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Alicia M. Chen; Andrew Palacci; Natalia Vélez; Robert D. Hawkins; Samuel J. Gershman – Cognitive Science, 2024
How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N…
Descriptors: Bayesian Statistics, Models, Teaching Methods, Evaluation
Smith, Lynette M.; Yu, Fang; Schmid, Kendra K. – Journal of Statistics and Data Science Education, 2021
Replication and reproducibility are an important component of scientific research. One reason research is not replicable is the misuse of statistical techniques. Educators can teach the importance of research replication by having students perform a replication study as part of a graduate assistantship or their coursework. In this article, we…
Descriptors: Replication (Evaluation), Scientific Research, Biology, Statistics
Williamson, J. Charles; Silverstein, Todd P. – Journal of Chemical Education, 2021
We have expanded Stein's "Sweetness of Aspartame" laboratory project (Stein, P. J. "J. Chem. Educ." 1997, 74, 1112, DOI: 10.1021/ed074p1112) to include extensive use of statistical testing. Students test the statistical significance of a nonzero intercept in a linear regression, bias in comparison to a true value, and…
Descriptors: Chemistry, Science Experiments, Food, Regression (Statistics)
Mikyung Shin; Jiyeon Park – Society for Research on Educational Effectiveness, 2025
Background: A single-case design focuses on individual performance and measures the causal relationships between variables (Kazdin, 2019). This experimental design enables researchers to measure the learning behaviors of individual participants over time across phases and assess the effectiveness of an instructional strategy in improving or…
Descriptors: Causal Models, Statistical Inference, Statistical Data, Research Design
Sahil Luthra; Austin Luor; Adam T. Tierney; Frederic Dick; Lori L. Holt – npj Science of Learning, 2025
Humans implicitly pick up on probabilities of stimuli and events, yet it remains unclear how statistical learning builds expectations that affect perception. Across 29 experiments, we examine the influence of task-irrelevant distributions--defined across acoustic frequency--on both tone detection in noise and tone duration judgments. The shape and…
Descriptors: Probability, Statistics, Expectation, Auditory Perception
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
Marci S. DeCaro; Campbell R. Bego; Lianda Velic; Phillip M. Newman – Instructional Science: An International Journal of the Learning Sciences, 2025
Instructors traditionally lecture on new content before providing practice problems, but learning is often superficial. Exploratory learning before instruction deepens conceptual understanding by giving students a novel activity to explore before direct instruction. We examined how increasing the salience of contrasting cases in exploration versus…
Descriptors: Discovery Learning, Undergraduate Students, Statistics Education, Learning Activities
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
Changiz Mohiyeddini – Anatomical Sciences Education, 2025
This article presents a step-by-step guide to using R and SPSS to bootstrap exam questions. Bootstrapping, a versatile nonparametric analytical technique, can help to improve the psychometric qualities of exam questions in the process of quality assurance. Bootstrapping is particularly useful in disciplines such as medical education, where student…
Descriptors: Test Items, Sampling, Statistical Inference, Nonparametric Statistics
Paul W. T. Fijn; Alba Santin Garcia – Mathematics Education Research Group of Australasia, 2025
We present the preliminary results from a project investigating a large statistics class designed for and taught using a flipped classroom model, with pre-recorded videos. The study, undertaken in 2021 during the COVID-19 pandemic, utilised student surveys in conjunction with metadata on their engagement with electronic resources. This preliminary…
Descriptors: Statistics Education, Flipped Classroom, Learner Engagement, Student Attitudes
Lee, Daniel Y.; Harring, Jeffrey R. – Journal of Educational and Behavioral Statistics, 2023
A Monte Carlo simulation was performed to compare methods for handling missing data in growth mixture models. The methods considered in the current study were (a) a fully Bayesian approach using a Gibbs sampler, (b) full information maximum likelihood using the expectation-maximization algorithm, (c) multiple imputation, (d) a two-stage multiple…
Descriptors: Monte Carlo Methods, Research Problems, Statistical Inference, Bayesian Statistics
Marek Arendarczyk; Tomasz J. Kozubowski; Anna K. Panorska – Journal of Statistics and Data Science Education, 2023
We provide tools for identification and exploration of data with very large variability having power law tails. Such data describe extreme features of processes such as fire losses, flood, drought, financial gain/loss, hurricanes, population of cities, among others. Prediction and quantification of extreme events are at the forefront of the…
Descriptors: Natural Disasters, Probability, Regression (Statistics), Statistical Analysis
Roberto Silva de Souza Jr.; Endler Marcel Borges – Journal of Chemical Education, 2023
This laboratory experiment was divided into four parts. In the first part, students evaluate previously published data and check their normality using histograms, Q-Q plots, the Shapiro-Wilk test, and boxplots. In the second part, two different groups were compared. First, data normality and homoskedasticity were checked by using the Shapiro-Wilk…
Descriptors: Statistics Education, Hypothesis Testing, Chemistry, Comparative Analysis
Melissa A. Shepherd; Elizabeth J. Richardson – Teaching Statistics: An International Journal for Teachers, 2024
Statistical software is commonly used in undergraduate social sciences statistics courses. Due to the increase in online/hybrid courses and the cost of SPSS, instructors may wish to switch to another statistical software. We cover seven programs: Excel, Google Sheets, jamovi, JASP, PSPP, R, and SOFA. We compare programs using the following…
Descriptors: Open Source Technology, Statistics, Computer Software, Computer Software Reviews
Nadide Yilmaz – Electronic Journal for Research in Science & Mathematics Education, 2024
Many countries do not include the normal distribution concept in their middle school mathematics curriculum, but on the grounds that middle school mathematics teachers need to know more than just mathematics, researchers argue that preservice teachers (PTs) ought to have knowledge and skills in this area. This study was aimed to investigate PTs'…
Descriptors: Mathematics Instruction, Pedagogical Content Knowledge, Mathematical Concepts, Preservice Teachers

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