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Xinhong Zhang; Xiangyu Wang; Jiayin Zhao; Boyan Zhang; Fan Zhang – IEEE Transactions on Education, 2024
Contribution: This study proposes a student dropout prediction model, named image convolutional and bi-directional temporal convolutional network (IC-BTCN), which makes dropout prediction for learners based on the learning clickstream data of students in massive open online courses (MOOCs) courses. Background: The MOOCs learning platform attracts…
Descriptors: MOOCs, Dropout Characteristics, Dropout Research, Predictor Variables
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Xiao Wen; Hu Juan – Interactive Learning Environments, 2024
To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows:…
Descriptors: MOOCs, Dropouts, Dropout Characteristics, Dropout Research
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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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Stefanie Findeisen; Alexander Brodsky; Christian Michaelis; Beatrice Schimmelpenningh; Jürgen Seifried – Empirical Research in Vocational Education and Training, 2024
Evidence on the extent to which dropout intention can serve as a valid predictor of dropout decisions remains scarce. This study first presents the results of a systematic literature review of 14 studies examining the relationship between dropout intention and actual dropout in post-secondary education (vocational education and training [VET] or…
Descriptors: At Risk Students, Intention, Dropouts, Predictor Variables
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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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Marcell Nagy; Roland Molontay – International Journal of Artificial Intelligence in Education, 2024
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…
Descriptors: Dropout Characteristics, Dropout Research, Intervention, At Risk Students
Deyonna M. Grant – ProQuest LLC, 2024
The achievement gaps between Black and white students in math and science have remained unchanged over the last decade. Additionally, disparities in academic achievement and academic outcomes, like dropout, have persisted across genders. Existing research has explored the intricacies of various forms of identity -- namely racial and gender…
Descriptors: Achievement Gap, Minority Group Students, Racial Differences, Gender Differences
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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
Nancy Montes; Fernanda Luna – UNESCO International Institute for Educational Planning, 2024
This article characterizes and reflects on the possible uses of early warning systems (hereafter, EWS) in the region as effective tools to support educational pathways, whenever they identify risks of dropout, difficulties for the achievement of substantive learning, and the possibility of organizing specific actions. This article was developed in…
Descriptors: Data Collection, Data Use, At Risk Students, Foreign Countries
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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
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Elisabeth Lackner – Teachers College Record, 2024
Background/Context: There is a misalignment in contemporary society between the assumption that all high school graduates can and should attend college and the lack of funding and support to make that happen. This is especially apparent at community colleges that enroll disproportionally low-income students, who deal with a variety of obstacles…
Descriptors: Community College Students, Access to Education, Student Characteristics, Dropout Characteristics
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Thao-Trang Huynh-Cam; Long-Sheng Chen; Tzu-Chuen Lu – Journal of Applied Research in Higher Education, 2025
Purpose: This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability. Design/methodology/approach: The real-world…
Descriptors: Foreign Countries, Undergraduate Students, At Risk Students, Dropout Characteristics