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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
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
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
A Sequential Bayesian Changepoint Detection Procedure for Aberrant Behaviors in Computerized Testing
Jing Lu; Chun Wang; Jiwei Zhang; Xue Wang – Grantee Submission, 2023
Changepoints are abrupt variations in a sequence of data in statistical inference. In educational and psychological assessments, it is pivotal to properly differentiate examinees' aberrant behaviors from solution behavior to ensure test reliability and validity. In this paper, we propose a sequential Bayesian changepoint detection algorithm to…
Descriptors: Bayesian Statistics, Behavior Patterns, Computer Assisted Testing, Accuracy
Peer reviewedDashiell, 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
Peer reviewedCarter, 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
Matthews, Paul G.; Atkinson, Richard C. – 1975
This paper reports an experiment designed to test theoretical relations among fast problem solving, more complex and slower problem solving, and research concerning fundamental memory processes. Using a cathode ray tube, subjects were presented with propositions of the form "Y is in list X" which they memorized. In later testing they were asked to…
Descriptors: Algorithms, Graphs, Information Processing, Logical Thinking
Peer reviewedEgan, 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
Sternberg, Robert J. – 1979
About 25 children in each of grades 3, 5, 7, 9, and 11 were tested in their ability to solve linear syllogisms, such as: John is taller than Mary. Mary is taller than Pete. Who is tallest--John, Mary, or Pete? Response latencies and error rates decreased across grade levels and sessions. Component latencies also generally decreased with increasing…
Descriptors: Abstract Reasoning, Age Differences, Algorithms, Cognitive Development

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