Políticas para Adoção de Learning Analytics: Uma Proposta Baseada nas Opiniões dos Estudantes
Resumo
Learning Analytics (LA) visa a análise de dados educacionais para melhorar o processo de ensino e aprendizagem. Para sua efetiva adoção, é essencial considerar a opinião dos stakeholders. Assim, este artigo tem por objetivo conhecer as expectativas dos estudantes de uma Instituição de Ensino Superior (IES) pública brasileira sobre o uso de seus dados, com o objetivo final de propor diretrizes para a definição de pol´ıticas que apoiem a adoção de LA e atendam às expectativas desses estudantes. Para isso, conduziu-se um estudo de caso com a utilização do questionário do projeto SHEILA para coleta de dados; a análise de dados foi realizada por meio de técnicas estatísticas e Mineração de Dados Educacionais (MDE).
Palavras-chave:
Learning Analytics, dados educacionais, pesquisa quantitativa
Referências
Avella, J. T., Kebritchi, M., Nunn, S. G., and Kanai, T. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning, 20(2):13–29.
Bholowalia, P. and Kumar, A. (2014). Ebk-means: A clustering technique based on elbow method and k-means in wsn. International Journal of Computer Applications, 105(9):17–24.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R., et al. (2000). Crisp-dm 1.0: Step-by-step data mining guide. SPSS inc, 9:13.
Falcao, T. P., Ferreira, R., Rodrigues, R. L., Diniz, J., and Gasevic, D. (2019). Students’ perceptions about learning analytics in a brazilian higher education institution. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), volume 2161, pages 204–206. IEEE.
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6):304–317.
Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Hilliger, I., Ortiz, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y.-S., Merino, P., Broos, T., Whitelock-Wainwright, A., Gasevic, D., and Pérez-Sanagustín, M. (2020). Towards learning analytics adoption: A mixed methods study of data-related practices and policies in latin american universities: Data practices and policies in latin america. British Journal of Educational Technology, 51.
Hoel, T., Mason, J., and Chen,W. (2015). Data sharing for learning analytics–questioning the risks and benefits. In Proceedings of the 23rd International Conference on Computers in Education. China: Asia-Pacific Society for Computers in Education.
Kaufman, L. and Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis, volume 344. John Wiley & Sons.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology.
Lim, C. and Tinio, V. (2018). Learning analytics for the global south. Quezon City, Philippines: Foundation for Information Technology Education and Development.
Memarsadeghi, N. and O’Leary, D. P. (2003). Classified information: the data clustering problem. Computing in Science & Engineering, 5(5):54–60.
Runeson, P. and Höst, M. (2009). Guidelines for conducting and reporting case study research in software engineering. Empirical software engineering, 14(2):131–164.
Sheng, W. and Liu, X. (2006). A genetic k-medoids clustering algorithm. Journal of Heuristics, 12(6):447–466.
Silvestre, A. L. (2007). Análise de dados e estatística descritiva. Escolar editora.
Tsai, Y.-S. and Gasevic, D. (2017). Learning analytics in higher education—challenges and policies: a review of eight learning analytics policies. In Proceedings of the seventh International Learning Analytics & Knowledge Conference, pages 233–242.
Tsai, Y.-S., Gasevic, D., Whitelock-Wainwright, A., Moreno-Marcos, P. M., Fernandez, A. R., Muñoz-Merino, P. J., Kloos, C. D., Tammets, K., Kollom, K., Scheffel, M., and Drachsler, H. (2018a). Teacher and student perspectives on learning analytics – executive summary.
Tsai, Y.-S., Moreno-Marcos, P. M., Tammets, K., Kollom, K., and Gasevic, D. (2018b). Sheila policy framework: informing institutional strategies and policy processes of learning analytics. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pages 320–329.
Bholowalia, P. and Kumar, A. (2014). Ebk-means: A clustering technique based on elbow method and k-means in wsn. International Journal of Computer Applications, 105(9):17–24.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R., et al. (2000). Crisp-dm 1.0: Step-by-step data mining guide. SPSS inc, 9:13.
Falcao, T. P., Ferreira, R., Rodrigues, R. L., Diniz, J., and Gasevic, D. (2019). Students’ perceptions about learning analytics in a brazilian higher education institution. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), volume 2161, pages 204–206. IEEE.
Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6):304–317.
Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Hilliger, I., Ortiz, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y.-S., Merino, P., Broos, T., Whitelock-Wainwright, A., Gasevic, D., and Pérez-Sanagustín, M. (2020). Towards learning analytics adoption: A mixed methods study of data-related practices and policies in latin american universities: Data practices and policies in latin america. British Journal of Educational Technology, 51.
Hoel, T., Mason, J., and Chen,W. (2015). Data sharing for learning analytics–questioning the risks and benefits. In Proceedings of the 23rd International Conference on Computers in Education. China: Asia-Pacific Society for Computers in Education.
Kaufman, L. and Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis, volume 344. John Wiley & Sons.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology.
Lim, C. and Tinio, V. (2018). Learning analytics for the global south. Quezon City, Philippines: Foundation for Information Technology Education and Development.
Memarsadeghi, N. and O’Leary, D. P. (2003). Classified information: the data clustering problem. Computing in Science & Engineering, 5(5):54–60.
Runeson, P. and Höst, M. (2009). Guidelines for conducting and reporting case study research in software engineering. Empirical software engineering, 14(2):131–164.
Sheng, W. and Liu, X. (2006). A genetic k-medoids clustering algorithm. Journal of Heuristics, 12(6):447–466.
Silvestre, A. L. (2007). Análise de dados e estatística descritiva. Escolar editora.
Tsai, Y.-S. and Gasevic, D. (2017). Learning analytics in higher education—challenges and policies: a review of eight learning analytics policies. In Proceedings of the seventh International Learning Analytics & Knowledge Conference, pages 233–242.
Tsai, Y.-S., Gasevic, D., Whitelock-Wainwright, A., Moreno-Marcos, P. M., Fernandez, A. R., Muñoz-Merino, P. J., Kloos, C. D., Tammets, K., Kollom, K., Scheffel, M., and Drachsler, H. (2018a). Teacher and student perspectives on learning analytics – executive summary.
Tsai, Y.-S., Moreno-Marcos, P. M., Tammets, K., Kollom, K., and Gasevic, D. (2018b). Sheila policy framework: informing institutional strategies and policy processes of learning analytics. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge, pages 320–329.
Publicado
22/11/2021
Como Citar
KELVIN, Thiago; LEANDRO, Flávio; FAGUNDES, Roberta; FREITAS, Elyda.
Políticas para Adoção de Learning Analytics: Uma Proposta Baseada nas Opiniões dos Estudantes. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 32. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 885-896.
DOI: https://doi.org/10.5753/sbie.2021.218599.
