Journal of Educational Measurement最新文献_第8页

A Unified Comparison of IRT-Based Effect Sizes for DIF Investigations DIF研究中基于IRT的效应大小的统一比较

IF 1.3 4区心理学

Journal of Educational Measurement Pub Date : 2022-11-07 DOI: 10.1111/jedm.12347

R. Philip Chalmers

引用次数: 3

A Statistical Test for the Detection of Item Compromise Combining Responses and Response Times 结合响应和响应时间检测项目折衷的统计检验

IF 1.3 4区心理学

Journal of Educational Measurement Pub Date : 2022-10-28 DOI: 10.1111/jedm.12346

Wim J. van der Linden, Dmitry I. Belov

引用次数: 1

Fully Gibbs Sampling Algorithms for Bayesian Variable Selection in Latent Regression Models 潜在回归模型中贝叶斯变量选择的全吉布斯采样算法

IF 1.3 4区心理学

Journal of Educational Measurement Pub Date : 2022-10-25 DOI: 10.1111/jedm.12348

Kazuhiro Yamaguchi, Jihong Zhang

引用次数: 0

A Factor Mixture Model for Item Responses and Certainty of Response Indices to Identify Student Knowledge Profiles 项目反应与反应指标确定性的因子混合模型识别学生知识概况

IF 1.3 4区心理学

Journal of Educational Measurement Pub Date : 2022-10-10 DOI: 10.1111/jedm.12344

Chia-Wen Chen, Björn Andersson, Jinxin Zhu

{"title":"A Factor Mixture Model for Item Responses and Certainty of Response Indices to Identify Student Knowledge Profiles","authors":"Chia-Wen Chen, Björn Andersson, Jinxin Zhu","doi":"10.1111/jedm.12344","DOIUrl":"10.1111/jedm.12344","url":null,"abstract":"The certainty of response index (CRI) measures respondents' confidence level when answering an item. In conjunction with the answers to the items, previous studies have used descriptive statistics and arbitrary thresholds to identify student knowledge profiles with the CRIs. Whereas this approach overlooked the measurement error of the observed item responses and indices, we address this by proposing a factor mixture model that integrates a latent class model to detect student subgroups and a measurement model to control for student ability and confidence level. Applying the model to 773 seventh graders' responses to an algebra test, where some items were related to new material that had not been taught in class, we found two subgroups: (1) students who had high confidence in answering items involving the new material; and (2) students who had low confidence in answering items involving the new material but higher general self-confidence than the first group. We regressed the posterior probability of the group membership on gender, prior achievement, and preview behavior and found preview behavior a significant factor associated with the membership. Finally, we discussed the implications of the current study for teaching practices and future research.","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":"60 1","pages":"28-51"},"PeriodicalIF":1.3,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jedm.12344","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43460732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Betty Lanteigne, Christine Coombe, & James Dean Brown. 2021. Challenges in Language Testing around the World: Insights for language test users. Singapore: Springer, 2021, 129.99 € (hardcover), ISBN 978-981-33-4232-3 (eBook). xxiii + 553 pp. https://doi.org/10.1007/978-981-33-4232-3 Betty Lanteigne、Christine Coombe和James DeanBrown。2021.世界各地语言测试的挑战：语言测试用户的见解。新加坡：施普林格出版社，2021，129.99欧元（精装本），ISBN 978-981-33-4232-3（电子书）。xxiii+553页。https://doi.org/10.1007/978-981-33-4232-3

IF 1.3 4区心理学

Journal of Educational Measurement Pub Date : 2022-09-25 DOI: 10.1111/jedm.12343

Bahram Kazemian, Shafigeh Mohammadian

引用次数: 0

Using Item Scores and Distractors in Person-Fit Assessment 在个人适合度评估中使用项目分数和干扰因素

IF 1.3 4区心理学

Journal of Educational Measurement Pub Date : 2022-09-16 DOI: 10.1111/jedm.12345

Kylie Gorney, James A. Wollack

{"title":"Using Item Scores and Distractors in Person-Fit Assessment","authors":"Kylie Gorney, James A. Wollack","doi":"10.1111/jedm.12345","DOIUrl":"10.1111/jedm.12345","url":null,"abstract":"In order to detect a wide range of aberrant behaviors, it can be useful to incorporate information beyond the dichotomous item scores. In this paper, we extend the <math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>l</mi>\u0000 <mi>z</mi>\u0000 </msub>\u0000 <annotation>$l_z$</annotation>\u0000 </semantics></math> and <math>\u0000 <semantics>\u0000 <msubsup>\u0000 <mi>l</mi>\u0000 <mi>z</mi>\u0000 <mo>∗</mo>\u0000 </msubsup>\u0000 <annotation>$l_z^*$</annotation>\u0000 </semantics></math> person-fit statistics so that unusual behavior in item scores and unusual behavior in item distractors can be used as indicators of aberrance. Through detailed simulations, we show that the new statistics are more powerful than existing statistics in detecting several types of aberrant behavior, and that they are able to control the Type I error rate in instances where the model does not exactly fit the data. A real data example is also provided to demonstrate the utility of the new statistics in an operational setting.","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":"60 1","pages":"3-27"},"PeriodicalIF":1.3,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jedm.12345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48816866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A New Bayesian Person-Fit Analysis Method Using Pivotal Discrepancy Measures 使用关键差异度量的贝叶斯人-拟合分析新方法

IF 1.3 4区心理学

Journal of Educational Measurement Pub Date : 2022-09-02 DOI: 10.1111/jedm.12342

Adam Combs

{"title":"A New Bayesian Person-Fit Analysis Method Using Pivotal Discrepancy Measures","authors":"Adam Combs","doi":"10.1111/jedm.12342","DOIUrl":"10.1111/jedm.12342","url":null,"abstract":"A common method of checking person-fit in Bayesian item response theory (IRT) is the posterior-predictive (PP) method. In recent years, more powerful approaches have been proposed that are based on resampling methods using the popular <math>\u0000 <semantics>\u0000 <msubsup>\u0000 <mi>L</mi>\u0000 <mi>z</mi>\u0000 <mo>∗</mo>\u0000 </msubsup>\u0000 <annotation>$L_{z}^{*}$</annotation>\u0000 </semantics></math> statistic. There has also been proposed a new Bayesian model checking method based on pivotal discrepancy measures (PDMs). A PDM T is a discrepancy measure that is a pivotal quantity with a known reference distribution. A posterior sample of T can be generated using standard Markov chain Monte Carlo output, and a p-value is obtained from probability bounds computed on order statistics of the sample. In this paper, we propose a general procedure to apply this PDM method to person-fit checking in IRT models. We illustrate this using the <math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>L</mi>\u0000 <mi>z</mi>\u0000 </msub>\u0000 <annotation>$L_{z}$</annotation>\u0000 </semantics></math> and <math>\u0000 <semantics>\u0000 <msubsup>\u0000 <mi>L</mi>\u0000 <mi>z</mi>\u0000 <mo>∗</mo>\u0000 </msubsup>\u0000 <annotation>$L_{z}^{*}$</annotation>\u0000 </semantics></math> measures. Simulation studies are done comparing these with the PP method and one of the more recent resampling methods. The results show that the PDM method is more powerful than the PP method. Under certain conditions, it is more powerful than the resampling method, while in others, it is less. The PDM method is also applied to a real data set.","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":"60 1","pages":"52-75"},"PeriodicalIF":1.3,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46358680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Several Variations of Simple-Structure MIRT Equating 简单结构MIRT方程的几种变体

IF 1.3 4区心理学

Journal of Educational Measurement Pub Date : 2022-07-28 DOI: 10.1111/jedm.12341

Stella Y. Kim, Won-Chan Lee

引用次数: 1

Validity Arguments Meet Artificial Intelligence in Innovative Educational Assessment 创新教育评估中的有效性论证与人工智能

IF 1.3 4区心理学

Journal of Educational Measurement Pub Date : 2022-07-08 DOI: 10.1111/jedm.12331

David W. Dorsey, Hillary R. Michaels

{"title":"Validity Arguments Meet Artificial Intelligence in Innovative Educational Assessment","authors":"David W. Dorsey, Hillary R. Michaels","doi":"10.1111/jedm.12331","DOIUrl":"https://doi.org/10.1111/jedm.12331","url":null,"abstract":"We have dramatically advanced our ability to create rich, complex, and effective assessments across a range of uses through technology advancement. Artificial Intelligence (AI) enabled assessments represent one such area of advancement—one that has captured our collective interest and imagination. Scientists and practitioners within the domains of organizational and workforce assessment have increasingly used AI in assessment, and its use is now becoming more common in education. While these types of solutions offer their users the promise of efficiency, effectiveness, and a “wow factor,” users need to maintain high standards for validity and fairness in high stakes settings. Due to the complexity of some AI methods and tools, this requirement for adherence to standards may challenge our traditional approaches to building validity and fairness arguments. In this edition, we review what these challenges may look like as validity arguments meet AI in educational assessment domains. We specifically explore how AI impacts Evidence-Centered Design (ECD) and development from assessment concept and coding to scoring and reporting. We also present information on ways to ensure that bias is not built into these systems. Lastly, we discuss future horizons, many that are almost here, for maximizing what AI offers while minimizing negative effects on test takers and programs.","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":"59 3","pages":"267-271"},"PeriodicalIF":1.3,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137805809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deterministic Gated Lognormal Response Time Model to Identify Examinees with Item Preknowledge 用项目先验知识识别考生的确定性门控对数正态响应时间模型

IF 1.3 4区心理学

Journal of Educational Measurement Pub Date : 2022-07-07 DOI: 10.1111/jedm.12340

Murat Kasli, Cengiz Zopluoglu, Sarah L. Toton

引用次数: 0