Predicting student dropouts with machine learning
An empirical study in Finnish higher education
Bibliographic Data
| ID | 11239413 |
|---|---|
| Authors | Matti Vaarma (0000-0002-1198-4709, Tampere University of Applied Sciences, corresponding author), Hongxiu Li (0000-0002-7447-1222, Tampere University) |
| Year | 2024 |
| Volume | 76 |
| Pages | 102474 |
| Publication date | 2024-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Technology in Society (JOURNAL) |
| Journal identifiers | ISSN: 0160-791X • E-ISSN: 1879-3274 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.techsoc.2024.102474 |
| OpenAlex | W4391513906 |
| Language | EN |
| Citations received | 9 |
| References cited | 43 |
This study uses three machine learning models to predict student dropouts based on students' transcript, demographic, and learning management system (LMS) data from a Finnish university. The contribution of this research lies in 1) comparing the relative importance of LMS (Moodle) data with transcript and demographic data in degree program dropout prediction, 2) examining the predictive importance of different data features monthly as a function of time from enrollment, hence extending the prior end-of-semester research to a midsemester analysis, and 3) measuring the prediction performance of the models monthly. The results identify “accumulated credits” (transcript) the “number of failed courses” (transcript), and “Moodle activity count” (LMS) as the most important features, suggesting LMS has significant predictive power and should be considered alongside transcript and demographic data when predicting degree program dropouts. Moreover, we visualize how these factors' importance and prediction performance vary over time, revealing general longitudinal trends and fluctuations within semesters. Finally, we elaborate upon this study's contributions before highlighting its limitations
Empirical research · Machine learning · Mathematics education · Political science · Statistics · Computer Science · Mathematics · Online Learning and Analytics · Psychology · Artificial Intelligence
Machine Learning-Based Dropout Prediction for Undergraduates
Student Performance Prediction Using Machine Learning Algorithms
Structuring a factor-based framework for student retention
Extracurricular Activities and Academic Performance
Developing an Early Warning System with Personalized Interventions to Enhance Academic Outcomes for At-Risk Students in Taiwanese Higher Education
A Privacy-Conscious AI Framework for Early Identification of At-Risk Students Across Disciplines Using LMS Engagement Data
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Mining LMS data to develop an “early warning system” for educators
Study engagement and burnout profiles among Finnish higher education students
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
Research and Practice of Student Retention
Dropout from Higher Education
Random Forests
Predicting Student Performance Using Clickstream Data and Machine Learning
Mooc student dropout prediction model based on learning behavior features and parameter optimization
Prediction of students’ early dropout based on their interaction logs in online learning environment
Student dropout and retention
Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced Moocs
Taking action to reduce dropout in Moocs
A longitudinal analysis of the reciprocal relationship between academic procrastination, study satisfaction, and dropout intentions in higher education
Bachelor’s degree student dropouts
A Temporal Investigation of Factors Related to Timely Degree Completion
Studying Attrition and Degree Completion Behavior among First-Generation College Students in the United States
University Life of Non-Traditional Students
Investigating the Impact of Financial Aid on Student Dropout Risks
Predictors of Long-Term Enrollment and Degree Outcomes for Community College Students
Persistence Patterns in Massive Open Online Courses (Moocs)
Data mining in course management systems
Early-predicting dropout of university students
| Citations per year | 4.5 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |