Early Warning Indicators in Education: Innovations, Uses, and Optimal Conditions for Effectiveness

Abstract

Background/Context The passage of the No Child Left Behind Act in 2002 and the Every Student Succeeds Act in 2015 spurred changes in the way educators use data. On the one hand, the policies inspired educators’ awareness of large gaps in achievement between subgroups based on gender, race, and socioeconomic status. On the other hand, the policies inspired the use of data-based indicators in day-to-day routines in schools. In some cases, practitioners started working with researchers to analyze data using advanced measurement techniques to develop “early warning indicators.” These indicators identify when students are at risk of not achieving key milestones, such as graduating from high school, in hopes of providing practitioners with time to intervene early enough to change the students’ trajectories. Educational leaders have created “early warning systems” that produce reports summarizing these indicators, provide a process for the leaders to organize interventions based on the indicator reports, and a further process to examine whether students are back on track toward milestones such as high school graduation as a result of the intervention. Given the pace at which the science and use of these indicators is advancing, the topic is in need of a new resource explaining the most up-to-date elements involved with early warning indicators. Purpose/Focus of the Study This article will describe the evolution of the research, use, and conditions that influence the use of early warning indicators. The review of the literature includes a description of each of the yearbook articles and how they summarize some of the most recent advancements in early warning indicators in education. Research Design This is a review of literature. Findings As indicators in education have improved, these indicators show practitioners when students are not on track to reach certain milestones and provide practitioners with time to intervene early enough to change the child's trajectory. Allensworth and other authors (e.g., Balfanz et al.; Faria et al.; and Soland) provide evidence that these early warning indicators are impactful when used by practitioners in certain ways. Earlier efforts around early warning indicators focused on identifying measures that were predictive of key outcomes, such as high school graduation. As efforts to use indicators in practice became more mature, the focus expanded from providing information through indicators and information systems to ensuring that practitioners had the capacity and opportunity to engage in indicator use to guide their daily practice, and that the conditions in schools, school districts, and states supported indicator use. More recently, methodological advances around data analytics and a growing interest in social–emotional learning have expanded the approach and scope of indicators and their use. That said, the research on the nature and use of these indicators needs further documentation to keep up with the rapid pace at which the indicators are advancing and becoming more precise. The field still lacks a robust research base on how to use the indicators to improve practices and policies, and what conditions support the development and use of the indicators in education. Conclusions/Recommendations We will explain how the studies in this yearbook provide more up-to-date information on measurement related to the indicators, on the influence of certain approaches to using the indicators, as well as research on how conditions interact with the development and use of the indicators. The article demonstrates an important slice of what the field knows, as well as challenges and opportunities for further work to improve the development and use of early warning indicators.

Learning at the Boundaries of Research and Practice: A Framework for Understanding Research–Practice Partnerships

“…Knowledge developed within an RPP can have a broad reach, as when routines, strategies, or interventions developed in one RPP spread to other settings. For example, the idea of an early warning indicator system, initially developed by the University of Chicago Consortium on School Research and Chicago Public Schools, has been adapted in multiple locales, including Philadelphia, New York, and Baltimore (Wentworth & Nagaoka, 2020).…”

A Method of Early Warning for Course Learning Based on SMOTE and OCSVM

“…After combing and analyzing the literature at home and abroad, we find that most of the literature starts from the field of education, takes learners as the center, and finds the problems in the learning process through the learning process data [5][6][7][8]. Data mining, machine learning, and deep learning technologies are the most widely used methods in early warning for learning [9][10][11]. However, the existing studies mostly elaborate the use of machine learning or deep learning to design learning early warning models from a macro perspective.…”

Research on the Construction and Application of Dynamic Early Warning Mechanism of Student Civics in Colleges and Universities

“…The importance of mining the use of student management mechanism of the ideological and political education function, the content and concept of ideological and political education and student management early warning mechanism cleverly integrated, the use of flexible, harmonious content of ideological and political education on the rigid student management early warning mechanism to soften the implementation of rigid and flexible management of students, from the ideological and behavioral college students to do a good job of guiding the work, prevention, and reduction of campus emergencies. The occurrence of campus emergencies, so as to maintain the stability and harmony of the campus [7][8].…”