共查询到19条相似文献，搜索用时 171 毫秒
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计算机自适应测验依据被试作答的反应自动选择测验项目,是项目反应理论与计算机技术相结合的一种考试组织形式。随着计算机自适应测验的使用越来越广,特别是在大型考试的实施过程中,服务器的处理能力成为制约考试规模的重要因素之一。分布式计算可以有效提高系统的载荷,为网络服务提供更快的响应速度。贵州师范大学CAT实验室开发的计算机自适应测验系统PowerCAT,运用分布式计算的方式,为大规模考试提供了可用的负载能力。 相似文献
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林邦国 《湖北广播电视大学学报》2009,29(4):151-152
计算机自适应考试是项目反应理论和计算机技术想结合的产物,本文依据项目反应理论,对自适应考试系统的中的能力估计、选题策略和终止规则等关键模块的设计进行了较为深入的探讨,并提出了基于J2EE系统实现的模型框架。 相似文献
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本文针对《计算机基础》课程考试的特点和普通计算机化考试系统的不足,以项目反应理论为基础,阐述了基于局域网的计算机自适应考试系统的功能、组成和设计方法。 相似文献
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利用计算机进行考试已普遍被各种考试，特别是多种能力测试所采用，针对不同考试不同的计算机考试系统也层出不穷。大部分的考试系统可以很好地解决标准化试题问题，但对于一些具有学科或技能特点的实际操作题的考试，却没有较好的解决办法。不同的学科或同一学科的不同考试，除了有相同的标准化题型，如选择题、判断题、多项选择等，还有具有其学科特点的非标准化试题，比如计算机应用能力考核中文字录入和WORD、EXCEL、POWERPOINT等的操作题，电子商务考核中的操作题，数学考试中的论证题，计算机语言类考试中的程序设计题等。这… 相似文献
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一帆 《教育测量与评价(理论版)》2014,(3):9-9
计算机自适应测验（CAT）是建立在项目反应理论基础上,由计算机根据被试能力水平自动选择测题,从而对被试能力做出估计的新型测验。计算机自适应测验呈现给考生的试题是依据被试在前一个试题作答的表现好坏来决定的,其实现条件应囊括以下五个部分。 相似文献
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本文介绍了项目反应理论及计算机自适应考试系统的相关理论,对计算机自适应考试系统的需求进行了分析,并设计了功能模块及数据库。 相似文献
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基于项目反应理论的计算机自适应考试是一种新型的考试形式,文中阐述了项目反应理论的基本原理,在.NET平台上以VB.NET为编程语言结合ADO.NET和SQL Server,设计并开发了一个功能比较完善的,基于W eb的自适应考试系统.该系统实现了网络化的计算机自适应考试以及较完善的题库、考务管理等辅助功能. 相似文献
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李慧 《中国现代教育装备》2009,(3):27-29
本文介绍了基于项目反应理论（IRT）的计算机自适应考试系统（CAT）的基本理论、设计思想和实现方法,探讨了CAT在语言测试应用中的优越性,指出在大学英语测试中采用CAT是大势所趋,我国高校英语教师应尽早熟悉和开展对CAT的研究与开发。 相似文献
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基于项目反应理论的计算机自适应考试是一种新型的考试形式,文中阐述了项目反应理论的基本原理,在.NET平台上以VB.NET为编程语言结合ADO.NET和SQL Server,设计并开发了一个功能比较完善的,基于Web的自适应考试系统.该系统实现了网络化的计算机自适应考试以及较完善的题库、考务管理等辅助功能. 相似文献
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漆书青 《江西师范大学学报(哲学社会科学版)》2005,38(5):107-109
在认知心理学、现代测量模型探索与信息技术的推动下，21世纪的能力测量，出现了测验连续性校订、计算机化自适应测验、智能化项目创编以及跟教学结合在一起的动态测量等新趋势，从而使能力测量技术革新和对教育与社会生活的影响，出现崭新局面。 相似文献
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Bernard P. Veldkamp 《Journal of Educational Measurement》2016,53(2):212-228
Many standardized tests are now administered via computer rather than paper‐and‐pencil format. The computer‐based delivery mode brings with it certain advantages. One advantage is the ability to adapt the difficulty level of the test to the ability level of the test taker in what has been termed computerized adaptive testing (CAT). A second advantage is the ability to record not only the test taker's response to each item (i.e., question), but also the amount of time the test taker spends considering and answering each item. Combining these two advantages, various methods were explored for utilizing response time data in selecting appropriate items for an individual test taker. Four strategies for incorporating response time data were evaluated, and the precision of the final test‐taker score was assessed by comparing it to a benchmark value that did not take response time information into account. While differences in measurement precision and testing times were expected, results showed that the strategies did not differ much with respect to measurement precision but that there were differences with regard to the total testing time. 相似文献
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以项目反应理论IRT（ItemResponseTheory）为基础,介绍项目反应理论IRT的特点,以及基于项目反应理论IRT的计算机自适应测试的工作原理,并在此基础上总结了起点选择的方法,提出了测试流程两步制的改进方案,通过对测试流程的改进,大大减少了与被试能力值相差较远的测试项目,缩短了测试时间和计算量,同时能准确地估计被试能力值。 相似文献
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In this study we evaluated and compared three item selection procedures: the maximum Fisher information procedure (F), the a-stratified multistage computer adaptive testing (CAT) (STR), and a refined stratification procedure that allows more items to be selected from the high a strata and fewer items from the low a strata (USTR), along with completely random item selection (RAN). The comparisons were with respect to error variances, reliability of ability estimates and item usage through CATs simulated under nine test conditions of various practical constraints and item selection space. The results showed that F had an apparent precision advantage over STR and USTR under unconstrained item selection, but with very poor item usage. USTR reduced error variances for STR under various conditions, with small compromises in item usage. Compared to F, USTR enhanced item usage while achieving comparable precision in ability estimates; it achieved a precision level similar to F with improved item usage when items were selected under exposure control and with limited item selection space. The results provide implications for choosing an appropriate item selection procedure in applied settings. 相似文献
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Wim J. van der Linden 《Journal of Educational Measurement》2005,42(3):283-302
In test assembly, a fundamental difference exists between algorithms that select a test sequentially or simultaneously. Sequential assembly allows us to optimize an objective function at the examinee's ability estimate, such as the test information function in computerized adaptive testing. But it leads to the non-trivial problem of how to realize a set of content constraints on the test—a problem more naturally solved by a simultaneous item-selection method. Three main item-selection methods in adaptive testing offer solutions to this dilemma. The spiraling method moves item selection across categories of items in the pool proportionally to the numbers needed from them. Item selection by the weighted-deviations method (WDM) and the shadow test approach (STA) is based on projections of the future consequences of selecting an item. These two methods differ in that the former calculates a projection of a weighted sum of the attributes of the eventual test and the latter a projection of the test itself. The pros and cons of these methods are analyzed. An empirical comparison between the WDM and STA was conducted for an adaptive version of the Law School Admission Test (LSAT), which showed equally good item-exposure rates but violations of some of the constraints and larger bias and inaccuracy of the ability estimator for the WDM. 相似文献
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Matthew Finkelman Michael L. Nering Louis A. Roussos 《Journal of Educational Measurement》2009,46(1):84-103
In computerized adaptive testing (CAT), ensuring the security of test items is a crucial practical consideration. A common approach to reducing item theft is to define maximum item exposure rates, i.e., to limit the proportion of examinees to whom a given item can be administered. Numerous methods for controlling exposure rates have been proposed for tests employing the unidimensional 3-PL model. The present article explores the issues associated with controlling exposure rates when a multidimensional item response theory (MIRT) model is utilized and exposure rates must be controlled conditional upon ability. This situation is complicated by the exponentially increasing number of possible ability values in multiple dimensions. The article introduces a new procedure, called the generalized Stocking-Lewis method, that controls the exposure rate for students of comparable ability as well as with respect to the overall population. A realistic simulation set compares the new method with three other approaches: Kullback-Leibler information with no exposure control, Kullback-Leibler information with unconditional Sympson-Hetter exposure control, and random item selection. 相似文献
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Stphanie Berger Angela J. Verschoor Theo J. H. M. Eggen Urs Moser 《Journal of Educational Measurement》2019,56(1):121-146
Calibration of an item bank for computer adaptive testing requires substantial resources. In this study, we investigated whether the efficiency of calibration under the Rasch model could be enhanced by improving the match between item difficulty and student ability. We introduced targeted multistage calibration designs, a design type that considers ability‐related background variables and performance for assigning students to suitable items. Furthermore, we investigated whether uncertainty about item difficulty could impair the assembling of efficient designs. The results indicated that targeted multistage calibration designs were more efficient than ordinary targeted designs under optimal conditions. Limited knowledge about item difficulty reduced the efficiency of one of the two investigated targeted multistage calibration designs, whereas targeted designs were more robust. 相似文献
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One of the methods of controlling test security in adaptive testing is imposing random item-ineligibility constraints on the selection of the items with probabilities automatically updated to maintain a predetermined upper bound on the exposure rates. Three major improvements of the method are presented. First, a few modifications to improve the initialization of the method and accelerate the impact of its feedback mechanism on the observed item-exposure rates are introduced. Second, the case of conditional item-exposure control given the uncertainty of examinee's ability parameter is addressed. Third, although rare for a well-designed item pool, when applied in combination with the shadow-test approach to adaptive testing the method may meet occasional infeasibility of the shadow-test model. A big M method is proposed that resolves the issue. The practical advantages of the improvements are illustrated using simulated adaptive testing from a real-world item pool under a variety of conditions. 相似文献