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Resumen
Introducción: En los últimos años, el proceso de enseñanza-aprendizaje ha ido cambiando del modo presencial al virtual de forma progresiva a nivel mundial, esto se aceleró significativamente a causa de la pandemia del COVID-19 afectando todos los niveles de la educación, muchos países tuvieron que dar un salto al conocimiento digital, más por necesidad que por crecimiento tecnológico, lo cual originó buscar soluciones a los nuevos problemas a partir del entorno virtual. Hoy en la nueva normalidad, el entorno virtual se desarrollará paralelamente con el entorno presencial. El objetivo de la presente investigación fue identificar el estado emocional que tienen los estudiantes en el aula virtual, para permitir al docente evaluar la percepción que tienen los estudiantes durante su sesión de clase y así mejorar sus estrategias de enseñanza-aprendizaje en tiempo real.
Método: Se propuso una aplicación de inteligencia artificial con redes neuronales que permiten capturar el estado emocional de los estudiantes dentro del aula virtual en tiempo real para mostrar al docente la percepción de sus estudiantes durante la sesión de clase virtual.
Resultados: Los resultados obtenidos muestran el estado emocional de los estudiantes dentro del aula, para que el docente pueda evaluar y así mejore en tiempo real sus estrategias dentro del proceso enseñanza-aprendizaje.
Conclusiones: Se concluye que es una forma eficiente de mejora continua para los procesos del aprendizaje activo dentro del aula en tiempo real.
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Detalles del artículo
Referencias
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- Corvalán, J. G. (2018). Inteligencia artificial: Retos, desafíos y oportunidades - Prometea: la primera inteligencia artificial de Latinoamérica al servicio de la Justicia. Revista de Investigações Constitucionais, 5, 295-316. https://doi.org/10.5380/rinc.v5i1.55334
- de Mello, F. L., & de Souza, S. A. (2019). Psychotherapy and Artificial Intelligence: A Proposal for Alignment. Frontiers in Psychology, 10. https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00263
- Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2019.00482
- El Hechi, M., Ward, T. M., An, G. C., Maurer, L. R., El Moheb, M., Tsoulfas, G., & Kaafarani, H. M. (2021). Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype. Journal of Surgical Research, 264, A1-A9. https://doi.org/10.1016/j.jss.2021.01.046
- Fakhoury, M. (2019). Artificial Intelligence in Psychiatry. En Y.-K. Kim (Ed.), Frontiers in Psychiatry: Artificial Intelligence, Precision Medicine, and Other Paradigm Shifts (pp. 119-125). Springer. https://doi.org/10.1007/978-981-32-9721-0_6
- Gomez, A., & Gomez, K. (2019). Muestreo estadístico para docentes y estudiantes (1ª Ed.). Independently published.
- Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism-clinical and experimental, 69, S36-S40. https://doi.org/10.1016/j.metabol.2017.01.011
- Hasnine, M. N., Ahmed, M. M. H., & Ueda, H. (2021). Learner-Centric Technologies to Support Active Learning Activity Design in New Education Normal: Exploring the Disadvantageous Educational Contexts. International Journal of Emerging Technologies in Learning (IJET), 16(10), 150-162. https://doi.org/10.3991/ijet.v16i10.20081
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.90
- Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917-926. https://doi.org/10.1002/ajim.23037
- Jirapanthong, W. (2020). A Tool for Supporting the Evaluation of Active Learning Activities. En Y. Tan, Y. Shi, & M. Tuba (Eds.), Advances in Swarm Intelligence (pp. 476-484). Springer International Publishing. https://doi.org/10.1007/978-3-030-53956-6_43
- King, D. E. (2009). Dlib-ml: A Machine Learning Toolkit. Journal of machine learning research, 10, 1755-1758.
- León, O., & Romero, J. (2020). Ambientes de aprendizaje accesibles que fomentan la afectividad en contextos universitarios. Universidad Distrital Francisco José de Caldas.
- Montaño, J. (2002) Redes Neuronales Artificiales aplicadas al Análisis de Datos [Tesis doctoral]. Universitat De Les Illes Balears. http://hdl.handle.net/11201/2511
- Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(1), 81. https://doi.org/10.1007/s13278-021-00776-6
- Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of Artificial Intelligence powered digital writing assistant in higher education: Randomized controlled trial. Heliyon, 7(5), e07014. https://doi.org/10.1016/j.heliyon.2021.e07014
- Orrù, G., Monaro, M., Conversano, C., Gemignani, A., & Sartori, G. (2020). Machine Learning in Psychometrics and Psychological Research. Frontiers in Psychology, 10. https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02970
- Pulido, F. P., & Herrera, F. H. (2017). La influencia de las emociones sobre el rendimiento académico. Ciencias Psicológicas, 29-39. https://doi.org/10.22235/cp.v11i2.1344
- Ramos, C. A. (2015). Los paradigmas de la investigación científica. Avances en Psicología, 23(1), 9-17. https://doi.org/10.33539/avpsicol.2015.v23n1.167
- Roberts, K., & Rosselot, C. (2019). Experiencia de acompañamiento a estudiantes para la permanencia en la educación superior desde una perspectiva Socioeducativa: El caso de la Universidad de Santiago de Chile. Congresos CLABES.
- Santoso, K., & Kusuma, G. P. (2018). Face Recognition Using Modified OpenFace. Procedia Computer Science, 135, 510-517. https://doi.org/10.1016/j.procs.2018.08.203
- Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2015.7298682
- Serengil, S. I., & Ozpinar, A. (2020). LightFace: A Hybrid Deep Face Recognition Framework. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU). https://doi.org/10.1109/ASYU50717.2020.9259802
- Serengil, S. I., & Ozpinar, A. (2021). HyperExtended LightFace: A Facial Attribute Analysis Framework. 2021 International Conference on Engineering and Emerging Technologies (ICEET). https://doi.org/10.1109/ICEET53442.2021.9659697
- Sharma, S., Shanmugasundaram, K., & Ramasamy, S. K. (2016). FAREC — CNN based efficient face recognition technique using Dlib. 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). https://doi.org/10.1109/ICACCCT.2016.7831628
- Shetty, A. B., Bhoomika, Deeksha, Rebeiro, J., & Ramyashree. (2021). Facial recognition using Haar cascade and LBP classifiers. Global Transitions Proceedings, 2(2), 330-335. https://doi.org/10.1016/j.gltp.2021.08.044
- Shrestha, S. K., & Furqan, F. (2020). IoT for Smart Learning/Education. 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA). https://doi.org/10.1109/CITISIA50690.2020.9371774
- Tahan, M. (2019). Artificial Intelligence applications and psychology: An overview. Neuropsychopharmacologia Hungarica, 21(3), 8.
- Talan, T. (2021). Artificial Intelligence in Education: A Bibliometric Study. International Journal of Research in Education and Science, 7(3), 822-837. https://doi.org/10.46328/ijres.2409
- Tarik, A., Aissa, H., & Yousef, F. (2021). Artificial Intelligence and Machine Learning to Predict Student Performance during the COVID-19. Procedia Computer Science, 184, 835-840. https://doi.org/10.1016/j.procs.2021.03.104
- Tautz, D., Sprenger, D. A., & Schwaninger, A. (2021). Evaluation of four digital tools and their perceived impact on active learning, repetition and feedback in a large university class. Computers & Education, 175, 104338. https://doi.org/10.1016/j.compedu.2021.104338
- von Davier, A. A., Deonovic, B., Yudelson, M., Polyak, S. T., & Woo, A. (2019). Computational Psychometrics Approach to Holistic Learning and Assessment Systems. Frontiers in Education, 4. https://www.frontiersin.org/articles/10.3389/feduc.2019.00069
- Wang, G., Yin, J., Hossain, M. S., & Muhammad, G. (2021). Incentive mechanism for collaborative distributed learning in Artificial Intelligence of Things. Future Generation Computer Systems, 125, 376-384. https://doi.org/10.1016/j.future.2021.06.015
- Wang, M., & Deng, W. (2021). Deep Face Recognition: A Survey. Neurocomputing, ScienceDirect, 429, 215-244. https://doi.org/10.1016/j.neucom.2020.10.081
- Xu, J. J., & Babaian, T. (2021). Artificial intelligence in business curriculum: The pedagogy and learning outcomes. The International Journal of Management Education, 19(3), 100550. https://doi.org/10.1016/j.ijme.2021.100550
- Yang, L., Li, Z., Ma, S., & Yang, X. (2022). Artificial intelligence image recognition based on 5G deep learning edge algorithm of Digestive endoscopy on medical construction. Alexandria Engineering Journal, 61(3), 1852-1863. https://doi.org/10.1016/j.aej.2021.07.007
- Yee Chung, J. W., Fuk So, H. C., Tak Choi, M. M., Man Yan, V. C., & Shing Wong, T. K. (2021). Artificial Intelligence in education: Using heart rate variability (HRV) as a biomarker to assess emotions objectively. Computers and Education: Artificial Intelligence, 2, 100011. https://doi.org/10.1016/j.caeai.2021.100011
Referencias
Aiquipa, W. A., Flores, E., Sernaque, F., Fuentes, A., Cueva, J., & Núñez, E. O. (2019). Integrated Low-Cost Platform for the Capture, Processing, Analysis and Control in Real Time of Signals and Images. 2nd International Conference on Sensors, Signal and Image Processing. https://doi.org/10.1145/3365245.3365249
Corvalán, J. G. (2018). Inteligencia artificial: Retos, desafíos y oportunidades - Prometea: la primera inteligencia artificial de Latinoamérica al servicio de la Justicia. Revista de Investigações Constitucionais, 5, 295-316. https://doi.org/10.5380/rinc.v5i1.55334
de Mello, F. L., & de Souza, S. A. (2019). Psychotherapy and Artificial Intelligence: A Proposal for Alignment. Frontiers in Psychology, 10. https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00263
Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2019.00482
El Hechi, M., Ward, T. M., An, G. C., Maurer, L. R., El Moheb, M., Tsoulfas, G., & Kaafarani, H. M. (2021). Artificial Intelligence, Machine Learning, and Surgical Science: Reality Versus Hype. Journal of Surgical Research, 264, A1-A9. https://doi.org/10.1016/j.jss.2021.01.046
Fakhoury, M. (2019). Artificial Intelligence in Psychiatry. En Y.-K. Kim (Ed.), Frontiers in Psychiatry: Artificial Intelligence, Precision Medicine, and Other Paradigm Shifts (pp. 119-125). Springer. https://doi.org/10.1007/978-981-32-9721-0_6
Gomez, A., & Gomez, K. (2019). Muestreo estadístico para docentes y estudiantes (1ª Ed.). Independently published.
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism-clinical and experimental, 69, S36-S40. https://doi.org/10.1016/j.metabol.2017.01.011
Hasnine, M. N., Ahmed, M. M. H., & Ueda, H. (2021). Learner-Centric Technologies to Support Active Learning Activity Design in New Education Normal: Exploring the Disadvantageous Educational Contexts. International Journal of Emerging Technologies in Learning (IJET), 16(10), 150-162. https://doi.org/10.3991/ijet.v16i10.20081
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.90
Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917-926. https://doi.org/10.1002/ajim.23037
Jirapanthong, W. (2020). A Tool for Supporting the Evaluation of Active Learning Activities. En Y. Tan, Y. Shi, & M. Tuba (Eds.), Advances in Swarm Intelligence (pp. 476-484). Springer International Publishing. https://doi.org/10.1007/978-3-030-53956-6_43
King, D. E. (2009). Dlib-ml: A Machine Learning Toolkit. Journal of machine learning research, 10, 1755-1758.
León, O., & Romero, J. (2020). Ambientes de aprendizaje accesibles que fomentan la afectividad en contextos universitarios. Universidad Distrital Francisco José de Caldas.
Montaño, J. (2002) Redes Neuronales Artificiales aplicadas al Análisis de Datos [Tesis doctoral]. Universitat De Les Illes Balears. http://hdl.handle.net/11201/2511
Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(1), 81. https://doi.org/10.1007/s13278-021-00776-6
Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of Artificial Intelligence powered digital writing assistant in higher education: Randomized controlled trial. Heliyon, 7(5), e07014. https://doi.org/10.1016/j.heliyon.2021.e07014
Orrù, G., Monaro, M., Conversano, C., Gemignani, A., & Sartori, G. (2020). Machine Learning in Psychometrics and Psychological Research. Frontiers in Psychology, 10. https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02970
Pulido, F. P., & Herrera, F. H. (2017). La influencia de las emociones sobre el rendimiento académico. Ciencias Psicológicas, 29-39. https://doi.org/10.22235/cp.v11i2.1344
Ramos, C. A. (2015). Los paradigmas de la investigación científica. Avances en Psicología, 23(1), 9-17. https://doi.org/10.33539/avpsicol.2015.v23n1.167
Roberts, K., & Rosselot, C. (2019). Experiencia de acompañamiento a estudiantes para la permanencia en la educación superior desde una perspectiva Socioeducativa: El caso de la Universidad de Santiago de Chile. Congresos CLABES.
Santoso, K., & Kusuma, G. P. (2018). Face Recognition Using Modified OpenFace. Procedia Computer Science, 135, 510-517. https://doi.org/10.1016/j.procs.2018.08.203
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2015.7298682
Serengil, S. I., & Ozpinar, A. (2020). LightFace: A Hybrid Deep Face Recognition Framework. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU). https://doi.org/10.1109/ASYU50717.2020.9259802
Serengil, S. I., & Ozpinar, A. (2021). HyperExtended LightFace: A Facial Attribute Analysis Framework. 2021 International Conference on Engineering and Emerging Technologies (ICEET). https://doi.org/10.1109/ICEET53442.2021.9659697
Sharma, S., Shanmugasundaram, K., & Ramasamy, S. K. (2016). FAREC — CNN based efficient face recognition technique using Dlib. 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). https://doi.org/10.1109/ICACCCT.2016.7831628
Shetty, A. B., Bhoomika, Deeksha, Rebeiro, J., & Ramyashree. (2021). Facial recognition using Haar cascade and LBP classifiers. Global Transitions Proceedings, 2(2), 330-335. https://doi.org/10.1016/j.gltp.2021.08.044
Shrestha, S. K., & Furqan, F. (2020). IoT for Smart Learning/Education. 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA). https://doi.org/10.1109/CITISIA50690.2020.9371774
Tahan, M. (2019). Artificial Intelligence applications and psychology: An overview. Neuropsychopharmacologia Hungarica, 21(3), 8.
Talan, T. (2021). Artificial Intelligence in Education: A Bibliometric Study. International Journal of Research in Education and Science, 7(3), 822-837. https://doi.org/10.46328/ijres.2409
Tarik, A., Aissa, H., & Yousef, F. (2021). Artificial Intelligence and Machine Learning to Predict Student Performance during the COVID-19. Procedia Computer Science, 184, 835-840. https://doi.org/10.1016/j.procs.2021.03.104
Tautz, D., Sprenger, D. A., & Schwaninger, A. (2021). Evaluation of four digital tools and their perceived impact on active learning, repetition and feedback in a large university class. Computers & Education, 175, 104338. https://doi.org/10.1016/j.compedu.2021.104338
von Davier, A. A., Deonovic, B., Yudelson, M., Polyak, S. T., & Woo, A. (2019). Computational Psychometrics Approach to Holistic Learning and Assessment Systems. Frontiers in Education, 4. https://www.frontiersin.org/articles/10.3389/feduc.2019.00069
Wang, G., Yin, J., Hossain, M. S., & Muhammad, G. (2021). Incentive mechanism for collaborative distributed learning in Artificial Intelligence of Things. Future Generation Computer Systems, 125, 376-384. https://doi.org/10.1016/j.future.2021.06.015
Wang, M., & Deng, W. (2021). Deep Face Recognition: A Survey. Neurocomputing, ScienceDirect, 429, 215-244. https://doi.org/10.1016/j.neucom.2020.10.081
Xu, J. J., & Babaian, T. (2021). Artificial intelligence in business curriculum: The pedagogy and learning outcomes. The International Journal of Management Education, 19(3), 100550. https://doi.org/10.1016/j.ijme.2021.100550
Yang, L., Li, Z., Ma, S., & Yang, X. (2022). Artificial intelligence image recognition based on 5G deep learning edge algorithm of Digestive endoscopy on medical construction. Alexandria Engineering Journal, 61(3), 1852-1863. https://doi.org/10.1016/j.aej.2021.07.007
Yee Chung, J. W., Fuk So, H. C., Tak Choi, M. M., Man Yan, V. C., & Shing Wong, T. K. (2021). Artificial Intelligence in education: Using heart rate variability (HRV) as a biomarker to assess emotions objectively. Computers and Education: Artificial Intelligence, 2, 100011. https://doi.org/10.1016/j.caeai.2021.100011