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Koch, Marco; Spinath, Frank M.; Greiff, Samuel; Becker, Nicolas – Journal of Intelligence, 2022
Figural matrices tasks are one of the most prominent item formats used in intelligence tests, and their relevance for the assessment of cognitive abilities is unquestionable. However, despite endeavors of the open science movement to make scientific research accessible on all levels, there is a lack of royalty-free figural matrices tests. The Open…
Descriptors: Intelligence, Intelligence Tests, Computer Assisted Testing, Test Items
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Feuerstahler, Leah M.; Waller, Niels; MacDonald, Angus, III – Educational and Psychological Measurement, 2020
Although item response models have grown in popularity in many areas of educational and psychological assessment, there are relatively few applications of these models in experimental psychopathology. In this article, we explore the use of item response models in the context of a computerized cognitive task designed to assess visual working memory…
Descriptors: Item Response Theory, Psychopathology, Intelligence Tests, Psychological Evaluation
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Kaufman, Alan S. – Journal of Intelligence, 2021
U.S. Supreme Court justices and other federal judges are, effectively, appointed for life, with no built-in check on their cognitive functioning as they approach old age. There is about a century of research on aging and intelligence that shows the vulnerability of processing speed, fluid reasoning, visual-spatial processing, and working memory to…
Descriptors: Judges, Federal Government, Aging (Individuals), Decision Making
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Storme, Martin; Myszkowski, Nils; Baron, Simon; Bernard, David – Journal of Intelligence, 2019
Assessing job applicants' general mental ability online poses psychometric challenges due to the necessity of having brief but accurate tests. Recent research (Myszkowski & Storme, 2018) suggests that recovering distractor information through Nested Logit Models (NLM; Suh & Bolt, 2010) increases the reliability of ability estimates in…
Descriptors: Intelligence Tests, Item Response Theory, Comparative Analysis, Test Reliability
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Navarro, Juan-José; Mourgues-Codern, Catalina V. – Journal of Cognitive Education and Psychology, 2018
The development of novel educational assessment models founded on item response theory (IRT), as well as software tools designed to implement these models, has contributed to the surge in computerized adaptive tests (CATs). The distinguishing characteristic of CATs is that the sequence of items on a test progressively adapts to the performance…
Descriptors: Reading Processes, Computer Assisted Testing, Adaptive Testing, Item Response Theory
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Stevenson, Claire E. – International Journal of Artificial Intelligence in Education, 2017
This study contrasted the effects of tutoring, multiple try and no feedback on children's progression in analogy solving and examined individual differences herein. Feedback that includes additional hints or explanations leads to the greatest learning gains in adults. However, children process feedback differently from adults and effective…
Descriptors: Tutoring, Feedback (Response), Children, Short Term Memory
Foorman, Barbara R.; Petscher, Yaacov; Schatschneider, Chris – Florida Center for Reading Research, 2015
The FAIR-FS consists of computer-adaptive reading comprehension and oral language screening tasks that provide measures to track growth over time, as well as a Probability of Literacy Success (PLS) linked to grade-level performance (i.e., the 40th percentile) on the reading comprehension subtest of the Stanford Achievement Test (SAT-10) in the…
Descriptors: Reading Instruction, Screening Tests, Reading Comprehension, Oral Language
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Verguts, Tom; De Boeck, Paul – Applied Psychological Measurement, 2000
Developed an extension of the Rasch model from a Bayesian point of view and used the model to study whether learning occurred throughout a computer-administered intelligence test. Results from 137 college students indicate that learning did occur and that there might be individual differences in learning rate. (SLD)
Descriptors: Bayesian Statistics, College Students, Computer Assisted Testing, Higher Education