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Wyse, Adam E.; McBride, James R. – Journal of Educational Measurement, 2021
A key consideration when giving any computerized adaptive test (CAT) is how much adaptation is present when the test is used in practice. This study introduces a new framework to measure the amount of adaptation of Rasch-based CATs based on looking at the differences between the selected item locations (Rasch item difficulty parameters) of the…
Descriptors: Item Response Theory, Computer Assisted Testing, Adaptive Testing, Test Items
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Kezer, Fatih – International Journal of Assessment Tools in Education, 2021
Item response theory provides various important advantages for exams carried out or to be carried out digitally. For computerized adaptive tests to be able to make valid and reliable predictions supported by IRT, good quality item pools should be used. This study examines how adaptive test applications vary in item pools which consist of items…
Descriptors: Item Banks, Adaptive Testing, Computer Assisted Testing, Item Response Theory
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Wang, Wenhao; Kingston, Neal M.; Davis, Marcia H.; Tiemann, Gail C.; Tonks, Stephen; Hock, Michael – Educational Measurement: Issues and Practice, 2021
Adaptive tests are more efficient than fixed-length tests through the use of item response theory; adaptive tests also present students questions that are tailored to their proficiency level. Although the adaptive algorithm is straightforward, developing a multidimensional computer adaptive test (MCAT) measure is complex. Evidence-centered design…
Descriptors: Evidence Based Practice, Reading Motivation, Adaptive Testing, Computer Assisted Testing
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Yu, Albert; Douglas, Jeffrey A. – Journal of Educational and Behavioral Statistics, 2023
We propose a new item response theory growth model with item-specific learning parameters, or ISLP, and two variations of this model. In the ISLP model, either items or blocks of items have their own learning parameters. This model may be used to improve the efficiency of learning in a formative assessment. We show ways that the ISLP model's…
Descriptors: Item Response Theory, Learning, Markov Processes, Monte Carlo Methods
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Chen, Chia-Wen; Wang, Wen-Chung; Chiu, Ming Ming; Ro, Sage – Journal of Educational Measurement, 2020
The use of computerized adaptive testing algorithms for ranking items (e.g., college preferences, career choices) involves two major challenges: unacceptably high computation times (selecting from a large item pool with many dimensions) and biased results (enhanced preferences or intensified examinee responses because of repeated statements across…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Selection
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Wyse, Adam E. – Educational and Psychological Measurement, 2021
An essential question when computing test--retest and alternate forms reliability coefficients is how many days there should be between tests. This article uses data from reading and math computerized adaptive tests to explore how the number of days between tests impacts alternate forms reliability coefficients. Results suggest that the highest…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Reliability, Reading Tests
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Wang, Xi; Liu, Yang; Robin, Frederic; Guo, Hongwen – International Journal of Testing, 2019
In an on-demand testing program, some items are repeatedly used across test administrations. This poses a risk to test security. In this study, we considered a scenario wherein a test was divided into two subsets: one consisting of secure items and the other consisting of possibly compromised items. In a simulation study of multistage adaptive…
Descriptors: Identification, Methods, Test Items, Cheating
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Betts, Joe; Muntean, William; Kim, Doyoung; Kao, Shu-chuan – Educational and Psychological Measurement, 2022
The multiple response structure can underlie several different technology-enhanced item types. With the increased use of computer-based testing, multiple response items are becoming more common. This response type holds the potential for being scored polytomously for partial credit. However, there are several possible methods for computing raw…
Descriptors: Scoring, Test Items, Test Format, Raw Scores
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Yi-Hsuan Lee; Yue Jia – Applied Measurement in Education, 2024
Test-taking experience is a consequence of the interaction between students and assessment properties. We define a new notion, rapid-pacing behavior, to reflect two types of test-taking experience -- disengagement and speededness. To identify rapid-pacing behavior, we extend existing methods to develop response-time thresholds for individual items…
Descriptors: Adaptive Testing, Reaction Time, Item Response Theory, Test Format
Ashley Anne Grant; Michael A. Cook; Steven M. Ross – Center for Research and Reform in Education, 2023
The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction on ELA achievement, as measured by SBA scores. We compared "striving learner" students who were assigned to use i-Ready Personalized Instruction (Treatment students) and "striving learner" students assigned to only receive i-Ready…
Descriptors: Individualized Instruction, Reading Instruction, Reading Achievement, Language Arts
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Greving, Sven; Lenhard, Wolfgang; Richter, Tobias – Journal of Computer Assisted Learning, 2020
Retrieval practice promotes retention more than restudying (i.e., the "testing effect") and is applied to many educational settings. However, little research has investigated means to enhance this effect in educational settings. Theoretical accounts assume retrieval practice to be the most effective whenever retrieval is difficult but…
Descriptors: Multiple Choice Tests, Recall (Psychology), Retention (Psychology), Adaptive Testing
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Yang, Lihong; Reckase, Mark D. – Educational and Psychological Measurement, 2020
The present study extended the "p"-optimality method to the multistage computerized adaptive test (MST) context in developing optimal item pools to support different MST panel designs under different test configurations. Using the Rasch model, simulated optimal item pools were generated with and without practical constraints of exposure…
Descriptors: Item Banks, Adaptive Testing, Computer Assisted Testing, Item Response Theory
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Kjersti Karlsen; Ellen Kathrine Munkhaugen; Hanne Kari Fossum; Trine Lise Bakken; Arvid Nikolai Kildahl – Journal of Applied Research in Intellectual Disabilities, 2025
Background: Individuals with intellectual disabilities are at increased risk of mental health disorders, but may struggle to access appropriate services. While assessment/treatment may need to be adapted, knowledge is limited about what such adaptations may entail. Method: During a service development project, the participants (33 professionals,…
Descriptors: Intellectual Disability, Mental Health, Comorbidity, Adults
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Wise, Steven L.; Soland, James; Dupray, Laurence M. – Journal of Applied Testing Technology, 2021
Technology-Enhanced Items (TEIs) have been purported to be more motivating and engaging to test takers than traditional multiple-choice items. The claim of enhanced engagement, however, has thus far received limited research attention. This study examined the rates of rapid-guessing behavior received by three types of items (multiple-choice,…
Descriptors: Test Items, Guessing (Tests), Multiple Choice Tests, Achievement Tests
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Wyse, Adam E.; McBride, James R. – Measurement: Interdisciplinary Research and Perspectives, 2022
A common practical challenge is how to assign ability estimates to all incorrect and all correct response patterns when using item response theory (IRT) models and maximum likelihood estimation (MLE) since ability estimates for these types of responses equal -8 or +8. This article uses a simulation study and data from an operational K-12…
Descriptors: Scores, Adaptive Testing, Computer Assisted Testing, Test Length
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