Investigation of College Dropout with the Fuzzy C-Means Algorithm

Macedo, Mariana; Santana, Clodomir J.; Siqueira, Hugo Valadares; Rodrigues, Rodrigo Lins; Ramos, Jorge Luis Cavalcanti; Silva, Joao Carlos S.; Maciel, Alexandre Magno Andrade; Bastos-Filho, Carmelo J. A.

doi:10.1109/icalt.2019.00055

Cited by 9 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The highlight for the tool Waikato Environment for Knowledge Analysis (Weka), developed by the University of Waikato, et al (2018) in New Zealand, used in 8 works for this purpose, an essential ally in combating dropout in distance education. An article can use more than one tool, as in Macedo et al (2019) and Brito et al (2019).…”

Section: Rq3 What Were Computational Tools Used To Address Dropout In Distance Education?mentioning

confidence: 99%

“…Kostopoulos et al (2018a), for example, used 12 algorithms in order to predict student performance in final exams. , Macedo et al (2019), Chen and Zhang (2017), Whitehill et al (2017), Niu et al (2018) and Santos et al (2015) used only 1 algorithm, and Brito et al (2019) and Imran et al (2019) did not mention the algorithms used. For innovation, new algorithms were developed by some authors of the primary articles studied in an attempt to contribute to the identification and mitigation of dropout in distance education.…”

Section: Rq4 What Were the Algorithms Used To Deal With Dropout In Distance Education?mentioning

confidence: 99%

“…(2016); Cobos and Olmos (2019); De La Peña et al (2018); Isidro et al (2018); Wang and Wang (2019); Waheed et al (2020); Heidrich et al (2018); Tomasevic et al (2020);Wang et al (2017); Kang andWang (2018);Wu et al (2019); Santos et al (2016); Ramos et al (2017); Ramos et al et al (2016); Cobos and Olmos (2019); Wang and Wang (2019); Wang et al (2017); Wu et al . (2018); Tomasevic et al (2020); Ramos et al (2017);Ramos et al et al (2019);Imran et al (2019)…”

mentioning

confidence: 99%

See 2 more Smart Citations

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

Andrade¹,

Rigo²,

Barbosa³

2021

Informatics in Education

View full text Add to dashboard Cite

Distance Learning has enabled educational practices based on digital platforms, generating massive amounts of data. Several initiatives use this data to identify dropout contexts, mainly providing teacher support about student behavior. Approaches such as Active Methodologies are known as having good potential to involve and motivate students. This article presents a systematic mapping aiming to identify current Educational Data Mining and Learning Analytics methods. Besides, we identify Active Methodologies' application to mitigate dropout in Distance Learning. We evaluated 668 papers published from January 2015 to March 2020. The results indicate a growing application of Educational Data Mining and Learning Analytics to identify and mitigate students' abandonment in Distance Learning. However, studies with Active Methodologies to minimize dropout and enhance student permanence are scarce. Some works suggest Active Methods as a possible complement of Learning Analytics in dropout.

show abstract

Section: Rq3 What Were Computational Tools Used To Address Dropout In Distance Education?mentioning

confidence: 99%

Section: Rq4 What Were the Algorithms Used To Deal With Dropout In Distance Education?mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

Andrade¹,

Rigo²,

Barbosa³

2021

Informatics in Education

View full text Add to dashboard Cite

show abstract

“…O ensino a distância proporciona aos estudantes acesso facilitadoà educação, contudó e sabido que este tipo de modalidade traz consigo dificuldades que requerem um alto grau de comprometimento, e, não por acaso em muitos casos resultam em abandono e descrédito (Macedo, et al 2019).…”

Section: Introductionunclassified

“…Já (Beer, et al 2010) afirma que o engajamento está associado ao desempenho escolar e ao sucesso institucional. No ensino a distância a análise de perfis de engajamentoé fundamental para a melhoria no processo de ensino-aprendizagem e consequentemente diminuição de abandono escolar (Macedo, et al 2019).…”

Section: Introductionunclassified

Análise de Perfis de Engajamento de Estudantes de Ensino a Distância

Macêdo

Santos

Maciel

2021

RENOTE

View full text Add to dashboard Cite

uso de ambientes virtuais de aprendizagem permite mais interação no ensino a distância, mas ao mesmo tempo faz com que os professores tenham dificuldade em identificar seu aluno. Esse trabalho tem como objetivo detectar os perfis de engajamento dos estudantes de ensino a distância a partir de técnicas de agrupamento através dos bancos de dados de um ambiente virtual de aprendizagem. O estudo utilizou 2 algoritmos de agrupamento. Os perfis encontrados na base utilizada possuem uma fraca relação entre eles, porem, é possível detectar os traços comportamentais dos estudantes nas plataformas de ensino a distância.

show abstract

Predicting Student Retention in Smart Learning Environments Using Machine Learning

Abujadallah,

Abudalfa

2024

Technical and Vocational Education and Training: Issues, Concerns and Prospects

View full text Add to dashboard Cite

Investigation of College Dropout with the Fuzzy C-Means Algorithm

Cited by 9 publications

References 6 publications

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study

Análise de Perfis de Engajamento de Estudantes de Ensino a Distância

Predicting Student Retention in Smart Learning Environments Using Machine Learning

Contact Info

Product

Resources

About