Maximum information stratification method for controlling item exposure in computerized adaptive testing

The proposal for increasing the security in Computerized Adaptive Tests that has received most attention in recent years is the a-stratified method (AS - Chang and Ying, 1999): at the beginning of the test only items with low discrimination parameters ( a ) can be administered, with the values of the a parameters increasing as the test goes on. With this method, distribution of the exposure rates of the items is less skewed, while efficiency is maintained in trait-level estimation. The pseudo-guessing parameter ( c ), present in the three-parameter logistic model, is considered irrelevant, and is not used in the AS method. The Maximum Information Stratified (MIS) model incorporates the c parameter in the stratification of the bank and in the item-selection rule, improving accuracy by comparison with the AS, for item banks with a and b parameters correlated and uncorrelated. For both kinds of banks, the blocking b methods (Chang, Qian and Ying, 2001) improve the security of the item bank.     

The maximum priority index method for severely constrained item selection in computerized adaptive testing

This paper introduces a new heuristic approach, the maximum priority index (MPI) method, for severely constrained item selection in computerized adaptive testing. Our simulation study shows that it is able to accommodate various non‐statistical constraints simultaneously, such as content balancing, exposure control, answer key balancing, and so on. Compared with the weighted deviation modelling method, it leads to fewer constraint violations and better exposure control while maintaining the same level of measurement precision.     

Rotating item banks versus restriction of maximum exposure rates in computerized adaptive testing

If examinees were to know, beforehand, part of the content of a computerized adaptive test, their estimated trait levels would then have a marked positive bias. One of the strategies to avoid this consists of dividing a large item bank into several sub-banks and rotating the sub-bank employed (Ariel, Veldkamp & van der Linden, 2004). This strategy permits substantial improvements in exposure control at little cost to measurement accuracy, However, we do not know whether this option provides better results than using the master bank with greater restriction in the maximum exposure rates (Sympson & Hetter, 1985). In order to investigate this issue, we worked with several simulated banks of 2100 items, comparing them, for RMSE and overlap rate, with the same banks divided in two, three... up to seven sub-banks. By means of extensive manipulation of the maximum exposure rate in each bank, we found that the option of rotating banks slightly outperformed the option of restricting maximum exposure rate of the master bank by means of the Sympson-Hetter method.     

考虑题目选项信息的非参数认知诊断计算机自适应测验

选择题中的作答选项能提供额外诊断信息, 为充分利用选项信息, 研究提出认知诊断计算机自适应测验(CD-CAT)中两种处理选择题选项信息的非参数选题策略和变长终止规则。模拟研究的结果发现：(1)定长条件下两种非参数选题策略的分类准确性整体要高于参数选题策略; (2)两种非参数选题策略较参数选题策略具有更加均衡的题库使用情况; (3)非参数选题策略在两种新的变长终止规则下具有更高的分类准确率; (4)两种非参数选题策略均适用于选择题CD-CAT情境, 使用者可任选其一进行测验分析。     

Maximum information per time unit designs for continuous online item calibration

Previous designs for online calibration have only considered examinees’ responses to items. However, the use of response time, a useful metric that can easily be collected by a computer, has not yet been embedded in calibration designs. In this article we utilize response time to optimize the assignment of new items online, and accordingly propose two new adaptive designs. These are the D-optimal per expectation time unit design (D-ET) and the D-optimal per time unit design (D-T). The former method uses the conditional maximum likelihood estimation (CMLE) method to estimate the expected response times, while the latter employs the nonparametric k-nearest-neighbour method to predict the response times. Simulations were conducted to compare the two new designs with the D-optimal online calibration design (D design) in the context of continuous online calibration. In addition, a preliminary study was carried out to evaluate the performance of CMLE prior to its application in D-ET. The results showed that, compared to the D design, the D-ET and D-T designs saved response time and accrued larger calibration information per time unit, without sacrificing item calibration precision.     

基于CAT的在线标定:设计与方法

项目增补是题库建设和维护的重要手段, 而标定新题参数是项目增补的重要内容。在线标定设计和在线标定方法分别研究新题的施测方式和参数估计方法, 是计算机化自适应测验(computerized adaptive testing, CAT)情景下项目增补的核心技术。重点厘清在线标定设计与在线标定方法的发展思路和脉络, 并对它们的特点、联系和表现进行介绍和评价。未来应基于其他信息指标进一步研究在线标定设计, 可基于联合估计和误差校正的思路探究在线标定方法, 应加强研究认知诊断CAT和多维CAT的在线标定技术, 深入开展项目增补方法的实证研究。     

Predicting item exposure parameters in computerized adaptive testing

The purpose of this study is to find a formula that describes the relationship between item exposure parameters and item parameters in computerized adaptive tests by using genetic programming (GP) – a biologically inspired artificial intelligence technique. Based on the formula, item exposure parameters for new parallel item pools can be predicted without conducting additional iterative simulations. Results show that an interesting formula between item exposure parameters and item parameters in a pool can be found by using GP. The item exposure parameters predicted based on the found formula were close to those observed from the Sympson and Hetter (1985) procedure and performed well in controlling item exposure rates. Similar results were observed for the Stocking and Lewis (1998) multinomial model for item selection and the Sympson and Hetter procedure with content balancing. The proposed GP approach has provided a knowledge‐based solution for finding item exposure parameters.     

Classification accuracy and consistency of computerized adaptive testing

In this article, four item selection methods in computerized adaptive testing are examined in terms of classification accuracy and consistency, including two popular heuristics for constraint management, the maximum priority index (MPI) method and the weighted deviation modeling method, as well as the widely known maximum Fisher information method and randomized item selection as baselines. Results suggest that the MPI method is able to meet constraints and keep test overlap rate low. Among the four methods, it is the only one that manages to produce parallel forms in terms of content coverage and, consequently, the only method to which the idea of classification consistency applies. With tests as short as 12 items, the MPI method does fairly well in classifying examinees accurately and consistently. Its performance improves with longer tests. The effects of number of decision categories and cut score locations are also examined. Recommendations are made in the Discussion section.     

Varying the valuating function and the presentable bank in computerized adaptive testing

In computerized adaptive testing, the most commonly used valuating function is the Fisher information function. When the goal is to keep item bank security at a maximum, the valuating function that seems most convenient is the matching criterion, valuating the distance between the estimated trait level and the point where the maximum of the information function is located. Recently, it has been proposed not to keep the same valuating function constant for all the items in the test. In this study we expand the idea of combining the matching criterion with the Fisher information function. We also manipulate the number of strata into which the bank is divided. We find that the manipulation of the number of items administered with each function makes it possible to move from the pole of high accuracy and low security to the opposite pole. It is possible to greatly improve item bank security with much fewer losses in accuracy by selecting several items with the matching criterion. In general, it seems more appropriate not to stratify the bank.     

On initial item selection in cognitive diagnostic computerized adaptive testing

There has recently been much interest in computerized adaptive testing (CAT) for cognitive diagnosis. While there exist various item selection criteria and different asymptotically optimal designs, these are mostly constructed based on the asymptotic theory assuming the test length goes to infinity. In practice, with limited test lengths, the desired asymptotic optimality may not always apply, and there are few studies in the literature concerning the optimal design of finite items. Related questions, such as how many items we need in order to be able to identify the attribute pattern of an examinee and what types of initial items provide the optimal classification results, are still open. This paper aims to answer these questions by providing non‐asymptotic theory of the optimal selection of initial items in cognitive diagnostic CAT. In particular, for the optimal design, we provide necessary and sufficient conditions for the Q ‐matrix structure of the initial items. The theoretical development is suitable for a general family of cognitive diagnostic models. The results not only provide a guideline for the design of optimal item selection procedures, but also may be applied to guide item bank construction.     

Item-presentation controls for multidimensional item pools in computerized adaptive testing

Item pools or item banks used in most testing situations are inherently multidimensional. This is especially a problem in computerized adaptive testing (CAT), which is driven by item response theory; item response theory requires that the item pool be unidimensional. This series of computer simulations demonstrates how alternative item-presentation controls (content-balancing and “mini-CATs”) may be employed in CAT to estimate ability accurately in spite of the violation of unidimensionality. Averaged, shorter mini-CATs provide the most accurate estimation of ability and ameliorate problems intrinsic to violating the unidimensionality assumption of item response theory.     

Comparability and validity of computerized adaptive testing with the MMPI-2

The comparability and validity of a computerized adaptive (CA) Minnesota Multiphasic Personality Inventory-2 (MMPI-2) were assessed in a sample of 571 undergraduate college students. The CA MMPI-2 administered adaptively Scales L, E the 10 clinical scales, and the 15 content scales, utilizing the countdown method (Butcher, Keller, & Bacon, 1985). All subjects completed the MMPI-2 twice, with three experimental conditions: booklet test-retest, booklet-CA, and conventional computerized (CC)-CA. Profiles across administration modalities show a high degree of similarity, providing evidence for the comparability of the three forms. Correlations between MMPI-2 scales and other psychometric measures (Beck Depression Inventory; Symptom Checklist-Revised; State-Trait Anxiety and Anger Scales; and the Anger Expression Scale) support the validity of the CA MMPI-2. Substantial item savings may be realized with the implementation of the countdown procedure.     

Comparability of computerized adaptive and conventional testing with the MMPI-2

A computerized adaptive version and the standard version of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) were administered 1 week apart to a sample of 155 college students to assess the comparability of the two versions. The countdown method was used to adaptively administer Scales L, F, the I0 clinical scales, and the 15 new content scales. Profiles across administration modalities show a high degree of similarity, providing evidence for the comparability of computerized adaptive and conventional testing with the MMPI-2. Substantial item savings were found with the adaptive version. Future directions in the study of adaptive testing with the MMPI-2 are discussed.     

可修改答案的计算机化自适应测验的方法

与传统的纸笔测验(Paper And Pencil Based Test, P&P)相比计算机化自适应测验(Computerized Adaptive Testing, CAT)根据被试的作答反应自适应地选择题目, 它不仅缩短了测验长度, 还极大地提高了测验的准确性。然而, 目前绝大多数CAT不允许被试修改答案, 研究者主要担心修改答案会降低CAT的有效性。允许修改答案符合被试一贯的测验习惯, 修改之后的分数更能反映被试真实的水平, 从而能够进一步促进CAT在实际中的应用。现有的研究主要从三个方面提出了可修改答案CAT的控制方法：一是测验设计; 二是改进选题策略; 三是建构模型。未来的研究应进一步探讨这些方法之间的比较与结合, 以及对可修改答案认知诊断CAT (Cognitive Diagnostic CAT, CD-CAT)的研究。     

Using response times to detect aberrant responses in computerized adaptive testing

A lognormal model for response times is used to check response times for aberrances in examinee behavior on computerized adaptive tests. Both classical procedures and Bayesian posterior predictive checks are presented. For a fixed examinee, responses and response times are independent; checks based on response times offer thus information independent of the results of checks on response patterns. Empirical examples of the use of classical and Bayesian checks for detecting two different types of aberrances in response times are presented. The detection rates for the Bayesian checks outperformed those for the classical checks, but at the cost of higher false-alarm rates. A guideline for the choice between the two types of checks is offered.This study received funding from the Law School Admission Council (LSAC). The opinions and conclusions contained in this paper are those of the authors and do not necessarily reflect the policy and position of LSAC. The authors are most indebted to Wim M. M. Tielen for his computational assistance and to the US Defense Manpower Data Center for the permission to use the ASVAB data set in the empirical examples.     

Controlling item exposure and test overlap on the fly in computerized adaptive testing

This paper proposes an on‐line version of the Sympson and Hetter procedure with test overlap control (SHT) that can provide item exposure control at both the item and test levels on the fly without iterative simulations. The on‐line procedure is similar to the SHT procedure in that exposure parameters are used for simultaneous control of item exposure rates and test overlap rate. The exposure parameters for the on‐line procedure, however, are updated sequentially on the fly, rather than through iterative simulations conducted prior to operational computerized adaptive tests (CATs). Unlike the SHT procedure, the on‐line version can control item exposure rate and test overlap rate without time‐consuming iterative simulations even when item pools or examinee populations have been changed. Moreover, the on‐line procedure was found to perform better than the SHT procedure in controlling item exposure and test overlap for examinees who take tests earlier. Compared with two other on‐line alternatives, this proposed on‐line method provided the best all‐around test security control. Thus, it would be an efficient procedure for controlling item exposure and test overlap in CATs.     

A simplified version of the SYMLOG Trait Rating Form

This paper describes a brief measure of Bales's three SYMLOG dimensions. A sample of 204 undergraduates with a mean age of 24.7 yr., 72.4% of whom were women, completed the usual version of the SYMLOG Trait Rating Form. This could also be scored as if it were a simplified version, omitting some items. The Simplified Measure of the SYMLOG Trait Rating Form correlated fairly highly with the full form--with r between .87 and .89 for the corresponding versions of all three scale dimension scores--making it a suitable substitute for research or illustrative purposes on occasions when respondents' time is of the essence.     

Look-ahead content balancing method in variable-length computerized classification testing

Content balancing is one of the most important issues in computerized classification testing. To adapt to variable-length forms, special treatments are needed to successfully control content constraints without knowledge of test length during the test. To this end, we propose the notions of ‘look-ahead’ and ‘step size’ to adaptively control content constraints in each item selection step. The step size gives a prediction of the number of items to be selected at the current stage, that is, how far we will look ahead. Two look-ahead content balancing (LA-CB) methods, one with a constant step size and another with an adaptive step size, are proposed as feasible solutions to balancing content areas in variable-length computerized classification testing. The proposed LA-CB methods are compared with conventional item selection methods in variable-length tests and are examined with different classification methods. Simulation results show that, integrated with heuristic item selection methods, the proposed LA-CB methods result in fewer constraint violations and can maintain higher classification accuracy. In addition, the LA-CB method with an adaptive step size outperforms that with a constant step size in content management. Furthermore, the LA-CB methods generate higher test efficiency while using the sequential probability ratio test classification method.