Collecting Movement and Activity Information from Children in School with Wearables: Experience Report
Abstract
Objective data about human movement can generate information about students in learning situations, and help the teacher. This work reports the experience of collecting movement data from 20 children in the classroom, using a wearable accelerometer sensor. The collection methodology, procedures, opinions of teachers and children are presented, as well as limitations and difficulties in the process. The research was carried out during a period of 3 weeks with two classes of the 2nd year of EF. 68 million lines of data and more than 100 pages of field notes were generated, which are in the process of being analyzed to support future studies. The feedback from the class and teachers was positive and attests to the quality of the proposed methodology.
Keywords:
Wearables, Data Collection, Classroom, Primary School
References
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Kwet, M. and Prinsloo, P. (2020). The smart classroom: a new frontier in the age of the smart university. Teaching in Higher Education, 25(4):510–526.
Lee, V. R., Fischback, L., and Cain, R. (2019). A wearables-based approach to detect and identify momentary engagement in afterschool makerspace programs. Contemporary Educational Psychology, 59:101789.
Lopes, V., Medina, R., Bernardi, G., and Nunes, F. B. (2018). Smart classroom utilizando dispositivos iot: uma revisao sistemática da literatura. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 308.
MacLeod, J., Yang, H. H., Zhu, S., and Li, Y. (2018). Understanding students preferences toward the smart classroom learning environment: Development and validation of an instrument. Computers & Education, 122:80–91.
Motti, V. G. (2019). Wearable technologies in education: a design space. In International Conference on Human-Computer Interaction, pages 55–67. Springer
Saquib, N., Bose, A., George, D., and Kamvar, S. (2018). Sensei: Sensing educational interaction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(4):1–27.
Watanabe, J.-I., Yano, K., and Matsuda, S. (2013). Relationship between physical behaviors of students and their scholastic performance. In 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing, pages 170–177. IEEE.
Zhu, Z., Ober, S., and Jafari, R. (2017). Modeling and detecting student attention and interest level using wearable computers. In 2017 IEEE 14th international conference on wearable and implantable body sensor networks (BSN), pages 13–18. IEEE.
Castro Filho, J. A., Freire, R. S., and Maia, D. L. (2021). Estudo de caso como método de pesquisa em informática na educação. In Metodologia de pesquisa científica em Informática na Educação: abordagem qualitativa, volume 3 of Série Metodologia de Pesquisa em Informática na Educação. SBC.
Costa, J. A., Dorça, F. A., and Araújo, R. D. (2020). Avaliação do comportamento de estudantes em um ambiente educacional ubíquo. In Anais do XXXI Simpósio Brasileiro de Informática na Educação, pages 182–191. SBC.
Ferreira, P. N., Rodriguez, C. L., and Motti, V. G. (2020). Wearables para coleta de dados de estudantes em ambiente escolar: Mapeamento sistemático. In Anais do XXXI Simpósio Brasileiro de Informática na Educação, pages 1353–1362. SBC.
Fronza, C. d. A. (2020). Submissão de projeto de pesquisa ao comitê de Ética: da plataforma brasil ao parecer consubstanciado. In Metodologia de pesquisa científica em Informática na Educação: Concepção de Pesquisa, volume 1 of Série Metodologia de Pesquisa em Informática na Educação. SBC.
Gersak, V., Vitulic, H. S., Prosen, S., Starc, G., Humar, I., and Geršak, G. (2020). Use of wearable devices to study activity of children in classroom; case study—learning geometry using movement. Computer Communications, 150:581–588.
Gligoric, N., Uzelac, A., and Krco, S. (2012). Smart classroom: real-time feedback on lecture quality. In 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pages 391–394. IEEE.
Heravi, B. M., Gibson, J. L., Hailes, S., and Skuse, D. (2018). Playground social interaction analysis using bespoke wearable sensors for tracking and motion capture. In Proceedings of the 5th International Conference on Movement and Computing.
Kaur, A., Bhatia, M., and Stea, G. (2022). A survey of smart classroom literature. Education Sciences, 12(2):86.
Kim, P. W. (2019). Ambient intelligence in a smart classroom for assessing students engagement levels. Journal of ambient intelligence and humanized computing, 10(10):3847–3852.
Kwet, M. and Prinsloo, P. (2020). The smart classroom: a new frontier in the age of the smart university. Teaching in Higher Education, 25(4):510–526.
Lee, V. R., Fischback, L., and Cain, R. (2019). A wearables-based approach to detect and identify momentary engagement in afterschool makerspace programs. Contemporary Educational Psychology, 59:101789.
Lopes, V., Medina, R., Bernardi, G., and Nunes, F. B. (2018). Smart classroom utilizando dispositivos iot: uma revisao sistemática da literatura. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 29, page 308.
MacLeod, J., Yang, H. H., Zhu, S., and Li, Y. (2018). Understanding students preferences toward the smart classroom learning environment: Development and validation of an instrument. Computers & Education, 122:80–91.
Motti, V. G. (2019). Wearable technologies in education: a design space. In International Conference on Human-Computer Interaction, pages 55–67. Springer
Saquib, N., Bose, A., George, D., and Kamvar, S. (2018). Sensei: Sensing educational interaction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1(4):1–27.
Watanabe, J.-I., Yano, K., and Matsuda, S. (2013). Relationship between physical behaviors of students and their scholastic performance. In 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing, pages 170–177. IEEE.
Zhu, Z., Ober, S., and Jafari, R. (2017). Modeling and detecting student attention and interest level using wearable computers. In 2017 IEEE 14th international conference on wearable and implantable body sensor networks (BSN), pages 13–18. IEEE.
Published
2022-11-16
How to Cite
FERREIRA, Poliana Nascimento; SANTOS, Camila L. F. dos; BEDA, Juliana L.; BELARMINO, Guilherme D.; RODRIGUEZ, Carla Lopes; MOTTI, Vivian Genaro.
Collecting Movement and Activity Information from Children in School with Wearables: Experience Report. In: BRAZILIAN SYMPOSIUM ON COMPUTERS IN EDUCATION (SBIE), 33. , 2022, Manaus.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 1102-1113.
DOI: https://doi.org/10.5753/sbie.2022.225239.
