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Melisa Diaz Lema; Melvin Vooren; Marta Cannistrà; Chris van Klaveren; Tommaso Agasisti; Ilja Cornelisz – Studies in Higher Education, 2024
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…
Descriptors: Foreign Countries, Dropout Research, College Students, Dropouts
Dahir Abdi Ali; Ali Mohamud Hussein – Journal of Applied Research in Higher Education, 2024
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…
Descriptors: Foreign Countries, College Students, At Risk Students, Potential Dropouts
Andrea Zanellati; Stefano Pio Zingaro; Maurizio Gabbrielli – IEEE Transactions on Learning Technologies, 2024
Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an…
Descriptors: Dropouts, Dropout Characteristics, Potential Dropouts, Artificial Intelligence
Chuan Cai; Adam Fleischhacker – Journal of Educational Data Mining, 2024
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…
Descriptors: College Students, Student Attrition, Dropouts, Potential Dropouts
Roberts, Nicola – Journal of Further and Higher Education, 2023
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…
Descriptors: Predictor Variables, Academic Achievement, Academic Failure, College Freshmen
Castro-Lopez, Adrián; Cervero, Antonio; Galve-González, Celia; Puente, Javier; Bernardo, Ana B. – Higher Education Research and Development, 2022
University dropout is a phenomenon of growing interest due to the high financial costs that it involves for both families and states. Various variables have been studied in order to understand why this problem occurs. Satisfaction with the degree choice, study self-regulation and social adaptation within the university are some of the variables…
Descriptors: Higher Education, College Students, Student Satisfaction, Self Management
Geisler, Sebastian; Rolka, Katrin; Rach, Stefanie – Educational Studies in Mathematics, 2023
The transition from school to university mathematics is a challenging process for many students. This phenomenon is reflected by high dropout rates from mathematics programs especially during the first year at university that may be related to the development of students' mathematical interest and self-concept. Taking a learning psychological…
Descriptors: College Freshmen, Student Interests, Self Concept, College Mathematics
Solveig Cornér; Lotta Tikkanen; Henrika Anttila; Kirsi Pyhältö – Studies in Graduate and Postdoctoral Education, 2024
Purpose: This study aims to advance the understanding on individual variations in PhD candidates' personal interest in their doctorate and supervisory and research community support, and several individual and structural attributes potentially having an impact on the profiles. Design/methodology/approach: The authors explored the interrelationship…
Descriptors: Foreign Countries, Doctoral Students, Doctoral Programs, Student Motivation
Gallego, María Gómez; Perez de los Cobos, Alfonso Palazón; Gallego, Juan Cándido Gómez – Education Sciences, 2021
A main goal of the university institution should be to reduce the desertion of its students, in fact, the dropout rate constitutes a basic indicator in the accreditation processes of university centers. Thus, evaluating the cognitive functions and learning skills of students with an increased risk of academic failure can be useful for the adoption…
Descriptors: Identification, At Risk Students, Potential Dropouts, Cognitive Processes
Ajjawi, Rola; Dracup, Mary; Zacharias, Nadine; Bennett, Sue; Boud, David – Higher Education Research and Development, 2020
Academic failure is an important and personal event in the lives of university students, and the ways they make sense of experiences of failure matters for their persistence and future success. Academic failure contributes to attrition, yet the extent of this contribution and precipitating factors of failure are not well understood. To illuminate…
Descriptors: Academic Persistence, Academic Failure, Student Attitudes, Emotional Response
Jongile, Sonwabo – International Journal on E-Learning, 2022
The identification of predictor variables for students at-risk of dropping out of university has received increased attention in higher education settings internationally concerning the context of origin in which they are developed and the different academic context in which they are introduced, often lacking schema-theoretic perspectives to offer…
Descriptors: Predictor Variables, At Risk Students, Potential Dropouts, College Students
Cannistrà, Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria – Studies in Higher Education, 2022
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…
Descriptors: Dropouts, Potential Dropouts, Dropout Prevention, Dropout Characteristics
Arhin, Vera; Laryea, John Ekow – Open Praxis, 2020
The tutor's role in enhancing student retention in distance learning is paramount. This study aims to predict retention and not actual retention by investigating how tutoring support predicts student retention in distance learning at the University of Cape Coast in Ghana. Moore Transactional Distance Theory underpinned the theoretical framework of…
Descriptors: Tutoring, Tutors, Academic Support Services, Predictor Variables
Coleman, Shannon L. – ProQuest LLC, 2019
Online education has been experiencing steadily increasing enrollment rates and it is therefore vital to study student and institutional factors related to dropout risk for online students. Currently, prior research examining this rapidly developing field is limited. With online graduate programs experiencing continuous growth in enrollment rates,…
Descriptors: Graduate Students, Predictor Variables, Potential Dropouts, Online Courses
Alvarez, Niurys Lázaro; Callejas, Zoraida; Griol, David – Journal of Technology and Science Education, 2020
We present an educational data analytics case study aimed at the early detection of potential dropout in Computer Engineering studies in Cuba. We have employed institutional data of 456 students and performed several experiments for predicting their permanency into three (promotion, repetition, and dropout) or two classes (promoting, not…
Descriptors: Foreign Countries, College Students, Computer Science Education, Engineering Education
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