Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/61465
Type: Artigo de Periódico
Title: A proposed framework for evaluating the academic-failure prediction in distance learning
Authors: Patrícia Takaki
Moisés Lima Dutra
Gustavo de Araújo
Eugênio Monteiro da Silva Júnior
Abstract: Academic failure is a crucial problem that affects not only students but also institutions and countries. Lack of success in the educational process can lead to health and social disorders and economic losses. Consequently, predicting in advance the occurrence of this event is a good prevention and mitigation strategy. This work proposes a framework to evaluate machine learning-based predictive models of academic failure, to facilitate early pedagogical interventions. We took a Brazilian undergraduate course in the distance learning modality as a case study. We run seven classification models on normalized datasets, which comprised grades for three weeks of classes for a total of six weeks. Since it is an imbalanced-data context, adopting a single metric to identify the best predictive model of student failure would not be efficient. Therefore, the proposed framework considers 11 metrics generated by the classifiers run and the application of exclusion and ordering criteria to produce a list of best predictors. Finally, we discussed and presented some possible applications for minimizing the students’ failure.
Subject: Ensino à distância
Fracasso escolar
Mineração de dados (Computação)
Aprendizado do computador
language: eng
metadata.dc.publisher.country: Brasil
Publisher: Universidade Federal de Minas Gerais
Publisher Initials: UFMG
metadata.dc.publisher.department: ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
Rights: Acesso Restrito
metadata.dc.identifier.doi: https://doi.org/10.1007/s11036-022-01965-z
URI: http://hdl.handle.net/1843/61465
Issue Date: 12-Apr-2022
metadata.dc.url.externa: https://link.springer.com/article/10.1007/s11036-022-01965-z
metadata.dc.relation.ispartof: Mobile Networks and Applications
Appears in Collections:Artigo de Periódico

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