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Schneider, Stefan; Jin, Haomiao; Orriens, Bart; Junghaenel, Doerte U.; Kapteyn, Arie; Meijer, Erik; Stone, Arthur A. – Field Methods, 2023
Researchers have become increasingly interested in response times to survey items as a measure of cognitive effort. We used machine learning to develop a prediction model of response times based on 41 attributes of survey items (e.g., question length, response format, linguistic features) collected in a large, general population sample. The…
Descriptors: Surveys, Response Rates (Questionnaires), Test Items, Artificial Intelligence
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Zhichen Guo; Daxun Wang; Yan Cai; Dongbo Tu – Educational and Psychological Measurement, 2024
Forced-choice (FC) measures have been widely used in many personality or attitude tests as an alternative to rating scales, which employ comparative rather than absolute judgments. Several response biases, such as social desirability, response styles, and acquiescence bias, can be reduced effectively. Another type of data linked with comparative…
Descriptors: Item Response Theory, Models, Reaction Time, Measurement Techniques
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Gorgun, Guher; Bulut, Okan – Large-scale Assessments in Education, 2023
In low-stakes assessment settings, students' performance is not only influenced by students' ability level but also their test-taking engagement. In computerized adaptive tests (CATs), disengaged responses (e.g., rapid guesses) that fail to reflect students' true ability levels may lead to the selection of less informative items and thereby…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Algorithms
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Seyma N. Yildirim-Erbasli; Guher Gorgun – Technology, Knowledge and Learning, 2025
Exploring the relationship between student ability and test-taking effort is an important area of study, offering insights into their approach to educational assessments. Previous research shows this relationship, yet there is a scarcity of research comparing the test-taking effort of students. In addition, researchers have frequently employed…
Descriptors: Ability, Test Wiseness, Predictor Variables, Student Reaction
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Dashiell, William; Killian, Paul W., Jr. – Perceptual and Motor Skills, 1981
Eighteen college students solved addition problems using the Hutchings Low Fatigue Addition Algorithm, which requires a written record of running sums, and the standard algorithm, which does not. Students using the Hutchings algorithm had significantly higher reaction times to a tone, indicating that the Hutchings method requires less cognitive…
Descriptors: Addition, Adolescents, Algorithms, Cognitive Processes
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Carter, Philip; And Others – Journal of Experimental Child Psychology, 1983
Two experiments studied nine-year-olds, l3-year-olds, and adults in their encoding of two kinds of stimuli taken from a psychometric measure of spatial aptitude. The first experiment used letter-like stimuli; the second employed multi-element flags. (CI)
Descriptors: Adults, Age Differences, Algorithms, Children
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Meghabghab, George – Information Processing & Management, 2001
Discusses the evaluation of search engines and uses neural networks in stochastic simulation of the number of rejected Web pages per search query. Topics include the iterative radial basis functions (RBF) neural network; precision; response time; coverage; Boolean logic; regression models; crawling algorithms; and implications for search engine…
Descriptors: Algorithms, Computer Simulation, Evaluation Methods, Mathematical Formulas
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Egan, Vincent; Deary, Ian J. – Intelligence, 1992
To assess whether movement artifacts reported in visual inspection time (IT) tasks were under metacognitive control, 29 young adults in Edinburgh (Scotland) were tested on a dual-task paradigm in which IT was conducted along with a concurrent task. Reports of movement artifacts are not usually examples of metacognitive processing. (SLD)
Descriptors: Algorithms, Foreign Countries, Intelligence Quotient, Metacognition