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Deeva, Galina; De Smedt, Johannes; De Weerdt, Jochen – IEEE Transactions on Learning Technologies, 2022
Due to the unprecedented growth in available data collected by e-learning platforms, including platforms used by massive open online course (MOOC) providers, important opportunities arise to structurally use these data for decision making and improvement of the educational offering. Student retention is a strategic task that can be supported by…
Descriptors: Electronic Learning, MOOCs, Dropouts, Prediction
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
José M. Ortiz-Lozano; Pilar Aparicio-Chueca; Xavier M. Triadó-Ivern; Jose Luis Arroyo-Barrigüete – Studies in Higher Education, 2024
Student dropout is a major concern in studies investigating retention strategies in higher education. This study identifies which variables are important to predict student dropout, using academic data from 3583 first-year students on the Business Administration (BA) degree at the University of Barcelona (Spain). The results indicate that two…
Descriptors: Dropouts, Predictor Variables, Social Sciences, Law Students
Pei, Bo; Xing, Wanli – Journal of Educational Computing Research, 2022
This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for…
Descriptors: At Risk Students, Learning Analytics, Information Retrieval, Models
Siebra, Clauirton Albuquerque; Santos, Ramon N.; Lino, Natasha C. Q. – International Journal of Distance Education Technologies, 2020
This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it…
Descriptors: Dropouts, Predictor Variables, At Risk Students, Distance Education
Adelman, Melissa; Haimovich, Francisco; Ham, Andres; Vazquez, Emmanuel – Education Economics, 2018
School dropout is a growing concern across Latin America because of its negative social and economic consequences. Identifying who is likely to drop out, and therefore could be targeted for interventions, is a well-studied prediction problem in countries with strong administrative data. In this paper, we use new data in Guatemala and Honduras to…
Descriptors: Foreign Countries, Dropouts, At Risk Students, Identification
Coleman, Chad; Baker, Ryan S.; Stephenson, Shonte – International Educational Data Mining Society, 2019
Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…
Descriptors: Prediction, At Risk Students, Predictor Variables, Elementary Secondary Education
Gottfried, Michael A.; Plasman, Jay Stratte – American Educational Research Journal, 2018
While prior studies have examined the efficacy of career and technical education (CTE) courses on high school students' outcomes, there is little knowledge on timing of these courses and a potential link to student outcomes. We asked if the timing of these courses predicted differences in the likelihood of dropout and on-time high school…
Descriptors: Vocational Education, Dropouts, College Bound Students, High School Students
Gumus, Sedat – International Review of Education, 2014
Turkey, like many developing countries, is facing considerable problems in terms of low school attendance rates, late enrolment and early dropout of girls in particular. Numerous studies have already been conducted, both in Turkey and elsewhere, to determine the factors affecting school enrolment of boys and girls. Existing studies in Turkey,…
Descriptors: Foreign Countries, Community Influence, Attendance, Enrollment
Abdel-Salam, Sami; Gunter, Whitney D. – Journal of Child & Adolescent Substance Abuse, 2014
The adolescent drug problem places a huge toll on society and a heavy burden on the criminal justice system. Research regarding the benefits of therapeutic community (TC) treatment for adolescents has shown it to be effective. Despite the ability of therapeutic communities to lower drug relapse and reduce criminality, a great deal remains unknown…
Descriptors: Adolescents, Therapy, Correlation, Predictor Variables
Dupéré, Véronique; Leventhal, Tama; Dion, Eric; Crosnoe, Robert; Archambault, Isabelle; Janosz, Michel – Review of Educational Research, 2015
High school dropout is commonly seen as the result of a long-term process of failure and disengagement. As useful as it is, this view has obscured the heterogeneity of pathways leading to dropout. Research suggests, for instance, that some students leave school not as a result of protracted difficulties but in response to situations that emerge…
Descriptors: High School Students, Stress Variables, Dropouts, Models
Goodman, Christie L., Ed. – Intercultural Development Research Association, 2021
This year's study is the 35th in a series of annual reports on trends in dropout and attrition rates in Texas public schools. The 2019-20 study builds on a series of studies by the Intercultural Development Research Association (IDRA) that track the number and percent of students in Texas who are lost from public school enrollment prior to…
Descriptors: Public Schools, Student Attrition, Dropout Rate, Educational Trends
Ferreira, Regardt J.; Buttell, Frederick P. – Research on Social Work Practice, 2016
Objective: The purpose of the study was to evaluate the psychosocial predictors of propensity for abusiveness among a large sample of women ordered into a 26-week batterer intervention program (BIP). Method: The study employed a nonequivalent, control group design (comparing program completers to dropouts) in a secondary analysis of 485 women.…
Descriptors: Females, Family Violence, Dropouts, Predictor Variables
Kerby, Molly B. – Journal of College Student Retention: Research, Theory & Practice, 2015
Theoretical models designed to predict whether students will persist or not have been valuable tools for retention efforts relative to the creation of services in academic and student affairs. Some of the early models attempted to explain and measure factors in the "college dropout process." For example, in his seminal work, Tinto…
Descriptors: Predictor Variables, Models, School Holding Power, Academic Persistence
Obade, Masela Anyango – ProQuest LLC, 2013
Despite the increase in their college enrollment, nontraditional students in U.S. postsecondary institutions are less likely to stay in college until they earn their degree. What could explain nontraditional student high attrition rates and overall success beyond what their demographic characteristics reveal? The purpose of this study was to…
Descriptors: Nontraditional Students, Academic Achievement, Academic Persistence, Online Surveys