Early detection of students at risk of failure from a small dataset
| Autor: | João Fausto Lorenzato de Oliveira, Rodrigo E. Carneiro, Denis Leite, Alexandre Magno Andrade Maciel, Edson Mata da Silva Filho |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Distance education Feature extraction Psychological intervention Early detection Machine learning computer.software_genre Data modeling Task (project management) Time windows ComputingMilieux_COMPUTERSANDEDUCATION Artificial intelligence business computer Dropout (neural networks) |
| Zdroj: | ICALT |
| DOI: | 10.1109/icalt52272.2021.00021 |
| Popis: | Predicting that a student is likely to fail in a course is critical for performing early interventions, prevent dropout and increase performance on distance learning. This work investigates the most promising machine learning model to perform this task using a small (35 samples) dataset that concerns two classes of one undergraduate course subject. The results bring evidence that the implemented ensemble can perform a prediction at the end of the first week of the course, with a mean accuracy of 78%, when presented to unseen data. This paper also investigates the influence of past data on the results of the classifiers by building datasets with different time window configurations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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