Colleges and universities are increasingly turning to algorithms that predict college-student success to inform various decisions, including those related to admissions, budgeting, and student-success interventions. Because predictive algorithms rely on historical data, they capture societal injustices, including racism. In this study, we examine…

This study investigates how youth from two cities in the United States engage in critical data practices as they learn about and take action in their lives and communities in relation to COVID-19 and its intersections with justice-related concerns. Guided by theories of critical data literacies and data justice, a historicized and future-oriented…

Although numerous survey-based studies have found that students who identify as lesbian, gay, bisexual, or questioning (LGBQ) have elevated risk for many negative academic, disciplinary, psychological, and health outcomes, the validity of the types of data on which these results rest have come under increased scrutiny. Over the past several years,…

This multiple case study examines the extent and ways in which leaders and administrators in Florida College System (FCS) institutions engage in distributed leadership through data sharing with frontline staff. Based on focus groups and individual interviews with administrators, faculty, and staff (659 participants) from 21 state colleges, we…

Big data from massive open online courses (MOOCs) have enabled researchers to examine learning processes at almost infinite levels of granularity. Yet, such data sets do not track every important element in the learning process. Many strategies that MOOC learners use to overcome learning challenges are not captured in clickstream and log data. In…

Research has shown that mathematical proficiency gaps are related to students' and schools' indicators of poverty, with fewer studies on neighborhood effects on achievement gaps. Although this literature has accounted for students' nesting within schools, so far, methodological constraints have not allowed researchers to formally account for…

Research consistently has found teachers' use of assessment data for instructional purposes challenging and inconsistent. To support teachers' use of data, we need to develop shared knowledge about how data are and can be used to advance teaching and learning. However, the literature on the specific actions teachers take is inconsistent, creating…

Currently, the state of California has dedicated much focus to reducing absenteeism in schools through the In School + On Track initiative, which revitalizes efforts made to keep accurate and informative attendance data. Additionally, absenteeism has been integrated into California's Local Control and Accountability Plan to monitor district…

Classroom quality measures, such as the Early Childhood Environment Rating Scale, Revised (ECERS-R), are widely used in research, practice, and policy. Increasingly, these uses have been for purposes not originally intended, such as contributing to consequential policy decisions. The current study adds to the recent evidence of problems with the…

Despite substantial federal, state, and local investments in improving early care and education (ECE), we know little about whether ECE program quality has improved over time. The lack of data tracking the quality of publicly funded ECE programs at scale creates a substantial evidence gap for policymakers attempting to weigh the returns on, and…

Districts make and implement policies aiming to improve structures, practices, and outcomes. Instructional coaching has become a popular lever to catalyze instructional improvement efforts. However, many questions remain about the alignment between coaching and reforms. This article draws on coupling theory to analyze the relationship between…

This mixed-methods observational study analyzes Advanced Placement (AP) Biology teachers' engagement in microblogging for professional learning. Data from three hashtag-based Twitter communities--#apbiochat, #apbioleaderacad, and #apbioleaderacademy (121 users; 2,253 tweets)--are analyzed using educational data mining, qualitative two-cycle…

The Stanford Educational Data Archive (SEDA) is the first data set to allow comparisons of district academic achievement and growth from Grades 3 to 8 across the United States, shining a light on the distribution of educational opportunities. This study describes a convergent validity analysis of the SEDA growth estimates in mathematics and…

Aggregate survey responses collected from students are commonly used by universities to compare effective educational practices across program majors, and to make high-stakes decisions about the effectiveness of programs. Yet if there is too much heterogeneity among student responses within programs, the program-level averages may not…

Does data use make a difference in student achievement? Despite the field's optimism on this matter, relatively few studies have attempted to quantify the effects of data use. These studies have often used the presence of a data use intervention (e.g., a data system or data coaching) as a proxy for use, as opposed to tracking teachers' direct…