A major problem in human cognition is to understand how newly acquired information and long-standing beliefs about the environment combine to make decisions and plan behaviors. Over-dependence on long-standing beliefs may be a significant source of suboptimal decision-making in unusual circumstances. While the contribution of long-standing beliefs…

When physicians are asked to determine the positive predictive value from the a priori probability of a disease and the sensitivity and false positive rate of a medical test (Bayesian reasoning), it often comes to misjudgments with serious consequences. In daily clinical practice, however, it is not only important that doctors receive a tool with…

Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks…

There is growing interest in how to better prepare K-12 students to work with data. In this article, we assert that these discussions of teaching and learning must attend to the human dimensions of data work. Specifically, we draw from several established lines of research to argue that practices involving the creation and manipulation of data are…

The traditional instructional approach combined with standard textbook examples provide a disengaging learning environment for students in introductory statistics courses. This article proposes and examines an experiential music-based single-class-period learning activity for an introductory statistics course. This activity teaches students how to…

Teacher attrition is a chronic challenge facing many education systems, and has been shown to negatively impact education quality and equity. Common explanations rooted in occupational choice theory identify pecuniary and non-pecuniary rewards as critical factors in motivating and retaining teachers. Using China Household Income Project (CHIP)…

Art therapy students' art practice might influence professional identity and career commitment. Art therapy graduate students (n = 202) in South Korea participated in a survey. This study introduces the Art Practice Scale and associated exploratory factor analysis, which identified 2 subvariables: art competency and attitude toward artwork. The…

This study presents a latent (item response theory--like) framework of a recently developed classical approach to test scoring, equating, and item analysis, referred to as "D"-scoring method. Specifically, (a) person and item parameters are estimated under an item response function model on the "D"-scale (from 0 to 1) using…

In this article we present and discuss the importance and relevance of the inclusion of spatial data analysis as part of the undergraduate statistics curriculum. Given its usefulness and applicability across a range of disciplines, spatial data are a topic that should receive more emphasis within undergraduate statistics curricula. There exist…

This is the fourth in a series of statistical articles for mathematics teachers. In this article, the authors discuss topics in General Mathematics in Unit 2 Topic 1 (Univariate data analysis and the statistical investigation process) and topics in Essential Mathematics, Unit 2 Topic 1 (Representing and comparing data).

Best practices in statistics and data science courses include the use of real and relevant data as well as teaching the entire data science cycle starting with importing data. A rich source of real and current data is the web, where data are often presented and stored in a structure that needs some wrangling and transforming before they can be…

Data science is collaborative and its students should learn teamwork and collaboration. Yet it can be a challenge to fit the teaching of such skills into the data science curriculum. Team-Based Learning (TBL) is a pedagogical strategy that can help educators teach data science better by flipping the classroom to employ small-group collaborative…

Monte Carlo methods are an accepted methodology in regards to generation critical values for a Maximum test. The same methods are also applicable to the evaluation of the robustness of the new created test. A table of critical values was created, and the robustness of the new maximum test was evaluated for five different distributions. Robustness…

This dissertation's idea began with my class notes and questions in the statistics courses I attended in my doctoral program. These notes and questions originally were about the concepts of the bell shape, statistical distribution, and hypothesis testing. They then became my inquiries of p-values because what I learned in the courses about how the…

With the development of technology-enhanced learning platforms, eye-tracking biometric indicators can be recorded simultaneously with students item responses. In the current study, visual fixation, an essential eye-tracking indicator, is modeled to reflect the degree of test engagement when a test taker solves a set of test questions. Three…