教育学最新文献

{"title":"Using Machine Learning to Assess Factors Associated with North American Pharmacist Licensure Examination Performance.","authors":"Douglas R Oyler, Esther P Black, Hope H Brandon, Clark D Kebodeaux, Jeffery C Talbert, Frank Romanelli","doi":"10.1016/j.ajpe.2025.101885","DOIUrl":"https://doi.org/10.1016/j.ajpe.2025.101885","url":null,"abstract":"<p><strong>Objective: </strong>Pharmacy graduates' declining performance on the North American Pharmacist Licensure Examination (NAPLEX) remains concerning, but specific student factors related to success remain unclear. Machine learning (ML) algorithms may offer improved methods to identify potentially at-risk students before they take the examination.</p><p><strong>Methods: </strong>Individuals graduating from the University of Kentucky College of Pharmacy in 2024 who passed (n=93) or failed (n=30) the NAPLEX on their first attempt were included. Over 20 characteristics related to demographics (e.g., age, sex, residence), undergraduate work (e.g., university, degree obtained, grade point average (GPA)), performance in the Doctor of Pharmacy program (e.g., GPA, elective courses taken, etc.), and engagement with NAPLEX preparatory software (RxPrep) were assessed for each student. CLASSify, a web-based platform for analysis of tabular data, was used to assess each of 8ML algorithms' ability to accurately predict whether a given student passed or failed. Area under the receiver operating curve (AUC-ROC) was primarily used to assess model accuracy. Average absolute SHapley Additive exPlanation (SHAP) value ranks were used to assess feature importance across models.</p><p><strong>Results: </strong>Four of eight ML algorithms outperformed logistic regression (AUC-ROC 0.860), with the highest AUC-ROC in the random forest model (0.930). Across high performing models, the most important features were score on the college's high stakes progression examination (MileMarker 1), engagement with RxPrep, and traditional measures of academic performance.</p><p><strong>Conclusion: </strong>ML algorithms accurately classified students' NAPLEX first-time performance in this cohort and could offer notable improvements to existing strategies colleges use to identify potentially at-risk students.</p>","PeriodicalId":55530,"journal":{"name":"American Journal of Pharmaceutical Education","volume":" ","pages":"101885"},"PeriodicalIF":3.5,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From taskforce to blueprint: implementation of literacy policy in Kansas.","authors":"David P Hurford, Alex C Fender, Michaela R Ozier, Molly J Baumgardner, Cynthia R Lane, Laurie J Curtis","doi":"10.1007/s11881-025-00340-3","DOIUrl":"https://doi.org/10.1007/s11881-025-00340-3","url":null,"abstract":"<p><p>The Kansas Legislative Task Force on Dyslexia was established in 2018 to advise and make recommendations to educators, administrators, and policymakers in Kansas. The Kansas State Department of Education adopted the recommendations of the Task Force by creating a position to coordinate issues related to dyslexia, developing and publishing the Kansas Dyslexia Handbook, addressing professional development requirements, and universal screening protocols for dyslexia were created, published, and modified. New accreditation standards for colleges of education and school districts were created that were aligned with the Knowledge and Practice Standards of the International Dyslexia Association. In July 2023, Structured Literacy was named in the state's eligibility indicators for special education and related services for students with dyslexia (KSDE, 2023b). In April 2024, Kansas Senate Bill 438 was signed into law by Governor Laura Kelly, delineating the Kansas Blueprint for Literacy and including funding to be applied to state literacy and education workforce development. This comprehensive initiative establishes a literacy advisory committee, aligns teacher training and curricular materials with Structured Literacy, prohibits the use of discredited methodologies, develops a plan for six centers to be housed across the state for direct and indirect services, and sets teacher training and student performance goals for the state. The Kansas Blueprint for Literacy requires collaboration from various stakeholders, including educators, school systems, teacher trainers, literacy experts, parents, businesses, and policymakers, to systematically improve teacher training and student literacy instruction in the state. We review the history of recent literacy legislation in Kansas and describe the cyclical process through which practice has informed policy, which has informed practice. Finally, we will report on the state's progress to date in achieving its goals.</p>","PeriodicalId":47273,"journal":{"name":"Annals of Dyslexia","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Mining on the Fundamental Factors Influencing Mathematics Achievement: Traditional and Modern Perspectives","authors":"Burcu Koca Guler,&nbsp;Fulya Gokalp Yavuz","doi":"10.1111/ejed.70281","DOIUrl":"https://doi.org/10.1111/ejed.70281","url":null,"abstract":"<div>\u0000 \u0000 <p>Assessing achievement is a complex task due to its dependence on multiple factors and the hierarchical structure of educational data, yet surveys like TIMSS offer valuable insights into its determining factors like students' mathematics anxiety. However, disregarding the nested structure of data and ignoring the assumptions of models causes poor performance such as inaccurate predictions and biased estimates. Our research utilises linear mixed models (LMMs) and machine learning (ML) techniques (e.g., REEM-tree and GP boosting) especially chosen for their abilities to model nested data and capture non-linear relationships. This study is a pioneer in the literature as these ML algorithms are implemented for the first time in TIMSS. Accordingly, mathematical tendency and emotional factors are the two primary predictors of mathematics achievement across all methods, acknowledging the possibility of potential bias due to reliance on self-report responses. However, there are variations in the effect size of the students' origins among the methods. This indicates different algorithms yield distinct results according to their inner processes and priorities, such as revealing statistical significance of predictors or contributing to predictive performance. Moreover, gender has a negligible impact across all models in our analysis, caused by cultural differences in the sample. Overall, while LMMs are widely accepted, ML methods remain competitive alternatives in prediction and flexibility. All three methods yield similar benchmarks, yet ML methods offer slightly better performance in RMSE, MAE, and MAPE while exhibiting high predictive power and capturing nonlinearity and interaction. Although they take more computation time, parallel processing mitigates this in larger datasets. Consequently, ML methods and LMMs concurrently provide broader and more precise insights in terms of predictive and inferential gains.</p>\u0000 </div>","PeriodicalId":47585,"journal":{"name":"European Journal of Education","volume":"60 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating AI in Construction Estimation Education: A Comparative Study of Togal AI and Bluebeam Revu 20","authors":"Tianjiao Zhao,&nbsp;Xi Lin,&nbsp;Ri Na","doi":"10.1111/ejed.70287","DOIUrl":"https://doi.org/10.1111/ejed.70287","url":null,"abstract":"<p>The integration of artificial intelligence (AI) into construction education is transforming how future professionals approach estimation tasks. This study examines the role of Togal AI—an AI-powered estimation tool—alongside the industry-preferred Bluebeam Revu 20 in undergraduate construction education. Through a structured experiment with 60 students, we tracked flooring area estimations for a school building, collecting both quantitative performance metrics and qualitative survey responses. Key findings show Togal AI accelerated task completion by 51.3%, improved measurement accuracy by 20.4%, enhanced team coordination by 28.4% and sped up change order processing by 75.7%, while boosting confidence by 55.2%. However, semi-structured interviews revealed concerns that over-reliance on automation might hinder critical thinking. This highlights the importance of curricular frameworks positioning AI as an educational support tool rather than a replacement for essential competencies. This study offers practical strategies for integrating AI tools into estimation education. While Togal AI automates measurement, freeing cognitive capacity for large, complex projects, its technical limitations, oversimplified markups and risk of over-reliance on AI underscore the need for curricula that balance AI efficiency with manual estimation skills. These findings inform the modernization of academic curricula, ensuring AI enhances rather than replaces essential competencies in construction education.</p>","PeriodicalId":47585,"journal":{"name":"European Journal of Education","volume":"60 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejed.70287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The influencing factors of growth mindset: A systematic literature review","authors":"Xinglin Jin, Jun Guan, Tongji Li","doi":"10.1016/j.edurev.2025.100735","DOIUrl":"https://doi.org/10.1016/j.edurev.2025.100735","url":null,"abstract":"Since the concept of growth mindset was introduced by Dweck in 2006, it has been extensively explored within various educational contexts. However, previous literature reviews have primarily focused on the effects of growth mindset rather than examining its influencing factors. The current literature review seeks to clarify the factors that contribute to the formation of a growth mindset among students. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a sample of 54 empirical articles from the past decade was selected for analysis. These articles were analysed using a four-step bottom-up coding process within the framework of Bronfenbrenner's ecological systems theory. Many influencing factors within the individual system and microsystems were revealed. The most frequently examined factor categories in previous studies include students' psychological features, teachers' teaching strategies and methods, parenting styles, specific interventions, and school environment. Two synergistic paths were identified to illustrate the interplay of these influencing factors. Finally, the current study discusses the significance of factors within different ecological systems and proposes suggestions for future studies.","PeriodicalId":48125,"journal":{"name":"Educational Research Review","volume":"3 1","pages":""},"PeriodicalIF":11.7,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315062","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":"Empowering educators to create AI-based pedagogical apps.","authors":"Minyang Chow, Pao Hup Lim, Yng Miin Loke, Olivia Ng","doi":"10.1111/medu.70070","DOIUrl":"https://doi.org/10.1111/medu.70070","url":null,"abstract":"","PeriodicalId":18370,"journal":{"name":"Medical Education","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145286228","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":"What do we know about cyberbullying assessment tools for Primary and Secondary Education students? A systematic review and meta-analytical study","authors":"Mercedes Chicote-Beato, Ana Rosa Bodoque-Osma, Manuel Jacob Sierra Díaz, Sixto González-Víllora","doi":"10.1016/j.edurev.2025.100734","DOIUrl":"https://doi.org/10.1016/j.edurev.2025.100734","url":null,"abstract":"New forms of violence, such as cyberbullying, have emerged partly due to the increased use of technological devices at an early age. This rise in risk situations has led to a proliferation in the number of instruments that assess these behaviors, requiring an exhaustive analysis to determine the contents they address, the age range in which they are validated, their psychometric properties, and the way in which they approach the conceptualization of cyberbullying. To this end, a systematic review and meta-analysis was conducted, obtaining 3859 results in the selected databases (Web of Science, Scopus, PsycInfo, PsicoDoc, ProQuest, Psychology and Behavioural Sciences and PubPsych), which through a screening process, resulted in the analysis of 28 cyberbullying assessment tools that met the established inclusion criteria ((I) scientific articles that were published in peer-reviewed scientific journals; (II) that were validated in the selected age range (Primary or Secondary Education); (III) that addressed cyberbullying; (IV) that were written in English, Spanish or Portuguese; (V) that validity and/or reliability be analyzed). Taking into consideration the meta-analytical approach, the internal consistency of the cyberbullying detection instruments and scales was found to be significant, and the meta-analysis showed satisfactory results. According to the qualitative interpretation of this statistic, all of the aforementioned scales showed reliable results (between 0.84 and 0.90). The data indicated a medium-to-large effect of the precision and reliability of the psychological scales of bullying and cyberbullying.","PeriodicalId":48125,"journal":{"name":"Educational Research Review","volume":"113 8 1","pages":""},"PeriodicalIF":11.7,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315063","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":"Concept Maps as a Content and Language Integrated Learning Facilitator in Digital Gaming Contexts: Triangulated Evidence From Students' Learning Achievements, Perceptions and Behavioural Patterns","authors":"Shu-Fang Hsieh,&nbsp;Gwo-Jen Hwang","doi":"10.1111/jcal.70136","DOIUrl":"https://doi.org/10.1111/jcal.70136","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>While both Content and Language Integrated Learning (CLIL) and Digital Game-Based Learning (DGBL) are widely recognised as effective methods to enhance bilingual education, their combination often leads to cognitive overload and fragmented learning experiences. In Taiwan's bilingual education context, where integrating English into subject teaching is a national priority, finding effective instructional models remains a critical issue. To address this, the present study introduces the Concept Map-Facilitated Content and Language Integrated Digital Gaming (CM-CLDG) model, aiming to enhance bilingual science learning for elementary students by embedding concept mapping into a digital role-playing game.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>Although DGBL has demonstrated potential to increase motivation and engagement, prior studies suggest that without structured guidance, learning outcomes may vary. The CM-CLDG model was designed to scaffold knowledge construction and reduce cognitive overload by integrating concept maps directly into the gaming tasks. This study aims to evaluate the effects of the CM-CLDG approach on students' English and science learning achievements, motivation, flow experience, anxiety and behavioural patterns.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>A quasi-experimental design was adopted with 78 sixth-grade students from an elementary school in central Taiwan. Participants were divided into the CM-CLDG group and the conventional CLIL gaming (C-CLDG) group. Pre- and posttests, questionnaires, behavioural log analyses and interviews were conducted to collect triangulated data. Learning behaviours were examined using sequential analysis via GSEQ.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>The CM-CLDG group significantly outperformed the C-CLDG group in both English and science achievements. Although no significant differences were found in motivation or anxiety, the C-CLDG group experienced greater flow. Behavioural analysis revealed that the CM-CLDG group exhibited more reflective, structured and strategic learning patterns. Interview findings supported these results, highlighting better memory retention and self-directed learning in the CM-CLDG group. These findings confirm that concept maps, when embedded in digital gameplay, can enhance bilingual learning outcomes and support more effective learning behaviours in CLIL-based educational games.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 6","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three Windows Into Self-Regulated Learning: A Multimodal Triangulation Approach in EFL Classrooms","authors":"Katrin Saks","doi":"10.1111/ejed.70285","DOIUrl":"https://doi.org/10.1111/ejed.70285","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurately assessing students' self-regulated learning remains a persistent challenge due to the construct's complexity and the limitations of individual measurement methods. This study explores the reliability and meaningfulness of assessing upper-secondary students' SRL in English as a Foreign Language classrooms using a convergent triangulation design. Integrating behavioural observation, self-report questionnaires, and teacher judgements, the research examines the convergence of these methods and their predictive power for academic outcomes. The study involved 14 Estonian students and applied both within- and cross-case analyses. While each method provided complementary insights, notable discrepancies emerged, particularly between observed behaviours and self-reported data. Teacher judgements demonstrated the strongest correlation with academic performance, although all methods were shaped by contextual and methodological limitations. The findings highlight the multifaceted nature of SRL and underscore the need for multimodal assessment strategies that acknowledge the interplay between methods. Triangulation, in this context, is conceptualised not as a means to confirm results but as a framework for obtaining a richer and more nuanced understanding of learner self-regulation. Recognising the limitations of single-method approaches, this study underscores the necessity of multimodal triangulation, one that accounts for contextual variables and integrates complementary data sources, to capture the complex interplay of cognitive and motivational dimensions in SRL.</p>\u0000 </div>","PeriodicalId":47585,"journal":{"name":"European Journal of Education","volume":"60 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Role of Chinese EFL Teachers' Interpersonal Behaviours in Their Students' Acceptance and Adoption of AI Tools Through the Lens of Technology Acceptance Model (TAM)","authors":"Peilin Kong","doi":"10.1111/ejed.70275","DOIUrl":"https://doi.org/10.1111/ejed.70275","url":null,"abstract":"<div>\u0000 \u0000 <p>The influence of teachers on the acceptance of educational technologies has been largely highlighted in the literature. However, the way their interpersonal behaviours mediate students' acceptance and adoption of artificial intelligence (AI) technologies has received limited attention. To address the gap, this study drew on the technology acceptance model (TAM) to examine such interplay in the context of China. To do so, 103 Chinese English as a foreign language (EFL) students were interviewed online. The findings, obtained from manual thematic analysis, indicated that teachers' interpersonal behaviours affected Chinese EFL students' AI acceptance and adoption in five ways, namely by showcasing practical values of AI, establishing a positive learning environment, increasing ease of use, reducing negative AI-induced emotions and encouraging autonomy and reflectivity. A comprehensive discussion of the findings is provided along with a list of implications on different grounds for EFL teachers, students and trainers augmenting their familiarity with innovative educational technologies in L2 education.</p>\u0000 </div>","PeriodicalId":47585,"journal":{"name":"European Journal of Education","volume":"60 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145317061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}