Understanding student engagement with the institution from the first day of classes to the end of the semester would help inform the institution of the potential risk that a student will drop out of a class or of the school. Learning Management Systems (LMS) record student interactions with the system and might be able to be used to identify…

Dropout is one of the major problems online higher education faces. Early identification of the dropout risk level and an intervention mechanism to revert the potential risk have been proved as the key answers to solving the challenge. Predictive modeling has been extensively studied on course dropout. However, intervention practices are scarce,…

Course dropout is a concern in online higher education, mainly in first-year courses when different factors negatively influence the learners' engagement leading to an unsuccessful outcome or even dropping out from the university. The early identification of such potential at-risk learners is the key to intervening and trying to help them before…

Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for…

U.S. Department of Education research indicates that early warning indicator systems are being used in at least half of high schools in the United States. Previous findings from an efficacy study of one early warning indicator and response system, the Early Warning Indicator (EWI) team model, indicated that ninth grade students in schools using…

Emphasis in school dropout literature has shifted from exploring wide-ranging causes of dropping out to soliciting a smaller number of predictive indicators to identify students at increased risk for dropping out. However, much of the past decade's Early Warning research excludes indicators that do not add to the predictive nature of the model…

Research, nonprofit, and philanthropic organizations have increasingly had a larger influence in public educational institutions as they support particular programs, practices, and policy reforms. This study investigates the case of local organizations that have initiated and institutionalized high school dropout prediction systems called ninth…

This study focuses on the implementation of an early warning indicator and intervention system in 20 southern high schools. This model included a team of teachers, counselors, and student support services personnel who analyzed ninth-grade student-level data and implemented and monitored interventions. The team was led by a half-time coach who…

This study focuses on the implementation of an early warning indicator and intervention system in 20 southern high schools. This model included a team of teachers, counselors, and student support services personnel who analyzed ninth grade student-level data and implemented and monitored interventions. The team was led by a half-time coach who…

Higher education institutions are increasingly seeking technological solutions to not only enhance the learning environment but also support students. In this study, we explored the case of an early alert system (EAS) at a regional university engaged in both on-campus and online teaching. Using a total of 16,142 observations captured between 2011…

Grade retention and early dropout are two of the biggest challenges facing education systems in middle-income countries today, representing waste in school resources. We investigate whether reducing parent-school information gaps can improve outcomes that are early-warning signals for grade retention and dropout. We conducted an experiment in…

Early warning systems (EWS), in the context of secondary transition, are tools that analyze individual student-level data and estimate each student's risk of dropping out of school or completing school on time. Such tools generally consider three primary types of data--commonly referred to as the A, B, Cs: attendance/absence data,…

Ninth grade is a critical juncture for students--and can be a jarring transition. Even students a strong track record in the middle grades can experience academic difficulty, and those who enter high school with poor course grades, weak attendance, or behavior problems are especially at risk. An early misstep can have lasting implications:…

Although high school graduation rates continue to rise in the United States, reaching 81 percent in the 2012-2013 school year (U.S. Department of Education, 2015), dropout remains a pervasive issue for education systems across the nation. In recent years, Early Warning Systems (EWS), which utilize administrative data to identify students at risk…

The purpose of this guide is to provide information on how to use early warning data, including the Massachusetts Early Warning Indicator System (EWIS), to identify, diagnose, support and monitor students in grades 1-12. It offers educators an overview of EWIS and how to effectively use these data in conjunction with local data by following a…