Lessons learned from the student dropout patterns on Covid ‐19 pandemic
An analysis supported by machine learning
Bibliographic Data
| ID | 21297394 |
|---|---|
| Authors | Miriam Pizzatto Colpo (0000-0002-6477-3227, Programa de Pós‐Graduação em Computação Universidade Federal de Pelotas (UFPel) Pelotas Brazil, corresponding author), Tiago Thompsen Primo (0000-0003-3870-097X, Programa de Pós‐Graduação em Computação Universidade Federal de Pelotas (UFPel) Pelotas Brazil), Marilton Sanchotene De Aguiar (0000-0002-5247-6022, Programa de Pós‐Graduação em Computação Universidade Federal de Pelotas (UFPel) Pelotas Brazil) |
| Year | 2024 |
| Volume | 55 |
| Issue | 2 |
| Pages | 560-585 |
| Publication date | 2024-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | British Journal of Educational Technology (JOURNAL) |
| Journal identifiers | ISSN: 0007-1013 • E-ISSN: 1467-8535 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/bjet.13380 |
| OpenAlex | W4386252521 |
| Language | EN |
| Citations received | 6 |
| References cited | 27 |
During the COVID‐19 pandemic, the challenges associated with the transition from face‐to‐face to emergency remote education increased concerns about student dropout. Aligned with this concern, this study investigates the impact of the pandemic on the dropout patterns of 3371 undergraduate students from a Brazilian institution. Using data mining and machine learning techniques, we developed predictive dropout models based on student data preceding and succeeding the onset of the pandemic. Through the interpretation and comparison of these models and with the support of statistical and graphical analyses, we identify that the patterns persistently indicate that young students in their initial semesters, characterized by lower income, academic performance, and interaction, remain most susceptible to dropping out. Despite the pandemic leading to an enhanced predictive capability of data regarding student interaction within the virtual learning environment, our analysis revealed a lack of significant variation in dropout patterns. Institutionally, this indicates that a considerable number of dropouts likely encountered challenges in adapting to higher education, both before and throughout the pandemic. Practitioner notes
Context (archaeology) · Coronavirus disease 2019 (COVID-19) · Dropout (neural networks) · Geography · Machine learning · Mathematics education · Pandemic · Artificial Intelligence · Computer Science · COVID-19 and Mental Health · Educational Innovations and Technology · Medicine · Online Learning and Analytics · Psychology
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Abandono E Atraso Escolar E Sua Relação Com Indicadores Socioeconômicos
| Unique citing works | 6 |
|---|---|
| Citations per year | 3 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |