A Visual Perception-Based Approach to Support Cervical Cytology Learning
Abstract
The emergence of new hardware and software has allowed the improvement of activities in different contexts by including technological resources to support the execution of these activities. One context that benefits from this improvement is education since these resources allow exploring various strategies to enhance the teaching-learning process. This incorporation of technology into regular activities also aligns with the expectactions of users, who expects technological resources to be integrated into their daily lives. This work presents a computational approach to support the teaching-learning process of content based on visual perception in cytology. To achieve this goal, we designed a framework. We implemented a proof of concept to help evaluate the model and build the database necessary for activities in the context of cytology. Users with knowledge of the area evaluated the framework. The resultsdemonstrate that the interaction model developed could represent real-world activity and expose students to specific unusual situations. The evaluation collected information and indications on how the tool can improve the teaching-learning process.
References
Bahia, N. S., da Silva, W. R., Vianna, J. B., Rodrigues, H. G., Silva, M. T. B., and Bacchi, R. R. (2019). O uso das tdic’s como estratégia para aprendizagem em morfologia microscópica. Informática na Educação: teoria & prática, 22(2).
Chen, L.-S., Lay, Y.-J., Yang, C.-C., and Chang, S.-H. (2015). A virtual microscope system for histology learning in education. In 2015 8th International Conference on Ubi-Media Computing (UMEDIA), pages 355–359. IEEE.
Dankbaar, M. E. and de Jong, P. G. (2014). Technology for learning: how it has changed education. Perspectives on medical education, 3:257–259.
Dargham, J., Saeed, D., and Mcheick, H. (2012). E-learning at school level: Challenges and benefits.
de Melo, C. A. and de Sousa, M. S. (2021). Tecnologia educacional como estratégia integrativa de complementação na formação de estudantes e profissionais da área da saúde: Revisão integrativa. Research, Society and Development, 10(10):e87101018796–e87101018796.
Guiter, G. E., Sapia, S., Wright, A. I., Hutchins, G. G., and Arayssi, T. (2021). Development of a remote online collaborative medical school pathology curriculum with clinical correlations, across several international sites, through the covid-19 pandemic. Medical Science Educator, 31:549–556.
Guze, P. A. (2015). Using technology to meet the challenges of medical education. Transactions of the American clinical and climatological association, 126:260.
Hanna, M. G., Reuter, V. E., Ardon, O., Kim, D., Sirintrapun, S. J., Schüffler, P. J., Busam, K. J., Sauter, J. L., Brogi, E., Tan, L. K., et al. (2020). Validation of a digital pathology system including remote review during the covid-19 pandemic. Modern Pathology, 33(11):2115–2127.
Hussin, A. A. (2018). Education 4.0 made simple: Ideas for teaching. International Journal of Education and Literacy Studies, 6(3):92–98.
Keller, B., Guimarães, T., Malaquias, P. I., Ferreira, G., Resende, M., Carneiro, C., and Bianchi, A. (2021). Citofocus: Uma plataforma para colaboração e aprendizado em citopatologia. In Anais do XXI Simpósio Brasileiro de Computação Aplicada à Saúde, pages 404–409, Porto Alegre, RS, Brasil. SBC.
Kellman, P. J. and Krasne, S. (2018). Accelerating expertise: Perceptual and adaptive learning technology in medical learning. Medical Teacher, 40(8):797–802.
Maley, M. A., Harvey, J. R., Boer, W. B. d., Scott, N. W., and Arena, G. E. (2008). Addressing current problems in teaching pathology to medical students: blended learning. Medical teacher, 30(1):e1–e9.
Manou, E., Lazari, E.-C., Lazaris, A. C., Agrogiannis, G., Kavantzas, N. G., and Thomopoulou, G.-E. (2022). Evaluating e-learning in the pathology course during the covid-19 pandemic. Advances in Medical Education and Practice, pages 285–300.
Marris, J., Perfors, A., Mitchell, D., Wang, W., McCusker, M. W., Lovell, T. J. H., Gibson, R. N., Gaillard, F., and Howe, P. D. (2021). How effective is perceptual training? evaluating two perceptual training methods on a difficult visual categorisation task. In Proceedings of the Annual Meeting of the Cognitive Science Society, volume 43.
Rezende, M. T., Silva, R., Bernardo, F. d. O., Tobias, A. H., Oliveira, P. H., Machado, T. M., Costa, C. S., Medeiros, F. N., Ushizima, D. M., Carneiro, C. M., et al. (2021). Cric searchable image database as a public platform for conventional pap smear cytology data. Scientific data, 8(1):151.
Sasaki, Y., Nanez, J. E., and Watanabe, T. (2010). Advances in visual perceptual learning and plasticity. Nature Reviews Neuroscience, 11(1):53–60.
Thakkar, S. R. and Joshi, H. D. (2015). E-learning systems: a review. In 2015 IEEE seventh international conference on Technology for education (T4E), pages 37–40. IEEE.
Wiecha, J., Heyden, R., Sternthal, E., Merialdi, M., et al. (2010). Learning in a virtual world: experience with using second life for medical education. Journal of medical Internet research, 12(1):e1337.
