Policies for Adopting Learning Analytics: A Proposal Based on Student Opinions
Abstract
Learning Analytics (LA) aims to analyze educational data to support teachers and students in the teaching and learning process. Aspiring LA effective adoption, it is essential to consider the opinion of the stakeholders. For that so, this paper aims to have knowledge about the expectation of students from a Brazilian public Higher Education Institution (HEI) concerning the use of their data. The ultimate goal is to propose guidelines to define policies that support the adoption of LA and attend to the expectation of these students. To achieve this goal, a case study was conducted using the SHEILA project questionnaire to collect the data. Data were analyzed by statistical techniques and Educational Data Mining.
Keywords:
Learning Analytics, Educational Data, Quantitative Research
References
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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.
Published
2021-11-22
How to Cite
KELVIN, Thiago; LEANDRO, Flávio; FAGUNDES, Roberta; FREITAS, Elyda.
Policies for Adopting Learning Analytics: A Proposal Based on Student Opinions. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 32. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 885-896.
DOI: https://doi.org/10.5753/sbie.2021.218599.
