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Zhongzhou Chen; Tong Wan – Physical Review Physics Education Research, 2025
This study examines the feasibility and potential advantages of using large language models, in particular GPT-4o, to perform partial credit grading of large numbers of student written responses to introductory level physics problems. Students were instructed to write down verbal explanations of their reasoning process when solving one conceptual…
Descriptors: Grading, Technology Uses in Education, Student Evaluation, Science Education
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Theophile Musengimana; Lakhan Lal Yadav; Jean Uwamahoro; Gabriel Nizeyimana – Discover Education, 2025
Physics problem-solving ability is among the crucial skills physics students require as it enhances their conceptual understanding and develops their abilities for real-world problem-solving. This study employed a descriptive design to investigate the physics problem-solving skills of Senior-Five physics students from Rwandan secondary schools.…
Descriptors: Student Evaluation, Science Process Skills, Problem Solving, Secondary School Students
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Shelby J. Haberman; Sabine Meinck; Ann-Kristin Koop – Large-scale Assessments in Education, 2024
This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal…
Descriptors: Elementary Secondary Education, Foreign Countries, Achievement Tests, Mathematics Achievement
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Lyu, Weicong; Kim, Jee-Seon; Suk, Youmi – Journal of Educational and Behavioral Statistics, 2023
This article presents a latent class model for multilevel data to identify latent subgroups and estimate heterogeneous treatment effects. Unlike sequential approaches that partition data first and then estimate average treatment effects (ATEs) within classes, we employ a Bayesian procedure to jointly estimate mixing probability, selection, and…
Descriptors: Hierarchical Linear Modeling, Bayesian Statistics, Causal Models, Statistical Inference