Methods based on machine learning have become increasingly popular in many areas as they allow models to be fitted in a highly-data driven fashion and often show comparable or even increased performance in comparison to classical methods. However, in the area of educational sciences, the application of machine learning is still quite uncommon.…

Learning to interpret data in context is an important educational outcome. To assess students' attainment of this outcome, it is necessary to examine the interplay between their contextual and statistical reasoning. We describe a research method designed to do so. The method draws upon Toulmin's (1958, 2003) model of argumentation for the first…

J.D. Brown (JD) served as the author of an instructional column titled "Statistics Corner" in the "Shiken": JALT "Testing & Evaluation SIG Newsletter" for an extensive period spanning over two decades, from 1997 to 2019. This publication was under the auspices of the Testing and Evaluation Special Interest Group,…

This paper reports the statistical and probabilistic reasoning of young children in terms of randomness, variability, and data representations in the context of informal inferential reasoning (IIR). Using the IIR approach, a task was designed and conducted one-on-one with 28 children aged 5 to 6 years old, in a case study setting. The researcher…

The design and analysis of experiments (DOE) has historically been an important part of an education in statistics, and with the increasing complexity of modern production processes and the advent of large-scale online experiments, it continues to be highly relevant. In this article, we provide an extensive review of the literature on DOE…

Measurement in the social sciences involves assigning values to particular empirical cases and plays a vital role in the research process, but it can be tricky to teach. Unless an undergraduate social sciences research methods course includes the collection of original data, many of the issues of measurement can seem abstract and arcane to…

Drawing from research in the learning sciences, this article argues that educators, like Jesus, should use stories as we mentor students to move beyond passive reception of information and truly apply their learning to novel situations. Thus, students can go beyond "hearing" to "bearing fruit." This storytelling approach to…

Every year, the national high school graduation rate is declining and impacting the number of students applying to colleges. Moreover, the majority of students are applying to more than one college. This makes a lot of colleges to be highly competitive in student recruitment for enrollment and thus, the necessity for institutions to anticipate…

There is a growing concern that much of the published research literature is distorted by the pursuit of statistically significant results. In a seminal article, Ioannidis and Trikalinos (2007, "Clinical Trials") proposed an omnibus (I&T) test for significance chasing (SC) biases. This test compares the observed number of studies…

Violations of normality and homogeneity are common in educational data. When this occurs, the use of parametric statistics may be inappropriate. A generalized form of nonparametric analyses based on the Puri and Sen L statistic provides an alternative approach. Using a chi-square distribution, this technique is easy to apply and has significant…

Statistical literacy, encompassing the interpretation and evaluation of statistical reports, is a skill developed in schools. However, the pedagogical approaches to statistical literacy skills have not been sufficiently investigated. This study investigated whether statistical literacy skills could be demonstrated through high school lessons.…

Racism impacts the lives of students who identify as Black, Indigenous, or People of Color (BIPOC) in a myriad of ways. It is important that future teachers go beyond individual acts of racism to understand how racism operates as a system. To this end, we designed and implemented a statistical investigation with 13 preservice teachers using real…

Bayesian Knowledge Tracing (BKT) is a well-established model for formative assessment, with optimization typically using expectation maximization, conjugate gradient descent, or brute force search. However, one of the flaws of existing optimization techniques for BKT models is convergence to undesirable local minima that negatively impact…

Purpose: This meta-analysis study aimed to determine the optimal level of talker variability in training to maximize second-language speech learning. Method: We conducted a systematic search for studies comparing different levels of talker variability in nonnative speech training, published through July 2024. Two independent reviewers screened…

Small sample sizes contribute to several problems in research and knowledge advancement. This conceptual replication study confirmed and extended the inflation of type II errors and confidence intervals in correlation analyses of small sample sizes common in kinesiology/exercise science. Current population data (N = 18, 230, & 464) on four…