Computerized Adaptive Testing (CAT) is a new testing mode that utilizes the adaptive measurement concept of "tailored to fit." Compared with traditional paper-and-pencil testing, CAT has the advantages of improving measurement accuracy, reducing test length, and ensuring test security. Therefore, it is highly regarded by researchers and practitioners both domestically and internationally. However, the platform construction of CAT involves complex statistical measurement theory and tedious numerical calculations, which hinder the application and promotion of CAT in practice. This article mainly introduces the development platform of computerized adaptive testing - flexCAT. Users can quickly build their own CAT system using the convenient human-computer interactive interface provided by the flexCAT platform. This article will introduce the first web-based computerized adaptive testing development platform in China - flexCAT, from the perspectives of its advantages, basic theory, module functions, etc. The aim is to provide free adaptive testing platform development services for research and application personnel in the fields of education, psychology, and further promote the development of psychological and educational measurement theory and technology in China. The URL for the flexCAT platform is: http://www.psychometrics-studio.cn/app/cat_demo/index.html?Id=false&Block=false.
{"title":"flexCAT: Computerized Adaptive Test Development Platform","authors":"Dongbo Tu, Fen Luo, Daxun Wang, Yan Cai","doi":"10.59863/gxzd9076","DOIUrl":"https://doi.org/10.59863/gxzd9076","url":null,"abstract":"Computerized Adaptive Testing (CAT) is a new testing mode that utilizes the adaptive measurement concept of \"tailored to fit.\" Compared with traditional paper-and-pencil testing, CAT has the advantages of improving measurement accuracy, reducing test length, and ensuring test security. Therefore, it is highly regarded by researchers and practitioners both domestically and internationally. However, the platform construction of CAT involves complex statistical measurement theory and tedious numerical calculations, which hinder the application and promotion of CAT in practice. This article mainly introduces the development platform of computerized adaptive testing - flexCAT. Users can quickly build their own CAT system using the convenient human-computer interactive interface provided by the flexCAT platform. This article will introduce the first web-based computerized adaptive testing development platform in China - flexCAT, from the perspectives of its advantages, basic theory, module functions, etc. The aim is to provide free adaptive testing platform development services for research and application personnel in the fields of education, psychology, and further promote the development of psychological and educational measurement theory and technology in China. The URL for the flexCAT platform is: http://www.psychometrics-studio.cn/app/cat_demo/index.html?Id=false&Block=false.","PeriodicalId":72586,"journal":{"name":"Chinese/English journal of educational measurement and evaluation","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77169213","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}
Dengying Cai, K. Cheung, Pou-seong Sit, Man Kai Leong
Using mediation analyses, this study analyzes PISA 2015 scientific literacy computerized assessment data collected from students in Beijing, Shanghai, Jiangsu, and Guangdong, and examines the relationship between students' familiarity with information and communications technology and educational equity. The study also offers recommendations based on the results while providing empirical support for reducing digital educational inequities as well as differences in academic literacy performance between students of different socioeconomic and cultural statuses.
{"title":"Examining the Impact of Students' Familiarity with Information and Communications Technology on Education Equity Using Data from Students in Four Provinces and Cities in China","authors":"Dengying Cai, K. Cheung, Pou-seong Sit, Man Kai Leong","doi":"10.59863/kowf1981","DOIUrl":"https://doi.org/10.59863/kowf1981","url":null,"abstract":"Using mediation analyses, this study analyzes PISA 2015 scientific literacy computerized assessment data collected from students in Beijing, Shanghai, Jiangsu, and Guangdong, and examines the relationship between students' familiarity with information and communications technology and educational equity. The study also offers recommendations based on the results while providing empirical support for reducing digital educational inequities as well as differences in academic literacy performance between students of different socioeconomic and cultural statuses.","PeriodicalId":72586,"journal":{"name":"Chinese/English journal of educational measurement and evaluation","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84296712","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}
This essay sketches the historical development of latent variable scoring procedures in the item response theory (IRT) and factor analysis literatures, observing that the most commonly used score estimates in both traditions are fundamentally the same; only methods of calculation differ. Different procedures have been used to derive factor score estimates and latent variable estimates in IRT, and different computational procedures have been the result. Due to differences in the context of score usage, challenges have led to different solutions in the IRT and factor analytic traditions. The needs for bias corrections differ, as do the corrections that have been proposed. While the standard factor analysis model has naturally Gaussian likelihoods, IRT does not, but in IRT normal approximations have been used in various contexts to make the IRT computations more like those of factor analysis. Finally, factor analysis alone has been the home of decades of controversy over factor score indeterminacy, while IRT has not, even though the scores in question are the same. That is an artifact of history and the ways the models have been written in the IRT and factor analytic literatures. IRT has never been plagued with questions of indeterminacy, which helps to clarify the position that what is referred to as indeterminacy is not a problem.
{"title":"Latent Variable Estimation in Factor Analysis and Item Response Theory","authors":"D. Thissen, Anne Thissen-Roe","doi":"10.59863/optz4045","DOIUrl":"https://doi.org/10.59863/optz4045","url":null,"abstract":"This essay sketches the historical development of latent variable scoring procedures in the item response theory (IRT) and factor analysis literatures, observing that the most commonly used score estimates in both traditions are fundamentally the same; only methods of calculation differ. Different procedures have been used to derive factor score estimates and latent variable estimates in IRT, and different computational procedures have been the result. Due to differences in the context of score usage, challenges have led to different solutions in the IRT and factor analytic traditions. The needs for bias corrections differ, as do the corrections that have been proposed. While the standard factor analysis model has naturally Gaussian likelihoods, IRT does not, but in IRT normal approximations have been used in various contexts to make the IRT computations more like those of factor analysis. Finally, factor analysis alone has been the home of decades of controversy over factor score indeterminacy, while IRT has not, even though the scores in question are the same. That is an artifact of history and the ways the models have been written in the IRT and factor analytic literatures. IRT has never been plagued with questions of indeterminacy, which helps to clarify the position that what is referred to as indeterminacy is not a problem.","PeriodicalId":72586,"journal":{"name":"Chinese/English journal of educational measurement and evaluation","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79445276","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}
{"title":"中国四省市学生之信息通信技术熟悉度对教育公平的影响","authors":"Dengying Cai, K. Cheung, Pou-seong Sit, Man Kai Leong","doi":"10.59863/gedl8244","DOIUrl":"https://doi.org/10.59863/gedl8244","url":null,"abstract":"本研究旨在透过中介效应分析PISA 2015电子化科学素养测试数据，检验北京、上海、江苏与广东四个省市学生的信息通信技术熟悉程度与教育公平的关系，并根据研究结果提出建言，以期为改善数码教育不公、缩小不同社会经济文化地位学生的学业素养表现差距提供实证支持。","PeriodicalId":72586,"journal":{"name":"Chinese/English journal of educational measurement and evaluation","volume":"252 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86191126","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}
Digital problem-solving competence is widely recognized as one of the core skills of the 21st century. A number of important factors influence this competence; some are task-specific pertaining to the problem-solving processes while others are non-task-specific related to knowledge, skills, attitudes and beliefs of the problem solvers, as well as the student learning environment. This study sought to determine important factors that classify student problem-solver as “high-performing expert” versus “low-performing novice”, using computer-generated log files of an exemplary digital problem task assessed in Organization for Economic Co-operation and Development (OECD)’s Programme for International Student Assessment (PISA) 2012 Study. The participants comprise 11,599 fifteen-year-old students from 42 economies. Apart from multilevel logistic regression of problem-solving process and student questionnaire data, the secondary data analysis employed was a data-mining approach involving classification and regression trees. Five important factors were identified that are key to the discrimination of the “expert vs novice” dichotomy.
{"title":"Important Factors Discriminating Between Problem-Solving Experts and Novices: A Data Mining Approach","authors":"Song Li Jin, K. Cheung, Pou-seong Sit","doi":"10.59863/bpea3210","DOIUrl":"https://doi.org/10.59863/bpea3210","url":null,"abstract":"Digital problem-solving competence is widely recognized as one of the core skills of the 21st century. A number of important factors influence this competence; some are task-specific pertaining to the problem-solving processes while others are non-task-specific related to knowledge, skills, attitudes and beliefs of the problem solvers, as well as the student learning environment. This study sought to determine important factors that classify student problem-solver as “high-performing expert” versus “low-performing novice”, using computer-generated log files of an exemplary digital problem task assessed in Organization for Economic Co-operation and Development (OECD)’s Programme for International Student Assessment (PISA) 2012 Study. The participants comprise 11,599 fifteen-year-old students from 42 economies. Apart from multilevel logistic regression of problem-solving process and student questionnaire data, the secondary data analysis employed was a data-mining approach involving classification and regression trees. Five important factors were identified that are key to the discrimination of the “expert vs novice” dichotomy.","PeriodicalId":72586,"journal":{"name":"Chinese/English journal of educational measurement and evaluation","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88082847","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}
{"title":"高等教育中的男性：美国正在经历一场性别危机?","authors":"O. Liu, B. Bridgeman, Daniel Fishtein","doi":"10.59863/nzwx3174","DOIUrl":"https://doi.org/10.59863/nzwx3174","url":null,"abstract":"男性在美国大学占比不足（underrepresentation）的现象引起了广泛关注。截至2021年，男性学生仅占大学入学人数的40.5%。鉴于大学学位的经济和社会效益已经得到证明，男性中大学学位的缺少预计会对美国的经济与社会产生不利影响，同时也会导致个人错失各种机会。我们回顾了不同学位等级的性别构成，讨论男性占比不足带来的可能影响，并分析男性低入学率的潜在原因。我们还回顾了各院校用以吸引更多男性申请者的策略。尽管女性在大学入学人数中占比过高，但是促进性别平等的努力仍应继续；因为在学术和企业环境中，女性在高薪和高级职位的比例仍然不足。","PeriodicalId":72586,"journal":{"name":"Chinese/English journal of educational measurement and evaluation","volume":"37 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91551917","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}
Men’s significant underrepresentation in U.S. colleges has attracted widespread attention. As of 2021, men make up only 40.5% of the college enrollments. Given the proven economic and social benefits of a college degree, men’s lack of a college degree is projected to generate detrimental effects to the U.S. economy and society, as well as bring missed opportunities for individuals. We review the gender composition at various degree levels, discuss possible impacts of men’s underrepresentation, and analyze underlying reasons of men’s low college enrollment. We also review strategies that institutions use to attract more male applicants. Despite women’s overrepresentation in college enrollment, efforts to promote gender equality should continue, as females are still underrepresented in high-paying and high-ranking positions, in both academic and corporate settings.
{"title":"Men in Higher Education: A Gender Crisis in the United States?","authors":"O. Liu, B. Bridgeman, Daniel Fishtein","doi":"10.59863/ymmx7538","DOIUrl":"https://doi.org/10.59863/ymmx7538","url":null,"abstract":"Men’s significant underrepresentation in U.S. colleges has attracted widespread attention. As of 2021, men make up only 40.5% of the college enrollments. Given the proven economic and social benefits of a college degree, men’s lack of a college degree is projected to generate detrimental effects to the U.S. economy and society, as well as bring missed opportunities for individuals. We review the gender composition at various degree levels, discuss possible impacts of men’s underrepresentation, and analyze underlying reasons of men’s low college enrollment. We also review strategies that institutions use to attract more male applicants. Despite women’s overrepresentation in college enrollment, efforts to promote gender equality should continue, as females are still underrepresented in high-paying and high-ranking positions, in both academic and corporate settings.","PeriodicalId":72586,"journal":{"name":"Chinese/English journal of educational measurement and evaluation","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81915401","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：