{"title":"Designing effective professional development for technology integration in schools","authors":"Z. Avci, Laura M. O’Dwyer, J. Lawson","doi":"10.1111/jcal.12394","DOIUrl":"https://doi.org/10.1111/jcal.12394","url":null,"abstract":"","PeriodicalId":350985,"journal":{"name":"J. Comput. Assist. Learn.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124871516","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}
Pub Date : 2020-03-23DOI: 10.1111/jcal.12491/v2/response1
Sietske Tacoma, P. Drijvers, J. Jeuring
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.
{"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}
Pub Date : 2020-03-09DOI: 10.1111/jcal.12474/v1/review3
C. Lo, K. Hew
{"title":"Developing a flipped learning approach to support student engagement: A design-based research of secondary school mathematics teaching","authors":"C. Lo, K. Hew","doi":"10.1111/jcal.12474/v1/review3","DOIUrl":"https://doi.org/10.1111/jcal.12474/v1/review3","url":null,"abstract":"","PeriodicalId":350985,"journal":{"name":"J. Comput. Assist. Learn.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134233168","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}
Ming-Puu Chen, Asta Y. Z. Lord, Yu-Yao Cheng, Ku-Chou Tai, W. Pan
{"title":"Collective reflection strategy for moderating conformity tendency and promoting reflective judgment performance","authors":"Ming-Puu Chen, Asta Y. Z. Lord, Yu-Yao Cheng, Ku-Chou Tai, W. Pan","doi":"10.1111/jcal.12419","DOIUrl":"https://doi.org/10.1111/jcal.12419","url":null,"abstract":"","PeriodicalId":350985,"journal":{"name":"J. Comput. Assist. Learn.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124690052","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}
Pub Date : 2020-02-25DOI: 10.1111/jcal.12457/v2/response1
Nicolas Michinov, É. Anquetil, E. Michinov
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.
{"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}
Pub Date : 2020-02-23DOI: 10.1111/jcal.12444/v1/decision1
Roberto Martínez-Maldonado, J. Schulte, Vanessa Echeverría, Yuveena Gopalan, S. B. Shum
{"title":"Where is the teacher? Digital analytics for classroom proxemics","authors":"Roberto Martínez-Maldonado, J. Schulte, Vanessa Echeverría, Yuveena Gopalan, S. B. Shum","doi":"10.1111/jcal.12444/v1/decision1","DOIUrl":"https://doi.org/10.1111/jcal.12444/v1/decision1","url":null,"abstract":"","PeriodicalId":350985,"journal":{"name":"J. Comput. Assist. Learn.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124225984","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}
Pub Date : 2020-02-21DOI: 10.1111/jcal.12577/v2/review1
J. Gardner, M. O’Leary, Li Yuan
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.
Michael O'Leary，爱尔兰都柏林都柏林城市大学教育政策与实践评估研究中心(CARPE)。摘要人工智能是现代社会的核心，计算机现在能够在人类活动的许多领域做出决策。在教育方面，通过在线开放教育资源和大规模在线开放课程，使正式和非正式学习成为数十亿人随时随地的活动的系统得到了密集的发展。此外，与人工智能相关的教育评估的新发展作为提高评估效率和有效性的手段正在引起越来越多的兴趣，人们非常关注对从数字评估环境中捕获的大量过程数据的分析。在评估人工智能在形成性和总结性教育评估中的作用状态时，本文提供了两个核心应用的关键视角:自动作文评分系统和计算机化自适应测试，以及支撑它们的机器学习的大数据分析方法。
{"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}
Tino Endres, Steffen Weyreter, A. Renkl, Alexander Eitel
{"title":"When and why does emotional design foster learning? Evidence for situational interest as a mediator of increased persistence","authors":"Tino Endres, Steffen Weyreter, A. Renkl, Alexander Eitel","doi":"10.1111/jcal.12418","DOIUrl":"https://doi.org/10.1111/jcal.12418","url":null,"abstract":"","PeriodicalId":350985,"journal":{"name":"J. Comput. Assist. Learn.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127653060","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}
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