Use of the Fullcode platform in teaching Clinical Emergencies I to Emergency Medical students
DOI:
https://doi.org/10.62305/alcon.v6i1.1027Keywords:
Fullcode; clinical emergencies; digital platforms; quasi-experimentalAbstract
Higher education currently faces the challenge of integrating digital technologies that foster autonomous and accelerated learning, with particular emphasis on health programs where practical skills and problem solving are essential. This study examined the effectiveness of the Fullcode platform in teaching the course Clinical Emergencies I to second-semester students in the Emergency Medical Services program at the Instituto Superior Tecnológico Consulting Group Ecuador. Teaching clinical content in prehospital contexts requires combining active methodologies with technological tools that support comprehension, diagnosis, and rapid intervention in critical situations. Within this framework, a quasi-experimental pretest–posttest design without a control group was implemented with a sample of 20 students. The intervention consisted of using Fullcode over eight weeks through multimedia resources, interactive schemas, and integrated assessments. Results show a significant increase in academic performance: the pretest mean was 12.20 (SD = 2.15), whereas the posttest mean reached 16.55 (SD = 2.47). Inferential analysis using a paired-samples Student’s t-test revealed a statistically significant difference between the two time points, t(19) = 6.41, p < .001, with a mean difference of 4.35. These findings confirm that Fullcode is an effective pedagogical resource for strengthening clinical learning and promoting the integration of digital technologies in the training of future emergency medical professionals.
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Ahmed, A. A., Al-Husban, A. A., & Alzubi, M. A. (2021). Predicting student performance in anatomy course using machine learning algorithms. Journal of Educational Technology Systems, 49(3), 387-404.
Azer, S. A., & Azer, N. S. (2016). 21st century anatomy education: What is the best approach?. Anatomical Sciences Education, 9(1), 15-19.
Beam, A. L., Ambrosy, D., Oberst, M., Richter, J., & Kohane, I. S. (2020). Challenges, opportunities, and the future of artificial intelligence in healthcare. Journal of the American Medical Informatics Association, 27(3), 395-402.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Dawson, P., Gašević, D., & Siemens, G. (2018). Learning analytics: Towards data-driven improvement of learning and teaching in higher education. British Journal of Educational Technology, 49(5), 889-906.
Drake, R. L., Vogl, W., & Mitchell, A. W. M. (2017). Gray's anatomy for students (4th ed.). Churchill Livingstone.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swani, S. M., Blau, H. M., ... & Thrun, S. (2017). Dermatologist-level classification of skin cancer using deep neural networks. Nature, 542(7639), 115-118.
Ferrer-Torres, A., Jiménez-Rodríguez, M. A., & Tornero-Montes, P. (2020). Augmented reality in anatomy education: A systematic review. Medical Teacher, 42(2), 136-145.
Garg, A., Aggarwal, P., Nayyar, A., & Raja, L. (2020). Role of machine learning in medical image analysis: A review of the last decade. Archives of Computational Methods in Engineering, 27(3), 1209-1231.
Giler-Medina, P., et al. (2024). Uso del Atlas 3D en el aprendizaje de la Anatomía Humana. Sociedad & Tecnología, 7(2), 146-162. https://doi.org/10.51247/st.v7i2.421
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Herrera Herrera, F. J., et al. (2022). La realidad aumentada como recurso formativo en la educación superior. https://doi.org/10.26871/killkanasocial.v6i4.1187
Hecht-López, P., Maturana-Arancibia, J. C., & Parra-Villegas, E. (2023). Nuevos recursos digitales y 3D en la enseñanza de Anatomía. International Journal of Morphology, 41(3), 690-698.
Hinton, G. (2016). Deep learning—a technology with the potential to transform health care. Jama, 315(22), 2443-2444.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Liaw, S. S., Lin, C. H., Lin, K. L., & Yeh, C. H. (2021). Applications of artificial intelligence in medical education: A scoping review. BMC Medical Education, 21(1), 1-15.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88.
Lombardero Rodil, L. (2015) Trabajar en la era digital: tecnología y competencias para la transformación digital: (1.ª ed.). LID Editorial España. https://elibro.net/es/lc/istcge/titulos/270614
Luna de la Luz, Verónica, & González-Flores, Patricia. (2020). Transformaciones en educación médica: innovaciones en la evaluación de los aprendizajes y avances tecnológicos (parte 2). Investigación en educación médica, 9(34), 87-99. Epub 02 de diciembre de 2020. https://doi.org/10.22201/facmed.20075057e.2020.34.20220
McMenamin, P. G., & McLachlan, J. C. (2015). Does the study of anatomy in a modern medical curriculum still require cadaveric dissection?. Medical Teacher, 37(7), 603-611.
Mitchell, T. M. (1997). Machine learning. McGraw-Hill Education.
Moro, C., Stromberga, Z., Raikos, A., & Stirling, A. (2017). Virtual and augmented reality in education, training and serious games: A systematic review. Australasian Journal of Educational Technology, 33(6), 1-17.
Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
Patel, R. S., Shah, M., Doshi, N., & Shukla, S. (2020). Applications of machine learning in healthcare. Cureus, 12(5).
Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. International Journal of Educational Technology in Higher Education, 14(1), 1-10.
Prince, K. J. A. H., Vleuten, C. P. M. van der, Scherpbier, A. J. J. A., Dolmans, D. H. J. M., & Muijtjens, A. M. M. (2005). Why a modern approach to teaching anatomy is indispensable for medical practice. Clinical Anatomy, 18(1), 14-18.
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson Education Limited.
Sacristán, A. (2018). Sociedad digital, tecnología y educación: (ed.). UNED - Universidad Nacional de Educación a Distancia. https://elibro.net/es/lc/istcge/titulos/117247
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
Silva, P. H., Maestro, J. A. & Cortés, M. V. (2020). Metodologías para una educación innovadora. LA LEY Soluciones Legales S.A.
Singhal, S., Bansal, P., Chaudhary, N., Singh, P., & Sharma, P. (2019). Role of technology in anatomy learning: A review of current trends and future directions. Journal of Clinical and Diagnostic Research, 13(8).
Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
Turney, B. W. (2007). Anatomy in a modern medical curriculum. The Royal College of Surgeons of England, 89(4), 246-248.
Zhang, J., Lu, H., Zhang, L., Jin, G., & Chen, X. (2010). A review of medical image segmentation based on deformable models. Medical Imaging Technology, 28(6), 421-428.
Zumba, G. R., Mora Aristega, A. M., & Sánchez Soto, M. A. (2021). Estrategias y metodologías de enseñanza para el aprendizaje activo en la Educación Superior. Editorial Tecnocientífica Americana.
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