Predicting student performance has been a critical focus of educational research. With an effective predictive model, schools can identify potentially at-risk students and implement timely interventions to support student success. Recent developments in educational data mining (EDM) have introduced several machine learning techniques that can…

The Data Quality Campaign (DQC) has been reviewing state report cards for the past seven years. They continue to examine the landscape of state report cards because they believe states must increase transparency and build trust by sharing information. But after many years, it was time to look at state report cards with fresh eyes. In addition to…

Background: Emotions are inherently intertwined with learning and disciplinary identity, and this relationship demands that teachers attend and respond to students' emotions. In this study, we forward a theory that the development of "pedagogical empathy," or the capacity to understand the implications of student emotions and use…

"Citizen science" popularly refers to a participatory approach in which lay persons voluntarily contribute data toward scientific enterprise. Although citizen science may provide unique opportunities for pre-college students to engage in data-intensive science practices, little is known about whether or how citizen science interacts with…

Research indicates that data-informed practice helps teachers change their teaching and promotes teacher professional development (TPD). Although educational data are often collected from digital spaces, in-action evidence from physical spaces is seldom gathered, providing an incomplete view of the classroom reality. Also, most learning analytics…

Community-based instruction (CBI) is one type of community experience in which students with disabilities work toward instructional goals while engaged in activities occurring in a natural environment outside of a typical school setting (Hoover, 2016; Rowe et al., 2015). Educators who implement CBI capitalize on their students' time in the…

Technological advances, artificial intelligence innovations, and widespread computing have all combined to necessitate a new generation of knowledge workers where data becomes a ubiquitous part of decision making (Sutton, 2006). Teaching today's students through the application of this "new" knowledge to long-established fields…

The Vermont Agency of Education (AOE) has prioritized initiatives that help promote regional equity across the state. In Year 2, AOE wanted to continue using geographic information systems (GIS) data to support policymaking and requested using GIS to understand the landscape of student mental health support providers in schools and districts…

The primary purpose of Title 1, Part D programs is to improve the educational outcomes for youth who are categorized as "neglected" (n), "delinquent" (d), or at-risk under the statute. This brief is designed to help State Coordinators, grantees involved in data collection or analyses, and personnel involved in the design of…

Systems thinking (ST) is a holistic leadership approach that puts the study of wholes before that of parts. It focuses attention on how organization members act together in networks of interactions. This is done by researching organization members' mental models, which are deeply ingrained assumptions, generalizations, or even mental pictures or…

Using data for decision-making is critical for schoolwide leadership teams and has been shown to enhance both social and academic outcomes for students (Faria et al., 2017). Using data effectively, however, requires that teams have a clear vision about the type of data, format of data presentation, and process for using data. To avoid expending…

Statistical modeling is a core component of statistical thinking and has been identified by several countries as a curricular goal for secondary education. However, many secondary teachers have minimal preparation for teaching this topic. The goal of this research study is to learn about teachers' perceptions of the role statistical models play in…

Given the importance of data skills to the economy and the skills shortage within data science, educational policy makers have identified the importance of including technical and analytical data skills in the school curriculum. An equally important aim is to educate children and young people to become "data citizens" who are aware of…

Participatory sensing is a data collection method in which communities of people collect and share data to investigate large-scale processes. These data have many features often associated with the big data paradigm: they are rich and multivariate, include non-numeric data, and are collected as determined by an algorithm rather than by traditional…

This special issue of "ETR&D" is devoted to ethics in the broad domain of educational technology. Many ethical issues arise involving the study and use of educational technologies. A well-known issue involves the digital divide and the degree to which the introduction of new technologies is increasing the digital divide and…