British Journal of Educational Technology最新文献

{"title":"“ChatGPT can make mistakes. Check important info.” Epistemic beliefs and metacognitive accuracy in students' integration of ChatGPT content into academic writing","authors":"Marek Urban, Cyril Brom, Jiří Lukavský, Filip Děchtěrenko, Veronika Hein, Filip Svacha, Petra Kmoníčková, Kamila Urban","doi":"10.1111/bjet.13591","DOIUrl":"10.1111/bjet.13591","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <p>Recent studies have conceptualized ChatGPT as an epistemic authority; however, no research has yet examined how epistemic beliefs and metacognitive accuracy affect students' actual use of ChatGPT-generated content, which often contains factual inaccuracies. Therefore, the present experimental study aimed to examine how university students integrate correct and incorrect information from expert-written and ChatGPT-generated articles when writing independently (<i>N</i> = 49) or with ChatGPT assistance (<i>N</i> = 49). Students working with ChatGPT-4o integrated more correct information from both expert-written (<i>d</i> = 0.64) and ChatGPT-generated articles (<i>d</i> = 0.95), but ChatGPT-assisted writing did not affect the amount of incorrect information sourced from the ChatGPT-generated article. Regardless of the condition, hierarchical regressions revealed that lower metacognitive bias was moderately associated with increased inclusion of correct information from the expert-written article (<i>R</i><sup>2</sup> = 12%). Conversely, a higher metacognitive bias (<i>R</i><sup>2</sup> = 10%) and epistemic beliefs (<i>R</i><sup>2</sup> = 12%) were moderately related to the inclusion of incorrect information from ChatGPT-generated articles. These findings suggest that while ChatGPT assistance enhances the integration of correct human- and AI-generated content, metacognitive skills remain essential to mitigate the risks of incorporating incorrect AI-generated information.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <div>\u0000 \u0000 <div>\u0000 \u0000 <h3>Practitioner notes</h3>\u0000 <p>What is already known about this topic\u0000\u0000 </p><ul>\u0000 \u0000 <li>Generative AI tools, such as ChatGPT, are increasingly regarded as epistemic authorities due to their authoritative tone and human-like interaction.</li>\u0000 \u0000 <li>ChatGPT has demonstrated utility in providing correct information and improving productivity in educational and professional contexts, but it is also prone to inaccuracies, hallucinations and misleading content.</li>\u0000 \u0000 <li>Students' epistemic beliefs and metacognitive skills predict their ability to critically evaluate and integrate conflicting information from multiple resources, particularly when searching for information on the Internet.</li>\u0000 </ul>\u0000 <p>What this paper adds\u0000\u0000 </p><ul>\u0000 \u0000 <li>This study experimentally examines how students integrate correct and incorrect information from expert-written and ChatGPT-generated articles when writing independently or with ChatGPT's assistance.</li>\u0000 \u0000 <li>The findings show that ChatGPT as","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1897-1918"},"PeriodicalIF":8.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysing nontraditional students' ChatGPT interaction, engagement, self-efficacy and performance: A mixed-methods approach","authors":"Mohan Yang, Shiyan Jiang, Belle Li, Kristin Herman, Tian Luo, Shanan Chappell Moots, Nolan Lovett","doi":"10.1111/bjet.13588","DOIUrl":"10.1111/bjet.13588","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <p>Generative artificial intelligence brings opportunities and unique challenges to nontraditional higher education students, stemming, in part, from the experience of the digital divide. Providing access and practice is critical to bridge this divide and equip students with needed digital competencies. This mixed-methods study investigated how nontraditional higher education students interact with ChatGPT in multiple courses and examined relationships between ChatGPT interactions, engagement, self-efficacy and performance. Data were collected from 73 undergraduate and graduate students through chat logs, course reflections and artefacts, surveys and interviews. ChatGPT interactions were analysed using four metrics: prompt number, depth of knowledge (DoK), prompt relevance and originality. Results showed that ChatGPT prompt numbers (<i>β</i> = 0.256, <i>p</i> < 0.03) and engagement (<i>β</i> = 0.267, <i>p</i> < 0.05) significantly predicted performance, while self-efficacy did not. Students' DoK (<i>r</i> = 0.40, <i>p</i> < 0.01) and prompt relevance (<i>r</i> = 0.42, <i>p</i> < 0.01) were positively correlated with performance. Text mining analysis identified distinct interaction patterns, with ‘strategic inquirers’ demonstrating significantly higher performance than ‘exploratory inquirers’ through more sophisticated follow-up questioning. Qualitative findings revealed that while most students were first-time ChatGPT users who initially showed resistance, they developed growing acceptance. Still, students tended to use ChatGPT sparingly and, even then, as only a starting point for assignments. The study highlights the need for targeted guidance in prompt engineering and AI literacy training to help nontraditional higher education students leverage ChatGPT more effectively for higher-order thinking tasks.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <div>\u0000 \u0000 <div>\u0000 \u0000 <h3>Practitioner notes</h3>\u0000 <p>What is already known about this topic\u0000\u0000 </p><ul>\u0000 \u0000 <li>Nontraditional students face unique challenges in higher education, such as limited technological literacy and digital access.</li>\u0000 \u0000 <li>The emergence of generative AI tools presents both opportunities and challenges for addressing educational disparities.</li>\u0000 \u0000 <li>Existing studies on AI implementation predominantly focus on traditional students.</li>\u0000 </ul>\u0000 <p>What this paper adds\u0000\u0000 </p><ul>\u0000 \u0000 <li>Empirical evidence of how nontraditional students interact with ChatGPT through multiple metrics (prompt n","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 5","pages":"1973-2000"},"PeriodicalIF":8.1,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BJET Editorial Spring 2025: Reporting on AIED research and ethical considerations","authors":"Manolis Mavrikis, Cathy Lewin, Mutlu Cukurova, Louis Major, Laura Outhwaite, Elisa Rubegni","doi":"10.1111/bjet.13581","DOIUrl":"10.1111/bjet.13581","url":null,"abstract":"","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 3","pages":"1118-1121"},"PeriodicalIF":8.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI for data generation in education: Towards learning and teaching support at scale","authors":"Mohammad Khalil,&nbsp;Qinyi Liu,&nbsp;Jelena Jovanovic","doi":"10.1111/bjet.13580","DOIUrl":"10.1111/bjet.13580","url":null,"abstract":"<p>Supporting learning and teaching at scale requires access to large and high-quality content and datasets for analysis and innovation. With rapid advances in artificial intelligence (AI) and the growing demand for data, synthetic data has emerged as a potential solution for addressing these challenges. This editorial introduces the contributions of five accepted articles to the special section AI for Synthetic Data Generation in Education: Scaling Teaching and Learning. These articles explore key themes in leveraging AI-generated synthetic data to support learning and teaching as well as enhance educational practices at scale. The editorial emphasizes that hybrid strategies that leverage AI alongside human judgment are essential for scaling support for learning and teaching through synthetic data generation.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 3","pages":"993-998"},"PeriodicalIF":8.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging LLM respondents for item evaluation: A psychometric analysis","authors":"Yunting Liu,&nbsp;Shreya Bhandari,&nbsp;Zachary A. Pardos","doi":"10.1111/bjet.13570","DOIUrl":"10.1111/bjet.13570","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Effective educational measurement relies heavily on the curation of well-designed item pools. However, item calibration is time consuming and costly, requiring a sufficient number of respondents to estimate the psychometric properties of items. In this study, we explore the potential of six different large language models (LLMs; GPT-3.5, GPT-4, Llama 2, Llama 3, Gemini-Pro and Cohere Command R Plus) to generate responses with psychometric properties comparable to those of human respondents. Results indicate that some LLMs exhibit proficiency in College Algebra that is similar to or exceeds that of college students. However, we find the LLMs used in this study to have narrow proficiency distributions, limiting their ability to fully mimic the variability observed in human respondents, but that an ensemble of LLMs can better approximate the broader ability distribution typical of college students. Utilizing item response theory, the item parameters calibrated by LLM respondents have high correlations (eg, &gt;0.8 for GPT-3.5) with their human calibrated counterparts. Several augmentation strategies are evaluated for their relative performance, with resampling methods proving most effective, enhancing the Spearman correlation from 0.89 (human only) to 0.93 (augmented human).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;The collection of human responses to candidate test items is common practice in educational measurement when designing an assessment tool.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Large language models (LLMs) have been found to rival human abilities in a variety of subject areas, making them a low-cost option for testing the efficacy of educational assessment items.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Data augmentation using AI has been an effective strategy for enhancing machine learning model performance.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 \u0000 &lt;p&gt;What this paper adds\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;This paper provides the first psychometric analysis of the ability distribution of a variety of open-source and proprietary LLMs as compared to humans.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The study finds that item parameters similar to those produced by 50 undergraduate respondents.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Using LLM respondents to augment human response data yields mixed results.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 \u0000 &lt;p&gt;Implications ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 3","pages":"1028-1052"},"PeriodicalIF":8.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensuring privacy through synthetic data generation in education","authors":"Qinyi Liu,&nbsp;Ronas Shakya,&nbsp;Jelena Jovanovic,&nbsp;Mohammad Khalil,&nbsp;Javier de la Hoz-Ruiz","doi":"10.1111/bjet.13576","DOIUrl":"10.1111/bjet.13576","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;High-volume, high-quality and diverse datasets are crucial for advancing research in the education field. However, such datasets often contain sensitive information that poses significant privacy challenges. Traditional anonymisation techniques fail to meet the privacy standards required by regulations like GDPR, prompting the need for more robust solutions. Synthetic data have emerged as a promising privacy-preserving approach, allowing for the generation and sharing of datasets that mimic real data while ensuring privacy. Still, the application of synthetic data alone on educational datasets remains vulnerable to privacy threats such as linkage attacks. Therefore, this study explores for the first time the application of &lt;i&gt;private synthetic data&lt;/i&gt;, which combines synthetic data with differential privacy mechanisms, in the education sector. By considering the dual needs of data utility and privacy, we investigate the performance of various synthetic data generation techniques in safeguarding sensitive educational information. Our research focuses on two key questions: the capability of these techniques to prevent privacy threats and their impact on the utility of synthetic educational datasets. Through this investigation, we aim to bridge the gap in understanding the balance between privacy and utility of advanced privacy-preserving techniques within educational contexts.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Traditional privacy-preserving methods for educational datasets have not proven successful in ensuring a balance of data utility and privacy. Additionally, these methods often lack empirical evaluation and/or evidence of successful application in practice.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Synthetic data generation is a state-of-the-art privacy-preserving method that has been increasingly used as a substitute for real datasets for data publishing and sharing. However, recent research has demonstrated that even synthetic data are vulnerable to privacy threats.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Differential privacy (DP) is the gold standard for quantifying and mitigating privacy concerns. Its combination with synthetic data, often referred to as &lt;i&gt;private synthetic data,&lt;/i&gt; is presently the best available approach to ensuring data privacy. However, private synthetic data have not been studied in the educational domain.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 \u0000 &lt;p&gt;What this study contributes\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;The study has applied synthetic data generation methods with D","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 3","pages":"1053-1073"},"PeriodicalIF":8.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scaffolding middle school mathematics curricula with large language models","authors":"Rizwaan Malik,&nbsp;Dorna Abdi,&nbsp;Rose Wang,&nbsp;Dorottya Demszky","doi":"10.1111/bjet.13571","DOIUrl":"10.1111/bjet.13571","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Despite well-designed curriculum materials, teachers often face challenges implementing them due to diverse classroom needs. This paper investigates whether large language models (LLMs) can support middle school math teachers by helping create high-quality curriculum scaffolds, which we define as the adaptations and supplements teachers employ to ensure all students can access and engage with the curriculum. Through cognitive task analysis with expert teachers, we identify a three-stage process for curriculum scaffolding: observation, strategy formulation and implementation. We incorporate these insights into three LLM approaches to create warmup tasks that activate students' background knowledge. The best-performing approach provides the model with the original curriculum materials and an expert-informed prompt; this approach generates warmups that are rated significantly higher than those created by expert teachers in terms of alignment to learning objectives, accessibility to students working below grade level and teacher preference. This research demonstrates the potential of LLMs to support teachers in creating effective scaffolds and provides a methodology for developing artificial intelligence-driven educational tools.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Scaffolding is essential for enabling students to access and engage with curriculum materials.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Large language models (LLMs) have shown promise in generating educational content and supporting teachers.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Teachers frequently need to adapt and supplement standardized curricula to meet the diverse needs of their students.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 \u0000 &lt;p&gt;What this paper adds\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Identifies a three-stage curriculum scaffolding process (observation, strategy formulation, implementation) used by expert teachers.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Demonstrates that providing LLMs with additional context from the curriculum, such as the original warmup task, helps to ground the model and improve the quality of the generated warmup tasks.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Demonstrates that, when prompted well, LLMs can generate warmup tasks that are of similar or better quality compared to those created by expert teachers in terms of alignment to learning objectives, accessibility and teacher preference.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 \u0000 &lt;p&gt;Implications for practice and/or policy","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 3","pages":"999-1027"},"PeriodicalIF":8.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Value-sensitive design in the praxis of instructional design: A view of designers in situ","authors":"Victoria Abramenka-Lachheb,&nbsp;Ahmed Lachheb,&nbsp;Gamze Ozogul","doi":"10.1111/bjet.13574","DOIUrl":"10.1111/bjet.13574","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Philosophical stances and design frameworks, such as value-sensitive design, manifest in design praxis through enacting specific design approaches and employing a variety of methods by the designers. Although it could overlap with other frameworks and approaches in the Instructional Design and Technology (IDT) field, value-sensitive design remains a largely unexplored topic in the praxis of instructional design for several reasons. As it focuses on different stakeholders and their values, and by recognizing the contested issue of universal values, we report in this paper on our empirical work that sought to describe the values that instructional designers hold/express in relation to their instructional design work for online courses. In this study, instructional designers communicated their values while discussing their design philosophies and how they manifested in designing human-computer interactions to promote online authentic learning. Through the theoretical lens of value-sensitive design, we provide a detailed account of instructional designers' values as well as describe and showcase how their values manifested in specific design artefacts. Through this investigation of instructional designers' values, we contribute to the ongoing discussion on value-sensitive design and generate implications for instructional design research and education. These implications contribute to the evolution of the instructional design field.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Philosophical stances and design frameworks, such as value-sensitive design (VSD), manifest in design praxis through designers enacting specific design approaches and employing a variety of methods.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;VSD overlaps with other frameworks and approaches in the Instructional Design and Technology (IDT) field and manifests through different terms/frameworks.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;VSD is a largely unexplored topic in the praxis of instructional design.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Designers' philosophies, values and design judgements play a significant role in design practice, and they are the driving force behind the enactment of frameworks and philosophical stances such as VSD.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The IDT field has not sufficiently addressed the role of the designer in carrying out design work.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;p&gt;What this paper adds\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 4","pages":"1311-1349"},"PeriodicalIF":8.1,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144273225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling cognitive processes in digital reading through behavioural cues: A hybrid intelligence (HI) approach","authors":"Yoon Lee,&nbsp;Gosia Migut,&nbsp;Marcus Specht","doi":"10.1111/bjet.13551","DOIUrl":"10.1111/bjet.13551","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Learner behaviours often provide critical clues about learners' cognitive processes. However, the capacity of human intelligence to comprehend and intervene in learners' cognitive processes is often constrained by the subjective nature of human evaluation and the challenges of maintaining consistency and scalability. The recent widespread AI technology has been applied to learning analytics (LA), aiming at a more accurate, consistent and scalable understanding of learning to compensate for challenges that human intelligence faces. However, machine intelligence has been criticized for lacking contextual understanding and difficulties dealing with complex human emotions and social cues. In this work, we aim to understand learners' internal cognitive processes based on the external behavioural cues of learners in a digital reading context, using a hybrid intelligence (HI) approach, bridging human and machine intelligence. Based on the behavioural frameworks and the insights from human experts, we scope specific behavioural cues that are known to be relevant to learners' attention regulation, which is highly relevant for learners' cognitive processes. We utilize the public WEDAR dataset with 30 subjects' video data, behaviour annotation and pre–post tests on multiple choice and summarization tasks. We apply the explainable AI (XAI) approach to train the machine learning model so that human evaluators can also understand which behavioural features were essential for predicting the usage of the cognitive processes (ie, higher-order thinking skills [HOTS] and lower-order thinking skills [LOTS]) of learners, providing insights for the next-round feature engineering and intervention design. The result indicates that the dominant use of attention regulation behaviours is a reliable indicator of low use of LOTS with 79.33% prediction accuracy, while reading speed is a valuable indicator for predicting the overall usage of HOTS and LOTS, ranging from 60.66% to 78.66% accuracy, highly surpassing random guess of 33.33%. Our study demonstrates how various combinations of behavioural features supported by HI can inform learners' cognitive processes accurately and interpretably, integrating human and machine intelligence.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;div&gt;\u0000 \u0000 &lt;h3&gt;Practitioner notes&lt;/h3&gt;\u0000 &lt;p&gt;What is already known about this topic\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;Human attention is a cognitive process that allows us to choose and concentrate on relevant information, which leads to successful learning.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;In affective computing, certain behavioural cues (eg, attention regulation behaviours) are used to indicate learners' ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 2","pages":"678-711"},"PeriodicalIF":8.1,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13551","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"‘Internationalisation at a Distance’ at the intersections of educational technologies and the internationalisation of higher education","authors":"Jenna Mittelmeier,&nbsp;Daian Huang,&nbsp;Ashley Gunter","doi":"10.1111/bjet.13567","DOIUrl":"10.1111/bjet.13567","url":null,"abstract":"<p>In this editorial, we introduce the special section focusing on ‘Internationalisation at a Distance’ (IaD), highlighting how educational technologies contribute to internationalisation practices and processes in higher education. These practices exist in transitional spaces which interrogate conventional distinctions between mobility/immobility and home/abroad by establishing ‘third spaces’ where students and staff interact with institutions across borders without the necessity of geographic relocation. This special section evaluates these practices through a critical lens, reflecting on limitations and inequalities associated with IaD, despite its often-assumed transformative potential for expanding access to global higher education. Here, this special section argues that IaD fundamentally opposes fixed representations of place and interrogates simplistic binary frameworks, positioning itself as a concept that is inherently post-structuralist. This reframing necessitates a sophisticated comprehension of essential concepts including mobility, home, and cultural exchange. Collectively, this special section addresses these issues by underscoring how IaD continues to reinforce pre-existing disparities in resource access, epistemic (in)justices, and power imbalances. The findings discussed herein underscore the significance of ethical considerations for educational technologies in the realm of IaD and the necessity for evidence-based strategies aimed at fostering more inclusive and sustainable practices. Ultimately, IaD signifies a profound reconsideration of global higher education, presenting technology-influenced avenues for innovation while necessitating a critical examination of its functions within an unequal world.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 2","pages":"755-761"},"PeriodicalIF":8.1,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}