While there are many different types of education data, policymakers and education leaders often place heavy emphasis on data from large-scale quantitative measures, such as annual state assessments. But data from these sources alone do not provide a complete picture of learning and are often not well suited to informing improvements at the local…

Predictive analytics methods in education are seeing widespread use and are producing increasingly accurate predictions of students' outcomes. With the increased use of predictive analytics comes increasing concern about fairness for specific subgroups of the population. One approach that has been proposed to increase fairness is using demographic…

Early childhood research and policy have promoted the use of language and literacy assessment data to inform instruction. Yet, there is a limited understanding of preschool teachers' data practices and sensemaking, particularly when considered from the perspectives of practicing teachers. In this multicase study, we used a phenomenological…

Speculative reform jumps the gun on notions of data-driven reform, requiring administrators to anticipate and act to ensure problems (and the data that would show them) do not materialize. Speculative reforms are incapable of delivering the outcomes they promise, as they are fueled by a fear of the future that their reforms do not extinguish. In…

Computer-based learning environments (CBLEs) are powerful tools to support student learning. Increasingly of interest is the data that is recorded as learners interact with a CBLE. This "process data" yields opportunities for researchers to examine learners' engagement with a CBLE and explore whether specific interactions are associated…

The University of Missouri Kansas City (UMKC) is an urban research university with a special emphasis on fostering diversity. In 2021, the Center for Advancing Faculty Excellence created a Faculty Fellows program to develop comprehensive resources and support in four areas: teaching and learning, service and engagement, research and creativity,…

Teacher data use is often centered around standardized testing. Such data use that is commonly centered on standardized tests and used during professional development does not necessarily transform teacher practice toward equity and fails to change teacher conceptualizations of students while also perpetuating inequitable practices. Conversely,…

This article explores utilizing a post-graduation success lens to help community college leaders frame the challenges of achieving equitable improvement for their students. Specifically, it posits that providing and exploring customized labor market data presented in an accessible format can help institutional leaders provide a "true…

The continuous development of Educational Data Mining (EDM) and Learning Analytics (LA) technologies has provided more effective technical support for accurate early warning and interventions for student academic performance. However, the existing body of research on EDM and LA needs more empirical studies that provide feedback interventions, and…

For decades, assessing income has served as the tried-and-true method for creating financial aid packages--scholarships, grants, and loans--for the nation's college students. Each year, students and families living with low and moderate incomes submit income documentation to colleges, states, and the federal government in hopes of qualifying for…

The European Education Area aims to support Member States' efforts in enhancing the educational attainment of younger generations. In this policy context, there is a need for an objective tool to assess the educational outcomes of EU countries. The present report addresses this need by pursuing two objectives: (1) providing a comprehensive method…

Institutions of higher education are presented as vital pathways to economic mobility, particularly for working class, first generation, and students of color. Yet the inequitable outcomes produced by post-secondary institutions suggest structures and systems of exclusion and enclosure. In this critical mixed methods case study, we explore the…

Purpose: This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course. Design/methodology/approach: The exploration and visualisation of the…

The tremendous growth of educational institutions' electronic data provides the opportunity to extract information that can be used to predict students' overall success, predict students' dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students' needs, and much more. This paper aims to…

The focus of this study was on identifying patterns of student behavior to support data-informed decision-making which would then improve the learning experience and learning outcomes of online English language courses. Learning analytics approach (or more specifically cluster analysis) was used to identify engagement patterns in online learning.…