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William D. Riihiluoma; Zeynep Topdemir; John R. Thompson – Physical Review Physics Education Research, 2025
The ability to relate physical concepts and phenomena to multiple mathematical representations--and to move fluidly between these representations--is a critical outcome expected of physics instruction. In upper-division quantum mechanics, students must work with multiple symbolic notations, including some that they have not previously encountered.…
Descriptors: Undergraduate Students, College Faculty, Physics, Science Instruction
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Umut Atasever; Francis L. Huang; Leslie Rutkowski – Large-scale Assessments in Education, 2025
When analyzing large-scale assessments (LSAs) that use complex sampling designs, it is important to account for probability sampling using weights. However, the use of these weights in multilevel models has been widely debated, particularly regarding their application at different levels of the model. Yet, no consensus has been reached on the best…
Descriptors: Mathematics Tests, International Assessment, Elementary Secondary Education, Foreign Countries
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Liqun Yin; Ummugul Bezirhan; Matthias von Davier – International Electronic Journal of Elementary Education, 2025
This paper introduces an approach that uses latent class analysis to identify cut scores (LCA-CS) and categorize respondents based on context scales derived from largescale assessments like PIRLS, TIMSS, and NAEP. Context scales use Likert scale items to measure latent constructs of interest and classify respondents into meaningful ordered…
Descriptors: Multivariate Analysis, Cutting Scores, Achievement Tests, Foreign Countries
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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
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Hong, Jeehye; Kim, Hyunjung; Hong, Hun-Gi – Asia-Pacific Science Education, 2022
This study explored science-related variables that have an impact on the prediction of science achievement groups by applying the educational data mining (EDM) method of the random forest analysis to extract factors associated with students categorized in three different achievement groups (high, moderate, and low) in the Korean data from the 2015…
Descriptors: Science Achievement, Prediction, Teaching Methods, Science Teachers
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Young, Nicholas T.; Caballero, Marcos D. – Physical Review Physics Education Research, 2021
One argument for keeping the physics Graduate Record Exam (GRE) is that it can help applicants who might otherwise be missed in the admissions process stand out. In this work, we evaluate whether this claim is supported by physics graduate school admissions decisions. We used admissions data from five Ph.D.-granting physics departments over a…
Descriptors: College Entrance Examinations, Graduate Study, College Applicants, Selective Admission
Phelan, Julia; Egger, Jeffrey; Kim, Junok; Choi, Kilchan; Keum, Eunhee; Chung, Gregory K. W. K.; Baker, Eva L. – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2021
The National Math + Science Initiative (NMSI) is a nonprofit organization committed to improving educational outcomes that traces its roots back to the early 1990s. NMSI's College Readiness Program (CRP) is a long-standing program with the goal of promoting STEM education in high schools to improve students' preparation for college. The three-year…
Descriptors: College Readiness, Nonprofit Organizations, Program Evaluation, STEM Education