with 399 students and 17 teachers, utilising an MMLA system in reflective debriefings in the context of healthcare education. We analysed the survey data of 74 students and 11 teachers regarding their perceptions of the MMLA system. We applied the Evaluation Framework for Learning Analytics, augmented by complexity, accuracy and trust measures, to assess both teachers' and students' perspectives. The findings illustrated that teachers and students both had generally positive perceptions of the MMLA solution. Teachers found the MMLA solution helpful in facilitating feedback provision and reflection during debriefing sessions. Similarly, students found the MMLA solution effective in providing clarity on the data collected, stimulating reflection on their learning behaviours, and prompting considerations for adaptation in their learning behaviours. However, the complexity of the MMLA solution and the need for qualitative measures of communication emerged as areas for improvement. Additionally, the study highlighted the importance of data accuracy, transparency, and privacy protection to maintain user trust. The findings provide valuable contributions to advancing our understanding of the use of MMLA in supporting feedback and reflection practices in intricate collaborative learning while identifying avenues for further research and improvement. We also provided several insights and practical recommendations for successful MMLA implementation in authentic learning contexts.</p> </section> <section> <div> <div> <h3>Practitioner notes</h3> <p>What is currently known about this topic </p><ul> <li>Multimodal learning analytics (MMLA) seeks to generate data-informed insights about learners' metacognitive and emotional states as well as their learning behaviours, by utilising intricate physical and physiological signals.</li> <li>MMLA has not only pioneered novel data analytic methods but also aspired to complete the learning analytics loop by crafting innovative, tangible solutions that relay these insights to the concerned stakeholders.</li> <li>A prominent directi

多模态学习分析（MMLA）可以为协作学习等复杂的学习现象提供基于证据的见解。然而，很少有 MMLA 应用程序通过在真实的教育环境中进行评估来实现学习分析的闭环。本研究评估了 MMLA 解决方案在复杂和高度动态的协作学习环境中加强反馈和反思的有效性。我们对 399 名学生和 17 名教师进行了为期两年的纵向研究，在医疗保健教育背景下的反思性汇报中使用了 MMLA 系统。我们分析了 74 名学生和 11 名教师对 MMLA 系统看法的调查数据。我们采用了学习分析评估框架，并辅以复杂性、准确性和信任度测量方法，对教师和学生的观点进行了评估。研究结果表明，教师和学生对 MMLA 解决方案的看法总体上都是积极的。教师认为 MMLA 解决方案有助于在汇报课上提供反馈和进行反思。同样，学生们也认为 MMLA 解决方案能有效地澄清所收集的数据，激发他们对自己学习行为的反思，并促使他们考虑调整自己的学习行为。然而，MMLA 解决方案的复杂性和对交流的定性测量的需求成为需要改进的方面。此外，研究还强调了数据准确性、透明度和隐私保护对于维护用户信任的重要性。研究结果为加深我们对使用 MMLA 支持错综复杂的协作学习中的反馈和反思实践的理解做出了宝贵贡献，同时也确定了进一步研究和改进的途径。我们还为在真实学习环境中成功实施 MMLA 提供了一些见解和实用建议。多模态学习分析（MMLA）旨在通过利用复杂的物理和生理信号，对学习者的元认知和情感状态以及学习行为进行数据化分析。MMLA 不仅开创了新颖的数据分析方法，而且还希望通过制定创新的、有形的解决方案，将这些见解传递给相关利益方，从而完成学习分析循环。鉴于 MMLA 能够辨别错综复杂的动态学习行为，MMLA 研究的一个突出方向是开发工具，以支持协作学习场景中的反馈和反思。本文的补充教师和学生对实施 MMLA 的积极看法分别激发了他们对教学实践和学习行为调整的考虑。经验证据证明，在错综复杂的协作学习情景中，多媒体课件可以帮助教师促进学生的反思实践。解决与设计复杂性、残疾用户的可解释性、汇总数据表示以及与信任有关的问题对于在实际学习环境中建立实用的 MMLA 解决方案的重要性。对实践和/或政策的影响 MMLA 解决方案可让教师全面了解学生的表现，指出需要改进的地方，并确认学习情景的结果。MMLA 解决方案可激发学生对其学习行为的反思，并促进学生考虑调整其学习行为。就如何解释分析结果提供清晰的解释和指导，以及解决与数据完整性和代表性有关的问题，对于最大限度地发挥效用至关重要。

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引用次数: 0