Early detection of students at risk of failure from a small dataset

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 concern...

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Bibliographic Details
Main Authors: Leite, Denis, Filho, Edson, de Oliveira, Joao F. L., Carneiro, Rodrigo E., Maciel, Alexandre
Format: Conference Proceeding
Language:English
Subjects:
Buildings
Computer aided instruction
Data models
early prediction
Feature extraction
learning analytics
Machine learning
Predictive models
students
Windows
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Summary: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.
ISSN:2161-377X
DOI:10.1109/ICALT52272.2021.00021