A Real Time Predictive Model for Identifying Course Dropout in Online Higher Education

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 t...

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Bibliographic Details
Published in:IEEE Transactions on Learning Technologies 2023-08, Vol.16 (4), p.1-16
Main Authors: Baneres, David, Rodriguez-Gonzalez, M. Elena, Guerrero-Roldan, Ana Elena
Format: Article
Language:English
Subjects:
Accuracy
Adaptive Learning System
Analytical models
At Risk Students
At-risk Learner
CAI
College Freshmen
College students
Colleges & universities
Complexity theory
Computer assisted instruction
Course Dropout
Distance learning
Distance measurement
Dropout Prevention
Dropouts
Education
Electronic Learning
First-year Learner
Higher Education
Identification
Intelligent System
Intelligent systems
Intervention
Online Courses
Online Learning
Potential Dropouts
Prediction
Prediction models
Predictive Methods
Predictive models
Probability
Real time
Real-time systems
Risk
Technological innovation
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Summary: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 they decide to drop out. This paper focuses on this challenging problem by providing a predictive dropout model with distinctive characteristics from previous approaches. First, the identification is in real time by providing a daily dropout prediction. Second, a temporal window of variable size is defined to evaluate the likelihood of being a dropout learner at the activity level. Such contributions will serve as a basis for designing and applying intervention mechanisms to reverse the course dropout at-risk situation. The predictive model and the temporal window have been evaluated on data from an authentic online learning setting in two first-year undergraduate courses. We show the accuracy of correctly identifying at-risk learners within activities and the model performance to detect actual course dropout learners.
ISSN:1939-1382
2372-0050
DOI:10.1109/TLT.2023.3267275