E-learning students have a tendency to get demotivated and easily dropout from online courses. Refining the learners' involvement and reducing dropout rates in these e-learning based scenarios is the main drive of this study. This study also shares the results obtained and crafts a comparison with new and emerging commercial solutions. In a…

Students in distance education face high demands for self-regulation, within which goal focus can play varying roles. The current study examined the (in)congruence between outcome and process-focused goals, and whether the nature and degree of this (in)congruence are associated with academic engagement in distance higher education, considering the…

At a community college in the state of Florida, student retention has become a critical concern because more than half of the first-year first-generation students fail to graduate. That rate is four times higher than for other first-year students. Guided by Tinto's Student Integration Model as the conceptual framework, this intrinsic qualitative…

Ph.D. dropout is a persistent and challenging issue in higher education, with significant implications for individual students, academic institutions, and the broader society. This research paper aims to explore the factors contributing to Ph.D. students' decision to dropout and their interrelationships. For this purpose, we employed the hybrid…

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…

Within OECD countries, 20% of university students continue no further than the first year. The objective of this research is to analyse the antecedents of student commitment, so as to design action plans for reducing dropout rates within higher education. Educational engagement, student--university identification and perception of performance were…

There is less attention on examining algorithmic fairness in secondary education dropout predictions. Also, the inclusion of protected attributes in machine learning models remains a subject of debate. This study delves into the use of machine learning models for predicting high school dropouts, focusing on the role of protected attributes like…

School non-completion and non-completers' subsequent pathways into employment or back into education are enduring issues for policy-makers in Australia. Understanding the factors that predict a higher probability of leaving school before completing Year 12, as well as those that increase the chance of reengaging with education, can inform action…

University dropout is a phenomenon of growing interest in the knowledge society. However, it is a complex phenomenon in which a variety of academic and social variables interact with each other. Cyberbullying is also something that has been the subject of increased research recently, and although it has most often been studied in primary and…

The present study addressed an area of research not yet sufficiently investigated: the environmental sensitivity trait in relation to perceived marginalization in the classroom, dropout intentions, and the quality of teacher-student relationship. Adopting a psychosocial perspective and based on a single study survey with a group of Italian…

In this report, the authors analyze student attrition before the census date (which is the date by which community colleges count students as officially enrolled for each term) across community colleges in Texas, Kentucky, and California. They explore the magnitude of this issue, delve into the factors contributing to early student departures, and…

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…

Higher education dropout has been considered a major concern for several researchers in the field of education around the World. Although different studies were carried out to deal with the topic, they all faced common limitations. This paper explores the twofold research conducted to investigate higher education dropout in Education studies at…

Student dropout can be conceptualized as a decision-making process, consisting of different phases. Based on previous literature on student dropout, decision-making, and action-phases, we proposed that the process of developing dropout intentions includes the following phases: "non-fit perception," "thoughts of…