A proposed framework for evaluating the academic-failure prediction in distance learning

Patrícia Takaki; Moisés Lima Dutra; Gustavo de Araújo; Eugênio Monteiro da Silva Júnior

Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/61465

Full metadata record

DC Field	Value	Language
dc.creator	Patrícia Takaki	pt_BR
dc.creator	Moisés Lima Dutra	pt_BR
dc.creator	Gustavo de Araújo	pt_BR
dc.creator	Eugênio Monteiro da Silva Júnior	pt_BR
dc.date.accessioned	2023-11-28T19:02:55Z	-
dc.date.available	2023-11-28T19:02:55Z	-
dc.date.issued	2022-04-12	-
dc.citation.volume	27	pt_BR
dc.citation.spage	1958	pt_BR
dc.citation.epage	1966	pt_BR
dc.identifier.doi	https://doi.org/10.1007/s11036-022-01965-z	pt_BR
dc.identifier.issn	1572-8153	pt_BR
dc.identifier.uri	http://hdl.handle.net/1843/61465	-
dc.description.resumo	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.	pt_BR
dc.language	eng	pt_BR
dc.publisher	Universidade Federal de Minas Gerais	pt_BR
dc.publisher.country	Brasil	pt_BR
dc.publisher.department	ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS	pt_BR
dc.publisher.initials	UFMG	pt_BR
dc.relation.ispartof	Mobile Networks and Applications	-
dc.rights	Acesso Restrito	pt_BR
dc.subject.other	Ensino à distância	pt_BR
dc.subject.other	Fracasso escolar	pt_BR
dc.subject.other	Mineração de dados (Computação)	pt_BR
dc.subject.other	Aprendizado do computador	pt_BR
dc.title	A proposed framework for evaluating the academic-failure prediction in distance learning	pt_BR
dc.type	Artigo de Periódico	pt_BR
dc.url.externa	https://link.springer.com/article/10.1007/s11036-022-01965-z	pt_BR
Appears in Collections:	Artigo de Periódico

Files in This Item:

There are no files associated with this item.

Show simple item record