J. Comput. Assist. Learn.最新文献_第10页

Designing effective professional development for technology integration in schools 为学校科技整合设计有效的专业发展

J. Comput. Assist. Learn. Pub Date : 2020-04-01 DOI: 10.1111/jcal.12394

Z. Avci, Laura M. O’Dwyer, J. Lawson

引用次数: 45

Combined inner and outer loop feedback in an intelligent tutoring system for statistics in higher education 基于内外环反馈的高等教育统计智能辅导系统

J. Comput. Assist. Learn. Pub Date : 2020-03-23 DOI: 10.1111/jcal.12491/v2/response1

Sietske Tacoma, P. Drijvers, J. Jeuring

{"title":"Combined inner and outer loop feedback in an intelligent tutoring system for statistics in higher education","authors":"Sietske Tacoma, P. Drijvers, J. Jeuring","doi":"10.1111/jcal.12491/v2/response1","DOIUrl":"https://doi.org/10.1111/jcal.12491/v2/response1","url":null,"abstract":"Peer Review The peer review history for this article is available at https://publons.com/publon/10. 1111/jcal.12491. Abstract Intelligent tutoring systems (ITSs) can provide inner loop feedback about steps within tasks, and outer loop feedback about performance on multiple tasks. While research typically addresses these feedback types separately, many ITSs offer them simultaneously. This study evaluates the effects of providing combined inner and outer loop feedback on social sciences students' learning process and performance in a first-year university statistics course. In a 2 x 2 factorial design (elaborate inner loop vs. minimal inner loop and outer loop vs. no outer loop feedback) with 521 participants, the effects of both feedback types and their combination were assessed through multiple linear regression models. Results showed mixed effects, depending on students' prior knowledge and experience, and no overall effects on course performance. Students tended to use outer loop feedback less when also receiving elaborate inner loop feedback. We therefore recommend introducing feedback types one by one and offering them for substantial periods of time.","PeriodicalId":350985,"journal":{"name":"J. Comput. Assist. Learn.","volume":"2290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130298248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Touchy feely vectors: A compensatory design approach to support model-based reasoning in developing country classrooms 在发展中国家的教室中支持基于模型的推理的补偿性设计方法

J. Comput. Assist. Learn. Pub Date : 2020-03-15 DOI: 10.1111/jcal.12500/v2/decision1

Durgaprasad Karnam, Harshit Agrawal, P. Parte, Saurabh Ranjan, Priyanka Borar, Prasanna Prakash Kurup, Amose Jebin Joel, Pattamadai Sankaran Srinivasan, Uddhav Suryawanshi, A. Sule, S. Chandrasekharan

引用次数: 4

Developing a flipped learning approach to support student engagement: A design-based research of secondary school mathematics teaching 发展支持学生参与的翻转学习方法:基于设计的中学数学教学研究

J. Comput. Assist. Learn. Pub Date : 2020-03-09 DOI: 10.1111/jcal.12474/v1/review3

C. Lo, K. Hew

引用次数: 22

Collective reflection strategy for moderating conformity tendency and promoting reflective judgment performance 调节从众倾向和促进反思判断绩效的集体反思策略

J. Comput. Assist. Learn. Pub Date : 2020-02-26 DOI: 10.1111/jcal.12419

Ming-Puu Chen, Asta Y. Z. Lord, Yu-Yao Cheng, Ku-Chou Tai, W. Pan

引用次数: 2

Guiding the use of collective feedback displayed on heatmaps to reduce group conformity and improve learning in Peer Instruction 指导使用热图上显示的集体反馈，以减少群体从众，改善同伴教学中的学习

J. Comput. Assist. Learn. Pub Date : 2020-02-25 DOI: 10.1111/jcal.12457/v2/response1

Nicolas Michinov, É. Anquetil, E. Michinov

{"title":"Guiding the use of collective feedback displayed on heatmaps to reduce group conformity and improve learning in Peer Instruction","authors":"Nicolas Michinov, É. Anquetil, E. Michinov","doi":"10.1111/jcal.12457/v2/response1","DOIUrl":"https://doi.org/10.1111/jcal.12457/v2/response1","url":null,"abstract":"Peer Instruction is an active learning method widely used in higher education, whereby students answer a series of questions twice, once before and once after peer discussion. There is an ongoing debate as to whether a collective feedback should be given after the students' initial answer, and if so, how the frequently observed group conformity can be avoided. This study examined whether guiding on the use of this feedback can reduce group conformity and improve learning using an interactive learning environment to administer a new type of quiz using graphics, and delivering collective feedback to the whole class in a novel heatmap format. In the experimental group, the teacher told the students that the answer indicated by the heatmap was not necessarily the correct one; this information was not given to students in the control group. Results revealed that guided students were less likely to adopt the (incorrect) majority answer than the non‐guided students, and consequently, they were more likely to improve their learning by reaching an agreement about the correct answer through discussion with their peers. These findings suggest that guiding students in their use of collective feedback may have a crucial role in Peer Instruction.","PeriodicalId":350985,"journal":{"name":"J. Comput. Assist. Learn.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130038634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Where is the teacher? Digital analytics for classroom proxemics 老师在哪里?课堂近体学的数字分析

J. Comput. Assist. Learn. Pub Date : 2020-02-23 DOI: 10.1111/jcal.12444/v1/decision1

Roberto Martínez-Maldonado, J. Schulte, Vanessa Echeverría, Yuveena Gopalan, S. B. Shum

引用次数: 16

Understanding student simultaneous smartphone use in learning settings: A conceptual framework 理解学生在学习环境中同时使用智能手机:一个概念框架

J. Comput. Assist. Learn. Pub Date : 2020-02-23 DOI: 10.1111/jcal.12471/v2/decision1

En Fu, Qiufeng Gao, Chuqian Wei, Qianyi Chen, Yijun Liu

引用次数: 10

Artificial intelligence in educational assessment: 'Breakthrough? Or buncombe and ballyhoo?' 人工智能在教育评估中的应用:突破?还是buncomb和ballhoo ?”

J. Comput. Assist. Learn. Pub Date : 2020-02-21 DOI: 10.1111/jcal.12577/v2/review1

J. Gardner, M. O’Leary, Li Yuan

{"title":"Artificial intelligence in educational assessment: 'Breakthrough? Or buncombe and ballyhoo?'","authors":"J. Gardner, M. O’Leary, Li Yuan","doi":"10.1111/jcal.12577/v2/review1","DOIUrl":"https://doi.org/10.1111/jcal.12577/v2/review1","url":null,"abstract":"Correspondence Michael O'Leary, Centre for Assessment Research, Policy and Practice in Education (CARPE), Dublin City University, Dublin, Ireland. Email: michael.oleary@dcu.ie Abstract Artificial Intelligence is at the heart of modern society with computers now capable of making process decisions in many spheres of human activity. In education, there has been intensive growth in systems that make formal and informal learning an anytime, anywhere activity for billions of people through online open educational resources and massive online open courses. Moreover, new developments in Artificial Intelligencerelated educational assessment are attracting increasing interest as means of improving assessment efficacy and validity, with much attention focusing on the analysis of the large volumes of process data being captured from digital assessment contexts. In evaluating the state of play of Artificial Intelligence in formative and summative educational assessment, this paper offers a critical perspective on the two core applications: automated essay scoring systems and computerized adaptive tests, along with the Big Data analysis approaches to machine learning that underpin them.","PeriodicalId":350985,"journal":{"name":"J. Comput. Assist. Learn.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130406928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 34

When and why does emotional design foster learning? Evidence for situational interest as a mediator of increased persistence 情感设计何时以及为何促进学习?情境兴趣作为持久性增强中介的证据

J. Comput. Assist. Learn. Pub Date : 2020-02-20 DOI: 10.1111/jcal.12418

Tino Endres, Steffen Weyreter, A. Renkl, Alexander Eitel

引用次数: 28