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…

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…

Cooperative education programs are usually based on a partnership between companies and universities. Dropouts have a particular impact here, for example the loss of junior staff in the companies. Most dropouts in cooperative education occur in the first academic year. In this multicausal dropout process, the influence of the cooperation partner…

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…

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…

In recent times, Educational Data Mining and Learning Analytics have been abundantly used to model decision-making to improve teaching/learning ecosystems. However, the adaptation of student models in different domains/courses needs a balance between the generalization and context specificity to reduce the redundancy in creating domain-specific…

This paper sought to examine psychological grit, defined as passion and perseverance for long-term goals, and its efficacy in determining postgraduate retention among historically disadvantaged students enrolled at the University of South Africa (UNISA). The Grit-S scale was used to gauge the level of grit among the participants (n = 594) followed…

This study uses a logistic regression model to analyze survey data (n = 341) and predict factors influencing online course success for underserved and academically at-risk undergraduate students at a small, broad access, four-year, public Midwestern university. Three blocks of predictor variables, demographic (first generation, low income,…

Student dropout is a significant concern for university administrators, students and other stakeholders. Dropout is recognised as highly complex due to its multi-causality, which is expressed in the existing relationship in its explanatory variables associated with students, their socio-economic and academic conditions, and the characteristics of…

Globally, statistical analyses have found a range of variables that predict the odds of first-year students failing to progress at their Higher Education Institution (HEI). Some of these studies have included students from a range of disciplines. Yet despite the rise in the number of criminology students in HEIs in the UK, little statistical…

Students with disabilities have higher rates of attention problems than those without disabilities. This can impede their academic success and postsecondary transition, but these effects have not been well-studied. Understanding these effects is especially critical among high school girls with disabilities who additionally experience significant…