Data mining has been successfully and widely utilized in educational information systems, and an important research field has been formed, which is educational data mining. Process mining inherits the characteristics of data mining which can not only use historical data in the system to analyze learning behavior and predict academic performance,…

In today's era of large-scale data, academic institutions, businesses, and government agencies are increasingly faced with heterogeneous datasets. Consequently, there is a growing need to develop effective methods for extracting meaningful insights from this type of data. Quantile, expectile, and expected shortfall regression methods offer useful…

Although various academic disciplines use data papers to support effective research practices, data papers are still uncommon in the educational sciences. Main goals of data papers are enhancing transparency regarding research processes and supporting data sharing among researchers and thus, open science. As many educational research projects…

Protection of student privacy is critical for scaling up the use of learning analytics (LA) in education. Poorly implemented frameworks for privacy protection may negatively impact LA outcomes and undermine trust in the discipline. To design and implement models and tools for privacy protection, we need to understand privacy itself. To develop…

Data reflects a series of decisions made by people--and those decisions affect the story that data tells, what it captures, and how it can and should be used to inform decision-making. Because of this, mistrust in data is often the result of incomplete information and a lack of context. This resource breaks down what it means to build trust in…

Because learning from failures is just as important as learning from successes, we used qualitative case study data gathered in the Netherlands and the United States to examine instances in which data teams struggle to contribute to school improvement. Similar factors in both the Dutch and U.S. case hindered the work of the data teams, such as…

The Stanford Education Data Archive (SEDA) launched in 2016 to provide nationally comparable, publicly available test score data for U.S. public school districts. I introduce a special collection of six articles that each use SEDA to lend their questions and findings a national scope. Together, these articles demonstrate a range of uses of SEDA…

This study has three primary objectives. First to provide insight into possibilities for moderation/mediation analysis in education research by discussing analytical methods based on the counterfactual and developed in other disciplines. Second is to highlight specific issues related to mediation and the estimation of effects when the outcome is…

"The Forum Guide to Strategies for Education Data Collection and Reporting (SEDCAR)" was created to provide timely and useful best practices for education agencies that are interested in designing and implementing a strategy for data collection and reporting, focusing on these as key elements of the larger data process. It builds upon…

The What Works Clearinghouse (WWC) at the Institute of Education Sciences reviews rigorous research on educational practices, policies, programs, and products with a goal of identifying 'what works' and making that information accessible to the public. One critique of the WWC is the need to more closely examine 'what works' for whom, in which…

This collection focuses on the role of higher education institutions concerning datafication as a complex phenomenon. It explores how the universities can develop data literac(ies) shaping tomorrow skills and "formae mentis" to face the most deleterious effects of datafication, but also to engage in creative and constructive ways with…

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…

As marketing continues to be transformed by technology and the explosion of big data, academic research has identified a significant need for analytics skills in marketing education. However, it is unclear whether current curriculum approaches to marketing analytics equip students with the skills employers need and prepare them effectively for…

The Open Science movement has gained considerable traction in the last decade. The Open Science movement tries to increase trust in research results and open the access to all elements of a research project to the public. Central to these goals, Open Science has promoted five critical tenets: Open Data, Open Analysis, Open Materials,…

More aggregate data on school performance is available than ever before, opening up new possibilities for applied researchers interested in assessing the effectiveness of school-level interventions quickly and at a relatively low cost by implementing comparative interrupted times series (CITS) designs. We examine the extent to which effect…