British Journal of Educational Technology最新文献

{"title":"Adaptive support for self-regulated learning in digital learning environments","authors":"Mohammad Khalil,&nbsp;Jacqueline Wong,&nbsp;Barbara Wasson,&nbsp;Fred Paas","doi":"10.1111/bjet.13479","DOIUrl":"10.1111/bjet.13479","url":null,"abstract":"<p>A core focus of self-regulated learning (SRL) research lies in uncovering methods to empower learners within digital learning environments. As digital technologies continue to evolve during the current hype of artificial intelligence (AI) in education, the theoretical, empirical and methodological nuances to support SRL are emerging and offering new ways for adaptive support and guidance for learners. Such affordances offer a unique opportunity for personalised learning experiences, including adaptive interventions. Exploring the application of adaptivity to enhance SRL is an important and emerging area of research that requires further attention. This editorial introduces the contributions of seven papers for the special section on adaptive support for SRL within digital learning environments. These papers explore various themes related to enhancing SRL strategies through technological interventions, offering valuable insights and paving the way for future advancements in this dynamic area.</p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 4","pages":"1281-1289"},"PeriodicalIF":6.6,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13479","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140887474","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":"Pre-service teachers' inclination to integrate AI into STEM education: Analysis of influencing factors","authors":"Fengyao Sun,&nbsp;Peiyao Tian,&nbsp;Daner Sun,&nbsp;Yanhua Fan,&nbsp;Yuqin Yang","doi":"10.1111/bjet.13469","DOIUrl":"10.1111/bjet.13469","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;In the ever-evolving AI-driven education, integrating AI technologies into teaching practices has become increasingly imperative for aspiring STEM educators. Yet, there remains a dearth of studies exploring pre-service STEM teachers' readiness to incorporate AI into their teaching practices. This study examined the factors influencing teachers' willingness to integrate AI (WIAI), especially from the perspective of pre-service STEM teachers' attitudes towards the application of AI in teaching. In the study, a comprehensive survey was conducted among 239 pre-service STEM teachers, examining the influences and interconnectedness of Technological Pedagogical Content Knowledge (TPACK), Perceived Usefulness (PU), Perceived Ease of Use (PE), and Self-Efficacy (SE) on WIAI. Structural Equation Modeling (SEM) was employed for data analysis. The findings illuminated direct influences of TPACK, PU, PE, and SE on WIAI. TPACK was found to directly affect PE, PU, and SE, while PE and PU also directly influenced SE. Further analysis revealed significant mediating roles of PE, PU, and SE in the relationship between TPACK and WIAI, highlighting the presence of a chain mediation effect. In light of these insights, the study offers several recommendations on promoting pre-service STEM teachers' willingness to integrate AI into their teaching practices.&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;The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Pre-service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The TAM and TPACK frameworks are used to analyse teacher factors in technology-supported learning environments.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Few studies have been conducted for examining factors of pre-service teachers' willingness to integrate AI into teaching practices in the context of STEM education.&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;A survey was designed and developed for exploring pre-service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;TPACK, SE, PU, and PE have direct impact on pre-service STEM teachers' WIAI.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;SE, PU, and PE have been identified as mediating variables in the relationship ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 6","pages":"2574-2596"},"PeriodicalIF":6.7,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140837388","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":"Primary school teachers' classroom-based e-assessment practices: Insights from the theory of planned behaviour","authors":"Ying Zhan,&nbsp;Daner Sun,&nbsp;Ho Man Kong,&nbsp;Ye Zeng","doi":"10.1111/bjet.13478","DOIUrl":"10.1111/bjet.13478","url":null,"abstract":"<p>There is a global trend in the increased adoption of e-assessment in school classrooms to enhance learning. Teachers, as classroom-based assessment designers and implementers, play a vital role in such assessment change. However, little is known about school teachers' classroom-based e-assessment practices and the underlying reasons. To address this research gap, this study identified the factors influencing Hong Kong primary school teachers' e-assessment practices underpinned by the theory of planned behaviour (TPB). A large-scale survey was issued to 878 teachers via Qualtrics. Structural equation modelling (SEM) analysis shows that primary school teachers' intentions of using e-assessment and perceived behavioural control of it were the two strongest factors predicting their e-assessment practices in a general way. Specifically, teachers' intentions outweighed perceived behavioural control in determining their use of alternative e-assessment tasks and e-feedback, but this reversed for e-tests/exercises. The impact of perceived behavioural control was consistent across the three types of e-assessment practices. Furthermore, teachers' attitudes significantly influenced their intentions to use alternative e-assessment tasks, while subject norms primarily predicted their intentions to use e-feedback. The findings have implications for primary schools to take countermeasures to facilitate the successful implementation of e-assessment at the classroom level.\u0000 </p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 6","pages":"2740-2759"},"PeriodicalIF":6.7,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141015211","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":"Co-designing enduring learning analytics prediction and support tools in undergraduate biology courses","authors":"Robert D. Plumley,&nbsp;Matthew L. Bernacki,&nbsp;Jeffrey A. Greene,&nbsp;Shelbi Kuhlmann,&nbsp;Mladen Raković,&nbsp;Christopher J. Urban,&nbsp;Kelly A. Hogan,&nbsp;Chaewon Lee,&nbsp;Abigail T. Panter,&nbsp;Kathleen M. Gates","doi":"10.1111/bjet.13472","DOIUrl":"10.1111/bjet.13472","url":null,"abstract":"<p>Even highly motivated undergraduates drift off their STEM career pathways. In large introductory STEM classes, instructors struggle to identify and support these students. To address these issues, we developed co-redesign methods in partnership with disciplinary experts to create high-structure STEM courses that better support students and produce informative digital event data. To those data, we applied theory- and context-relevant labels to reflect active and self-regulated learning processes involving LMS-hosted course materials, formative assessments, and help-seeking tools. We illustrate the predictive benefits of this process across two cycles of model creation and reapplication. In cycle 1, we used theory-relevant features from 3 weeks of data to inform a prediction model that accurately identified struggling students and sustained its accuracy when reapplied in future semesters. In cycle 2, we refit a model with temporally contextualized features that achieved superior accuracy using data from just two class meetings. This modelling approach can produce durable learning analytics solutions that afford scaled and sustained prediction and intervention opportunities that involve explainable artificial intelligence products. Those same products that inform prediction can also guide intervention approaches and inform future instructional design and delivery.\u0000 </p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 5","pages":"1860-1883"},"PeriodicalIF":6.7,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141015020","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":"The impact of an academic counselling learning analytics tool: Evidence from 3 years of use","authors":"Valeria Henríquez,&nbsp;Julio Guerra,&nbsp;Eliana Scheihing","doi":"10.1111/bjet.13474","DOIUrl":"10.1111/bjet.13474","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Despite the importance of academic counselling for student success, providing timely and personalized guidance can be challenging for higher education institutions. In this study, we investigate the impact of counselling instances supported by a learning analytics (LA) tool, called TrAC, which provides specific data about the curriculum and grades of each student. To evaluate the tool, we measured changes in students' performance ranking position over 3 years and compared the performance of students who received counselling with and without the tool. Our results show that using the tool is related to an improvement in cohort ranking. We further investigated the characteristics of counselled students using cluster analyses. The findings highlight the potential beneficial influence on academic outcomes arising from the provision of guidance to students regarding their course load decisions via TrAC-mediated counselling. This study contributes to the field of LA by providing evidence of the impact of counselling supported by an LA tool in a real-world setting over a long period of time. Our results suggest that incorporating LA into academic counselling practices can improve student success.&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;By analysing student performance, teaching strategies and resource impact, learning analytics (LA) empowers institutions to make informed changes in curriculum design, resource allocation and educational policies.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Through insights into academic progress, engagement and behaviour, LA counselling tools enable the identification of at-risk students and those needing additional support.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;In the related literature, there are areas for further exploration such as understanding the scalability and long-term effects of interventions on student success and retention.&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;Through rigorous data analysis, the paper establishes a connection between LA utilization and enhanced student performance, offering concrete evidence of the effectiveness of LA interventions.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;By examining various factors such as academic stage and course load, the research offers valuable insights into the contextual nuances that optimize the outcomes of LA tool-based support.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;It adds to the growing body of evidence that supports the efficacy of data-driven interventio","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 5","pages":"1884-1899"},"PeriodicalIF":6.7,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140812299","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":"Improving executive functions at school. Integrating metacognitive exercise in class and computerized training at home to ensure training intensity and generalization. A feasibility pilot study","authors":"Carlotta Rivella,&nbsp;Clara Bombonato,&nbsp;Chiara Pecini,&nbsp;Andrea Frascari,&nbsp;Paola Viterbori","doi":"10.1111/bjet.13470","DOIUrl":"10.1111/bjet.13470","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;Previous research has demonstrated the effectiveness of executive functions (EFs) training, both in computer-based and school-based formats. However, there is limited research on the combined effects of these training modalities. This study aims to assess the feasibility and preliminary efficacy of an EFs training programme for primary school children. The programme includes computerized training sessions for home use and school activities with metacognitive elements. The study included a sample of 53 second-grade children, with 21 children in the training group and 32 children in the control group. Feasibility questionnaires were completed by children, parents and teachers. The children also underwent an EFs evaluation. The results indicate that the training was enjoyable for children and feasible for parents and teachers. Furthermore, preliminary efficacy analysis revealed significant improvements in working memory. These findings suggest that the training model holds promise for enhancing EFs in children in the school context.&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;Individual differences in executive functions influence acquisitions, behaviours and competencies in several specific domains from infancy to adulthood.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Enhancing executive functions during school-age years can contribute to reducing or preventing academic, behavioural and social difficulties.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Among interventions targeting executive functions in school-aged children, school-based interventions have shown the highest effectiveness, followed by metacognitive interventions and computer-based interventions.&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;This paper presents the implementation of an innovative school-based training programme designed to improve executive functions (EFs). The programme combines metacognitive sessions conducted at school with computer-based sessions carried out at home. The goal was to enhance the effectiveness and generalizability of the training.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;The training programme was found to be enjoyable for children and feasible for both parents and teachers.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Preliminary efficacy data indicate promising results, suggesting that the training programme is effective in achieving its intended goals.&lt;/li&gt;\u0000 ","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 6","pages":"2719-2739"},"PeriodicalIF":6.7,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140812295","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":"Exploring the impact of gamification on students’ academic performance: A comprehensive meta-analysis of studies from the year 2008 to 2023","authors":"Jiyuan Zeng,&nbsp;Daner Sun,&nbsp;Chee-Kit Looi,&nbsp;Andy Chun Wai Fan","doi":"10.1111/bjet.13471","DOIUrl":"10.1111/bjet.13471","url":null,"abstract":"<p>Gamification, characterized by the integration of game design elements into non-game environments, has gained popularity in classrooms due to its potential for increased engagement and enjoyment compared to traditional lecture-based teaching methods. While students generally exhibit positive attitudes towards gamification, its impact on academic achievement remains a subject of debate. This study employed a meta-analysis approach to examine the overall influence of gamification on students' academic performance. The sample comprised 22 experimental studies conducted between 2008 and 2023, comparing the effects of gamified and non-gamified classes. Utilizing a random effects model, the results revealed a moderately positive effect of gamification on student academic performance (Hedges's g = 0.782, <i>p</i> &lt; 0.05). The paper further discussed the outcomes of various moderator analyses, providing valuable insights into the selection and utilization of game design elements, as well as considerations specific to different educational stages.\u0000 </p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 6","pages":"2478-2502"},"PeriodicalIF":6.7,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140812032","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":"Evaluating technology enhanced learning by using single-case experimental design: A systematic review","authors":"Nadira Dayo,&nbsp;Sameh Said Metwaly,&nbsp;Wim Van Den Noortgate","doi":"10.1111/bjet.13468","DOIUrl":"10.1111/bjet.13468","url":null,"abstract":"<p>Single-case experimental designs (SCEDs) may offer a reliable and internally valid way to evaluate technology-enhanced learning (TEL). A systematic review was conducted to provide an overview of what, why and how SCEDs are used to evaluate TEL. Accordingly, 136 studies from nine databases fulfilling the inclusion criteria were included. The results showed that most of the studies were conducted in the field of special education focusing on evaluating the effectiveness of computer-assisted instructions, video prompts and mobile devices to improve language and communication, socio-emotional, skills and mental health. The research objective of most studies was to evaluate the effects of the intervention; often no specific justification for using SCED was provided. Additionally, multiple baseline and phase designs were the most common SCED types, with most measurements in the intervention phase. Frequent data collection methods were observation, tests, questionnaires and task analysis, whereas, visual and descriptive analysis were common methods for data analysis. Nearly half of the studies did not acknowledge any limitations, while a few mentioned generalization and small sample size as limitations. The review provides valuable insights into utilizing SCEDs to advance TEL evaluation methodology and concludes with a reflection on further opportunities that SCEDs can offer for evaluating TEL.\u0000 </p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 6","pages":"2457-2477"},"PeriodicalIF":6.7,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799665","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":"Using explainable AI to unravel classroom dialogue analysis: Effects of explanations on teachers' trust, technology acceptance and cognitive load","authors":"Deliang Wang,&nbsp;Cunling Bian,&nbsp;Gaowei Chen","doi":"10.1111/bjet.13466","DOIUrl":"10.1111/bjet.13466","url":null,"abstract":"<p>Deep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt and valuable feedback on their teaching practices. However, these deep learning models often have intricate structures with numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding their classroom dialogue analysis likely leads teachers to distrust and underutilize these AI-powered models. To tackle this issue, we leveraged explainable AI to unravel classroom dialogue analysis and conducted an experiment to evaluate the effects of explanations. Fifty-nine pre-service teachers were recruited and randomly assigned to either a treatment (<i>n</i> = 30) or control (<i>n</i> = 29) group. Initially, both groups learned to analyse classroom dialogue using AI-powered models without explanations. Subsequently, the treatment group received both AI analysis and explanations, while the control group continued to receive only AI predictions. The results demonstrated that teachers in the treatment group exhibited significantly higher levels of trust in and technology acceptance of AI-powered models for classroom dialogue analysis compared to those in the control group. Notably, there were no significant differences in cognitive load between the two groups. Furthermore, teachers in the treatment group expressed high satisfaction with the explanations. During interviews, they also elucidated how the explanations changed their perceptions of model features and attitudes towards the models. This study is among the pioneering works to propose and validate the use of explainable AI to address interpretability challenges within deep learning-based models in the context of classroom dialogue analysis.\u0000 </p>","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 6","pages":"2530-2556"},"PeriodicalIF":6.7,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13466","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140666555","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":"Towards inclusivity in AI: A comparative study of cognitive engagement between marginalized female students and peers","authors":"Shiyan Jiang,&nbsp;Jeanne McClure,&nbsp;Cansu Tatar,&nbsp;Franziska Bickel,&nbsp;Carolyn P. Rosé,&nbsp;Jie Chao","doi":"10.1111/bjet.13467","DOIUrl":"10.1111/bjet.13467","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;p&gt;This study addresses the need for inclusive AI education by focusing on marginalized female students who historically lack access to learning opportunities in computing. It applies the theoretical framework of intersectionality to understand how gender, race and ethnicity intersect to shape these students' learning experiences and outcomes. Specifically, this study investigated 27 high-school students' cognitive engagement in machine learning practices. We conducted the Wilcoxon–Mann–Whitney test to explore differences in cognitive engagement between marginalized female students and their peers, employed comparative content analysis to delve into significant differences and analysed interview data thematically to gain deeper insights into students' machine learning model development processes. The findings indicated that, when engaging in machine learning practices requiring drawing diverse cultural perspectives, marginalized female students demonstrated significantly higher performance compared to their peers. In particular, marginalized female students exhibited strengths in holistic language analysis, paying attention to writers' intentions and recognizing cultural nuances in language. This study suggests that integrating language analysis and machine learning across subjects has the potential to empower marginalized female students and amplify their perspectives. Furthermore, it calls for a strengths-based approach to reshape the narrative of underrepresentation and promote equitable participation in machine learning and AI.&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;Female students, particularly those from underrepresented groups such as African American and Latina students, often experience low levels of cognitive engagement in computing.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Marginalized female students possess unique strengths that, when nurtured, have the potential to not only transform their own learning experiences but also contribute to the advancement of the computing field.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;It is critical to empower marginalized female students in K-12 AI (ie, a subfield of computing) education, seeking to bridge the gender and racial disparity in AI.&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;Marginalized female students outperformed their peers in responding to machine learning questions related to feature analysis and feature distribution interpretati","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"55 6","pages":"2557-2573"},"PeriodicalIF":6.7,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bjet.13467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670385","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}