This study explores various approaches to investigate participants' testing performance and learning behaviors in a computer-based spatial rotation learning program. Using multivariate learning and assessment data, including responses, response times, learning times and selected covariates, a comprehensive data analytic framework is developed that…

Despite the increasing use of the statistical programming language R in statistics and data analysis (SDA), its implementation in communication science education is limited. Experiences, recommendations, and a critical exchange are therefore scarce. The following contribution addresses this very gap. At the Department of Journalism and…

Effective undergraduate statistical education requires training using real-world data. Textbook datasets seldom match the complexities and messiness of real-world data and finding these datasets can be challenging for educators. Consulting and industrial datasets often have nondisclosure agreements. Academic datasets often require subject area…

In the present study, factor mixture models (FMMs) were used to examine the latent structure underlying the Autism-Spectrum Quotient (AQ) among a sample of 633 undergraduate students. FMM represents a combination of latent-class, person-centered approaches and common-factor, variable-centered approaches to modeling population heterogeneity.…

There is a growing need for chemistry students to be able to handle and manipulate large datasets and analyze them in an efficient and accessible way. This creates the need to develop course materials that introduce these topics early in the undergraduate curriculum. To address this growing need, this activity introduced RStudio to students…

Statistical knowledge is required for students in a range of disciplines. However, there are limited educator resources that exist for applying statistics to solve real-world problems. This investigation provides one approach to teaching statistics using entrepreneurial-minded learning (as a way to connect real-world applications and value…

Numerous variants of Kahneman and Tversky's (1972) hospital problem have been used to investigate intuitions about the Empirical Law of Large Numbers (eLLN). A largely separate line of research has focused on interactions between context knowledge and statistical reasoning. The present study merges these two lines of research by analyzing tertiary…

Election forecasting has become the centerpiece of media coverage of elections. Yet for all the attention paid to forecasting, public understanding remains low and increasingly distrustful. We can improve citizen knowledge and comprehension and increase student engagement by giving students the opportunity to develop their own election forecast.…

Text is a burgeoning data source for psychological researchers, but little methodological research has focused on adapting popular modeling approaches for text to the context of psychological research. One popular measurement model for text, topic modeling, uses a latent mixture model to represent topics underlying a body of documents. Recently,…

Quantitative reasoning (QR) is a key skill for undergraduate biology education. Despite this, many students struggle with QR. Here, we use the theoretical framework of student noticing to investigate why some students struggle with QR in introductory biology labs. Under this framework, what students notice when given new information and data…

This paper describes a course designed to introduce students to mathematical thinking and a variety of lower level mathematics topics using baseball while satisfying the goals of quantitative reasoning. We give suggestions for sources, topics, techniques, and examples so any mathematics teacher can design such a course to fit their needs. The…

This article will address predictors of success for online students. A survey questionnaire was used to gather data concerning online students' social and educational readiness levels at a four-year private university in the Midwestern United States. Of the 4,050 potential participants, 250 (6.23%) responded to the survey. Stepwise regression…

Quantitative modelling plays an important role as biology increasingly deals with big data sets, relies on modelling to understand system dynamics, makes predictions about impacts of changes, and revises our understanding of system interactions. An assessment of quantitative modelling in biology was administered to students (n = 612) in…

The present study illustrates how select data can be organized to guide researchers to HSIs proficient at graduating Hispanics with STEM bachelor's degrees to investigate best practices that less proficient HSIs can adopt for program improvement. The Integrated Postsecondary Education Data System (IPEDS) from the National Center for Education…

Educational reform priorities such as emphasis on quantitative modelling (QM) have positioned undergraduate biology instructors as designers of QM experiences to engage students in authentic science practices that support the development of data-driven and evidence-based reasoning. Yet, little is known about how biology instructors adapt to the…