This article conceptualizes the notion of magic(al)ing in relation to post-pandemic ways of thinking about data production and analyses. Revisiting old data produced pre-COVID-19 and engaging with new data produced during COVID-19, we consider the possibilities and potential of magic(al)ing as a theoretical concept. We think with several ideas…

"Tell me about your research." How does one begin to convey the importance of our life's work--our research? Enter stage right, Research-based Theatre, an innovative, arts-based methodology that takes research data and brings it to life, by showing, rather than telling. In this article, we tell the story of how Research-based Theatre…

In a time when fewer resources force school leaders to make critical decisions, the data-driven decision-making model continues to offer promise. This research project provides observations about factors used for decision making from 14 district leaders across five Iowa school districts. Placing these factors for decision making within the…

This study compared three approaches for handling a fourth level of nesting when analyzing cluster-randomized trial (CRT) data. Although CRT data analyses may include repeated measures, individual, and cluster levels, there may be an additional fourth level that is typically ignored. This study examined the impact of ignoring this fourth level,…

Low-skilled adults in literacy in English are those with skills insufficient to complete tasks requiring comparing and contrasting information, paraphrasing, or making low-level inferences. Low-skilled adults in numeracy in English are those who would have difficulty making calculations with whole numbers and percentages, estimating numbers or…

Fidelity is defined as doing what is intended. The purpose of measuring fidelity is to assist with supporting implementation. Collecting fidelity data provides an opportunity to problem-solve throughout the process versus waiting for outcome data. More often than not, purchased programs and curriculum materials are missing a research-validated…

Item Response Theory (IRT) models are a popular analytic method for self report data. We show how traditional IRT models can be vulnerable to specific kinds of asymmetric measurement error (AME) in self-report data, because the models spread the error to all estimates -- even those of items that do not contribute error. We quantify the impact of…

Hierarchical data is often observed in education data. Analyzing such data with Multilevel Modeling becomes crucial to understanding the relationship at the individual and group levels. However, one of the most significant problems with this kind of data is small sample sizes and very low Intraclass Correlations. The multivariate Latent Covariate…

Higher education institutions in the United States are under intense scrutiny to engage in data-driven decision-making to address systemic deficiencies. An innovative strategy to increase the use of data in higher education is through interactive data visualization whereby users are more involved in the creation of knowledge. The problem that was…

This case study reflects upon a mixed-methods exploratory study utilized by the Leadership in Times of Crisis Framework within the paradigm of pragmatism to survey 976 Minnesota teachers in April of 2020 to determine the demographic profile of teachers in Minnesota who were facing the COVID-19 pandemic and the greatest challenges in their…

Learning analytics platforms (LAPs) have become important modes of anticipatory governance in education. Educational futures are governed by utilizing various forms of learning analytics to track student data over time, suggesting that school leaders and teachers are expected to improve school quality by engaging with digital presentations of…

Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the…

Managers are increasingly being tasked with overseeing data-driven projects that incorporate prescriptive and predictive models. Furthermore, basic knowledge of the data analytics pipeline is a fundamental requirement in many modern organizations. Given the central importance of analytics in today's business environment, there is a growing demand…

The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or…

The advancement of technology has led to a growing interest in assessing scientific inquiry within digital platforms. This shift towards dynamic and interactive inquiry assessments enables researchers to investigate not only the accuracy of student responses ("product data") but also their steps and actions leading to those responses…