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Gabriel Felipe Arantes Bertochi; Jeffer Eidi Sasaki – Measurement in Physical Education and Exercise Science, 2025
This study compared the weekly training load (TL) variation across different measures. Fifty-two runners reported their heart rate and distance ran for each training session during four weeks of training. Heart rate measures were used to calculate the weekly TRaining IMPulse (W-TRIMP), whereas the distance ran was used to calculate the weekly…
Descriptors: Physical Education, Physical Activities, Athletics, Athletes
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David Voas; Laura Watt – Teaching Statistics: An International Journal for Teachers, 2025
Binary logistic regression is one of the most widely used statistical tools. The method uses odds, log odds, and odds ratios, which are difficult to understand and interpret. Understanding of logistic regression tends to fall down in one of three ways: (1) Many students and researchers come to believe that an odds ratio translates directly into…
Descriptors: Statistics, Statistics Education, Regression (Statistics), Misconceptions
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Beyza Aksu Dunya; Stefanie Wind – International Journal of Testing, 2025
We explored the practicality of relatively small item pools in the context of low-stakes Computer-Adaptive Testing (CAT), such as CAT procedures that might be used for quick diagnostic or screening exams. We used a basic CAT algorithm without content balancing and exposure control restrictions to reflect low stakes testing scenarios. We examined…
Descriptors: Item Banks, Adaptive Testing, Computer Assisted Testing, Achievement
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Ari Decter-Frain; Pratik Sachdeva; Loren Collingwood; Hikari Murayama; Juandalyn Burke; Matt Barreto; Scott Henderson; Spencer Wood; Joshua Zingher – Sociological Methods & Research, 2025
We consider the cascading effects of researcher decisions throughout the process of quantifying racially polarized voting (RPV). We contrast three methods of estimating precinct racial composition, Bayesian Improved Surname Geocoding (BISG), fully Bayesian BISG, and Citizen Voting Age Population (CVAP), and two algorithms for performing ecological…
Descriptors: Voting, Computation, Racial Composition, Bayesian Statistics
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Sohaib Ahmad; Javid Shabbir – Measurement: Interdisciplinary Research and Perspectives, 2025
This study aims to suggest a generalized class of estimators for population proportion under simple random sampling, which uses auxiliary attributes. The bias and MSEs are considered derived to the first degree approximation. The validity of the suggested and existing estimators is assessed via an empirical investigation. The performance of…
Descriptors: Computation, Sampling, Data Collection, Data Analysis
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Leonidas Zotos; Hedderik van Rijn; Malvina Nissim – International Educational Data Mining Society, 2025
In an educational setting, an estimate of the difficulty of Multiple-Choice Questions (MCQs), a commonly used strategy to assess learning progress, constitutes very useful information for both teachers and students. Since human assessment is costly from multiple points of view, automatic approaches to MCQ item difficulty estimation are…
Descriptors: Multiple Choice Tests, Test Items, Difficulty Level, Artificial Intelligence
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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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