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Wörner, C. H. – Physics Teacher, 2023
Bounded by the statements of Feynman and Galileo, I describe certain tricks that can be useful for the teaching of physics. In particular, I describe the calculation of the center of mass (centroid) of an arc of circumference and a circular sector. For this purpose, I also use Pappus's theorems. An Appendix is available with Archimedes' method to…
Descriptors: Physics, Computation, Scientific Concepts, Science Instruction
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Riley, Richard D.; Ensor, Joie; Hattle, Miriam; Papadimitropoulou, Katerina; Morris, Tim P. – Research Synthesis Methods, 2023
Individual participant data meta-analysis (IPDMA) projects obtain, check, harmonise and synthesise raw data from multiple studies. When undertaking the meta-analysis, researchers must decide between a two-stage or a one-stage approach. In a two-stage approach, the IPD are first analysed separately within each study to obtain aggregate data (e.g.,…
Descriptors: Data Analysis, Meta Analysis, Models, Computation
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Hernandez-Gonzalez, Jeronimo; Herrera, Pedro Javier – IEEE Transactions on Learning Technologies, 2023
In peer assessment, students assess a task done by their peers, provide feedback and usually a grade. The extent to which these peer grades can be used to formally grade the task is unclear, with doubts often arising regarding their validity. The instructor could supervise the peer assessments, but would not then benefit from workload reduction,…
Descriptors: Peer Evaluation, Supervision, Models, Computation
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Rrita Zejnullahi; Larry V. Hedges – Research Synthesis Methods, 2024
Conventional random-effects models in meta-analysis rely on large sample approximations instead of exact small sample results. While random-effects methods produce efficient estimates and confidence intervals for the summary effect have correct coverage when the number of studies is sufficiently large, we demonstrate that conventional methods…
Descriptors: Robustness (Statistics), Meta Analysis, Sample Size, Computation
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Yang Haodong; Liu Jialin; Wang Gaofeng – Research in Higher Education, 2025
With the increasingly prominent characteristics of data-intensive and AI-driven scientific paradigms, computing power has become a crucial pillar of research activities. This study aims to examine the knowledge innovation effects of university supercomputing development by theoretically proposing two mechanisms: the efficiency effect (including…
Descriptors: Foreign Countries, Universities, Computers, Innovation
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Kaitlyn G. Fitzgerald; Elizabeth Tipton – Journal of Educational and Behavioral Statistics, 2025
This article presents methods for using extant data to improve the properties of estimators of the standardized mean difference (SMD) effect size. Because samples recruited into education research studies are often more homogeneous than the populations of policy interest, the variation in educational outcomes can be smaller in these samples than…
Descriptors: Data Use, Computation, Effect Size, Meta Analysis
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Pu Wang; Yifeng Lin; Tiesong Zhao – Education and Information Technologies, 2025
With the emergence of Artificial Intelligence (AI), smart education has become an attractive topic. In a smart education system, automated classrooms and examination rooms could help reduce the economic cost of teaching, and thus improve teaching efficiency. However, existing AI algorithms suffer from low surveillance accuracies and high…
Descriptors: Supervision, Artificial Intelligence, Technology Uses in Education, Automation
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Tanja C. Roembke; Bob McMurray – Cognitive Science, 2025
Computational and animal models suggest that the unlearning or pruning of incorrect meanings matters for word learning. However, it is currently unclear how such pruning occurs during word learning and to what extent it depends on supervised and unsupervised learning. In two experiments (N[subscript 1] = 40; N[subscript 2] = 42), adult…
Descriptors: Vocabulary Development, Computation, Models, Accuracy
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J. S. Allison; L. Santana; I. J. H. Visagie – Teaching Statistics: An International Journal for Teachers, 2025
Given sample data, how do you calculate the value of a parameter? While this question is impossible to answer, it is frequently encountered in statistics classes when students are introduced to the distinction between a sample and a population (or between a statistic and a parameter). It is not uncommon for teachers of statistics to also confuse…
Descriptors: Statistics Education, Teaching Methods, Computation, Sampling
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Wei Zhang; Xinyao Zeng; Lingling Song – Education and Information Technologies, 2025
Computational thinking (CT) assessment is crucial for testing the effectiveness of CT skills development. However, the exploration of CT assessment in the context of text-based programming is in its initial stages. The intrinsic relationship between the core skills of text-based programming and the core elements of CT isn't analyzed in depth in…
Descriptors: Mental Computation, Programming, College Students, Evaluation
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Dana Christensen – Journal of Educational Computing Research, 2025
Increased technological advances within marine biology requires professionals to become versed in interdisciplinary computer-based skills. Computational thinking (CT) is a contemporary concept used in educational settings across the globe to meet this need. CT has been incorporated into many curricula; however, incorporation strategies are vague…
Descriptors: Computation, Thinking Skills, Marine Biology, Introductory Courses
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Roy Levy; Daniel McNeish – Journal of Educational and Behavioral Statistics, 2025
Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has…
Descriptors: Models, Statistical Analysis, Measurement, Data Interpretation
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Victoria Macann; Aman Yadav – Education and Information Technologies, 2025
Computational Thinking (CT) is viewed as a set of foundation skills required to solve problems efficiently and effectively, with or without the use of technology. It has also been argued that CT can provide connections between computing and other core curriculum areas which can be beneficial for student learning outcomes. However, there are still…
Descriptors: Computation, Thinking Skills, Elementary School Teachers, Teaching Methods
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Pablo A. Mitnik – Sociological Methods & Research, 2025
Although there is an extensive methodological literature on the measurement of intergenerational income mobility, there has been limited research on the conceptual interpretation of mobility measures and the methodological implications of those interpretations. In this article, I focus on the three measures of mobility most frequently used in the…
Descriptors: Social Mobility, Income, Correlation, Measurement Techniques
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Amber Simpson; Rebecca Borowski; Ashleigh Colquhoun; Zhengqi Hu – Early Childhood Education Journal, 2025
With the increase of computational thinking (CT) tools in education, there are questions as to whether and how CT might support and/or hinder algebraic thinking of young children. Utilizing seeds of algebraic thinking, we add to this scholarly discussion by presenting examples from a CT activity with four-year old children in which we illustrate…
Descriptors: Preschool Children, Mathematics Education, Computation, Thinking Skills
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