Computational Thinking (CT) skills are increasingly recognized as essential for junior high school students, especially in addressing the demands of the digital era. This study explores how CT skills--decomposition, pattern recognition, abstraction, and algorithmic thinking--manifest in learning statistics based on students' cognitive abilities. A…

This article makes a case for introducing moving averages into introductory statistics courses and contemporary modeling/data-based courses in college algebra and precalculus. The authors examine a variety of aspects of moving averages and draw parallels between them and similar topics in calculus, differential equations, and linear algebra. The…

Logistic regression is one of the most fundamental tools in predictive analytics. Graduate business analytics students are often familiarized with implementation of logistic regression using Python, R, SPSS, or other software packages. However, an understanding of the underlying maximum likelihood model and the mechanics of estimation are often…

As the demand for skilled data scientists has grown, university level statistics and data science courses have become more rigorous in training students to understand and utilize the tools that their future careers will likely require. However, the mechanisms to assess students' use of these tools while they are learning to use them are not well…

Nolan and Temple Lang's "Computing in the Statistics Curricula" (2010) advocated for a shift in statistical education to broadly include computing. In the time since, individuals with training in both computing and statistics have become increasingly employable in the burgeoning data science field. In response, universities have…