An Analysis of Clustering Algorithms for Discovery of Engagement Profiles
Abstract
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. How student engagement and interaction is directly related to academic performance factors. This article aimed to describe dif erent grouping methods that were used in a database for a given course, which was divided into the first four and the last four periods. The results point to three engagement profiles, which showed no significant differences between the two phases of the course.
Keywords:
Engagement, Distance Education, Cluster Analysis, Educational Data Mining
References
Altuwairqi, K., Jarraya, S. K., Allinjawi, A. et al. (2018) A new emotion–based affective model
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.
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.
Published
2020-11-24
How to Cite
OLIVEIRA, Pamella Letícia Silva de; RODRIGUES, Rodrigo Lins; RAMOS, Jorge Luis Cavalcanti; SILVA, João Carlos Sedraz.
An Analysis of Clustering Algorithms for Discovery of Engagement Profiles. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (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.
