Early warning systems (EWS) using analytical tools that have been trained against prior years' data, can reliably predict dropout risk in individual students so that educators may intervene early to help avert this from happening. Risk profiles for dropouts aren't always useful since students often do not conform to the profiles. Researchers with…

In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of first-year progression outcomes, and whether this data is more predictive than personal data (such as gender and prior achievement). Using a machine learning approach, results from a sample of…

Objective: Accelerated Second-Degree (ASD) programs play a crucial role in addressing the nursing shortage. However, when ASD students drop out, it negatively impacts all involved parties. Despite facing similar challenges as adult learners, some ASD students successfully graduate while others do not. By comparing and contrasting these two groups,…

Students in the foster care system tend to have lower educational outcomes than their peers, including more frequent disciplinary events. However, few studies have explored how transitions into and out of foster care placements are associated with educational outcomes. Using longitudinal data from four California school districts, this study…

Evidence on the extent to which dropout intention can serve as a valid predictor of dropout decisions remains scarce. This study first presents the results of a systematic literature review of 14 studies examining the relationship between dropout intention and actual dropout in post-secondary education (vocational education and training [VET] or…

Purpose: The main purpose of this study is to evaluate the extent of dropout students and identify the relationship between risk factors of dropout and the survival time of students. Design/methodology/approach: The Kaplan-Meier estimator (KM), also known as the product-limit technique, is a nonparametric model function that is commonly used in…

Many U.S. schools utilize grade retention (repeating grades when not meeting academic benchmarks) to allow more time for students to learn grade level material. However, some research suggests retention may increase inequalities and not help students progress. We use national data (Future of Families and Child Wellbeing Study 2014-2017) and…

Introduction: The current study examined the extent to which static and dynamic measures of vocabulary and word learning predicted response and identified poor responders to a vocabulary intervention. Methods: Participants were 46 preschool children in classrooms randomly assigned to complete the Story Friends intervention in two…

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…

Student drop-out is one of the most burning issues in STEM higher education, which induces considerable social and economic costs. Using machine learning tools for the early identification of students at risk of dropping out has gained a lot of interest recently. However, there has been little discussion on dropout prediction using interpretable…

This quantitative based applied research study examined data collected fromstudents who have withdrawnfromor completed aneducator preparation program (EPP) ina small rural public community college in WestVirginia. This study compared studentretention rates with Frontier andRemote (FAR) designation by home zip code. These data informedthe research…

We investigated changes in youths' intentions to quit school after following a group-based cognitive behaviour therapy (CBT) based intervention for depressed adolescents in upper secondary school: the Adolescent Coping with Depression Course (ACDC). Data were collected from 228 youths, 133 of whom received the 14-week ACDC intervention and 95 who…

Predicting academic performance for students majoring in computer science has long been a significant field of research in computing education. Previous studies described that accurate prediction of students' early-stage performance could identify low-performing students and take corrective action to improve performance. Besides, adopting machine…

In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a…

Students learn not only directly from their teachers and books, but also by using their computers, tablets, and phones. Monitoring these learning environments creates new opportunities for teachers to track students' progress. In particular, this article is based on gathering real-time events as students interact with learning tools and materials…