Controlling Item Exposure Conditional on Ability in Computerized Adaptive Testing

1998 ◽  
Vol 23 (1) ◽  
pp. 57-75 ◽  
Author(s):  
Martha L. Stocking ◽  
Charles Lewis
Keyword(s):  
Large Scale ◽  
Computerized Adaptive Testing ◽  
Simulated Data ◽  
Adaptive Testing ◽  
Testing Environment ◽  
Item Exposure ◽  
Continuous Testing ◽  
Conditional Control ◽  
Periodic Testing ◽  
Testing Algorithm

The interest in the application of large-scale adaptive testing for secure tests has served to focus attention on issues that arise when theoretical advances are made operational. One such issue is that of ensuring item and pool security in the continuous testing environment made possible by the computerized admin-istration of a test, as opposed to the more periodic testing environment typically used for linear paper-and-pencil tests. This article presents a new method of controlling the exposure rate of items conditional on ability level in this continuous testing environment. The properties of such conditional control on the exposure rates of items, when used in conjunction with a particular adaptive testing algorithm, are explored through studies with simulated data.

Evaluating a Computerized Adaptive Testing Version of a Cognitive Ability Test Using a Simulation Study

2021 ◽  
pp. 073428292110277
Author(s):  
Ioannis Tsaousis ◽  
Georgios D. Sideridis ◽  
Hannan M. AlGhamdi
Keyword(s):  
Cognitive Ability ◽  
Simulation Study ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Estimation Methods ◽  
Item Pool ◽  
Sequential Approach ◽  
Ability Test ◽  
Promising Alternative ◽  
Item Exposure

This study evaluated the psychometric quality of a computerized adaptive testing (CAT) version of the general cognitive ability test (GCAT), using a simulation study protocol put forth by Han, K. T. (2018a). For the needs of the analysis, three different sets of items were generated, providing an item pool of 165 items. Before evaluating the efficiency of the GCAT, all items in the final item pool were linked (equated), following a sequential approach. Data were generated using a standard normal for 10,000 virtual individuals ( M = 0 and SD = 1). Using the measure’s 165-item bank, the ability value (θ) for each participant was estimated. maximum Fisher information (MFI) and maximum likelihood estimation with fences (MLEF) were used as item selection and score estimation methods, respectively. For item exposure control, the fade away method (FAM) was preferred. The termination criterion involved a minimum SE ≤ 0.33. The study revealed that the average number of items administered for 10,000 participants was 15. Moreover, the precision level in estimating the participant’s ability score was very high, as demonstrated by the CBIAS, CMAE, and CRMSE). It is concluded that the CAT version of the test is a promising alternative to administering the corresponding full-length measure since it reduces the number of administered items, prevents high rates of item exposure, and provides accurate scores with minimum measurement error.

Adaptive Weight Estimation of Latent Ability: Application to Computerized Adaptive Testing With Response Revision

2020 ◽  
pp. 107699862097280
Author(s):  
Shiyu Wang ◽  
Houping Xiao ◽  
Allan Cohen
Keyword(s):  
Large Scale ◽  
Computerized Adaptive Testing ◽  
Estimation Method ◽  
Adaptive Testing ◽  
Ability Estimation ◽  
Weight Estimation ◽  
Data Set ◽  
Adaptive Weight ◽  
Latent Ability ◽  
Estimation Procedures

An adaptive weight estimation approach is proposed to provide robust latent ability estimation in computerized adaptive testing (CAT) with response revision. This approach assigns different weights to each distinct response to the same item when response revision is allowed in CAT. Two types of weight estimation procedures, nonfunctional and functional weight, are proposed to determine the weight adaptively based on the compatibility of each revised response with the assumed statistical model in relation to remaining observations. The application of this estimation approach to a data set collected from a large-scale multistage adaptive testing demonstrates the capability of this method to reveal more information regarding the test taker’s latent ability by using the valid response path compared with only using the very last response. Limited simulation studies were concluded to evaluate the proposed ability estimation method and to compare it with several other estimation procedures in literature. Results indicate that the proposed ability estimation approach is able to provide robust estimation results in two test-taking scenarios.

scholarly journals Some Alternatives to Sympson-Hetter Item-Exposure Control in Computerized Adaptive Testing

2003 ◽  
Vol 28 (3) ◽  
pp. 249-265 ◽  
Author(s):  
Wim J. van der Linden
Keyword(s):  
Iterative Process ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Law School ◽  
Exposure Control ◽  
Item Exposure ◽  
Item Exposure Control ◽  
Admission Test ◽  
Formal Properties ◽  
Ability Parameter

The Hetter and Sympson (1997 ; 1985 ) method is a method of probabilistic item-exposure control in computerized adaptive testing. Setting its control parameters to admissible values requires an iterative process of computer simulations that has been found to be time consuming, particularly if the parameters have to be set conditional on a realistic set of values for the examinees’ ability parameter. Formal properties of the method are identified that help us explain why this iterative process can be slow and does not guarantee admissibility. In addition, some alternatives to the SH method are introduced. The behavior of these alternatives was estimated for an adaptive test from an item pool from the Law School Admission Test (LSAT). Two of the alternatives showed attractive behavior and converged smoothly to admissibility for all items in a relatively small number of iteration steps.

scholarly journals A Dynamic Stratification Method for Improving Trait Estimation in Computerized Adaptive Testing Under Item Exposure Control

2019 ◽  
Vol 44 (3) ◽  
pp. 182-196
Author(s):  
Jyun-Hong Chen ◽  
Hsiu-Yi Chao ◽  
Shu-Ying Chen
Keyword(s):  
Computerized Adaptive Testing ◽  
Item Difficulty ◽  
Adaptive Testing ◽  
Item Selection ◽  
Exposure Control ◽  
Item Exposure ◽  
Stratification Method ◽  
High Discrimination ◽  
Item Exposure Control ◽  
Trait Estimation

When computerized adaptive testing (CAT) is under stringent item exposure control, the precision of trait estimation will substantially decrease. A new item selection method, the dynamic Stratification method based on Dominance Curves (SDC), which is aimed at improving trait estimation, is proposed to mitigate this problem. The objective function of the SDC in item selection is to maximize the sum of test information for all examinees rather than maximizing item information for individual examinees at a single-item administration, as in conventional CAT. To achieve this objective, the SDC uses dominance curves to stratify an item pool into strata with the number being equal to the test length to precisely and accurately increase the quality of the administered items as the test progresses, reducing the likelihood that a high-discrimination item will be administered to an examinee whose ability is not close to the item difficulty. Furthermore, the SDC incorporates a dynamic process for on-the-fly item–stratum adjustment to optimize the use of quality items. Simulation studies were conducted to investigate the performance of the SDC in CAT under item exposure control at different levels of severity. According to the results, the SDC can efficiently improve trait estimation in CAT through greater precision and more accurate trait estimation than those generated by other methods (e.g., the maximum Fisher information method) in most conditions.

scholarly journals A computerized adaptive testing advancing the measurement of subjective well-being

2019 ◽  
Vol 13 ◽  
Author(s):  
Yifang Wu ◽  
Yan Cai ◽  
Dongbo Tu
Keyword(s):  
Computerized Adaptive Testing ◽  
Simulated Data ◽  
Real Data ◽  
Well Being ◽  
Adaptive Testing ◽  
Test Time ◽  
Subjective Well Being ◽  
Test Items ◽  
Item Fit ◽  
Item Functioning

AbstractThis article aimed at developing an adaptive version of the subjective well-being (SWB) scale to measure a comprehensive concept of SWB among Chinese university students. Item response theory was employed to formulate the item bank of the SWB scale and computerized adaptive testing (CAT) for SWB (CAT-SWB), based on several commonly used SWB scales, after unidimensionality testing, model selection, local dependence testing, parameter estimation, item fit test and differential item functioning (DIF) analysis were performed. Finally, two CAT simulations using simulated-data and real-data were carried out to verify and evaluate the CAT-SWB. Results indicated that the proposed CAT-SWB had an excellent performance in that it largely reduces the number of test items and the length of test time without losing measurement precision.

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