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Aiman Mohammad Freihat; Omar Saleh Bani Yassin – Educational Process: International Journal, 2025
Background/purpose: This study aimed to reveal the accuracy of estimation of multiple-choice test items parameters following the models of the item-response theory in measurement. Materials/methods: The researchers depended on the measurement accuracy indicators, which express the absolute difference between the estimated and actual values of the…
Descriptors: Accuracy, Computation, Multiple Choice Tests, Test Items
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Matthew J. Madison; Seungwon Chung; Junok Kim; Laine P. Bradshaw – Grantee Submission, 2023
Recent developments have enabled the modeling of longitudinal assessment data in a diagnostic classification model (DCM) framework. These longitudinal DCMs were developed to provide measures of student growth on a discrete scale in the form of attribute mastery transitions, thereby supporting categorical and criterion-referenced interpretations of…
Descriptors: Models, Cognitive Measurement, Diagnostic Tests, Classification
Joseph Alan Lyon – ProQuest LLC, 2022
The concept of computational thinking (CT) has become more prevalent across the engineering education research and teaching landscape. Yet much of the research to date has been more definitional and has not offered many ways to convert CT theory to practice. One prominent set of tools used across engineering disciplines is modeling and simulation,…
Descriptors: Thinking Skills, Computation, Models, Simulation
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Boris Forthmann; Benjamin Goecke; Roger E. Beaty – Creativity Research Journal, 2025
Human ratings are ubiquitous in creativity research. Yet, the process of rating responses to creativity tasks -- typically several hundred or thousands of responses, per rater -- is often time-consuming and expensive. Planned missing data designs, where raters only rate a subset of the total number of responses, have been recently proposed as one…
Descriptors: Creativity, Research, Researchers, Research Methodology
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Gonczi, Amanda; Palosaari, Chuck; Mayer, Alex; Urban, Noel – Science Teacher, 2022
Computational modeling and thinking skill sets were previously relegated to computer scientists and programmers. As a result, computational tools are largely unfamiliar to K-12 science teachers and students. Using Mathematical and Computational Thinking and Developing and Using Models were included in the "Next Generation Science…
Descriptors: Learning Activities, High School Students, STEM Education, Computation
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Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2023
Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…
Descriptors: Educational Research, Data Analysis, Error of Measurement, Computation
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Alejandra J. Magana; Joreen Arigye; Abasiafak Udosen; Joseph A. Lyon; Parth Joshi; Elsje Pienaar – International Journal of STEM Education, 2024
Background: This study posits that scaffolded team-based computational modeling and simulation projects can support model-based learning that can result in evidence of representational competence and regulatory skills. The study involved 116 students from a second-year thermodynamics undergraduate course organized into 24 teams, who worked on…
Descriptors: Scaffolding (Teaching Technique), Thermodynamics, Science Education, Undergraduate Study
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Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Paul De Boeck – Grantee Submission, 2024
Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are…
Descriptors: Item Response Theory, Test Items, Models, Maximum Likelihood Statistics
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Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Paul De Boeck – Journal of Educational Measurement, 2024
Explanatory item response models (EIRMs) have been applied to investigate the effects of person covariates, item covariates, and their interactions in the fields of reading education and psycholinguistics. In practice, it is often assumed that the relationships between the covariates and the logit transformation of item response probability are…
Descriptors: Item Response Theory, Test Items, Models, Maximum Likelihood Statistics
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Kim, Su-Young; Huh, David; Zhou, Zhengyang; Mun, Eun-Young – International Journal of Behavioral Development, 2020
Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in…
Descriptors: Bayesian Statistics, Maximum Likelihood Statistics, Longitudinal Studies, Models
Cho, April E.; Wang, Chun; Zhang, Xue; Xu, Gongjun – Grantee Submission, 2020
Multidimensional Item Response Theory (MIRT) is widely used in assessment and evaluation of educational and psychological tests. It models the individual response patterns by specifying functional relationship between individuals' multiple latent traits and their responses to test items. One major challenge in parameter estimation in MIRT is that…
Descriptors: Item Response Theory, Mathematics, Statistical Inference, Maximum Likelihood Statistics
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Kangasrääsiö, Antti; Jokinen, Jussi P. P.; Oulasvirta, Antti; Howes, Andrew; Kaski, Samuel – Cognitive Science, 2019
This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional…
Descriptors: Inferences, Computation, Cognitive Processes, Models
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Aran, Meltem A.; Munoz-Boudet, Ana Maria; Aktakke, Nazli – International Journal of Child Care and Education Policy, 2018
Public financing of privately provided child care can allow for access to these services in places where public provision and capacity are low. However, the mechanisms of subsidy delivery will affect who benefits, and the overall cost-effectiveness of such subsidies. This paper sets out an ex-ante simulation model for estimating the benefit…
Descriptors: Foreign Countries, Simulation, Computation, Child Care
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Samlan, Robin A.; Story, Brad H. – Journal of Speech, Language, and Hearing Research, 2017
Purpose: The purpose of this study was to determine the vocal fold structural and vibratory symmetries that are important to vocal function and voice quality in a simulated paramedian vocal fold paralysis. Method: A computational kinematic speech production model was used to simulate an exemplar "voice" on the basis of asymmetric…
Descriptors: Speech, Human Body, Voice Disorders, Simulation
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Westera, Wim – Interactive Learning Environments, 2018
This paper presents a computational model for simulating how people learn from serious games. While avoiding the combinatorial explosion of a games micro-states, the model offers a meso-level pathfinding approach, which is guided by cognitive flow theory and various concepts from learning sciences. It extends a basic, existing model by exposing…
Descriptors: Computation, Models, Simulation, Games
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