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Brinley N. Zabriskie; Nolan Cole; Jacob Baldauf; Craig Decker – Research Synthesis Methods, 2024
Meta-analyses have become the gold standard for synthesizing evidence from multiple clinical trials, and they are especially useful when outcomes are rare or adverse since individual trials often lack sufficient power to detect a treatment effect. However, when zero events are observed in one or both treatment arms in a trial, commonly used…
Descriptors: Meta Analysis, Error Correction, Computation, Simulation
Ori Ossmy; Danyang Han; Patrick MacAlpine; Justine Hoch; Peter Stone; Karen E. Adolph – Developmental Science, 2024
What is the optimal penalty for errors in infant skill learning? Behavioral analyses indicate that errors are frequent but trivial as infants acquire foundational skills. In learning to walk, for example, falling is commonplace but appears to incur only a negligible penalty. Behavioral data, however, cannot reveal whether a low penalty for falling…
Descriptors: Physical Activities, Robotics, Error Patterns, Infants
Yuting Deng; Yanling Zhang; Ruibin Zhao – Education and Information Technologies, 2025
Computer simulation technology and virtual reality technology have gained considerable attention in the field of education due to their potential to create various 3D interactive learning environments, typically including simulated learning environments and immersive learning environments. To gain a deeper understanding of students' learning…
Descriptors: Computer Uses in Education, Computer Simulation, Assistive Technology, Computer Peripherals
Lee, Sooyong; Han, Suhwa; Choi, Seung W. – Educational and Psychological Measurement, 2022
Response data containing an excessive number of zeros are referred to as zero-inflated data. When differential item functioning (DIF) detection is of interest, zero-inflation can attenuate DIF effects in the total sample and lead to underdetection of DIF items. The current study presents a DIF detection procedure for response data with excess…
Descriptors: Test Bias, Monte Carlo Methods, Simulation, Models
Chunsong Jiang; Xuan Chen; Aiping Yu; Guiqin Liang – Education and Information Technologies, 2025
Assignments and tests are the main forms of evaluation in the educational process, students usually lose interest in boring exercises during course learning. In spired of elements from human-computer battle game, a course test system is designed to encourage students to take tests more frequently and actively to achieve better learning effect,…
Descriptors: Computer Games, Educational Games, Game Based Learning, Competition
Eglington, Luke G.; Pavlik, Philip I., Jr. – International Journal of Artificial Intelligence in Education, 2023
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
Yasar C. Kakdas; Sinan Kockara; Tansel Halic; Doga Demirel – IEEE Transactions on Learning Technologies, 2024
This article presents a 3-D medical simulation that employs reinforcement learning (RL) and interactive RL (IRL) to teach and assess the procedure of donning and doffing personal protective equipment (PPE). The simulation is motivated by the need for effective, safe, and remote training techniques in medicine, particularly in light of the COVID-19…
Descriptors: Medical Education, Error Patterns, Error Correction, Reinforcement
Weisberg, Steven M.; Schinazi, Victor R.; Ferrario, Andrea; Newcombe, Nora S. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
Relying on shared tasks and stimuli to conduct research can enhance the replicability of findings and allow a community of researchers to collect large data sets across multiple experiments. This approach is particularly relevant for experiments in spatial navigation, which often require the development of unfamiliar large-scale virtual…
Descriptors: Programming, Error Patterns, Computer Simulation, Spatial Ability
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
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
Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Jorge Salas – Journal of Educational Measurement, 2024
Despite the growing interest in incorporating response time data into item response models, there has been a lack of research investigating how the effect of speed on the probability of a correct response varies across different groups (e.g., experimental conditions) for various items (i.e., differential response time item analysis). Furthermore,…
Descriptors: Item Response Theory, Reaction Time, Models, Accuracy
Mangiulli, Ivan; Otgaar, Henry; Curci, Antonietta; Jelicic, Marko – Applied Cognitive Psychology, 2020
Research suggests that both internal (i.e., lying) and external (i.e., misinformation) factors can affect memory for a crime. We aimed to explore the effects of post-event misinformation on crime-related amnesia claims. We showed participants a mock crime and asked them to either simulate amnesia (simulators) or confess to it (confessors). Next,…
Descriptors: Deception, Memory, Crime, Recall (Psychology)
Hoppe, Dorothée B.; Rij, Jacolien; Hendriks, Petra; Ramscar, Michael – Cognitive Science, 2020
Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers ("premarkers," e.g., gendered articles) or succeeding category markers ("postmarkers," e.g., gendered suffixes). Given that numerous…
Descriptors: Discrimination Learning, Computational Linguistics, Natural Language Processing, Artificial Languages
Eglington, Luke G.; Pavlik, Philip I., Jr. – Grantee Submission, 2022
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
Sun-Joo Cho; Amanda Goodwin; Matthew Naveiras; Jorge Salas – Grantee Submission, 2024
Despite the growing interest in incorporating response time data into item response models, there has been a lack of research investigating how the effect of speed on the probability of a correct response varies across different groups (e.g., experimental conditions) for various items (i.e., differential response time item analysis). Furthermore,…
Descriptors: Item Response Theory, Reaction Time, Models, Accuracy

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