02111nas a2200193 4500008003900000245007100039210006900110300000900179490000700188520153100195653000701726653003201733653001701765653002301782100001501805700001501820700001901835856006301854 2020 d00aItem Calibration Methods With Multiple Subscale Multistage Testing0 aItem Calibration Methods With Multiple Subscale Multistage Testi a3-280 v573 aAbstract Many large-scale educational surveys have moved from linear form design to multistage testing (MST) design. One advantage of MST is that it can provide more accurate latent trait (θ) estimates using fewer items than required by linear tests. However, MST generates incomplete response data by design; hence, questions remain as to how to calibrate items using the incomplete data from MST design. Further complication arises when there are multiple correlated subscales per test, and when items from different subscales need to be calibrated according to their respective score reporting metric. The current calibration-per-subscale method produced biased item parameters, and there is no available method for resolving the challenge. Deriving from the missing data principle, we showed when calibrating all items together the Rubin's ignorability assumption is satisfied such that the traditional single-group calibration is sufficient. When calibrating items per subscale, we proposed a simple modification to the current calibration-per-subscale method that helps reinstate the missing-at-random assumption and therefore corrects for the estimation bias that is otherwise existent. Three mainstream calibration methods are discussed in the context of MST, they are the marginal maximum likelihood estimation, the expectation maximization method, and the fixed parameter calibration. An extensive simulation study is conducted and a real data example from NAEP is analyzed to provide convincing empirical evidence.10aEM10amarginal maximum likelihood10amissing data10amultistage testing1 aWang, Chun1 aChen, Ping1 aJiang, Shengyu uhttps://onlinelibrary.wiley.com/doi/abs/10.1111/jedm.1224102300nas a2200133 4500008004100000245006100041210005800102260005500160520182900215653000902044653002302053100001302076856007702089 2017 eng d00aAn Empirical Simulation Study Using mstR for MST Designs0 aEmpirical Simulation Study Using mstR for MST Designs aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Unlike other systems of adaptive testing, multistage testing (MST) provides many benefits of adaptive testing and linear testing, and has become the most sought-after form for computerized testing in educational assessment recently. It is greatly fit for testing educational achievement and can be adapted to practical educational surveys testing. However, there are many practical considerations for MST design for operational implementations including costs and benefits. As a practitioner, we need to start with various simulations to evaluate the various MST designs and their performances before the implementations. A recently developed statistical tool mstR, an open source R package, was released to support the researchers and practitioners to aid their MST simulations for implementations.

Conventional MST design has three stages of module (i.e., 1-2-3 design) structure. Alternatively, the composition of modules diverges from one design to another (e.g., 1-3 design). For advance planning of equivalence studies, this paper utilizes both 1-2-3 design and 1-3 design for the MST structures. In order to study the broad structure of these values, this paper evaluates the different MST designs through simulations using the R package mstR. The empirical simulation study provides an introductory overview of mstR and describes what mstR offers using different MST structures from 2PL item bank. Further comparisons will show the advantages of the different MST designs (e.g., 1-2-3 design and 1-3 design) for different practical implementations.

As an open-source statistical environment R, mstR provides a great simulation tool and allows psychologists, social scientists, and educational measurement scientists to apply it to innovative future assessments in the operational use of MST.

10amstR10amultistage testing1 aLee, Soo uhttp://www.iacat.org/empirical-simulation-study-using-mstr-mst-designs-002108nas a2200169 4500008004100000245005200041210005100093260005500144520156900199653002101768653000801789653002301797100001501820700001901835700001301854856007101867 2017 eng d00aMHK-MST Design and the Related Simulation Study0 aMHKMST Design and the Related Simulation Study aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

The MHK is a national standardized exam that tests and rates Chinese language proficiency. It assesses non-native Chinese minorities’ abilities in using the Chinese language in their daily, academic and professional lives; Computerized multistage adaptive testing (MST) is a combination of conventional paper-and-pencil (P&P) and item level computerized adaptive test (CAT), it is a kind of test forms based on computerized technology, take the item set as the scoring unit. It can be said that, MST estimate the Ability extreme value more accurate than conventional paper-and-pencil (P&P), also used the CAT auto-adapted characteristic to reduce the examination length and the score time of report. At present, MST has used in some large test, like Uniform CPA Examination and Graduate Record Examination(GRE). Therefore, it is necessary to develop the MST of application in China.

Based on consideration of the MHK characteristics and its future development, the researchers start with design of MHK-MST. This simulation study is conducted to validate the performance of the MHK -MST system. Real difficulty parameters of MHK items and the simulated ability parameters of the candidates are used to generate the original score matrix and the item modules are delivered to the candidates following the adaptive procedures set according to the path rules. This simulation study provides a sound basis for the implementation of MHK-MST.

Session Video

10alanguage testing10aMHK10amultistage testing1 aYuyu, Ling1 aChenglin, Zhou1 aJie, Ren uhttp://www.iacat.org/mhk-mst-design-and-related-simulation-study-003598nas 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-y02175nas a2200133 4500008004100000245006300041210006300104260005500167520168000222653002501902653002301927100002001950856007101970 2017 eng d00aUsing Determinantal Point Processes for Multistage Testing0 aUsing Determinantal Point Processes for Multistage Testing aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Multistage tests are a generalization of computerized adaptive tests (CATs), that allow to ask batches of questions before starting to adapt the process, instead of asking questions one by one. In order to be provided in real-world scenarios, they should be assembled on the fly, and recent models have been designed accordingly (Zheng & Chang, 2015). We will present a new algorithm for assembling multistage tests, based on a recent technique in machine learning called determinantal point processes. We will illustrate this technique on various student data that come from fraction subtraction items, or massive online open courses.

In multidimensional CATs, feature vectors are estimated for students and questions, and the probability that a student gets a question correct depends on how much their feature vector is correlated with the question feature vector. In other words, questions that are close in space lead to similar response patterns from the students. Therefore, in order to maximize the information of a batch of questions, the volume spanned by their feature vectors should be as large as possible. Determinantal point processes allow to sample efficiently batches of items from a bank that are diverse, i.e., that span a large volume: it is actually possible to draw k items among n with a O(nk3 ) complexity, which is convenient for large databases of 10,000s of items.

References

Zheng, Y., & Chang, H. H. (2015). On-the-fly assembled multistage adaptive testing. Applied Psychological Measurement, 39(2), 104-118.

Session Video

10aMultidimentional CAT10amultistage testing1 aVie, Jill-Jênn uhttps://drive.google.com/open?id=1GkJkKTEFWK3srDX8TL4ra_Xbsliemu1R00723nas a2200205 4500008004500000022001500045245013200060210006900192300000900261490000600270653002100276653003000297653003000327653001600357653002300373100002100396700002000417700002000437856006000457 2016 Engldsh  a2165-6592 00aEffect of Imprecise Parameter Estimation on Ability Estimation in a Multistage Test in an Automatic Item Generation Context 0 aEffect of Imprecise Parameter Estimation on Ability Estimation i a1-180 v410aAdaptive Testing10aautomatic item generation10aerrors in item parameters10aitem clones10amultistage testing1 aColvin, Kimberly1 aKeller, Lisa, A1 aRobin, Frederic uhttp://iacat.org/jcat/index.php/jcat/article/view/59/2700781nas a2200205 4500008004100000245003400041210003200075260001200107520027300119653000800392653000800400653002300408653001000431653001200441653000800453100002200461700001900483700001600502856005700518 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://www.iacat.org/content/test-assembly-model-mst