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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
Kevin Thomas Caffrey; Fredelito Yvan M. Tugas; Ricardo Clauden-Cross; Tiyacca Simms-Jones – ProQuest LLC, 2022
The Division of Student Engagement and Enrollment Services at Old Dominion University (ODU) submitted a Request for Assistance to examine the challenges sophomore students face that can lead to attrition. A doctoral research team conducted an exploratory, sequential, mixed-methods study consisting of a literature review, focus groups and…
Descriptors: College Students, Student Attrition, Barriers, Student Experience
Gontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S. – Interactive Learning Environments, 2022
Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure,…
Descriptors: Student Attrition, Distance Education, At Risk Students, Achievement
Gkontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S. – Interactive Learning Environments, 2022
Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze, and predict students' failure,…
Descriptors: Student Attrition, Distance Education, At Risk Students, Achievement
Clare Buckley Flack; John Sludden; James J. Kemple – Research Alliance for New York City Schools, 2024
There is currently a heavy emphasis on career-connected learning for high school students in New York City. ExpandED Schools' science, technology, engineering and mathematics (STEM) Options (ES Options) program predates the newest of these initiatives. Launched in 2019, ES Options combines a credit-bearing STEM apprenticeship in the spring with a…
Descriptors: After School Programs, Work Experience Programs, STEM Education, Student Participation
Fahd, Kiran; Venkatraman, Sitalakshmi; Miah, Shah J.; Ahmed, Khandakar – Education and Information Technologies, 2022
Recently, machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on…
Descriptors: Artificial Intelligence, Electronic Learning, Higher Education, Academic Achievement
Rosser-Majors, Michelle L.; Rebeor, Sandra; McMahon, Christine; Wilson, Andrea; Stubbs, Stephanie L.; Harper, Yolanda; Sliwinski, Laura – Online Learning, 2022
Considerable research on effective instruction in the virtual classroom exists. Yet very little is known about the extent to which instructor presence (IP) based on the Community of Inquiry model (CoI), are directly related to retention and student success. CoI includes three components of IP: teaching (TP), cognitive (CP), and social (SP). These…
Descriptors: Instructional Effectiveness, Online Courses, Teacher Student Relationship, Teacher Influence
Kemda, Lionel Establet; Murray, Michael – International Journal of Higher Education, 2021
Within students' attrition studies, it is necessary to assess the longitudinal evolution of students within a given course of study, from enrolment to exit from the university through degree completion and academic dropout. Here, the student's academic progress is monitored through the number of courses failed each semester enrolled. The students'…
Descriptors: Academic Failure, Student Behavior, College Students, Student Attrition
Radovan, Marko – Turkish Online Journal of Distance Education, 2019
Supporters of distance education highlight the many advantages of online learning as compared to face-toface education, such as greater openness, diversity of teaching materials, adjustment to student learning styles, the speed of learning, and more. Despite the advantages, the growing number of programs, and the increased acceptance of distance…
Descriptors: School Holding Power, Models, Distance Education, Electronic Learning
Burke, Adam – College and University, 2019
This literature review examines student retention in higher education institutions. Specifically, it looks at the background and history of student retention, three student retention theories, and current literature on student retention within the social system. The three theories are Spady's (1970, 1971) Undergraduate Dropout Process Model,…
Descriptors: School Holding Power, Models, Higher Education, Learner Engagement
Quille, Keith; Bergin, Susan – Computer Science Education, 2019
Background and Context: Computer Science attrition rates (in the western world) are very concerning, with a large number of students failing to progress each year. It is well acknowledged that a significant factor of this attrition, is the students' difficulty to master the introductory programming module, often referred to as CS1. Objective: The…
Descriptors: Computer Science Education, Introductory Courses, Programming, Student Attrition
Burns, Ellie Mae – ProQuest LLC, 2018
Attrition at any level of post-secondary education is costly to the institution and the students. At the doctoral level, students are often funding their education through personal finances while balancing the demands of their careers and families. The time to complete the doctoral degree is growing steadily and 40-60% of students are making the…
Descriptors: School Holding Power, Graduate Students, Doctoral Programs, Models
Manyanga, Fidelis; Sithole, Alec; Hanson, Shawn M. – Journal of Applied Learning in Higher Education, 2017
Student retention and completion rates are challenging issues in higher education. In the academic domain, pressure exists for every institution to come up with strategies that support student success from enrollment through graduation without compromising academic or accreditation standards. This paper presents the findings from a review of…
Descriptors: Comparative Analysis, School Holding Power, Models, Undergraduate Study
Arnold, Karen D.; Mihut, Georgiana – Teachers College Record, 2020
Context: Educational reform efforts have taken the form of different school models intended to reduce educational inequality. Personalized, interest-based schools and academically focused, "No Excuses" schools are two leading small-school designs with sharply contrasting approaches to innovation. Given mixed research findings about the…
Descriptors: Educational Innovation, High Schools, Equal Education, Models
Kopel, Jaclyn – ProQuest LLC, 2018
A private, not-for-profit, 4-year urban university had been struggling to improve its 1st-year retention rate despite conducting previous studies and implementing various initiatives. This study explored the influence that students' personal connections to the study site had on their experience in their 1st year in college. Tinto's student…
Descriptors: College Freshmen, Student Experience, Student Attrition, Student Participation