Computers & Education最新文献_第2页

Fifty Years of Topic Development, Thematic Combination, and AI’s Evolving Reach in Computers & Education 五十年的主题发展，主题组合，以及人工智能在计算机和教育领域的发展

IF 12 1区教育学

Computers & Education Pub Date : 2026-09-29 DOI: 10.1016/j.compedu.2026.105755

Jiun-Yu Wu, An-Ching Shih, Yuan-Hsuan Lee, Ching Sing Chai, Chin-Chung Tsai

{"title":"Fifty Years of Topic Development, Thematic Combination, and AI’s Evolving Reach in Computers & Education","authors":"Jiun-Yu Wu, An-Ching Shih, Yuan-Hsuan Lee, Ching Sing Chai, Chin-Chung Tsai","doi":"10.1016/j.compedu.2026.105755","DOIUrl":"https://doi.org/10.1016/j.compedu.2026.105755","url":null,"abstract":"Across the first fifty years of <ce:italic>Computers & Education</ce:italic>, successive technologies entered an expanding educational agenda centered on teaching, learning, learner regulation, assessment, participation, access, and institutional conditions. This study examined how that agenda developed from 1976 through partial 2026. We assembled 5,739 records, retained 5,388 eligible articles and reviews, and modeled 5,387 records using titles and available abstracts. An eighteen-topic structural topic model traced topic prevalence across five historical periods. Ordered primary–secondary topic combinations examined changing within-article co-occurrence patterns, while a validated dictionary and continuous piecewise binomial logistic models traced artificial intelligence (AI) prevalence and topical integration. Early volumes concentrated on computer-assisted learning, educational software, and intelligent systems; later research gave greater attention to teacher integration, collaboration, learning analytics, learner regulation, digital literacy, and feedback. Within-article topic combinations diversified substantially across the archive. The share represented by the ten most frequent topic combinations fell from 74.4% in 1976–1989 to 16.5% in 2010–2019, indicating substantial diversification. The human-validated AI corpus contained 390 records (7.2%) and extended across 17 of the 18 topics. Annual modeling identified marked acceleration in AI-related publication prevalence after 2018, as research on generative models expanded alongside established AI traditions. This acceleration reflects growing research attention, not demonstrated educational effectiveness. Across fifty years, the journal broadened how it addressed enduring educational questions. These findings call for evaluating AI within its educational configuration, with attention to human agency, theoretical and disciplinary grounding, and evidence of effectiveness in authentic learning settings.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"4 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149085804","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}

引用次数: 0

From Citation to Documented Use: Computers & Education Research in Selected Policy Reports 从引用到文档使用：选定政策报告中的计算机与教育研究

IF 12 1区教育学

Computers & Education Pub Date : 2026-09-28 DOI: 10.1016/j.compedu.2026.105754

Zvi Bekerman, Miguel Nussbaum

{"title":"From Citation to Documented Use: Computers & Education Research in Selected Policy Reports","authors":"Zvi Bekerman, Miguel Nussbaum","doi":"10.1016/j.compedu.2026.105754","DOIUrl":"https://doi.org/10.1016/j.compedu.2026.105754","url":null,"abstract":"Policy-citation databases show where academic research is cited but not what a cited paper does within a policy report. This study examines the documented roles of papers published in Computers & Education within 35 policy reports. Qualitative document analysis was applied to 168 report–paper linkages to determine whether a paper supported a position stated in the report, framed a problem, provided a benchmark, contributed to a tool, or appeared only in the bibliography. The analysis also examined whether the paper preceded the relevant policy activity, used verified cases to examine the separation of cited and report-generated evidence, and summarized visible contextual safeguards at report level. Most linkages supported positions stated in the reports; framing and problem-setting were the next most common role, whereas tool adoption and bibliography-only occurrence were rare. Most linkages with a traceable role in report text involved papers published before the relevant activity, but visible safeguards for applying evidence across contexts were uncommon. Because the corpus was drawn from the top 40 relevance-ranked Overton results and only a subset received complete second-pass verification, these results represent exploratory patterns within this corpus rather than stable estimates of the documented roles of Computers & Education papers across policy reports generally. By combining policy-citation data with qualitative document analysis, the study shows how bibliographic visibility can be distinguished from a paper’s documented role and the qualifications on that role. Its findings provide a journal-specific point of comparison for future studies of education journals and policy reports.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"4 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149085805","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}

引用次数: 0

Mapping fifty years of technology-enhanced science and mathematics education in Computers & Education: Trends and connections with technologies, functions, and theories 《计算机与教育：技术、功能和理论的趋势和联系》

IF 12 1区教育学

Computers & Education Pub Date : 2026-09-01 DOI: 10.1016/j.compedu.2026.105756

Min-Hsien Lee,Tzung-Jin Lin,Edwin Chng,Guo-Li Chiou,Aik-Ling Tan,Jyh-Chong Liang,Pin-Chi Huang,Seng Chee Tan,Chin-Chung Tsai

引用次数: 0

From Computer-Supported Collaborative Learning to Human–AI Collaboration: CSCL Research in Fifty Years of Computers & Education 从计算机支持的协同学习到人机协作：计算机与教育五十年的CSCL研究

IF 12 1区教育学

Computers & Education Pub Date : 2026-09-01 DOI: 10.1016/j.compedu.2026.105753

Ramazan Yilmaz,Fatma Gizem Karaoglan Yilmaz

引用次数: 0

Corrigendum to “LLM-derived metrics in second language writing assessment: an explainable AI approach” [Computers & Education 254 (2026) 105721] “法学硕士衍生的第二语言写作评估指标：可解释的人工智能方法”的勘误表[计算机与教育254 (2026)105721]

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-28 DOI: 10.1016/j.compedu.2026.105740

Jingying Hu, Yan Cong

引用次数: 0

The Design Framework for Pedagogical Agents Supporting Constructivist Learning with Generative AI 用生成式人工智能支持建构主义学习的教学代理设计框架

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-28 DOI: 10.1016/j.compedu.2026.105744

Chen-Chung Liu, Chin-Chung Tsai, Ramazan Yilmaz, Jiun-Yu Wu, Maria Ranieri, Di Zou, Leanna Archambault, Johan van Braak, Gautam Biswas

{"title":"The Design Framework for Pedagogical Agents Supporting Constructivist Learning with Generative AI","authors":"Chen-Chung Liu, Chin-Chung Tsai, Ramazan Yilmaz, Jiun-Yu Wu, Maria Ranieri, Di Zou, Leanna Archambault, Johan van Braak, Gautam Biswas","doi":"10.1016/j.compedu.2026.105744","DOIUrl":"https://doi.org/10.1016/j.compedu.2026.105744","url":null,"abstract":"While generative AI lowers the barrier to creating pedagogical agents, unguided designs risk undermining learner agency and hindering deep learning. This paper argues that a constructivist framing provides a strong theoretical basis for realigning pedagogical agent design to promote active knowledge construction and problem-solving. Guided by constructivist principles, we propose a design framework that (1) incorporates context-aware, in-the-moment guidance within authentic, open-ended learning activities; (2) supports the development of interpretation through productive cognitive conflict; (3) encourages collaboration and group discussions; (4) allows artifact co-creation with formative evaluation; and (5) fine-tunes cognitive apprenticeship strategies and intervention timing. We demonstrate how agents implement these principles by applying generative AI and multimodal learning analytics across various constructivist learning settings, including science labs, simulations, and computational modeling environments. We also highlight key safeguards essential for designing and deploying responsible pedagogical agents, including roles in collaboration, teacher oversight, prompt-before-answer rules, positive and open attitudes toward agent use, and maintaining activity authenticity. Grounding pedagogical agents in constructivist principles enables effective use of generative AI’s capabilities while addressing its risks, thereby supporting deeper constructivist learning.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"14 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148884732","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}

引用次数: 0

Corrigendum to ‘Assessing information problem solving through a simulation game: Validation evidence from a municipal enforcement official context’ [Computers & Education 244 (2026) 105533] “通过模拟游戏评估信息问题解决：来自市政执法官员背景的验证证据”的勘误表[计算机与教育244 (2026)105533]

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-20 DOI: 10.1016/j.compedu.2026.105741

Aranka Bijl, Sebastiaan de Klerk, Bernard P. Veldkamp, Saskia Wools

引用次数: 0

Corrigendum to “Mapping teacher education strategies to develop digital competence targets: A cross-framework analysis” [Computers & Education 254 (2026) 105699] “绘制教师教育策略以发展数字能力目标：跨框架分析”的勘误表[计算机与教育254 (2026)105699]

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-11 DOI: 10.1016/j.compedu.2026.105729

Els Tanghe, Katie Goeman, Annelies Raes, Tijs Rotsaert, Tammy Schellens, Katrien Struyven, Jo Tondeur, Adriaan Herremans

引用次数: 0

Introduction to “From crisis to continuity: Re-examining online teaching and learning in elementary and secondary education” “从危机到持续：重新审视中小学教育的在线教学”导言

IF 12 1区教育学

Computers & Education Pub Date : 2026-08-07 DOI: 10.1016/j.compedu.2026.105727

Leanna Archambault, Jered Borup, Chin-Hsi Lin

引用次数: 0

Measuring different types and domains of AI knowledge: Developing and validating a performance-based scale 衡量不同类型和领域的人工智能知识：开发和验证基于绩效的量表

IF 10.5 1区教育学

Computers & Education Pub Date : 2026-07-01 Epub Date: 2026-01-12 DOI: 10.1016/j.compedu.2026.105573

Inbal Klein-Avraham , Rut Ston , Osnat Atias , Ido Roll , Ayelet Baram-Tsabari

{"title":"Measuring different types and domains of AI knowledge: Developing and validating a performance-based scale","authors":"Inbal Klein-Avraham , Rut Ston , Osnat Atias , Ido Roll , Ayelet Baram-Tsabari","doi":"10.1016/j.compedu.2026.105573","DOIUrl":"10.1016/j.compedu.2026.105573","url":null,"abstract":"<div><div>As artificial intelligence (AI) and generative AI (GenAI) technologies become increasingly integrated into everyday life, the need for validated tools that measure people's knowledge about AI grows. Here, we present the development and validation of a theoretically driven, performance-based scale for assessing AI and GenAI knowledge. The scale is grounded in a two-axial framework. One axis captures three knowledge types: content knowledge (what AI is and where it is encountered), procedural knowledge (how AI systems operate and are used), and epistemic knowledge (what features and construction processes characterize AI outputs). The other axis encompasses three knowledge domains: technology-related knowledge (AI systems), user-related knowledge (users' interaction with AI), and society-related knowledge (the social and ethical implications of AI). Based on an online survey of 800 internet-using adults from Israel, the 26-item scale was evaluated using confirmatory factor analysis, which demonstrated an acceptable model fit. It was further validated through two-stage structural equation modeling and group comparisons. Overall, the scale was found to be both valid and practically insightful: while it reproduces the expected relationships with additional constructs (e.g., trust in GenAI, attitudes toward AI) and expected differences between demographic groups, it also provides nuanced insights on the intricacies of AI knowledge. For example, the scale indicates that the relationship between trust in GenAI and knowledge about AI is grounded in both epistemic and societal knowledge. Thus, this novel tool affords more precise investigations into how different types and domains of AI knowledge relate to perceptions, behaviors, and decision-making in an AI-mediated world.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"247 ","pages":"Article 105573"},"PeriodicalIF":10.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957052","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}

引用次数: 0