This case study focuses on a longitudinal study of changes in public acceptance of evolution in the United States. Change over time is often a central issue in social science research questions. There are two kinds of change over time. Time-series studies address change in populations or groups over time. Longitudinal studies address changes in…

Structured classroom observation protocols provide instructors with data about their teaching practices, but instructors may not meaningfully engage with those data without guidance. To facilitate instructor reflection, educational developers from the Centers for Teaching and Learning (CTLs) and educational researchers from STEM departments across…

Data modernization and security practices allow educational leaders to provide accurate, secure, and timely data that can be securely exchanged, shared, and connected in order to provide instant understanding of school performance, student attendance, academic performance, or funding from multiple sources. This matters because: (1) Data…

With Governor Newsom's signature on the state budget in June 2019, California is finally on the path to joining the majority of other states in the country who have robust data systems. Having this administration provide funding to begin to build and maintain a statewide longitudinal data system (SLDS) is an important win. Advocates have been…

This special issue's editors invited discussion of three broad questions. Slightly rephrased, they are: How well do self-report data represent theoretical constructs? How should analyses of data be conditioned by properties of self report data? In what ways do interpretations of self-report data shape interpretations of a study's findings? To…

Despite tremendous interest in social games and game studies, the potential of game heuristics for the field of mixed methods remains unknown. This article introduces game heuristics to mixed methods research, showing how it was used in a specific study on the survival probabilities on the "Titanic." Specifically, we describe how game…

Analyzing and interpreting data is essential to the practice of scientists and is also an essential science and engineering practice for science teaching and learning. Although working with data has benefits for student learning, it is also challenging, particularly with respect to aspects of work with data that are not yet very common in schools,…

For undergraduate students to be prepared for graduate school and industry, it is imperative that they understand how to merge the theoretical insights gleaned through their undergraduate education with the raw data sets acquired through materials analysis. Thus, the ability to implement data analysis is a vital skill that students should develop.…

Comprehensive and timely data on vocational education and training (VET) is important for increasing the efficiency and transparency of Australia's VET sector, in order to improve understanding of Australia's VET market and management of the national VET system. The National VET Data Policy (the Policy) brings together requirements for collecting…

Technological advances have led to an abundance of widely available data on every aspect of life today. Psychologists today have more information than ever before on human cognition, emotion, attitudes, and behavior. "Big Data in Psychological Research" addresses the opportunities and challenges that this data presents to psychological…

Special education and related professionals often understand the importance of adhering to intervention procedures (i.e., fidelity), but may be less familiar with concepts of intervention intensity. In some cases, professionals may correctly apply an intervention, but with insufficient intensity. This can result in an intervention failing to…

Audience analysis is essential for creating successful messages across communication fields. The audience persona is an effective tool for understanding the characteristics and needs of a target audience. Through this unit activity, students experience the process of persona development, build skills in data collection and analysis, and gain…

It is recommended that teacher-scholars of data science adopt reproducible workflows in their research as scholars and teach reproducible workflows to their students. In this article, we propose a third dimension to reproducibility practices and recommend that regardless of whether they teach reproducibility in their courses or not, data science…

The purpose of this study is to develop a valid and reliable Likert-type scale that can be used to measure the data literacy skills of educators. In the development process of the scale, after reviewing the relevant literature, a pool of 130 items was designed and presented to the experts for their view. After the evaluation of experts, the…

Many of the analytical models commonly used in educational research often aim to maximize explained variance and identify variable importance within models. These models are useful for understanding general ideas and trends, but give limited insight into the individuals within said models. Data envelopment analysis (DEA), is a method rooted in…