University dropout is a phenomenon of growing interest due to its negative consequences. Various variables have been studied in order to understand why this problem occurs. Satisfaction with the degree choice, self-regulation strategies and engagement within the university are some of the variables that have been studied in order to understand why…

This paper investigates the student dropout phenomenon in a technical Italian university from a time-to-event perspective. Shared frailty Cox time-dependent models are applied to analyse the careers of students enrolled in different engineering programs with the aim of identifying the determinants of student dropout through time, predicting the…

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…

We examine the mechanisms explaining the dropout intentions of students with disabilities by integrating Tinto's model of student integration, the student attrition model, the composite persistence model, and insights from social stratification research. The resulting theoretical model posits that not only students' academic and social…

In recent years, the rapid growth of Massive Open Online Courses (MOOCs) has attracted much attention for related research. Besides, one of the main challenges in MOOCs is the high dropout or low completion rate. Early dropout prediction algorithms aim the educational institutes to retain the students for the related course. There are several…

Study success in Higher Education is of primary importance in the European policy agenda. Yet, given the diverse educational landscape across countries and institutions, more coordinated action is needed to gain a more solid knowledge of the dropout phenomenon. This study aims to gain a better insight into students' dropout based on an integrated…

High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…

Since the advent of massive open online courses (MOOC), it has been the focus of educators and learners around the world, however the high dropout rate of MOOC has had a serious negative impact on its popularity and promotion. How to effectively predict students' dropout status in MOOC for early intervention has become a hot topic in MOOC…

Based on the classic models developed by Spady and Tinto on the link between social and academic integration and dropout, we propose a refined model to explain dropout intentions--relating to dropout from higher education (HE) and dropout from a specific study programme--that more strongly emphasises individual background characteristics (e.g.…

Over 80,000 children in England were being looked after in Local Authority care in 2020 and a further 40,000 people were defined as 'care leavers'. Although a significant body of research highlights the prevalence of educational low achievement in the care experienced population, official government figures show that around 13% of care experienced…

The purpose of the present investigation is to shed light on the intraindividual (i.e., within-person) process of distancing from the goal of obtaining a PhD. Based on the motivational theory of action crisis, we assume that a lack of both individual (here: self-directed career management) and external (here: social support) resources may fuel…

The Institute of Medicine has found that barriers exist that directly contribute to the underrepresentation of racial and ethnic groups in health professional education. Yet, little research has been done to evaluate the barriers that affect athletic training students. An integrative review was conducted to identify barriers that affect students…

Early dropout of students is one of the bigger problems that universities face currently. Several machine learning techniques have been used for detecting students at risk of dropout. By using sociodemographic data and qualifications of the previous level, the accuracy of these predictive models is good enough for implementing retention programs.…

Dropout refers to the phenomenon of students leaving school before completing their degree or program of study. Dropout is a major concern for educational institutions, as it affects not only the students themselves but also the institutions' reputation and funding. Dropout can occur for a variety of reasons, including academic, financial,…

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…