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Hong, Maxwell; Rebouças, Daniella A.; Cheng, Ying – Journal of Educational Measurement, 2021
Response time has started to play an increasingly important role in educational and psychological testing, which prompts many response time models to be proposed in recent years. However, response time modeling can be adversely impacted by aberrant response behavior. For example, test speededness can cause response time to certain items to deviate…
Descriptors: Reaction Time, Models, Computation, Robustness (Statistics)
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Zhang, Qian; Fiorella, Logan – Educational Psychologist, 2023
Errors are inevitable in most learning contexts, but under the right conditions, they can be beneficial for learning. Prior research indicates that generating and learning from errors can promote retention of knowledge, higher-level learning, and self-regulation. The present review proposes an integrated theoretical model to explain two major…
Descriptors: Models, Error Correction, Learning Processes, Feedback (Response)
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Ulitzsch, Esther; Domingue, Benjamin W.; Kapoor, Radhika; Kanopka, Klint; Rios, Joseph A. – Educational Measurement: Issues and Practice, 2023
Common response-time-based approaches for non-effortful response behavior (NRB) in educational achievement tests filter responses that are associated with response times below some threshold. These approaches are, however, limited in that they require a binary decision on whether a response is classified as stemming from NRB; thus ignoring…
Descriptors: Reaction Time, Responses, Behavior, Achievement Tests
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Raykov, Tenko – Measurement: Interdisciplinary Research and Perspectives, 2023
This software review discusses the capabilities of Stata to conduct item response theory modeling. The commands needed for fitting the popular one-, two-, and three-parameter logistic models are initially discussed. The procedure for testing the discrimination parameter equality in the one-parameter model is then outlined. The commands for fitting…
Descriptors: Item Response Theory, Models, Comparative Analysis, Item Analysis
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Guo, Wenjing; Choi, Youn-Jeng – Educational and Psychological Measurement, 2023
Determining the number of dimensions is extremely important in applying item response theory (IRT) models to data. Traditional and revised parallel analyses have been proposed within the factor analysis framework, and both have shown some promise in assessing dimensionality. However, their performance in the IRT framework has not been…
Descriptors: Item Response Theory, Evaluation Methods, Factor Analysis, Guidelines
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Maedeh Kazemitabar; Susanne P. Lajoie; Tenzin Doleck – Education and Information Technologies, 2024
For effective teamwork, especially in demanding learning situations like a hackathon, coordination is crucial as it contributes to mutual trust and shared mental models of team members. However, teams experience challenges that mar team coordination. Research has shown that interpersonal skills such as socially-shared emotion regulation (SSER) can…
Descriptors: Emotional Response, Self Control, Teamwork, Coordination
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Jacqueline Barfoot; Pamela Meredith; Koa Whittingham; Lachlan Kerley – Journal of Occupational Therapy, Schools & Early Intervention, 2024
The importance of parent-child relationships for child developmental outcomes suggests a need to incorporate a relationship focus into early intervention programs for children with developmental delays. Nevertheless, confusion exists about the definition and application of relationship-focussed interventions, and occupational therapists remain…
Descriptors: Occupational Therapy, Parent Child Relationship, Children, Developmental Delays
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Samantha Gregus; Sarah L. Smith; Timothy A. Cavell – Journal of School Violence, 2024
Described is an effort to develop and gather feedback on a competency-based framework designed to assist elementary school teachers in their support of children who are chronically bullied. Drawing from extant research, we identified 25 potential competencies to construct a guiding framework organized by knowledge, attitudes, and skills. In Study…
Descriptors: Bullying, Elementary School Teachers, Competency Based Teacher Education, Models
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Rashid, M. Parvez; Xiao, Yunkai; Gehringer, Edward F. – International Educational Data Mining Society, 2022
Peer assessment can be a more effective pedagogical method when reviewers provide quality feedback. But what makes feedback helpful to reviewees? Other studies have identified quality feedback as focusing on detecting problems, providing suggestions, or pointing out where changes need to be made. However, it is important to seek students'…
Descriptors: Peer Evaluation, Feedback (Response), Natural Language Processing, Artificial Intelligence
Nnamdi Chika Ezike – ProQuest LLC, 2022
Fitting wrongly specified models to observed data may lead to invalid inferences about the model parameters of interest. The current study investigated the performance of the posterior predictive model checking (PPMC) approach in detecting model-data misfit of the hierarchical rater model (HRM). The HRM is a rater-mediated model that incorporates…
Descriptors: Prediction, Models, Interrater Reliability, Item Response Theory
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Xieling Chen; Haoran Xie; Di Zou; Lingling Xu; Fu Lee Wang – Educational Technology & Society, 2025
In massive open online course (MOOC) environments, computer-based analysis of course reviews enables instructors and course designers to develop intervention strategies and improve instruction to support learners' learning. This study aimed to automatically and effectively identify learners' concerned topics within their written reviews. First, we…
Descriptors: Classification, MOOCs, Teaching Skills, Artificial Intelligence
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Hao Zhou; Wenge Rong; Jianfei Zhang; Qing Sun; Yuanxin Ouyang; Zhang Xiong – IEEE Transactions on Learning Technologies, 2025
Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises…
Descriptors: Learning Experience, Academic Achievement, Data, Artificial Intelligence
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Conrad Borchers; Tianze Shou – Grantee Submission, 2025
Large Language Models (LLMs) hold promise as dynamic instructional aids. Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)--where student knowledge and pedagogical strategies are explicitly modeled. We propose a prompt variation framework to assess LLM-generated instructional moves' adaptivity…
Descriptors: Benchmarking, Computational Linguistics, Artificial Intelligence, Computer Software
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Xiaoyan Zhang; Min Wang – Language Teaching Research, 2025
This study examines the effects of the continuation task and the model-as-feedback writing task (MAFW) on English as a foreign language (EFL) vocabulary learning. Three classes of intermediate-level Chinese EFL learners were randomly assigned to a continuation group, a MAFW group, and a control group. Three aspects of vocabulary knowledge --…
Descriptors: Task Analysis, Models, Feedback (Response), Second Language Learning
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Hyo Jeong Shin; Christoph König; Frederic Robin; Andreas Frey; Kentaro Yamamoto – Journal of Educational Measurement, 2025
Many international large-scale assessments (ILSAs) have switched to multistage adaptive testing (MST) designs to improve measurement efficiency in measuring the skills of the heterogeneous populations around the world. In this context, previous literature has reported the acceptable level of model parameter recovery under the MST designs when the…
Descriptors: Robustness (Statistics), Item Response Theory, Adaptive Testing, Test Construction
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