Although using machine learning for predicting which students are at risk of failing a course is indeed valuable, how can we identify which characteristics of individual students contribute to their being At-Risk? By characterising individual At-Risk students we could potentially advise on specific interventions or ways to reduce their probability…

Danish schools offering "preparatory basic education and training" (FGU schools) have students that are characterized by having different academic, social, or personal problems. In addition, many FGU students are at high risk of suicidal behavior. Many young people with suicide behavior do not seek help and early identification is…

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…

Preschool children considered at risk for future reading difficulties experience unique and complex combinations of risk factors. In this exploratory study, we used latent profile analysis (LPA) to investigate the underlying classifications of children identified as at-risk for reading difficulties (N = 281) along selected cognitive,…

Preschool children considered at risk for future reading difficulties experience unique and complex combinations of risk factors. In this exploratory study, we used latent profile analysis (LPA) to investigate the underlying classifications of children identified as at-risk for reading difficulties (N = 281) along selected cognitive,…

The anticipation, inclusion, responsiveness, and reflexivity (AIRR) framework (Stilgoe et al., 2013) is a novel framework that has helped those in science and technology fields shift their focus from products to the processes used to create those products. However, the framework has not been known to be applied to the development and…

We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most…

Regular attendance is integral for students' academic success; it also affects adolescents' physical and mental health. Very few studies consider (a) differences between partial- and full-day absences regarding chronic absenteeism (CA; missing school [greater than or equal to]15 days in an academic year); or (b) roles of school nurses in…

Early identification of schools with a high percentage of students at risk of learning poverty is crucial for effective and targeted interventions. This study investigates the use of an innovative combination of large-scale administrative datasets and advanced statistical techniques to predict schools at risk of learning poverty in Italy in the…

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…

This cluster-randomised study investigated the effects of a Norwegian early warning system, the IKO model. IKO is a Norwegian acronym for identification, assessment, and follow-up, and the model aims to improve schools' abilities to identify and support students who are at risk of dropping out during the school year. The study involved 7677…

State laws that mandate in-grade retention for struggling readers are widespread in the U.S., covering 34% of public-school third graders in 2023-24. This study investigates the impacts of Michigan's third-grade reading law on subsequent test scores and school progress outcomes for the 2020-21 and 2021-22 third-grade cohorts. Using a regression…

The collection of student data through screening and progress monitoring of social and emotional learning (SEL) skills is just as important as the implementation of curriculum and practices. Monitoring skill acquisition allows teachers to identify effective practices, provide intervention, and intensify support for students who need it. In this…

Over 1.5 million students in the United States experience homelessness. These students are entitled to educational support through the Education for Homeless Children and Youth program. However, many homeless students are not identified and therefore never receive this support. Across 1,732 local education agencies in New Jersey, New Mexico, and…