基于复合选择题的雷同答卷甄别探析——以某资格考试为例

基于某资格类考试考后数据,对包含单选题和多选题的复合选择题的雷同答卷进行分析,提出单选甄别、多选甄别和合并甄别3种甄别方案,设计不同测验长度、难度、被抄袭考生水平、题目抄袭比率、作弊考生比率及显著性水平的实验样本。研究结果显示:合并甄别和单选甄别都表现出较好的甄别性能,多选甄别由于多选题数量方面的劣势,甄别率低且Ⅰ型错误率高。据此,提出甄别雷同答卷的建议:如果对甄别出的雷同答卷考生进行违纪违规处理,可将单选甄别和合并甄别分别甄别出的结果取交集作为最后处理依据;如果不对甄别结果进行处理,可将2种方案分别甄别出的结果取并集,以此来对考区或考点的考风考纪进行监管。     

"破窗理论"及其对防范国家教育考试舞弊的启示

舞弊行为不仅影响了社会正义和公众秩序,而且严重破坏了教育公平和教育的形象.国家教育考试舞弊有如下的新特征:舞弊形式从传统走向现代,舞弊手段趋于高科技化;舞弊地点从考场内走向考场外,舞弊行为贯穿考试的整个流程;舞弊目的从单一走向多元,舞弊人员从个体走向集团.将"破窗理论"的主要观点与防范国家教育考试舞弊相结合,有利于更新考试管理理念,从而使有舞弊想法的考生不想舞弊、不能舞弊、不敢舞弊,从而提高国家教育考试管理工作的质量和水平.     

初探大学生考试舞弊动机与教育策略

考试舞弊已经成为高校的普遍现象．这种现象对高校学风建设和学生的成长都是有害的。从心理学的角度来考察,大学生考试舞弊动机有三类：认知偏差导致的舞弊动机、消极情绪造成的舞弊动机和人格缺陷产生的舞弊动机．这三种舞弊动机的产生根源是各种不良的心态。针对大学生考试舞弊的各种动机和心态。高校要采取相应的教育策略。     

2010年对口招生专业技能测试和部分专业理论考试安排

2010年我省普通高校对口招生考试继续分文化考试和专业考试两部分,满分750分。文化考试科目为语文、数学、英语,试卷满分均为120分;专业考试分专业理论考试和专业技能测试。报考除采矿及安全工程和学前教育类以外其他12类的考生,既要参加专业理论考试,还要进行专业技能测试,其中专业理论240分,专业技能测试150分。     

浅析地方高校考试舞弊的现状及改革措施

近几年来,考试舞弊无孔不入,层出不穷,但我们也不能断然地将考试制度与考试舞弊列入因果关系的行列,从而主张废除考试制度。基于当前高校考试舞弊愈演愈烈的严峻形势,本文针对性提出行之有效的防范和化解措施。     

基于题目反应理论的网络自适应考试

随着计算机的普及、网络的发展、教学和考试测评理论的更新,一种基于题目反应理论的计算机自适应考试已经越来越普及,它以其题目适应不同能力学生水平自动变化的特点,已经被越来越多的考试所采用,针对题目反应理论,需要对自适应考试实现等问题加以论述。     

高校学生考试舞弊问题诱因及对策探讨

考试是高校教学评价的基本手段和方式,也是检验学生学习是否合格、能否毕业的重要把关环节.高校学生考试舞弊问题屡禁不止,严重影响了考试的公平性以及教学评价和人才质量鉴定的公正性.产生考试舞弊问题的因素主要有学生自身认识偏差、学生评价手段单一、社会环境的不良影响等.解决高校学生考试舞弊问题应该治标与治本结合,不断创新教学评价机制,进一步改进对学生的教育和引导方式.     

使用R软件包CopyDetect识别考试抄袭

本文简要介绍了用于进行考试抄袭识别的R软件的CopyDetect,描述了CopyDetect所提供的考试抄袭识别指标ω、GBT、K指数及K系列指数的含义,并使用模拟数据对CopyDetect如何进行考试抄袭识别进行了实例操作分析。比较了在不同比例的雷同卷中,C0pyDetect包的不同指数识别的灵敏度。最后,指出了CopyDetect包存在的不足和需要针对我国考试作弊实际情况进行修订的地方。     

国家考试舞弊问题及其对策

市场经济加现代科技成为国家考试舞弊现状的基本特征,在制度安排上预防舞弊发生．在考试模式上堵塞舞弊漏洞．在操作流程上严防舞弊可能。成为国家亟待重视和需要解决的问题之一。本文从健全国家考试法规体系、创新国家考试管理系统、重构国家考试模式、建立社会诚信机制、改革人才选拔办法等方面提出了相应的对策与建议。     

高校学生考试舞弊问题诱因及对策探讨

考试是高校教学评价的基本手段和方式,也是检验学生学习是否合格、能否毕业的重要把关环节。高校学生考试舞弊问题屡禁不止,严重影响了考试的公平性以及教学评价和人才质量鉴定的公正性。产生考试舞弊问题的因素主要有学生自身认识偏差、学生评价手段单一、社会环境的不良影响等。解决高校学生考试舞弊问题应该治标与治本结合,不断创新教学评价机制,进一步改进对学生的教育和引导方式。     

Minimizing the Influence of Item Parameter Estimation Errors in Test Development: A Comparison of Three Selection Procedures

In test development, item response theory (IRT) is a method to determine the amount of information that each item (i.e., item information function) and combination of items (i.e., test information function) provide in the estimation of an examinee's ability. Studies investigating the effects of item parameter estimation errors over a range of ability have demonstrated an overestimation of information when the most discriminating items are selected (i.e., item selection based on maximum information). In the present study, the authors examined the influence of item parameter estimation errors across 3 item selection methods—maximum no target, maximum target, and theta maximum—using the 2- and 3-parameter logistic IRT models. Tests created with the maximum no target and maximum target item selection procedures consistently overestimated the test information function. Conversely, tests created using the theta maximum item selection procedure yielded more consistent estimates of the test information function and, at times, underestimated the test information function. Implications for test development are discussed.     

Embedded Field Test Item Statistics: Can They Be Trusted for Estimating Student Proficiency?

Assessment items are commonly field tested prior to operational use to observe statistical item properties such as difficulty. Item parameter estimates from field testing may be used to assign scores via pre-equating or computer adaptive designs. This study examined differences between item difficulty estimates based on field test and operational data and the relationship of such differences to item position changes and student proficiency estimates. Item position effects were observed for 20 assessments, with items in later positions tending to be more difficult. Moreover, field test estimates of item difficulty were biased slightly upward, which may indicate examinee knowledge of which items were being field tested. Nevertheless, errors in field test item difficulty estimates had negligible impacts on student proficiency estimates for most assessments. Caution is still warranted when using field test statistics for scoring, and testing programs should conduct investigations to determine whether the effects on scoring are inconsequential.     

Digital Module 08: Foundations of Operational Item Analysis https://ncme.elevate.commpartners.com

Item analysis is an integral part of operational test development and is typically conducted within two popular statistical frameworks: classical test theory (CTT) and item response theory (IRT). In this digital ITEMS module, Hanwook Yoo and Ronald K. Hambleton provide an accessible overview of operational item analysis approaches within these frameworks. They review the different stages of test development and associated item analyses to identify poorly performing items and effective item selection. Moreover, they walk through the computational and interpretational steps for CTT‐ and IRT‐based evaluation statistics using simulated data examples and review various graphical displays such as distractor response curves, item characteristic curves, and item information curves. The digital module contains sample data, Excel sheets with various templates and examples, diagnostic quiz questions, data‐based activities, curated resources, and a glossary.     

基于项目反应理论的测验编制方法研究

本文在简单介绍项目反应理论的基础上,从计量分析的角度,深入探讨了应用项目反应理论编制各种测验的一般步骤;探讨了项目反应理论题库建设方法及基于题库的测验编制方法;探讨了标准参照测验合格分数线的划分方法。     

Parameter Estimation Accuracy of the Effort-Moderated Item Response Theory Model Under Multiple Assumption Violations

As low-stakes testing contexts increase, low test-taking effort may serve as a serious validity threat. One common solution to this problem is to identify noneffortful responses and treat them as missing during parameter estimation via the effort-moderated item response theory (EM-IRT) model. Although this model has been shown to outperform traditional IRT models (e.g., two-parameter logistic [2PL]) in parameter estimation under simulated conditions, prior research has failed to examine its performance under violations to the model’s assumptions. Therefore, the objective of this simulation study was to examine item and mean ability parameter recovery when violating the assumptions that noneffortful responding occurs randomly (Assumption 1) and is unrelated to the underlying ability of examinees (Assumption 2). Results demonstrated that, across conditions, the EM-IRT model provided robust item parameter estimates to violations of Assumption 1. However, bias values greater than 0.20 SDs were observed for the EM-IRT model when violating Assumption 2; nonetheless, these values were still lower than the 2PL model. In terms of mean ability estimates, model results indicated equal performance between the EM-IRT and 2PL models across conditions. Across both models, mean ability estimates were found to be biased by more than 0.25 SDs when violating Assumption 2. However, our accompanying empirical study suggested that this biasing occurred under extreme conditions that may not be present in some operational settings. Overall, these results suggest that the EM-IRT model provides superior item and equal mean ability parameter estimates in the presence of model violations under realistic conditions when compared with the 2PL model.     

考试质量在线测评系统的设计——以经典测试理论和项目反应理论为基础

在总结目前考试测评系统不足的基础上提出科学有效的考试质量评价方法,并在该方法的指导下运用经典测试理论和项目反应理论设计考试质量在线测评系统。系统具有创建新表、成绩统计、质量分析以及基于经典测试理论下的信度和效度分析等功能,能够满足广大教师对考试质量分析的需求。     

Combining IRT and SEM: A Hybrid Model for Fitting Responses and Response Certainties

This article proposes a model-based procedure, intended for personality measures, for exploiting the auxiliary information provided by the certainty with which individuals answer every item (response certainty). This information is used to (a) obtain more accurate estimates of individual trait levels, and (b) provide a more detailed assessment of the consistency with which the individual responds to the test. The basis model consists of 2 submodels: an item response theory submodel for the responses, and a linear-in-the-coefficients submodel that describes the response certainties. The latter is based on the distance-difficulty hypothesis, and is parameterized as a factor-analytic model. Procedures for (a) estimating the structural parameters, (b) assessing model–data fit, (c) estimating the individual parameters, and (d) assessing individual fit are discussed. The proposal was used in an empirical study. Model–data fit was acceptable and estimates were meaningful. Furthermore, the precision of the individual trait estimates and the assessment of the individual consistency improved noticeably.     

浅析阅读理解考试中的测试方法效应问题

本文研究的是不同的测试方法-单项选择和信息转移-是否会在阅读理解考试中产生测试方法效应的问题.除对学生的考试成绩(分数)进行分析外,本研究还进一步对试题的难度值进行了分析,而本研究中试题难度是通过项目反应理论(Item Response Theory)计算得到的.结果显示不同测试方法的确会影响题目难度及考生的考试表现,就试题难度而言信息转移比单项选择更难.     

A Multidimensional Finite Mixture Structural Equation Model for Nonignorable Missing Responses to Test Items

We propose a structural equation model, which reduces to a multidimensional latent class item response theory model, for the analysis of binary item responses with nonignorable missingness. The missingness mechanism is driven by 2 sets of latent variables: one describing the propensity to respond and the other referred to the abilities measured by the test items. These latent variables are assumed to have a discrete distribution, so as to reduce the number of parametric assumptions regarding the latent structure of the model. Individual covariates can also be included through a multinomial logistic parameterization for the distribution of the latent variables. Given the discrete nature of this distribution, the proposed model is efficiently estimated by the expectation–maximization algorithm. A simulation study is performed to evaluate the finite-sample properties of the parameter estimates. Moreover, an application is illustrated with data coming from a student entry test for the admission to some university courses.     

Identification and Evaluation of Local Item Dependencies in the Medical College Admissions Test

Measurement specialists routinely assume examinee responses to test items are independent of one another. However, previous research has shown that many contemporary tests contain item dependencies and not accounting for these dependencies leads to misleading estimates of item, test, and ability parameters. The goals of the study were (a) to review methods for detecting local item dependence (LID), (b) to discuss the use of testlets to account for LID in context-dependent item sets, (c) to apply LID detection methods and testlet-based item calibrations to data from a large-scale, high-stakes admissions test, and (d) to evaluate the results with respect to test score reliability and examinee proficiency estimation. Item dependencies were found in the test and these were due to test speededness or context dependence (related to passage structure). Also, the results highlight that steps taken to correct for the presence of LID and obtain less biased reliability estimates may impact on the estimation of examinee proficiency. The practical effects of the presence of LID on passage-based tests are discussed, as are issues regarding how to calibrate context-dependent item sets using item response theory.