00542nas a2200169 4500008004500000245006600045210006600111300001000177490000700187653002100194653000800215653000800223653003000231653001300261100002000274856007800294 2023 Engldsh 00aExpanding the Meaning of Adaptive Testing to Enhance Validity0 aExpanding the Meaning of Adaptive Testing to Enhance Validity a22-310 v1010aAdaptive Testing10aCAT10aCBT10atest-taking disengagement10avalidity1 aWise, Steven, L uhttp://mail.iacat.org/expanding-meaning-adaptive-testing-enhance-validity01676nas a2200145 4500008004100000245004800041210004800089260005500137520120400192653000801396653002101404653001401425100002401439856006701463 2017 eng d00aAdaptivity in a Diagnostic Educational Test0 aAdaptivity in a Diagnostic Educational Test aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

During the past five years a diagnostic educational test for three subjects (writing Dutch, writing English and math) has been developed in the Netherlands. The test informs students and their teachers about the students’ strengths and weaknesses in such a manner that the learning process can be adjusted to their personal needs. It is a computer-based assessment for students in five different educational tracks midway secondary education that can yield diagnoses of many sub-skills. One of the main challenges at the outset of the development was to devise a way to deliver many diagnoses within a reasonably testing time. The answer to this challenge was to make the DET adaptive.

In this presentation we will discuss first how the adaptivity is shaped towards the purpose of the Diagnostic Educational Test. The adaptive design, particularly working with item blocks, will be discussed as well as the implemented adaptive rules. We will also show a simulation of different adaptive paths of students and some empirical information on the paths students took through the test

Session Video

10aCAT10aDiagnostic tests10aEducation1 aSchouwstra, Sanneke uhttp://mail.iacat.org/adaptivity-diagnostic-educational-test-002709nas a2200157 4500008004100000245007100041210006900112260005500181520213800236653000802374653002002382653001402402100002002416700002602436856008902462 2017 eng d00aAnalysis of CAT Precision Depending on Parameters of the Item Pool0 aAnalysis of CAT Precision Depending on Parameters of the Item Po aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

The purpose of this research project is to analyze the measurement precision of a latent variable depending on parameters of the item pool. The influence of the following factors is analyzed:

Factor A – range of variation of items in the pool. This factor varies on three levels with the following ranges in logits: a1 – [-3.0; +3.0], a2 - [-4.0; +4.0], a3 - [-5.0; +5.0].

Factor B – number of items in the pool. The factor varies on six levels with the following number of items for every factor: b1 - 128, b2 - 256, b3 – 512, b4 - 1024, b5 – 2048, b6 – 4096. The items are evenly distributed in each of the variation ranges.

Factor C – examinees’ proficiency varies at 30 levels (c1, c2, …, c30), which are evenly distributed in the range [-3.0; +3.0] logit.

The investigation was based on a simulation experiment within the framework of the theory of latent variables.

Response Y is the precision of measurement of examinees’ proficiency, which is calculated as the difference between the true levels of examinees’ proficiency and estimates obtained by means of adaptive testing. Three factor ANOVA was used for data processing.

The following results were obtained:

1. Factor A is significant. Ceteris paribus, the greater the range of variation of items in the pool, the higher the estimation precision is.

2. Factor B is significant. Ceteris paribus, the greater the number of items in the pool, the higher the estimation precision is.

3. Factor C is statistically insignificant at level α = .05. It means that the precision of estimation of examinees’ proficiency is the same within the range of their variation.

4. The only significant interaction among all interactions is AB. The significance of this interaction is explained by the fact that increasing the number of items in the pool decreases the effect of the range of variation of items in the pool. 

Session Video

10aCAT10aItem parameters10aPrecision1 aMaslak, Anatoly1 aPozdniakov, Stanislav uhttps://drive.google.com/file/d/1Bwe58kOQRgCSbB8x6OdZTDK4OIm3LQI3/view?usp=drive_web02101nas a2200181 4500008004100000245004600041210004600087260005500133520153800188653002501726653000801751100002801759700001901787700001301806700002001819700001301839856006701852 2017 eng d00aBayesian Perspectives on Adaptive Testing0 aBayesian Perspectives on Adaptive Testing aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Although adaptive testing is usually treated from the perspective of maximum-likelihood parameter estimation and maximum-informaton item selection, a Bayesian pespective is more natural, statistically efficient, and computationally tractable. This observation not only holds for the core process of ability estimation but includes such processes as item calibration, and real-time monitoring of item security as well. Key elements of the approach are parametric modeling of each relevant process, updating of the parameter estimates after the arrival of each new response, and optimal design of the next step.

The purpose of the symposium is to illustrates the role of Bayesian statistics in this approach. The first presentation discusses a basic Bayesian algorithm for the sequential update of any parameter in adaptive testing and illustrates the idea of Bayesian optimal design for the two processes of ability estimation and online item calibration. The second presentation generalizes the ideas to the case of 62 IACAT 2017 ABSTRACTS BOOKLET adaptive testing with polytomous items. The third presentation uses the fundamental Bayesian idea of sampling from updated posterior predictive distributions (“multiple imputations”) to deal with the problem of scoring incomplete adaptive tests.

Session Video 1

Session Video 2

 

10aBayesian Perspective10aCAT1 avan der Linden, Wim, J.1 aJiang, Bingnan1 aRen, Hao1 aChoi, Seung, W.1 aDiao, Qi uhttp://mail.iacat.org/bayesian-perspectives-adaptive-testing-003114nas a2200145 4500008004100000245004900041210004500090260005500135520264100190653002802831653000802859653002102867100001702888856006302905 2017 eng d00aIs CAT Suitable for Automated Speaking Test?0 aCAT Suitable for Automated Speaking Test aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

We have developed automated scoring system of Japanese speaking proficiency, namely SJ-CAT (Speaking Japanese Computerized Adaptive Test), which is operational for last few months. One of the unique features of the test is an adaptive test base on polytomous IRT.

SJ-CAT consists of two sections; Section 1 has sentence reading aloud tasks and a multiple choicereading tasks and Section 2 has sentence generation tasks and an open answer tasks. In reading aloud tasks, a test taker reads a phoneme-balanced sentence on the screen after listening to a model reading. In a multiple choice-reading task, a test taker sees a picture and reads aloud one sentence among three sentences on the screen, which describe the scene most appropriately. In a sentence generation task, a test taker sees a picture or watches a video clip and describes the scene with his/her own words for about ten seconds. In an open answer tasks, the test taker expresses one’s support for or opposition to e.g., a nuclear power generation with reasons for about 30 seconds.

In the course of the development of the test, we found many unexpected and unique characteristics of speaking CAT, which are not found in usual CATs with multiple choices. In this presentation, we will discuss some of such factors that are not previously noticed in our previous project of developing dichotomous J-CAT (Japanese Computerized Adaptive Test), which consists of vocabulary, grammar, reading, and listening. Firstly, we will claim that distribution of item difficulty parameters depends on the types of items. An item pool with unrestricted types of items such as open questions is difficult to achieve ideal distributions, either normal distribution or uniform distribution. Secondly, contrary to our expectations, open questions are not necessarily more difficult to operate in automated scoring system than more restricted questions such as sentence reading, as long as if one can set up suitable algorithm for open question scoring. Thirdly, we will show that the speed of convergence of standard deviation of posterior distribution, or standard error of theta parameter in polytomous IRT used for SJCAT is faster than dichotomous IRT used in J-CAT. Fourthly, we will discuss problems in equation of items in SJ-CAT, and suggest introducing deep learning with reinforcement learning instead of equation. And finally, we will discuss the issues of operation of SJ-CAT on the web, including speed of scoring, operation costs, security among others.

Session Video

10aAutomated Speaking Test10aCAT10alanguage testing1 aImai, Shingo uhttp://mail.iacat.org/cat-suitable-automated-speaking-test05045nas a2200145 4500008004100000245008900041210006900130260005500199520447200254653000804726653002904734100001804763700001504781856010304796 2017 eng d00aComparison of Pretest Item Calibration Methods in a Computerized Adaptive Test (CAT)0 aComparison of Pretest Item Calibration Methods in a Computerized aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Calibration methods for pretest items in a computerized adaptive test (CAT) are not a new area of research inquiry. After decades of research on CAT, the fixed item parameter calibration (FIPC) method has been widely accepted and used by practitioners to address two CAT calibration issues: (a) a restricted ability range each item is exposed to, and (b) a sparse response data matrix. In FIPC, the parameters of the operational items are fixed at their original values, and multiple expectation maximization (EM) cycles are used to estimate parameters of the pretest items with prior ability distribution being updated multiple times (Ban, Hanson, Wang, Yi, & Harris, 2001; Kang & Peterson, 2009; Pommerich & Segall, 2003).

Another calibration method is the fixed person parameter calibration (FPPC) method proposed by Stocking (1988) as “Method A.” Under this approach, candidates’ ability estimates are fixed in the calibration of pretest items and they define the scale on which the parameter estimates are reported. The logic of FPPC is suitable for CAT applications because the person parameters are estimated based on operational items and available for pretest item calibration. In Stocking (1988), the FPPC was evaluated using the LOGIST computer program developed by Wood, Wingersky, and Lord (1976). He reported that “Method A” produced larger root mean square errors (RMSEs) in the middle ability range than “Method B,” which required the use of anchor items (administered non-adaptively) and linking steps to attempt to correct for the potential scale drift due to the use of imperfect ability estimates.

Since then, new commercial software tools such as BILOG-MG and flexMIRT (Cai, 2013) have been developed to handle the FPPC method with different implementations (e.g., the MH-RM algorithm with flexMIRT). The performance of the FPPC method with those new software tools, however, has rarely been researched in the literature.

In our study, we evaluated the performance of two pretest item calibration methods using flexMIRT, the new software tool. The FIPC and FPPC are compared under various CAT settings. Each simulated exam contains 75% operational items and 25% pretest items, and real item parameters are used to generate the CAT data. This study also addresses the lack of guidelines in existing CAT item calibration literature regarding population ability shift and exam length (more accurate theta estimates are expected in longer exams). Thus, this study also investigates the following four factors and their impact on parameter estimation accuracy, including: (1) candidate population changes (3 ability distributions); (2) exam length (20: 15 OP + 5 PT, 40: 30 OP + 10 PT, and 60: 45 OP + 15 PT); (3) data model fit (3PL and 3PL with fixed C), and (4) pretest item calibration sample sizes (300, 500, and 1000). This study’s findings will fill the gap in this area of research and thus provide new information on which practitioners can base their decisions when selecting a pretest calibration method for their exams.

References

Ban, J. C., Hanson, B. A., Wang, T., Yi, Q., & Harris, D. J. (2001). A comparative study of online pretest item—Calibration/scaling methods in computerized adaptive testing. Journal of Educational Measurement, 38(3), 191–212.

Cai, L. (2013). flexMIRT® Flexible Multilevel Multidimensional Item Analysis and Test Scoring (Version 2) [Computer software]. Chapel Hill, NC: Vector Psychometric Group.

Kang, T., & Petersen, N. S. (2009). Linking item parameters to a base scale (Research Report No. 2009– 2). Iowa City, IA: ACT.

Pommerich, M., & Segall, D.O. (2003, April). Calibrating CAT pools and online pretest items using marginal maximum likelihood methods. Paper presented at the annual meeting of the National Council on Measurement in Education, Chicago, IL.

Stocking, M. L. (1988). Scale drift in online calibration (Research Report No. 88–28). Princeton, NJ: Educational Testing Service.

Wood, R. L., Wingersky, M. S., & Lord, F. M. (1976). LOGIST: A computer program for estimating examinee ability and item characteristic curve parameters (RM76-6) [Computer program]. Princeton, NJ: Educational Testing Service.

Session Video

10aCAT10aPretest Item Calibration1 aMeng, Huijuan1 aHan, Chris uhttp://mail.iacat.org/comparison-pretest-item-calibration-methods-computerized-adaptive-test-cat-001722nas a2200133 4500008004100000245009200041210006900133260005500202520120200257653000801459653001601467100001801483856008701501 2017 eng d00aA Comparison of Three Empirical Reliability Estimates for Computerized Adaptive Testing0 aComparison of Three Empirical Reliability Estimates for Computer aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Reliability estimates in Computerized Adaptive Testing (CAT) are derived from estimated thetas and standard error of estimated thetas. In practical, the observed standard error (OSE) of estimated thetas can be estimated by test information function for each examinee with respect to Item response theory (IRT). Unlike classical test theory (CTT), OSEs in IRT are conditional values given each estimated thetas so that those values should be marginalized to consider test reliability. Arithmetic mean, Harmonic mean, and Jensen equality were applied to marginalize OSEs to estimate CAT reliability. Based on different marginalization method, three empirical CAT reliabilities were compared with true reliabilities. Results showed that three empirical CAT reliabilities were underestimated compared to true reliability in short test length (< 40), whereas the magnitude of CAT reliabilities was followed by Jensen equality, Harmonic mean, and Arithmetic mean in long test length (> 40). Specifically, Jensen equality overestimated true reliability across all conditions in long test length (>50).

Session Video 

10aCAT10aReliability1 aSeo, Dong, Gi uhttps://drive.google.com/file/d/1gXgH-epPIWJiE0LxMHGiCAxZZAwy4dAH/view?usp=sharing03162nas a2200181 4500008004100000245010600041210006900147260005500216520249300271653000802764653001502772653002702787100002202814700002602836700001602862700001502878856008702893 2017 eng d00aEfficiency of Targeted Multistage Calibration Designs under Practical Constraints: A Simulation Study0 aEfficiency of Targeted Multistage Calibration Designs under Prac aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Calibration of an item bank for computer adaptive testing requires substantial resources. In this study, we focused on two related research questions. First, we investigated whether the efficiency of item calibration under the Rasch model could be enhanced by calibration designs that optimize the match between item difficulty and student ability (Berger, 1991). Therefore, we introduced targeted multistage calibration designs, a design type that refers to a combination of traditional targeted calibration designs and multistage designs. As such, targeted multistage calibration designs consider ability-related background variables (e.g., grade in school), as well as performance (i.e., outcome of a preceding test stage) for assigning students to suitable items.

Second, we explored how limited a priori knowledge about item difficulty affects the efficiency of both targeted calibration designs and targeted multistage calibration designs. When arranging items within a given calibration design, test developers need to know the item difficulties to locate items optimally within the design. However, usually, no empirical information about item difficulty is available before item calibration. Owing to missing empirical data, test developers might fail to assign all items to the most suitable location within a calibration design.

Both research questions were addressed in a simulation study in which we varied the calibration design, as well as the accuracy of item distribution across the different booklets or modules within each design (i.e., number of misplaced items). The results indicated that targeted multistage calibration designs were more efficient than ordinary targeted designs under optimal conditions. Especially, targeted multistage calibration designs provided more accurate estimates for very easy and 52 IACAT 2017 ABSTRACTS BOOKLET very difficult items. Limited knowledge about item difficulty during test construction impaired the efficiency of all designs. The loss of efficiency was considerably large for one of the two investigated targeted multistage calibration designs, whereas targeted designs were more robust.

References

Berger, M. P. F. (1991). On the efficiency of IRT models when applied to different sampling designs. Applied Psychological Measurement, 15(3), 293–306. doi:10.1177/014662169101500310

Session Video

10aCAT10aEfficiency10aMultistage Calibration1 aBerger, Stephanie1 aVerschoor, Angela, J.1 aEggen, Theo1 aMoser, Urs uhttps://drive.google.com/file/d/1ko2LuiARKqsjL_6aupO4Pj9zgk6p_xhd/view?usp=sharing02133nas a2200169 4500008004100000245006700041210006500108260005500173520156400228653000801792653000801800653002001808653002301828100002101851700002001872856007101892 2017 eng d00aEvaluation of Parameter Recovery, Drift, and DIF with CAT Data0 aEvaluation of Parameter Recovery Drift and DIF with CAT Data aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Parameter drift and differential item functioning (DIF) analyses are frequent components of a test maintenance plan. That is, after a test form(s) is published, organizations will often calibrate postpublishing data at a later date to evaluate whether the performance of the items or the test has changed over time. For example, if item content is leaked, the items might gradually become easier over time, and item statistics or parameters can reflect this.

When tests are published under a computerized adaptive testing (CAT) paradigm, they are nearly always calibrated with item response theory (IRT). IRT calibrations assume that range restriction is not an issue – that is, each item is administered to a range of examinee ability. CAT data violates this assumption. However, some organizations still wish to evaluate continuing performance of the items from a DIF or drift paradigm.

This presentation will evaluate just how inaccurate DIF and drift analyses might be on CAT data, using a Monte Carlo parameter recovery methodology. Known item parameters will be used to generate both linear and CAT data sets, which are then calibrated for DIF and drift. In addition, we will implement Randomesque item exposure constraints in some CAT conditions, as this randomization directly alleviates the range restriction problem somewhat, but it is an empirical question as to whether this improves the parameter recovery calibrations.

Session Video

10aCAT10aDIF10aParameter Drift10aParameter Recovery1 aThompson, Nathan1 aStoeger, Jordan uhttps://drive.google.com/open?id=1F7HCZWD28Q97sCKFIJB0Yps0H66NPeKq01714nas a2200157 4500008004100000245007500041210006900116260005500185520116600240653000801406653002401414653001201438100002001450700001501470856007101485 2017 eng d00aFrom Blueprints to Systems: An Integrated Approach to Adaptive Testing0 aFrom Blueprints to Systems An Integrated Approach to Adaptive Te aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

For years, test blueprints have told test developers how many items and what types of items will be included in a test. Adaptive testing adopted this approach from paper testing, and it is reasonably useful. Unfortunately, 'how many items and what types of items' are not all the elements one should consider when choosing items for an adaptive test. To fill in gaps, practitioners have developed tools to allow an adaptive test to behave appropriately (i.e. examining exposure control, content balancing, item drift procedures, etc.). Each of these tools involves the use of a separate process external to the primary item selection process.

The use of these subsidiary processes makes item selection less optimal and makes it difficult to prioritize aspects of selection. This discussion describes systems-based adaptive testing. This approach uses metadata concerning items, test takers and test elements to select items. These elements are weighted by the stakeholders to shape an expanded blueprint designed for adaptive testing. 

Session Video

10aCAT10aintegrated approach10aKeynote1 aKingsbury, Gage1 aZara, Tony uhttps://drive.google.com/open?id=1CBaAfH4ES7XivmvrMjPeKyFCsFZOpQMJ01456nas a2200133 4500008004100000245007100041210006900112260005500181520096500236653002101201653000801222100002101230856007101251 2017 eng d00aHow Adaptive is an Adaptive Test: Are all Adaptive Tests Adaptive?0 aHow Adaptive is an Adaptive Test Are all Adaptive Tests Adaptive aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

There are many different kinds of adaptive tests but they all have the characteristic that some feature of the test is customized to the purpose of the test. In the time allotted, it is impossible to consider the adaptation of all of this types so this address will focus on the “classic” adaptive test that matches the difficulty of the test to the capabilities of the person being tested. This address will first present information on the maximum level of adaptation that can occur and then compare the amount of adaptation that typically occurs on an operational adaptive test to the maximum level of adaptation. An index is proposed to summarize the amount of adaptation and it is argued that this type of index should be reported for operational adaptive tests to show the amount of adaptation that typically occurs.

Click for Presentation Video 

10aAdaptive Testing10aCAT1 aReckase, Mark, D uhttps://drive.google.com/open?id=1Nj-zDCKk3DvHA4Jlp1qkb2XovmHeQfxu03610nas a2200169 4500008004100000245006900041210006600110260005500176520302300231653000803254653002403262653003303286100001503319700001803334700001703352856007103369 2017 eng d00aAn Imputation Approach to Handling Incomplete Computerized Tests0 aImputation Approach to Handling Incomplete Computerized Tests aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

As technology advances, computerized adaptive testing (CAT) is becoming increasingly popular as it allows tests to be tailored to an examinee’s ability.  Nevertheless, examinees might devise testing strategies to use CAT to their advantage.  For instance, if only the items that examinees answer count towards their score, then a higher theta score might be obtained by spending more time on items at the beginning of the test and skipping items at the end if time runs out. This type of gaming can be discouraged if examinees’ scores are lowered or “penalized” based on the amount of non-response.

The goal of this study was to devise a penalty function that would meet two criteria: 1) the greater the omit rate, the greater the penalty, and 2) examinees with the same ability and the same omit rate should receive the same penalty. To create the penalty, theta was calculated based on only the items the examinee responded to ( ).  Next, the expected number correct score (EXR) was obtained using  and the test characteristic curve. A penalized expected number correct score (E ) was obtained by multiplying EXR by the proportion of items the examinee responded to. Finally, the penalized theta ( ) was identified using the test characteristic curve. Based on   and the item parameters ( ) of an unanswered item, the likelihood of a correct response,  , is computed and employed to estimate the imputed score ( ) for the unanswered item.

Two datasets were used to generate tests with completion rates of 50%, 80%, and 90%.  The first dataset included real data where approximately 4,500 examinees responded to a 21 -item test which provided a baseline/truth. Sampling was done to achieve the three completion rate conditions. The second dataset consisted of simulated item scores for 50,000 simulees under a 1-2-4 multi-stage CAT design where each stage contained seven items. Imputed item scores for unanswered items were computed using a variety of values for G (and therefore T).  Three other approaches to handling unanswered items were also considered: all correct (i.e., T = 0), all incorrect (i.e., T = 1), and random scoring (i.e., T = 0.5).

The current study investigated the impact on theta estimates resulting from the proposed approach to handling unanswered items in a fixed-length CAT. In real testing situations, when examinees do not finish a test, it is hard to tell whether they tried diligently but ran out of time or whether they attempted to manipulate the scoring engine.  To handle unfinished tests with penalties, the proposed approach considers examinees’ abilities and incompletion rates. The results of this study provide direction for psychometric practitioners when considering penalties for omitted responses.

Session Video

10aCAT10aimputation approach10aincomplete computerized test1 aChen, Troy1 aHuang, Chi-Yu1 aLiu, Chunyan uhttps://drive.google.com/open?id=1vznZeO3nsZZK0k6_oyw5c9ZTP8uyGnXh03841nas a2200145 4500008004100000245014000041210006900181260005500250520324600305653000803551653001103559653002903570100002503599856007103624 2017 eng d00aIssues in Trait Range Coverage for Patient Reported Outcome Measure CATs - Extending the Ceiling for Above-average Physical Functioning0 aIssues in Trait Range Coverage for Patient Reported Outcome Meas aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

The use of a measure which fails to cover the upper range of functioning may produce results which can lead to serious misinterpretation. Scores produced by such a measure may fail to recognize significant improvement, or may not be able to demonstrate functioning commensurate with an important milestone. Accurate measurement of this range is critical for the assessment of physically active adults, e.g., athletes recovering from injury and active military personnel who wish to return to active service. Alternatively, a PF measure with a low ceiling might fail to differentiate patients in rehabilitation who continue to improve, but for whom their score ceilings due to the measurement used.

The assessment of physical function (PF) has greatly benefited from modern psychometric theory and resulting scales, such as the Patient-Reported Outcomes Measurement Information System (PROMIS®) PF instruments. While PROMIS PF has extended the range of function upwards relative to older “legacy” instruments, few PROMIS PF items asses high levels of function. We report here on the development of higher functioning items for the PROMIS PF bank.

An expert panel representing orthopedics, sports/military medicine, and rehabilitation reviewed existing instruments and wrote new items. After internal review, cognitive interviews were conducted with 24 individuals of average and high levels of physical function. The remaining candidate items were administered along with 50 existing PROMIS anchor items to an internet panel screened for low, average, and high levels of physical function (N = 1,600), as well as members of Boston-area gyms (N= 344). The resulting data was subjected to standard psychometric analysis, along with multiple linking methods to place the new items on the existing PF metric. The new items were added to the full PF bank for simulated computerized adaptive testing (CAT).

Item response data was collected on 54 candidate items. Items that exhibited local dependence (LD) or differential item functioning (DIF) related to gender, age, race, education, or PF status. These items were removed from consideration. Of the 50 existing PROMIS PF items, 31 were free of DIF and LD and used as anchors. The parameters for the remaining new candidate items were estimated twice: freelyestimated and linked with coefficients and fixed-anchor calibration. Both methods were comparable and had appropriate fit. The new items were added to the full PF bank for simulated CATs. The resulting CAT was able to extend the ceiling with high precision to a T-score of 68, suggesting accurate measurement for 97% of the general population.

Extending the range of items by which PF is measured will substantially improve measurement quality, applicability, and efficiency. The bank has incorporated these extension items and is available for use in research and clinics for brief CAT administration (see www.healthmeasures.net). Future research projects should focus on recovery trajectories of the measure for individuals with above average function who are recovering from injury.

Session Video

10aCAT10aIssues10aPatient Reported Outcome1 aGershon, Richard, C. uhttps://drive.google.com/open?id=1ZC02F-dIyYovEjzpeuRdoXDiXMLFRuKb01393nas a2200169 4500008004100000245003600041210003600077260005500113520088800168653000801056653002101064100002101085700001201106700001601118700001801134856007101152 2017 eng d00aItem Pool Design and Evaluation0 aItem Pool Design and Evaluation aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Early work on CAT tended to use existing sets of items which came from fixed length test forms. These sets of items were selected to meet much different requirements than are needed for a CAT; decision making or covering a content domain. However, there was also some early work that suggested having items equally distributed over the range of proficiency that was of interest or concentrated at a decision point. There was also some work that showed that there was bias in proficiency estimates when an item pool was too easy or too hard. These early findings eventually led to work on item pool design and, more recently, on item pool evaluation. This presentation gives a brief overview of these topics to give some context for the following presentations in this symposium.

Session Video

10aCAT10aItem Pool Design1 aReckase, Mark, D1 aHe, Wei1 aXu, Jing-Ru1 aZhou, Xuechun uhttps://drive.google.com/open?id=1ZAsqm1yNZlliqxEHcyyqQ_vOSu20xxZs03464nas a2200145 4500008004100000245004500041210004500086260005500131520301500186653000803201653001803209653001603227100001403243856006103257 2017 eng d00aItem Response Time on Task Effect in CAT0 aItem Response Time on Task Effect in CAT aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Introduction. In addition to reduced test length and increased measurement efficiency, computerized adaptive testing (CAT) can provide new insights into the cognitive process of task completion that cannot be mined via conventional tests. Response time is a primary characteristic of the task completion procedure. It has the potential to inform us about underlying processes. In this study, the relationship between response time and response accuracy will be investigated.

Hypothesis. The present study argues that the relationship between response time on task and response accuracy, which may be positive, negative, or curvilinear, will depend on cognitive nature of task items, holding ability of the subjects and difficulty of the items constant. The interpretations regarding the associations are not uniform either.

Research question. Is there a homogeneous effect of response time on test outcome across Graduate

Proposed explanations. If the accuracy of cognitive test responses decreases with response time, then it is an indication that the underlying cognitive process is a degrading process such as knowledge retrieval. More accessible knowledge can be retrieved faster than less accessible knowledge. It is inherent to knowledge retrieval that the success rate declines with elapsing response time. For instance, in reading tasks, the time on task effect is negative and the more negative, the easier a task is. However, if the accuracy of cognitive test responses increases with response time, then the process is of an upgrading nature, with an increasing success rate as a function of response time. For example, problem-solving takes time, and fast responses are less likely to be well-founded responses. It is of course also possible that the relationship is curvilinear, as when an increasing success rate is followed by a decreasing success rate or vice versa.

Methodology. The data are from computer-based GRE quantitative and verbal tests and will be analyzed with generalized linear mixed models (GLMM) framework after controlling the effect of ability and item difficulty as possible confounding factors. A linear model means a linear combination of predictors determining the probability of person p for answering item i correctly. The models are equivalent with advanced IRT models that go beyond the regular modeling of test responses in terms of one or more latent variables and item parameters. The lme4 package for R will be utilized to conduct the statistical calculation.

Implications. The right amount of testing time in CAT is important—too much is wasteful and costly, too little impacts score validity. The study is expected to provide new perception on the relationship between response time and response accuracy, which in turn, contribute to a better understanding of time effects and relevant cognitive process in CA.

Session Video

10aCAT10aResponse time10aTask effect1 aShi, Yang uhttp://mail.iacat.org/item-response-time-task-effect-cat01708nas a2200145 4500008004100000245006200041210006200103260005500165520122100220653000801441653001401449653003001463100002101493856004801514 2017 eng d00aItem Selection Strategies for Developing CAT in Indonesia0 aItem Selection Strategies for Developing CAT in Indonesia aNiigata JapanbNiiagata Seiryo Universityc08/20173 a

Recently, development of computerized testing in Indonesia is quiet promising for the future. Many government institutions used the technology for recruitment. Starting from Indonesian Army acknowledged the benefits of computerized adaptive testing (CAT) over conventional test administration, ones of the issues of selection the first item have taken place of attention. Due to CAT’s basic philosophy, several methods can be used to select the first item such as educational level, ability estimation from item simulation, or other methods. In this case, the question is remains how apply the methods most effective in the context of constrained adaptive testing. This paper reviews such strategies that appeared in the relevant literature. The focus of this paper is on studies that have been conducted in order to evaluate the effectiveness of item selection strategies for dichotomous scoring. In this paper, also discusses the strength and weaknesses of each strategy group using examples from simulation studies. No new research is presented but rather a compendium of models is reviewed in term of learning in the newcomer context, a wide view of first item selection strategies.

 

10aCAT10aIndonesia10aitem selection strategies1 aChandra, Istiani uhttps://www.youtube.com/watch?v=2KuFrRATq9Q03826nas a2200157 4500008004100000245008500041210006900126260005500195520325800250653000803508653002203516653002303538100001603561700002003577856007103597 2017 eng d00aA Large-Scale Progress Monitoring Application with Computerized Adaptive Testing0 aLargeScale Progress Monitoring Application with Computerized Ada aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Many conventional assessment tools are available to teachers in schools for monitoring student progress in a formative manner. The outcomes of these assessment tools are essential to teachers’ instructional modifications and schools’ data-driven educational strategies, such as using remedial activities and planning instructional interventions for students with learning difficulties. When measuring student progress toward instructional goals or outcomes, assessments should be not only considerably precise but also sensitive to individual change in learning. Unlike conventional paper-pencil assessments that are usually not appropriate for every student, computerized adaptive tests (CATs) are highly capable of estimating growth consistently with minimum and consistent error. Therefore, CATs can be used as a progress monitoring tool in measuring student growth.

This study focuses on an operational CAT assessment that has been used for measuring student growth in reading during the academic school year. The sample of this study consists of nearly 7 million students from the 1st grade to the 12th grade in the US. The students received a CAT-based reading assessment periodically during the school year. The purpose of these periodical assessments is to measure the growth in students’ reading achievement and identify the students who may need additional instructional support (e.g., academic interventions). Using real data, this study aims to address the following research questions: (1) How many CAT administrations are necessary to make psychometrically sound decisions about the need for instructional changes in the classroom or when to provide academic interventions?; (2) What is the ideal amount of time between CAT administrations to capture student growth for the purpose of producing meaningful decisions from assessment results?

To address these research questions, we first used the Theil-Sen estimator for robustly fitting a regression line to each student’s test scores obtained from a series of CAT administrations. Next, we used the conditional standard error of measurement (cSEM) from the CAT administrations to create an error band around the Theil-Sen slope (i.e., student growth rate). This process resulted in the normative slope values across all the grade levels. The optimal number of CAT administrations was established from grade-level regression results. The amount of time needed for progress monitoring was determined by calculating the amount of time required for a student to show growth beyond the median cSEM value for each grade level. The results showed that the normative slope values were the highest for lower grades and declined steadily as grade level increased. The results also suggested that the CAT-based reading assessment is most useful for grades 1 through 4, since most struggling readers requiring an intervention appear to be within this grade range. Because CAT yielded very similar cSEM values across administrations, the amount of error in the progress monitoring decisions did not seem to depend on the number of CAT administrations.

Session Video

10aCAT10aLarge-Scale tests10aProcess monitoring1 aBulut, Okan1 aCormier, Damien uhttps://drive.google.com/open?id=1uGbCKenRLnqTxImX1fZicR2c7GRV6Udc02365nas a2200277 4500008004100000245007100041210006900112260005500181520152900236653000801765653001501773653002801788100001501816700002101831700001501852700001401867700001601881700001501897700001601912700001601928700001601944700001801960700001901978700001901997856007102016 2017 eng d00aNew Challenges (With Solutions) and Innovative Applications of CAT0 aNew Challenges With Solutions and Innovative Applications of CAT aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Over the past several decades, computerized adaptive testing (CAT) has profoundly changed the administration of large-scale aptitude tests, state-wide achievement tests, professional licensure exams, and health outcome measures. While many challenges of CAT have been successfully addressed due to the continual efforts of researchers in the field, there are still many remaining, longstanding challenges that have yet to be resolved. This symposium will begin with three presentations, each of which provides a sound solution to one of the unresolved challenges. They are (1) item calibration when responses are “missing not at random” from CAT administration; (2) online calibration of new items when person traits have non-ignorable measurement error; (3) establishing consistency and asymptotic normality of latent trait estimation when allowing item response revision in CAT. In addition, this symposium also features innovative applications of CAT. In particular, there is emerging interest in using cognitive diagnostic CAT to monitor and detect learning progress (4th presentation). Last but not least, the 5th presentation illustrates the power of multidimensional polytomous CAT that permits rapid identification of hospitalized patients’ rehabilitative care needs in health outcomes measurement. We believe this symposium covers a wide range of interesting and important topics in CAT.

Session Video

10aCAT10achallenges10ainnovative applications1 aWang, Chun1 aWeiss, David, J.1 aZhang, Xue1 aTao, Jian1 aHe, Yinhong1 aChen, Ping1 aWang, Shiyu1 aZhang, Susu1 aLin, Haiyan1 aGao, Xiaohong1 aChang, Hua-Hua1 aShang, Zhuoran uhttps://drive.google.com/open?id=1Wvgxw7in_QCq_F7kzID6zCZuVXWcFDPa03809nas a2200169 4500008004100000245008600041210006900127260005500196520318900251653000903440653000803449653002503457100002503482700001703507700001703524856009803541 2017 eng d00aNew Results on Bias in Estimates due to Discontinue Rules in Intelligence Testing0 aNew Results on Bias in Estimates due to Discontinue Rules in Int aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

The presentation provides new results on a form of adaptive testing that is used frequently in intelligence testing. In these tests, items are presented in order of increasing difficulty, and the presentation of items is adaptive in the sense that each subtest session is discontinued once a test taker produces a certain number of incorrect responses in sequence. The subsequent (not observed) responses are commonly scored as wrong for that subtest, even though the test taker has not seen these. Discontinuation rules allow a certain form of adaptiveness both in paper-based and computerbased testing, and help reducing testing time.

Two lines of research that are relevant are studies that directly assess the impact of discontinuation rules, and studies that more broadly look at the impact of scoring rules on test results with a large number of not administered or not reached items. He & Wolf (2012) compared different ability estimation methods for this type of discontinuation rule adaptation of test length in a simulation study. However, to our knowledge there has been no rigorous analytical study of the underlying distributional changes of the response variables under discontinuation rules. It is important to point out that the results obtained by He & Wolf (2012) agree with results presented by, for example, DeAyala, Plake & Impara (2001) as well as Rose, von Davier & Xu (2010) and Rose, von Davier & Nagengast (2016) in that ability estimates are biased most when scoring the not observed responses as wrong. Discontinuation rules combined with scoring the non-administered items as wrong is used operationally in several major intelligence tests, so more research is needed in order to improve this particular type of adaptiveness in the testing practice.

The presentation extends existing research on adaptiveness by discontinue-rules in intelligence tests in multiple ways: First, a rigorous analytical study of the distributional properties of discontinue-rule scored items is presented. Second, an extended simulation is presented that includes additional alternative scoring rules as well as bias-corrected ability estimators that may be suitable to improve results for discontinue-rule scored intelligence tests.

References: DeAyala, R. J., Plake, B. S., & Impara, J. C. (2001). The impact of omitted responses on the accuracy of ability estimation in item response theory. Journal of Educational Measurement, 38, 213-234.

He, W. & Wolfe, E. W. (2012). Treatment of Not-Administered Items on Individually Administered Intelligence Tests. Educational and Psychological Measurement, Vol 72, Issue 5, pp. 808 – 826. DOI: 10.1177/0013164412441937

Rose, N., von Davier, M., & Xu, X. (2010). Modeling non-ignorable missing data with item response theory (IRT; ETS RR-10-11). Princeton, NJ: Educational Testing Service.

Rose, N., von Davier, M., & Nagengast, B. (2016) Modeling omitted and not-reached items in irt models. Psychometrika. doi:10.1007/s11336-016-9544-7

Session Video

10aBias10aCAT10aIntelligence Testing1 avon Davier, Matthias1 aCho, Youngmi1 aPan, Tianshu uhttp://mail.iacat.org/new-results-bias-estimates-due-discontinue-rules-intelligence-testing-003772nas a2200145 4500008004100000245007300041210006900114260005500183520325500238653000803493653002203501653001803523100001403541856007103555 2017 eng d00aResponse Time and Response Accuracy in Computerized Adaptive Testing0 aResponse Time and Response Accuracy in Computerized Adaptive Tes aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Introduction. This study explores the relationship between response speed and response accuracy in Computerized Adaptive Testing (CAT). CAT provides a score as well as item response times, which can offer additional diagnostic information regarding behavioral processes of task completion that cannot be uncovered by paper-based instruments. The goal of this study is to investigate how the accuracy rate evolves as a function of response time. If the accuracy of cognitive test responses decreases with response time, then it is an indication that the underlying cognitive process is a degrading process such as knowledge retrieval. More accessible knowledge can be retrieved faster than less accessible knowledge. For instance, in reading tasks, the time on task effect is negative and the more negative, the easier a task is. However, if the accuracy of cognitive test responses increases with response time, then the process is of an upgrading nature, with an increasing success rate as a function of response time. For example, problem-solving takes time, and fast responses are less likely to be well-founded responses. It is of course also possible that the relationship is curvilinear, as when an increasing success rate is followed by a decreasing success rate or vice versa.

Hypothesis. The present study argues the relationship between response time on task and response accuracy can be positive, negative, or curvilinear, which depends on cognitive nature of task items holding ability of the subjects and difficulty of the items constant.

Methodology. Data from a subsection of GRE quantitative test were available. We will use generalized linear mixed models. A linear model means a linear combination of predictors determining the probability of person p for answering item i correctly. Modeling mixed effects means both random effects and fixed effects are included. Fixed effects refer to constants across test takers. The models are equivalent with advanced IRT models that go beyond the regular modeling of test responses in terms of one or more latent variables and item parameters. The lme4 package for R will be utilized to conduct the statistical calculation.

Research questions. 1. What is the relationship between response accuracy and response speed? 2. What is the correlation between response accuracy and type of response time (fast response vs slow response) after controlling ability of people?

Preliminary Findings. 1. There is a negative relationship between response time and response accuracy. The success rate declines with elapsing response time. 2. The correlation between the two response latent variables (fast and slow) is 1.0, indicating the time on task effects between respond time types are not different.

Implications. The right amount of testing time in CAT is important—too much is wasteful and costly, too little impacts score validity. The study is expected to provide new perception on the relationship between response time and response accuracy, which in turn, contribute to the best timing strategy in CAT—with or without time constraints.

Session Video

10aCAT10aresponse accuracy10aResponse time1 aShi, Yang uhttps://drive.google.com/open?id=1yYP01bzGrKvJnfLwepcAoQQ2F4TdSvZ203598nas a2200169 4500008004100000245004300041210004100084260005500125520306600180653000803246653002303254653002303277100001703300700002003317700002003337856007103357 2017 eng d00aScripted On-the-fly Multistage Testing0 aScripted Onthefly Multistage Testing aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

On-the-fly multistage testing (OMST) was introduced recently as a promising alternative to preassembled MST. A decidedly appealing feature of both is the reviewability of items within the current stage. However, the fundamental difference is that, instead of routing to a preassembled module, OMST adaptively assembles a module at each stage according to an interim ability estimate. This produces more individualized forms with finer measurement precision, but imposing nonstatistical constraints and controlling item exposure become more cumbersome. One recommendation is to use the maximum priority index followed by a remediation step to satisfy content constraints, and the Sympson-Hetter method with a stratified item bank for exposure control.

However, these methods can be computationally expensive, thereby impeding practical implementation. Therefore, this study investigated the script method as a simpler solution to the challenge of strict content balancing and effective item exposure control in OMST. The script method was originally devised as an item selection algorithm for CAT and generally proceeds as follows: For a test with m items, there are m slots to be filled, and an item is selected according to pre-defined rules for each slot. For the first slot, randomly select an item from a designated content area (collection). For each subsequent slot, 1) Discard any enemies of items already administered in previous slots; 2) Draw a designated number of candidate items (selection length) from the designated collection according to the current ability estimate; 3) Randomly select one item from the set of candidates. There are two distinct features of the script method. First, a predetermined sequence of collections guarantees meeting content specifications. The specific ordering may be determined either randomly or deliberately by content experts. Second, steps 2 and 3 depict a method of exposure control, in which selection length balances item usage at the possible expense of ability estimation accuracy. The adaptation of the script method to OMST is straightforward. For the first module, randomly select each item from a designated collection. For each subsequent module, the process is the same as in scripted CAT (SCAT) except the same ability estimate is used for the selection of all items within the module. A series of simulations was conducted to evaluate the performance of scripted OMST (SOMST, with 3 or 4 evenly divided stages) relative to SCAT under various item exposure restrictions. In all conditions, reliability was maximized by programming an optimization algorithm that searches for the smallest possible selection length for each slot within the constraints. Preliminary results indicated that SOMST is certainly a capable design with performance comparable to that of SCAT. The encouraging findings and ease of implementation highly motivate the prospect of operational use for large-scale assessments.

Presentation Video

10aCAT10amultistage testing10aOn-the-fly testing1 aChoe, Edison1 aWilliams, Bruce1 aLee, Sung-Hyuck uhttps://drive.google.com/open?id=1wKuAstITLXo6BM4APf2mPsth1BymNl-y04520nas a2200181 4500008004100000245010800041210006900149260005500218520381200273653000804085653002404093653002604117100001604143700001204159700001604171700002004187856013104207 2017 eng d00aUsing Computerized Adaptive Testing to Detect Students’ Misconceptions: Exploration of Item Selection0 aUsing Computerized Adaptive Testing to Detect Students Misconcep aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Owning misconceptions impedes learning, thus detecting misconceptions through assessments is crucial to facilitate teaching. However, most computerized adaptive testing (CAT) applications to diagnose examinees’ attribute profiles focus on whether examinees mastering correct concepts or not. In educational scenario, teachers and students have to figure out the misconceptions underlying incorrect answers after obtaining the scores from assessments and then correct the corresponding misconceptions. The Scaling Individuals and Classifying Misconceptions (SICM) models proposed by Bradshaw and Templin (2014) fill this gap. SICMs can identify a student’s misconceptions directly from the distractors of multiple-choice questions and report whether s/he own the misconceptions or not. Simultaneously, SICM models are able to estimate a continuous ability within the item response theory (IRT) framework to fulfill the needs of policy-driven assessment systems relying on scaling examinees’ ability. However, the advantage of providing estimations for two types of latent variables also causes complexity of model estimation. More items are required to achieve the same accuracies for both classification and estimation compared to dichotomous DCMs and to IRT, respectively. Thus, we aim to develop a CAT using the SICM models (SICM-CAT) to estimate students’ misconceptions and continuous abilities simultaneously using fewer items than a linear test.

To achieve this goal, in this study, our research questions mainly focus on establishing several item selection rules that target on providing both accurate classification results and continuous ability estimations using SICM-CAT. The first research question is which information criterion to be used. The Kullback–Leibler (KL) divergence is the first choice, as it can naturally combine the continuous and discrete latent variables. Based on this criterion, we propose an item selection index that can nicely integrate the two types of information. Based on this index, the items selected in real time could discriminate the examinee’s current misconception profile and ability estimates from other possible estimates to the most extent. The second research question is about how to adaptively balance the estimations of the misconception profile and the continuous latent ability. Mimic the idea of the Hybrid Design proposed by Wang et al. (2016), we propose a design framework which makes the item selection transition from the group-level to the item-level. We aim to explore several design questions, such as how to select the transiting point and which latent variable estimation should be targeted first.

Preliminary results indicated that the SICM-CAT based on the proposed item selection index could classify examinees into different latent classes and measure their latent abilities compared with the random selection method more accurately and reliably under all the simulation conditions. We plan to compare different CAT designs based on our proposed item selection rules with the best linear test as the next step. We expect that the SICM-CAT is able to use shorter test length while retaining the same accuracies and reliabilities.

References

Bradshaw, L., & Templin, J. (2014). Combining item response theory and diagnostic classification models: A psychometric model for scaling ability and diagnosing misconceptions. Psychometrika, 79(3), 403-425.

Wang, S., Lin, H., Chang, H. H., & Douglas, J. (2016). Hybrid computerized adaptive testing: from group sequential design to fully sequential design. Journal of Educational Measurement, 53(1), 45-62.

Session Video

10aCAT10aincorrect answering10aStudent Misconception1 aShen, Yawei1 aBao, Yu1 aWang, Shiyu1 aBradshaw, Laine uhttp://mail.iacat.org/using-computerized-adaptive-testing-detect-students%E2%80%99-misconceptions-exploration-item-selection-000628nas a2200169 4500008004100000245008900041210006900130260001200199653000800211653002500219653002700244653002100271653001200292100002300304700001600327856011500343 2011 eng d00aAdaptive Item Calibration and Norming: Unique Considerations of a Global Deployment0 aAdaptive Item Calibration and Norming Unique Considerations of a c10/201110aCAT10acommon item equating10aFigural Reasoning Test10aitem calibration10anorming1 aSchwall, Alexander1 aSinar, Evan uhttp://mail.iacat.org/content/adaptive-item-calibration-and-norming-%0Bunique-considerations-global-deployment00495nas a2200121 4500008004100000245009500041210006900136653001800205653000800223653000900231100001900240856011400259 2011 eng d00aBuilding Affordable CD-CAT Systems for Schools To Address Today's Challenges In Assessment0 aBuilding Affordable CDCAT Systems for Schools To Address Todays 10aaffordability10aCAT10acost1 aChang, Hua-Hua uhttp://mail.iacat.org/content/building-affordable-cd-cat-systems-schools-address-todays-challenges-assessment01634nas a2200145 4500008004100000245005200041210005000093260001200143520119300155653000801348653001601356653002001372100002301392856007301415 2011 eng d00aContinuous Testing (an avenue for CAT research)0 aContinuous Testing an avenue for CAT research c10/20113 a

Publishing an Adaptive Test

Problems with Publishing

Research Questions

10aCAT10aitem filter10aitem filtration1 aKingsbury, Gage, G uhttp://mail.iacat.org/content/continuous-testing-avenue-cat-research01232nas a2200193 4500008004100000245009400041210006900135260001200204520052700216653002600743653000800769653000800777653003100785653003200816653003000848100002000878700001900898856012100917 2011 eng d00aDetecting DIF between Conventional and Computerized Adaptive Testing: A Monte Carlo Study0 aDetecting DIF between Conventional and Computerized Adaptive Tes c10/20113 a

A comparison od two procedures, Modified Robust Z and 95% Credible Interval, were compared in a Monte Carlo study. Both procedures evidenced adequate control of false positive DIF results.

10a95% Credible Interval10aCAT10aDIF10adifferential item function10amodified robust Z statistic10aMonte Carlo methodologies1 aRiley, Barth, B1 aCarle, Adam, C uhttp://mail.iacat.org/content/detecting-dif-between-conventional-and-computerized-adaptive-testing-monte-carlo-study02532nas a2200205 4500008004100000245011900041210006900160260001200229520179400241653000802035653000802043653003402051653003002085653000802115653003102123653001602154653001302170100002002183856012302203 2011 eng d00aFrom Reliability to Validity: Expanding Adaptive Testing Practice to Find the Most Valid Score for Each Test Taker0 aFrom Reliability to Validity Expanding Adaptive Testing Practice c10/20113 a

CAT is an exception to the traditional conception of validity. It is one of the few examples of individualized testing. Item difficulty is tailored to each examinee. The intent, however, is increased efficiency. Focus on reliability (reduced standard error); Equivalence with paper & pencil tests is valued; Validity is enhanced through improved reliability.

How Else Might We Individualize Testing Using CAT?

An ISV-Based View of Validity

Test Event -- An examinee encounters a series of items in a particular context.

CAT Goal: individualize testing to address CIV threats to score validity (i.e., maximize ISV).

Some Research Issues:

10aCAT10aCIV10aconstruct-irrelevant variance10aIndividual Score Validity10aISV10alow test taking motivation10aReliability10avalidity1 aWise, Steven, L uhttp://mail.iacat.org/content/reliability-validity-expanding-adaptive-testing-practice-find-most-valid-score-each-test00483nas a2200145 4500008004100000245006100041210005900102653000800161653001600169653001300185100001900198700001600217700002200233856008200255 2011 eng d00aA Heuristic Of CAT Item Selection Procedure For Testlets0 aHeuristic Of CAT Item Selection Procedure For Testlets10aCAT10ashadow test10atestlets1 aChien, Yuehmei1 aShin, David1 aWay, Walter Denny uhttp://mail.iacat.org/content/heuristic-cat-item-selection-procedure-testlets00539nas a2200121 4500008004100000245011800041210006900159653000800228653002400236653001100260100002000271856012600291 2011 eng d00aHigh-throughput Health Status Measurement using CAT in the Era of Personal Genomics: Opportunities and Challenges0 aHighthroughput Health Status Measurement using CAT in the Era of10aCAT10ahealth applications10aPROMIS1 aKrishnan, Eswar uhttp://mail.iacat.org/content/high-throughput-health-status-measurement-using-cat-era-personal-genomics-opportunities-and01252nas a2200181 4500008004100000245012100041210006900162260001200231520055600243653003300799653000800832653001900840653003500859100001800894700001600912700002200928856012000950 2011 eng d00aItem Selection Methods based on Multiple Objective Approaches for Classification of Respondents into Multiple Levels0 aItem Selection Methods based on Multiple Objective Approaches fo c10/20113 a

Is it possible to develop new item selection methods which take advantage of the fact that we want to classify into multiple categories? New methods: Taking multiple points on the ability scale into account; Based on multiple objective approaches.

Conclusions

10aadaptive classification test10aCAT10aitem selection10asequential classification test1 aGroen, Maaike1 aEggen, Theo1 aVeldkamp, Bernard uhttp://mail.iacat.org/content/item-selection-methods-based-multiple-objective-approaches-classification-respondents01258nas a2200181 4500008004100000245008300041210006900124260001200193520063300205653000800838653002600846653000900872653003500881653001600916653001400932100002300946856010700969 2011 eng d00aMoving beyond Efficiency to Allow CAT to Provide Better Diagnostic Information0 aMoving beyond Efficiency to Allow CAT to Provide Better Diagnost c10/20113 a
Future CATs will provide better diagnostic information to
–Examinees
–Regulators, Educators, Employers
–Test Developers
This goal will be accomplished by
–Smart CATs which collect additional information during the test
–Psychomagic
The time is now for Reporting
10aCAT10adianostic information10aMIRT10aMultiple unidimensional scales10apsychomagic10asmart CAT1 aBontempo, Brian, D uhttp://mail.iacat.org/content/moving-beyond-efficiency-allow-cat-provide-better-diagnostic-information01104nas a2200169 4500008004100000245006600041210006600107260001200173520055300185653002600738653000800764653002100772653001700793653001000810100002200820856009200842 2011 eng d00aOptimal Calibration Designs for Computerized Adaptive Testing0 aOptimal Calibration Designs for Computerized Adaptive Testing c10/20113 a

Optimaztion

How can we exploit the advantages of Balanced Block Design while keeping the logistics manageable?

Homogeneous Designs: Overlap between test booklets as regular as possible

Conclusions:

10abalanced block design10aCAT10aitem calibration10aoptimization10aRasch1 aVerschoor, Angela uhttp://mail.iacat.org/content/optimal-calibration-designs-computerized-adaptive-testing01308nas a2200133 4500008004100000245005000041210004800091260001200139520089400151653000801045653003501053100001201088856007401100 2011 eng d00aA Paradigm for Multinational Adaptive Testing0 aParadigm for Multinational Adaptive Testing c10/20113 a

Impact of Issues in “Exported” Adaptive Testing

Goal is construct equivalency in the new environment

Research Questions

10aCAT10amultinational adaptive testing1 aZara, A uhttp://mail.iacat.org/content/paradigm-multinational-adaptive-testing00318nas a2200109 4500008004100000245003200041210003100073653000800104653001600112100001800128856006200146 2011 eng d00aSmall-Sample Shadow Testing0 aSmallSample Shadow Testing10aCAT10ashadow test1 aJudd, Wallace uhttp://mail.iacat.org/content/small-sample-shadow-testing00782nas a2200205 4500008004100000245003400041210003200075260001200107520027300119653000800392653000800400653002300408653001000431653001200441653000800453100002200461700001900483700001600502856005800518 2011 eng d00aA Test Assembly Model for MST0 aTest Assembly Model for MST c10/20113 a

This study is just a short exploration in the matter of optimization of a MST. It is extremely hard or maybe impossible to chart influence of item pool and test specifications on optimization process. Simulations are very helpful in finding an acceptable MST.

10aCAT10amst10amultistage testing10aRasch10arouting10atif1 aVerschoor, Angela1 aRadtke, Ingrid1 aEggen, Theo uhttp://mail.iacat.org/content/test-assembly-model-mst02121nas a2200193 4500008004100000245007900041210006900120260001200189520146800201653002801669653000801697653001801705100002001723700001701743700002301760700002301783700002301806856009801829 2011 eng d00aThe Use of Decision Trees for Adaptive Item Selection and Score Estimation0 aUse of Decision Trees for Adaptive Item Selection and Score Esti c10/20113 a

Conducted post-hoc simulations comparing the relative efficiency, and precision of decision trees (using CHAID and CART) vs. IRT-based CAT.

Conclusions

Decision tree methods were more efficient than CAT

But,...

Conclusions

CAT selects items based on two criteria: Item location relative to current estimate of theta, Item discrimination

Decision Trees select items that best discriminate between groups defined by the total score.

CAT is optimal only when trait level is well estimated.
Findings suggest that combining decision tree followed by CAT item selection may be advantageous.

10aadaptive item selection10aCAT10adecision tree1 aRiley, Barth, B1 aFunk, Rodney1 aDennis, Michael, L1 aLennox, Richard, D1 aFinkelman, Matthew uhttp://mail.iacat.org/content/use-decision-trees-adaptive-item-selection-and-score-estimation01393nas a2200145 4500008004100000245007100041210006900112260001200181520089200193653000801085653001801093653001701111100002601128856009301154 2011 eng d00aWalking the Tightrope: Using Better Content Control to Improve CAT0 aWalking the Tightrope Using Better Content Control to Improve CA c10/20113 a

All testing involves a balance between measurement precision and content considerations. CAT item-selection algorithms have evolved to accommodate content considerations. Reviews CAT evolution including: Original/”Pure” adaptive exams, Constrained CAT, Weighted-deviations method, Shadow-Test Approach, Testlets instead of fully adapted tests, Administration of one item may preclude the administration of other item(s), and item relationships.

Research Questions

 

10aCAT10aCAT evolution10atest content1 aGialluca, Kathleen, A uhttp://mail.iacat.org/content/walking-tightrope-using-better-content-control-improve-cat01350nas a2200229 4500008004100000020001300041245011700054210006900171300001200240490000700252520057500259653000800834653003400842653001900876653001500895100002000910700001600930700001800946700001500964700001400979856012700993 2010 eng d a0191886900aDetection of aberrant item score patterns in computerized adaptive testing: An empirical example using the CUSUM0 aDetection of aberrant item score patterns in computerized adapti a921-9250 v483 aThe scalability of individual trait scores on a computerized adaptive test (CAT) was assessed through investigating the consistency of individual item score patterns. A sample of N = 428 persons completed a personality CAT as part of a career development procedure. To detect inconsistent item score patterns, we used a cumulative sum (CUSUM) procedure. Combined information from the CUSUM, other personality measures, and interviews showed that similar estimated trait values may have a different interpretation.Implications for computer-based assessment are discussed.10aCAT10acomputerized adaptive testing10aCUSUM approach10aperson Fit1 aEgberink, I J L1 aMeijer, R R1 aVeldkamp, B P1 aSchakel, L1 aSmid, N G uhttp://mail.iacat.org/content/detection-aberrant-item-score-patterns-computerized-adaptive-testing-empirical-example-using01458nas a2200181 4500008004100000245008200041210006900123260001300192490000800205520080800213653000801021653001901029653003001048653003401078653004001112100001801152856010601170 2007 eng d00aA practitioner's guide to variable-length computerized classification testing0 apractitioners guide to variablelength computerized classificatio c7/1/20090 v12 3 aVariable-length computerized classification tests, CCTs, (Lin & Spray, 2000; Thompson, 2006) are a powerful and efficient approach to testing for the purpose of classifying examinees into groups. CCTs are designed by the specification of at least five technical components: psychometric model, calibrated item bank, starting point, item selection algorithm, and termination criterion. Several options exist for each of these CCT components, creating a myriad of possible designs. Confusion among designs is exacerbated by the lack of a standardized nomenclature. This article outlines the components of a CCT, common options for each component, and the interaction of options for different components, so that practitioners may more efficiently design CCTs. It also offers a suggestion of nomenclature. 10aCAT10aclassification10acomputer adaptive testing10acomputerized adaptive testing10aComputerized classification testing1 aThompson, N A uhttp://mail.iacat.org/content/practitioners-guide-variable-length-computerized-classification-testing02404nas a2200241 4500008004100000020002200041245008500063210006900148260002500217300001200242490000700254520161500261653000801876653003401884653002601918653003001944653002601974100001402000700001402014700001602028700001502044856010302059 2006 eng d a0439-755X (Print)00aThe comparison among item selection strategies of CAT with multiple-choice items0 acomparison among item selection strategies of CAT with multiplec bScience Press: China a778-7830 v383 aThe initial purpose of comparing item selection strategies for CAT was to increase the efficiency of tests. As studies continued, however, it was found that increasing the efficiency of item bank using was also an important goal of comparing item selection strategies. These two goals often conflicted. The key solution was to find a strategy with which both goals could be accomplished. The item selection strategies for graded response model in this study included: the average of the difficulty orders matching with the ability; the medium of the difficulty orders matching with the ability; maximum information; A stratified (average); and A stratified (medium). The evaluation indexes used for comparison included: the bias of ability estimates for the true; the standard error of ability estimates; the average items which the examinees have administered; the standard deviation of the frequency of items selected; and sum of the indices weighted. Using the Monte Carlo simulation method, we obtained some data and computer iterated the data 20 times each under the conditions that the item difficulty parameters followed the normal distribution and even distribution. The results were as follows; The results indicated that no matter difficulty parameters followed the normal distribution or even distribution. Every type of item selection strategies designed in this research had its strong and weak points. In general evaluation, under the condition that items were stratified appropriately, A stratified (medium) (ASM) had the best effect. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10aCAT10acomputerized adaptive testing10agraded response model10aitem selection strategies10amultiple choice items1 aHai-qi, D1 aDe-zhi, C1 aShuliang, D1 aTaiping, D uhttp://mail.iacat.org/content/comparison-among-item-selection-strategies-cat-multiple-choice-items