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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
Mendes Vieira, Kelmara; Bender Filho, Reisoli; Da Silva Costa Junior, Elizeu; Martins Santos, Gilberto – Turkish Online Journal of Distance Education, 2023
This research seeks to understand the determinants of student dropout in the courses offered at the Open University of Brazil system at the Federal University of Santa Maria. The research used the following methods: survival function, factorial analysis, and logistic function. Results indicated that male students, with higher levels of income, who…
Descriptors: Distance Education, Dropouts, Foreign Countries, College Students
Wild, Steffen; Rahn, Sebastian; Meyer, Thomas – European Journal of Psychology of Education, 2023
Student dropout in higher education is a challenge for higher education systems. In recent years, there has been an increasing focus on analyzing motivational aspects in order to counteract dropout. However, the detailed impact mechanisms and processes of motivation on dropout have not been sufficiently researched. For example, there is very…
Descriptors: Foreign Countries, College Students, Cooperative Education, Dropout Characteristics
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
Naaman, Hind – European Educational Researcher, 2021
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…
Descriptors: Dropouts, Dropout Research, Foreign Countries, College Students
Ntema, Ratoeba Piet – Journal of Student Affairs in Africa, 2022
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…
Descriptors: College Students, Dropout Characteristics, At Risk Students, Profiles
Edwin Buenaño; María José Beletanga; Mónica Mancheno – Journal of Latinos and Education, 2024
University dropout is a serious problem in higher education that is increasingly gaining importance, as it is essential to understand its causes and search for public and institutional policies that can help reduce it. This research uses conventional and extended Cox survival models to analyze the factors behind dropout rates at a co-financed…
Descriptors: Foreign Countries, College Students, Dropouts, Dropout Rate
Alfredo Guzmán Rincón; Pedro Aurelio Sotomayor Soloaga; Ruby Lorena Carrillo Barbosa; Sandra Patricia Barragán-Moreno – Cogent Education, 2024
Enrollment in online higher education (OHE) programs has witnessed a substantial increase, owing to the benefits and added value it offers students. However, one of the main challenges in this educational modality is attrition. While research on attrition in online settings is plentiful, studies on student satisfaction with higher education…
Descriptors: Student Satisfaction, College Students, Predictor Variables, Intention
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
Musso, Mariel F.; Hernández, Carlos Felipe Rodríguez; Cascallar, Eduardo C. – Higher Education: The International Journal of Higher Education Research, 2020
Predicting and understanding different key outcomes in a student's academic trajectory such as grade point average, academic retention, and degree completion would allow targeted intervention programs in higher education. Most of the predictive models developed for those key outcomes have been based on traditional methodological approaches.…
Descriptors: Classification, Prediction, Artificial Intelligence, College Students
Gansemer-Topf, Ann M.; Zhang, Yi; Beatty, Cameron C.; Paja, Scott – Journal of Student Affairs Research and Practice, 2014
Despite a diverse body of literature on college student retention, studies focusing on small, private, selective liberal arts colleges are limited. This study utilized a mixed methodology beginning with logistic regression analyses and followed with a qualitative inquiry that included interviews with students who had not persisted. While variables…
Descriptors: Liberal Arts, College Students, Regression (Statistics), Mixed Methods Research
Beck, Hall P.; Davidson, William B. – Journal of The First-Year Experience & Students in Transition, 2015
This investigation sought to determine when colleges should conduct assessments to identify first-year students at risk of dropping out. Thirty-five variables were used to predict the persistence of 2,024 first-year students from four universities in the southeastern United States. The predictors were subdivided into groups according to when they…
Descriptors: College Students, College Freshmen, Higher Education, School Holding Power
Hailikari, Telle Katriina; Nevgi, Anne – International Journal of Science Education, 2010
This study explores the relationship between different types of prior knowledge and student achievement in an introductory chemistry course. Student achievement was regarded as the pace of completing the course as well as the final grade. A model of prior knowledge is proposed; this distinguishes between different types of prior knowledge and…
Descriptors: Foreign Countries, College Students, Introductory Courses, Chemistry
Lampropoulos, Georgios K.; Schneider, Mercedes K.; Spengler, Paul M. – Journal of Counseling & Development, 2009
Despite the existence of counseling dropout research, there are limited predictive data for counseling in training clinics. Potential predictor variables were investigated in this archival study of 380 client files in a university counseling training clinic. Multinomial logistic regression, predictive discriminant analysis, and classification and…
Descriptors: Dropout Research, Dropouts, Predictor Variables, Discriminant Analysis
Jorgensen, Shirley; Fichten, Catherine; Havel, Alice – Online Submission, 2009
The main aim of this study was to gain a better understanding of why students abandon their studies, or perform less well than expected given their high school grades, and to develop predictive models that can help identify those students most at-risk at the time they enter college. This will allow teachers and those responsible for student…
Descriptors: High School Students, Grades (Scholastic), Academic Failure, Profiles
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