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Chiara Masci; Marta Cannistrà; Paola Mussida – Studies in Higher Education, 2024
This paper investigates the student dropout phenomenon in a technical Italian university from a time-to-event perspective. Shared frailty Cox time-dependent models are applied to analyse the careers of students enrolled in different engineering programs with the aim of identifying the determinants of student dropout through time, predicting the…
Descriptors: Foreign Countries, Dropouts, Dropout Prevention, Potential Dropouts
Jason Willard King – ProQuest LLC, 2024
The Great Mountain High School (GMHS) started a program to help support students at risk for not graduating high school. The focus of this study was to provide a formative program evaluation of the created program that (a) investigated the fidelity of implementation of the activities and processes of the program, (b) gathered an understanding of…
Descriptors: High School Seniors, Grade 12, At Risk Students, Dropout Prevention
Josep Figueroa-Cañas; Teresa Sancho-Vinuesa – Open Learning, 2024
Practitioners of the statistics course embedded in a computer science programme at a fully online university were concerned with the high dropout rate. In the academic year 2018-19, they decided to carry out a two-phase project in order to address this issue. In the first phase, an early classifier to identify students at risk of dropping out of…
Descriptors: Foreign Countries, College Students, Virtual Schools, Online Courses
Plak, Simone; Cornelisz, Ilja; Meeter, Martijn; van Klaveren, Chris – Higher Education Quarterly, 2022
Early Warning Systems (EWS) in higher education accommodate student counsellors by identifying at-risk students and allow them to intervene in a timely manner to prevent student dropout. This study evaluates an EWS that shares student-specific risk information with student counsellors, which was implemented at a large Dutch university. A…
Descriptors: At Risk Students, Identification, Counseling, Foreign Countries
Roger Sheng So – ProQuest LLC, 2024
Understanding student engagement with the institution from the first day of classes to the end of the semester would help inform the institution of the potential risk that a student will drop out of a class or of the school. Learning Management Systems (LMS) record student interactions with the system and might be able to be used to identify…
Descriptors: Learning Management Systems, Data Use, At Risk Students, Learner Engagement
Talamás-Carvajal, Juan Andrés; Ceballos, Héctor G. – Education and Information Technologies, 2023
Early dropout of students is one of the bigger problems that universities face currently. Several machine learning techniques have been used for detecting students at risk of dropout. By using sociodemographic data and qualifications of the previous level, the accuracy of these predictive models is good enough for implementing retention programs.…
Descriptors: College Students, Dropout Prevention, At Risk Students, Identification
Sanaa Shehayeb; Eman Shaaban – International Society for Technology, Education, and Science, 2023
Every year around 1.2 million students drop out of school in the US. According to a UNICEF report enrollment in educational institutions in Lebanon dropped from 60% in 2020-2021 to 43% in 2021-2022. The National Dropout Prevention Center (NDPC) at Clemson University has identified an extensive set of risk factors organized into four domains:…
Descriptors: Foreign Countries, High School Students, Dropouts, Dropout Attitudes
Cem Recai Çirak; Hakan Akilli; Yeliz Ekinci – Higher Education Quarterly, 2024
In this study, an early warning system predicting first-year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first-year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental…
Descriptors: Program Development, At Risk Students, Identification, College Freshmen
Baneres, David; Rodriguez-Gonzalez, M. Elena; Guerrero-Roldan, Ana Elena – IEEE Transactions on Learning Technologies, 2023
Course dropout is a concern in online higher education, mainly in first-year courses when different factors negatively influence the learners' engagement leading to an unsuccessful outcome or even dropping out from the university. The early identification of such potential at-risk learners is the key to intervening and trying to help them before…
Descriptors: Prediction, Models, Identification, Potential Dropouts
Eegdeman, Irene; Cornelisz, Ilja; Meeter, Martijn; van Klaveren, Chris – Education Economics, 2023
Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for…
Descriptors: Foreign Countries, Vocational Schools, Dropout Characteristics, Dropout Prevention
Shiao, Yi-Tzone; Chen, Cheng-Huan; Wu, Ke-Fei; Chen, Bae-Ling; Chou, Yu-Hui; Wu, Trong-Neng – Smart Learning Environments, 2023
In recent years, initiatives and the resulting application of precision education have been applied with increasing frequency in Taiwan; the accompanying discourse has focused on identifying potential applications for artificial intelligence and how to use learning analytics to improve teaching quality and learning outcomes. This study used the…
Descriptors: Foreign Countries, Dropout Prevention, Models, Sustainability
Oda Charlotte Larsen Saetre; Serap Keles; Thormod Idsoe – Scandinavian Journal of Educational Research, 2024
We investigated changes in youths' intentions to quit school after following a group-based cognitive behaviour therapy (CBT) based intervention for depressed adolescents in upper secondary school: the Adolescent Coping with Depression Course (ACDC). Data were collected from 228 youths, 133 of whom received the 14-week ACDC intervention and 95 who…
Descriptors: Depression (Psychology), Correlation, Intention, Dropouts
Kaara Lydner – ProQuest LLC, 2022
Dropping out of school before completing secondary education for special children is a significant problem in the world and particularly in America. Although high school dropout in America has been a major problem for more than two decades, it was not among the priorities of the policies of the 90s probably because it was thought that they would…
Descriptors: Dropout Prevention, High School Students, Students with Disabilities, Role of Education
Sherma Fleming – ProQuest LLC, 2023
The number of students choosing to drop out of school before earning their high school diploma continues to rise at alarming numbers. These students cause educators to question what strategies can assist struggling learners with academic, behavioral, or health issues to increase their chances of completing high school. Many dropout prevention…
Descriptors: High School Students, Nontraditional Education, Nontraditional Students, Dropout Prevention
Kerstin Wagner; Agathe Merceron; Petra Sauer; Niels Pinkwart – Journal of Educational Data Mining, 2024
In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a…
Descriptors: At Risk Students, Algorithms, Foreign Countries, Course Selection (Students)