British Journal of Educational Technology最新文献

{"title":"Investigating the impact of AR technologies on geometric learning in primary school: A comparison between marker-based and markerless AR","authors":"Hunhui Na, K. Bret Staudt Willet, Chaewon Kim","doi":"10.1111/bjet.13584","DOIUrl":"https://doi.org/10.1111/bjet.13584","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Over the past decade, augmented reality (AR) has gained traction in geometric learning for its pedagogical potential. However, research on how learners engage with different AR technologies and when and how to incorporate them has remained largely unexplored. Employing a learning analytics approach, this study investigates the impact of marker-based and markerless AR technologies on geometric learning and student engagement in primary school classrooms. We developed a mobile AR application that integrates both marker-based (ie, using predefined visual markers to trigger content) and markerless (ie, triggering content without predefined markers) AR modes for learning 3D shapes and conducted a quasi-experimental study with 43 sixth-grade students. To comprehensively capture student engagement, we collected pre- and posttests on geometric understanding, along with in-app log and device sensor data. Our findings showed that both AR technologies effectively enhance geometric understanding. However, engagement patterns varied significantly; marker-based AR led to more focused cognitive tasks, while markerless AR facilitated dynamic spatial navigation. The study highlights the distinct technical affordances of each AR technology that can lead to unique pedagogical advantages. Based on these findings, we propose a hybrid AR model for geometric learning that leverages the strengths of both marker-based and markerless AR.</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>Augmented reality (AR) is a powerful tool for enhancing geometric learning by providing immersive and interactive learning experiences.</li>\u0000 \u0000 <li>Marker-based AR—using predefined visual markers (eg, QR codes or images) to trigger content—has been widely used in education with its ease of use and setup.</li>\u0000 \u0000 <li>Markerless AR—using spatial recognition capabilities without predefined visual markers—has recently emerged as a new and accessible technology, offering the potential for more dynamic and immersive learning experiences in classroom settings.</li>\u0000 </ul>\u0000 <p>What this paper adds\u0000\u0000 </p><ul>\u0000 \u0000 <li>Past studies have predominantly focused on answering <i>whether</i> marker-based AR can be effectively used compared with traditional tools (eg, computers); this paper addresses <i>how and when</i> different AR technologies can be used.</li>\u0000 \u0000 <li>Findings show that both marker-based and markerless AR technologies enhance geometric und","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 6","pages":"2502-2521"},"PeriodicalIF":8.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248643","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":"When and how biases seep in: Enhancing debiasing approaches for fair educational predictive analytics","authors":"Lin Li, Namrata Srivastava, Jia Rong, Quanlong Guan, Dragan Gašević, Guanliang Chen","doi":"10.1111/bjet.13575","DOIUrl":"https://doi.org/10.1111/bjet.13575","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>The use of predictive analytics powered by machine learning (ML) to model educational data has increasingly been identified to exhibit bias towards marginalized populations, prompting the need for more equitable applications of these techniques. To tackle bias that emerges in training data or models at different stages of the ML modelling pipeline, numerous debiasing approaches have been proposed. Yet, research into state-of-the-art techniques for effectively employing these approaches to enhance fairness in educational predictive scenarios remains limited. Prior studies often focused on mitigating bias from a single source at a specific stage of model construction within narrowly defined scenarios, overlooking the complexities of bias originating from multiple sources across various stages. Moreover, these approaches were often evaluated using typical threshold-dependent fairness metrics, which fail to account for real-world educational scenarios where thresholds are typically unknown before evaluation. To bridge these gaps, this study systematically examined a total of 28 representative debiasing approaches, categorized by the sources of bias and the stage they targeted, for two critical educational predictive tasks, namely forum post classification and student career prediction. Both tasks involve a two-phase modelling process where features learned from upstream models in the first phase are fed into classical ML models for final predictions, which is a common yet under-explored setting for educational data modelling. The study observed that addressing local stereotypical bias, label bias or proxy discrimination in training data, as well as imposing fairness constraints on models, can effectively enhance predictive fairness. But their efficacy was often compromised when features from upstream models were inherently biased. Beyond that, this study proposes two novel strategies, namely Multi-Stage and Multi-Source debiasing to integrate existing approaches. These strategies demonstrated substantial improvements in mitigating unfairness, underscoring the importance of unified approaches capable of addressing biases from various sources across multiple stages.</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>Predictive analytics for educational data modelling often exhibit bias against students from certain demographic groups based on sensitive attributes.</li>\u0000 \u0000 <li>Bias can emerge in training data or models at different time points of the ML modelling pipeline, resulting in unfair final predictions.</li>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 6","pages":"2478-2501"},"PeriodicalIF":8.1,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bera-journals.onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248642","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":"Examining students' attitudes and intentions towards using ChatGPT in higher education","authors":"Muhammad Zia Ul Haq, Guangming Cao, Rawan Mazen Yousef Abukhait","doi":"10.1111/bjet.13582","DOIUrl":"https://doi.org/10.1111/bjet.13582","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <p>The release of ChatGPT has sparked an immense academic debate regarding its potential advantages and drawbacks for students. Despite its significance, there is a lack of empirical research on students' attitudes and behavioural intentions towards the utilization of ChatGPT. To fill this gap, we employed the integrated AI acceptance-avoidance model (IAAAM) and used partial least squares structural equation modelling to test the research model by collecting data from 287 university students in the UAE. Our key findings indicate that factors such as performance expectancy and effort expectancy positively influence students' attitudes towards ChatGPT. Additionally, personal development concerns demonstrate a negative association with attitudes and intentions to use ChatGPT, while perceived threat demonstrates a non-significant negative association with both. The findings of this research contribute to the emerging body of literature on ChatGPT's usage both conceptually and empirically. It also offers practical insights for future technology developers, emphasizing the significance of adopting a balanced approach that carefully considers both the benefits and potential drawbacks linked to the utilization of ChatGPT.</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>ChatGPT represents a significant advancement in natural language processing and has gained immense popularity since its launch, with rapid acceptance and adoption across various sectors.</li>\u0000 \u0000 <li>In higher education, ChatGPT has seen substantial use among university students and teachers.</li>\u0000 \u0000 <li>Previous studies have predominantly focused on investigating the attitudes and behavioural intentions of managers in organizational contexts and academic staff towards technology use in universities.</li>\u0000 </ul>\u0000 <p>What this paper adds\u0000\u0000 </p><ul>\u0000 \u0000 <li>This study addresses an empirical gap by focusing on students' behavioural intentions towards employing ChatGPT, offering a student-centric perspective.</li>\u0000 \u0000 <li>It draws upon established technology acceptance literature and employs the integrated AI acceptance-avoidance model (IAAAM) to explore factors shaping students' attitudes and intentions regarding ChatGPT utilization.</li>\u0000 \u0000 <li>By adopting a “net valence approach”, the study considers both positive and negative factors associated with ChatGPT use in higher education.</li>\u0000 \u0000 ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 6","pages":"2428-2452"},"PeriodicalIF":8.1,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248779","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":"Exploring peer facilitation and critical thinking in asynchronous online discussions: A lag sequential analysis approach","authors":"Erqi Zhang, Zhaoli Zhang, Hai Liu, Shuyun Han, Zengcan Xue","doi":"10.1111/bjet.13583","DOIUrl":"https://doi.org/10.1111/bjet.13583","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <p>Asynchronous online discussions (AODs) are increasingly prevalent in higher education to adapt to educational changes and promote critical thinking among learners. Past research has emphasized instructors' facilitation roles in encouraging learners' critical thinking in AODs, while fewer studies explored peer facilitation and peer participants' critical thinking from the students' perspective as facilitators. This study used a lag sequential analysis approach to examine peer facilitation techniques and critical thinking in a peer-facilitated AOD spanning six tasks over 12 weeks with 40 undergraduate participants. Results highlighted that the most frequently used peer facilitation techniques were <i>giving own opinions or experiences</i> and <i>questioning</i>, with the latter demonstrating the highest number of significant sequential patterns. Peer participants' critical thinking primarily involved <i>analyse</i> and <i>evaluate</i>, with significant sequential patterns observed in lower level and higher order critical thinking stages but not between them. Further investigation revealed the impact of peer facilitation techniques on critical thinking, and a new three-phase model was developed to describe their associations. These findings suggest that dynamic peer facilitation techniques effectively enhance critical thinking, with specific techniques targeting distinct phases of its development in AODs. The study provides actionable insights for educators, offering strategies to optimize facilitation approaches and foster critical thinking skills in higher education settings.</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>Asynchronous online discussions are widely used in higher education to encourage learners' critical thinking.</li>\u0000 \u0000 <li>Instructors as facilitators play a positive role in encouraging learners' critical thinking in asynchronous online discussions, while the role of peer facilitators is less discussed.</li>\u0000 \u0000 <li>In peer-facilitated asynchronous online discussions, the facilitation techniques used by peer facilitators affect the development of critical thinking in peer participants.</li>\u0000 </ul>\u0000 <p>What this paper adds\u0000\u0000 </p><ul>\u0000 \u0000 <li>Uses lag sequential analysis to examine the sequential patterns of peer facilitation techniques and critical thinking in peer-facilitated asynchronous online discussions.</li>\u0000 \u0000 <li>Reports common peer facilitation techniques used by peer facilitators","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 6","pages":"2453-2477"},"PeriodicalIF":8.1,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248777","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":"Long-term effects of an online math tool on U.S. adolescents' achievement","authors":"Mingyu Feng,&nbsp;Natalie Brezack,&nbsp;Chunwei Huang,&nbsp;Kelly Collins","doi":"10.1111/bjet.13579","DOIUrl":"https://doi.org/10.1111/bjet.13579","url":null,"abstract":"<p>In this study, we examined the long-term effects of ASSISTments, an educational technology platform that enables formative assessment. The intervention was implemented in schools in one U.S. state for 12–13-year-old students (7th grade). Students' math achievement was measured at the end of the following school year (8th grade, ages 13–14 years), one year after the intervention was complete. Students who received the intervention demonstrated improved math achievement one year later compared to students who received typical math instruction. The intervention improved math achievement to a greater extent for students of colour and Hispanic students, as well as those attending schools with higher proportions of economically disadvantaged students. Students' math achievement gains were related to their ASSISTments usage during 7th grade. These findings suggest that the intervention has the potential to significantly improve math achievement for students in the long run, one year after the intervention ended, particularly for students from marginalized backgrounds.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 6","pages":"2404-2427"},"PeriodicalIF":8.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248540","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":"BJET Editorial Spring 2025: Reporting on AIED research and ethical considerations","authors":"Manolis Mavrikis,&nbsp;Cathy Lewin,&nbsp;Mutlu Cukurova,&nbsp;Louis Major,&nbsp;Laura Outhwaite,&nbsp;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":"English vocabulary learning through gamification with Tic-Tac-Toe in Flippity-Connecto: The prediction of gameplay self-efficacy to anxiety, interest and flow experience","authors":"Jon-Chao Hong,&nbsp;Tzu-Yu Tai,&nbsp;Fen-Yuan Liang","doi":"10.1111/bjet.13577","DOIUrl":"https://doi.org/10.1111/bjet.13577","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;A wide variety of gamification tools are available for language learning; however, few studies have explored the impact of Flippity-Connecto (hereafter referred to as Connecto) on learners' cognitive and affective processes. Connecto, a game similar to Tic-Tac-Toe, was designed to assist students in learning English as a foreign language (EFL). This study utilized the Tic-Tac-Toe mechanism to engage students in competitive English vocabulary learning. Grounded in the achievement emotion model, this research examined the relationships among learning interest, gameplay anxiety and flow experience while students played the game. In a single-group quasi-experimental study, sixth-grade students from an elementary school played the game three times over a period of 3 weeks, followed by the completion of questionnaires. A total of 123 valid questionnaires were collected. Structural equation modelling results revealed that: (1) gameplay self-efficacy negatively predicted gameplay anxiety and positively predicted learning interest and (2) flow experience was negatively predicted by gameplay anxiety and positively predicted by learning interest. The finding that gameplay anxiety can enhance flow experience challenges conventional views, suggesting that anxiety may energize players when facing challenges, akin to being psyched up. These findings provide insights into the complex dynamics of gamified language learning, underscoring the importance of self-efficacy, anxiety management and interest in fostering EFL learning.&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;Gamification tools have the potential to enhance vocabulary learning.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Games like Tic-Tac-Toe, known for their uncertainty and quick outcomes, contribute to player motivation.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Flow, characterized by deep immersion and enjoyment during gameplay in language learning, can be hindered by learner anxiety.&lt;/li&gt;\u0000 &lt;/ul&gt;\u0000 &lt;p&gt;What this paper adds\u0000\u0000 &lt;/p&gt;&lt;ul&gt;\u0000 \u0000 &lt;li&gt;We investigated the relationship between players' self-efficacy, activated positive emotions and deactivated negative emotions during Connecto gameplay, shedding light on the intricate dynamics of gamified language learning.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;We utilized the achievement emotion model to demonstrate that gameplay self-efficacy correlates negatively with gameplay anxiety and positively with learning interest.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The results high","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"56 6","pages":"2387-2403"},"PeriodicalIF":8.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248741","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}