Uma Análise de Algoritmos de Clusterização para Descoberta de Perfis de Engajamento
Resumo
Distance education (EAD) has been taking great proportions in access to education, due to the flexibility of time and geographic location. However, despite the adoption by educational institutions, the number of graduates in this modality has low rates. Due to student engagement and interaction it is directly related to the factors of academic performance and motivation of students. This article aims to describe different grouping methods that were used in a database to identify groups with different engagement profiles. The results point to three profiles that, when analyzing them, methodologies can be studied to avoid dropouts in the course.
Palavras-chave:
Engajamento, Educação a Distância, Análise de Agrupamento, Mineração de Dados Educacionais
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Haron, H., Aziz, N. H. N. and Harun, A. (2017). A Conceptual Model Participatory Engagement
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Messias, I., Morgado, L. and Barbas, M. (2015). Students' engagement in Distance Learning:
Creating a scenario with LMS and Social Network aggregation. 2015 International
Symposium on Computers in Education (SIIE).
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Matemáticas e de Computação. Universidade de São Paulo. 2017.
Oliveira, P. L. S. D., Souza, A. J. D. and Rodrigues, R. (2019). Identificação de pesquisas
referentes ao engajamento de alunos em plataformas de LMS e suas relações com o
desempenho acadêmico. Anais do XXX Simpósio Brasileiro de Informática na Educação
(SBIE 2019).
Pineda-Báez, C., Manzuoli, C. H. and Sánchez, A. V. (2019). Supporting student cognitive and
agentic engagement: Students’ voices. International Journal of Educational Research, v.
96, p. 81–90.
Ramos, J. L. C., Silva, R. E. D. E., Silva, J. C. S., Rodrigues, R. L. and Gomes, A. S. (2016). A
Comparative Study between Clustering Methods in Educational Data Mining. IEEE Latin
America Transactions, v. 14, n. 8, p. 3755–3761.
Rodrigues, R., Ramos, J., Silva, J. and Gomes, A. (2016). Discovery engagement patterns
MOOCs through cluster analysis. IEEE Latin America Transactions, v. 14, n. 9, p.
4129–4135.
Shukor, N. A., Tasir, Z., Meijden, H. V. D. and Harun, J. (2014). A Predictive Model to
Evaluate Students’ Cognitive Engagement in Online Learning. Procedia - Social and
Behavioral Sciences, v. 116, p. 4844–4853.
Whitty, C. and Anane, R. (2014). Social Network Enhancement for Non-formal Learning. 2014 47th
Hawaii International Conference on System Sciences.
Williams, K. M., Stafford, R. E., Corliss, S. B. and Reilly, E. D. (2018). Examining student
characteristics, goals, and engagement in Massive Open Online Courses. Computers &
Education, v. 126, p. 433.
to detect student’s engagement. Journal of King Saud University - Computer and
Information Sciences.
Bergdahl, N., Nouri, J. and Fors, U. (2019). Disengagement, engagement and digital skills in
technology-enhanced learning. Education and Information Technologies, v. 25, n. 2, p.
957–983.
Capuano, N., Mangione, G. R., Pierri, A. and Lin, E. (2013). Engaging e-learning for Risk
Management: The ALICE Experience in Italian Schools. 2013 Seventh International
Conference on Complex, Intelligent, and Software Intensive Systems.
Coelho, U. M. and Vega, I. S. (2019). The Pedagogical Formation and the Knowledge of
Teachers in Computering in Teaching Strategies: Integration of Content, Didactic
Material and Interdisciplinary or Integrator Project. 2019 XIV Latin American
Conference on Learning Technologies (LACLO).
Fredricks, J. A., Blumenfeld, P. C. and Paris, A. H. (2004). School Engagement: Potential of the
Concept, State of the Evidence. Review of Educational Research, v. 74, n. 1, p. 59–109.
Goh, W., Ayub, E., Wong, S. Y. and Lim, C. L. (2017). The importance of teacher's presence
and engagement in MOOC learning environment: A case study. 2017 IEEE Conference
on e-Learning, e-Management and e-Services (IC3e).
Haron, H., Aziz, N. H. N. and Harun, A. (2017). A Conceptual Model Participatory Engagement
Within E-learning Community. Procedia Computer Science, v. 116, p. 242–250.
Messias, I., Morgado, L. and Barbas, M. (2015). Students' engagement in Distance Learning:
Creating a scenario with LMS and Social Network aggregation. 2015 International
Symposium on Computers in Education (SIIE).
Moubayed, A., Injadat, M., Shami, A. and Lutfiyya, H. (2018). Relationship Between Student
Engagement and Performance in E-Learning Environment Using Association Rules.
2018 IEEE World Engineering Education Conference (EDUNINE).
Padilha, V.A. and Carvalho, A.C.P.L.F. Mineração de Dados em Python. Instituto de Ciências
Matemáticas e de Computação. Universidade de São Paulo. 2017.
Oliveira, P. L. S. D., Souza, A. J. D. and Rodrigues, R. (2019). Identificação de pesquisas
referentes ao engajamento de alunos em plataformas de LMS e suas relações com o
desempenho acadêmico. Anais do XXX Simpósio Brasileiro de Informática na Educação
(SBIE 2019).
Pineda-Báez, C., Manzuoli, C. H. and Sánchez, A. V. (2019). Supporting student cognitive and
agentic engagement: Students’ voices. International Journal of Educational Research, v.
96, p. 81–90.
Ramos, J. L. C., Silva, R. E. D. E., Silva, J. C. S., Rodrigues, R. L. and Gomes, A. S. (2016). A
Comparative Study between Clustering Methods in Educational Data Mining. IEEE Latin
America Transactions, v. 14, n. 8, p. 3755–3761.
Rodrigues, R., Ramos, J., Silva, J. and Gomes, A. (2016). Discovery engagement patterns
MOOCs through cluster analysis. IEEE Latin America Transactions, v. 14, n. 9, p.
4129–4135.
Shukor, N. A., Tasir, Z., Meijden, H. V. D. and Harun, J. (2014). A Predictive Model to
Evaluate Students’ Cognitive Engagement in Online Learning. Procedia - Social and
Behavioral Sciences, v. 116, p. 4844–4853.
Whitty, C. and Anane, R. (2014). Social Network Enhancement for Non-formal Learning. 2014 47th
Hawaii International Conference on System Sciences.
Williams, K. M., Stafford, R. E., Corliss, S. B. and Reilly, E. D. (2018). Examining student
characteristics, goals, and engagement in Massive Open Online Courses. Computers &
Education, v. 126, p. 433.
Publicado
24/11/2020
Como Citar
OLIVEIRA, Pamella Letícia Silva de; RODRIGUES, Rodrigo Lins; RAMOS, Jorge Luis Cavalcanti; SILVA, João Carlos Sedraz.
Uma Análise de Algoritmos de Clusterização para Descoberta de Perfis de Engajamento. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 31. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 1012-1021.
DOI: https://doi.org/10.5753/cbie.sbie.2020.1012.
