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Showing 1 to 15 of 38 results Save | Export
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Robin Clausen – Grantee Submission, 2024
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…
Descriptors: Early Intervention, Predictor Variables, Potential Dropouts, At Risk Students
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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
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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
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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
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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
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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
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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
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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
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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
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Fandrem, Hildegunn; Tvedt, Maren Stabel; Virtanen, Tuomo; Bru, Edvin – Social Psychology of Education: An International Journal, 2021
Dropout from upper secondary education is a persistent educational problem, particularly among first-generation immigrant youth. This study examined factors associated with intentions to dropout to gain further insight into the process of leaving upper secondary education. The analyses of 1299 Norwegian first-year upper secondary school students'…
Descriptors: Intention, Potential Dropouts, Secondary School Students, Immigrants
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Soland, James; Domingue, Benjamin; Lang, David – Teachers College Record, 2020
Background/Context: Early warning indicators (EWI) are often used by states and districts to identify students who are not on track to finish high school, and provide supports/interventions to increase the odds the student will graduate. While EWI are diverse in terms of the academic behaviors they capture, research suggests that indicators like…
Descriptors: Identification, At Risk Students, Potential Dropouts, High School Students
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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
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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
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Odiel Estrada-Molina; Juanjo Mena; Alexander López-Padrón – International Review of Research in Open and Distributed Learning, 2024
No records of systematic reviews focused on deep learning in open learning have been found, although there has been some focus on other areas of machine learning. Through a systematic review, this study aimed to determine the trends, applied computational techniques, and areas of educational use of deep learning in open learning. The PRISMA…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Open Education, Educational Trends
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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
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