The robustness of qualitative comparative analysis (QCA) results features high on the agenda of methodologists and practitioners. This article aims at advancing this debate on several fronts. First, in line with the extant literature, we take a comprehensive view on robustness arguing that decisions on calibration, consistency, and frequency…

Academic resilience captures academic success despite adversity and thus is an important concept for promoting equity within education. However, our understanding of how and why rates of academic resilience differ between contexts is currently limited by variation in the ways that the construct has been operationalised in quantitative research.…

In this study, we examined the results and interpretations produced from two different IRTree models--one using paths consisting of only dichotomous decisions, and one using paths consisting of both dichotomous and polytomous decisions. We used data from two versions of an impulsivity measure. In the first version, all the response options had…

The impact of the COVID-19 pandemic on the higher education sector has been overwhelming, with emergency responses that have affected decision-making processes. Yet, our understanding of higher education instructors' perspectives regarding the process of data-driven decisions, especially in times of emergency, is still limited. We aimed at…

This article presents an evaluation of edCrumble, a blended learning authoring tool for teachers. The tool visually represents learning designs and integrates data analytics to scaffold teacher design decisions. In addition to assessing the usability of edCrumble using Usability Metric for User Experience questionnaire, analyses of participant…

Fact-based decision making is changing job functions within organizations more than any other technology. Analytics, once the purview of the data scientist, is now spread throughout organizations. No longer is there a single job title, job function, or set of required skills and credentials for an analytics career. Companies have moved away from…

Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…

Data reproducibility is becoming increasingly important in the social sciences, but it has yet to be incorporated into many undergraduate sociology programs. This note describes a service-learning activity that can be added to an introductory statistics course. Students partner with a nonprofit and analyze quantitative data to answer questions…

As the body of scientific evidence about effective policies and practices grows, so does the need to effectively communicate that evidence to policy-makers and practitioners. Clearinghouses have emerged to facilitate the evidence-based decision-making process for education practitioners. While the results and methods for developing and analyzing…

As the body of scientific evidence about effective policies and practices grows, so does the need to effectively communicate that evidence to policy-makers and practitioners. Clearinghouses have emerged to facilitate the evidence-based decision-making process for education practitioners. While the results and methods for developing and analyzing…

Engaging students in the creation of learning resources is an effective way of developing a repository of revision items. However, a selection process is needed to separate high- from low-quality resources as some of the materials created by students can be ineffective, inappropriate or incorrect. In this study, we share our experiences and…

Sophisticated technology advances are delivering new and powerful ways for marketers to collect and use consumer data. These data-driven marketing capabilities present a unique challenge for students, as they will soon be expected to manage consumer data and make business decisions based on ethical, legal, and fiscal considerations. This article…

Contribution: An automated methodology that provides visualizations of students' free text comments from course satisfaction surveys. Focusing on sentiment, these visualizations reveal learning and teaching aspects of the course that either may require improvement or are performing well. They provide educators with a simple, systematic way to…

Data-driven decision making (DDDM) helps educators identify children not responding to intervention, individualize instruction, and monitor response to intervention in multitiered systems of support (MTSS). More prevalent in K-12 special education, MTSS practices are emerging in early childhood. In previous reports, we described the Making Online…

Data-driven decision making (DDDM) helps educators identify children not responding to intervention, individualize instruction, and monitor response to intervention in multitiered systems of support (MTSS). More prevalent in K-12 special education, MTSS practices are emerging in early childhood. In previous reports, we described the Making Online…