Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 1 |
Since 2006 (last 20 years) | 2 |
Descriptor
Computer Simulation | 3 |
Markov Processes | 3 |
Monte Carlo Methods | 3 |
Evaluation Methods | 2 |
Adjustment (to Environment) | 1 |
Bayesian Statistics | 1 |
Cognitive Development | 1 |
College Students | 1 |
Computation | 1 |
Computer Mediated… | 1 |
Computer Software | 1 |
More ▼ |
Author
Briggs, Derek C. | 1 |
Casasanto, Daniel | 1 |
Casasanto, Laura Staum | 1 |
Gijssels, Tom | 1 |
Hagoort, Peter | 1 |
Jasmin, Kyle | 1 |
Levy, Roy | 1 |
Mislevy, Robert J. | 1 |
Wilson, Mark | 1 |
Publication Type
Journal Articles | 2 |
Reports - Research | 2 |
Reports - Evaluative | 1 |
Education Level
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Researchers | 1 |
Location
Netherlands | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Gijssels, Tom; Casasanto, Laura Staum; Jasmin, Kyle; Hagoort, Peter; Casasanto, Daniel – Discourse Processes: A multidisciplinary journal, 2016
People often accommodate to each other's speech by aligning their linguistic production with their partner's. According to an influential theory, the Interactive Alignment Model, alignment is the result of priming. When people perceive an utterance, the corresponding linguistic representations are primed and become easier to produce. Here we…
Descriptors: Speech Communication, Priming, Intonation, Phonology
Briggs, Derek C.; Wilson, Mark – Journal of Educational Measurement, 2007
An approach called generalizability in item response modeling (GIRM) is introduced in this article. The GIRM approach essentially incorporates the sampling model of generalizability theory (GT) into the scaling model of item response theory (IRT) by making distributional assumptions about the relevant measurement facets. By specifying a random…
Descriptors: Markov Processes, Generalizability Theory, Item Response Theory, Computation
Levy, Roy; Mislevy, Robert J. – US Department of Education, 2004
The challenges of modeling students' performance in simulation-based assessments include accounting for multiple aspects of knowledge and skill that arise in different situations and the conditional dependencies among multiple aspects of performance in a complex assessment. This paper describes a Bayesian approach to modeling and estimating…
Descriptors: Probability, Markov Processes, Monte Carlo Methods, Bayesian Statistics