Analysis of emotional behavior in high school students in virtual learning
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Published:
Feb 7, 2022
Keywords:
emotion, e-learning, self-regulation, digital platform.
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Abstract
Introduction. Education is constantly evolving and improving with the innovation of new methodologies, strategies, resources and technological advances. Objective. To compare the academic performance of education students through emotions to improve virtual learning. Methodology. Design science research was used. The methods applied were analytical, synthetic and experimental. The study was carried out in the "Nueva Concordia" Educational Unit, to a group of eleven high school students, obtaining a non-probabilistic sampling due to the world pandemic. The instrument applied in the survey was a questionnaire, which by means of the Likert scale allowed measuring the valuation of the student's emotions and for the internal consistency index the Cronbach's Alpha coefficient was used with a value of 0.82 of very high reliability and validity. Then, to indicate the degree of statistical relationship between emotions and grades, Pearson's correlation coefficient was applied. Results. The results showed that the emotions; happiness, surprise and disgust are positively correlated and the emotions fear and neutral negatively correlated with the virtual tutor's grade. Conclusion. The research allows us to conclude that emotions are not strongly correlated with the student's grade after the interaction, however they do influence it. This is due to the fact that all people are different and although some similarities are found in this sense it does not mean that they are fulfilled in a generalized way in all people.
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Sarmiento Saavedra, J. C., Chango Sailema, W. G., Mendoza Loor, J. J., & Troya Loor, M. A. (2022). Analysis of emotional behavior in high school students in virtual learning. ConcienciaDigital, 5(1), 130-152. https://doi.org/10.33262/concienciadigital.v5i1.2063
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References
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Khalfallah, J., & Ben Hadj Slama, J. (2019). The effect of emotional analysis on the improvement of experimental e-learning systems. Computer Applications in Engineering Education, 27(2), 303–318.
Kumar, J. A., Muniandy, B., & Wan Yahaya, W. A. J. (2019). Exploring the effects of emotional design and emotional intelligence in multimedia-based learning: an engineering educational perspective. New Review of Hypermedia and Multimedia, 25(1–2), 57–86.
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Mao, Q. R., Pan, X. Y., Zhan, Y. Z., & Shen, X. J. (2015). Using Kinect for real-time emotion recognition via facial expressions. Frontiers of Information Technology and Electronic Engineering, 16(4), 272–282.
Mega, C., Ronconi, L., & De Beni, R. (2014). What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic Achievement. Journal of Educational Psychology, 106(1), 121–131.
Molinari, G., Trannois, M., Tabard, A., & Lavoué, E. (2016). EMORE-L: An emotion reporting tool for distance learning. Actes de La 28ième Conférence Francophone Sur l’Interaction Homme-Machine, 167–176.
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Nikopoulou, R., Spyrou, E., Vernikos, I., & Mylonas, P. (2018). Emotion recognition from speech: A classroom experiment. ACM International Conference Proceeding Series, 104–105.
Picciano, A. G. (2013). Theories and Frameworks for Online Education : Seeking an Integrated Model. 166–190.
Ramachandiran, C. R., Mahmud, M. M., & Jomhari, N. (2019). A revolutionary approach in virtual learning: Usercentered kansei virtual agent. ACM International Conference Proceeding Series, 7–11.
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Saneiro, M., Santos, O., Salmeron-Majadas, S., & Boticario, J. (2015). Towards using influence diagram on social-network based analysis for managing students’ collaborations. Computer-Supported Collaborative Learning Conference, CSCL, 2, 647–648.
Sautu, R. (2005). TODO es TEORÍA Objetivos y métodos de investigación (Lumiere). Buenos Aires.
Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273–1296.
Taruchaín Pozo, L. F., Freire Palacios, V. A., & Mayorga Álvarez, M. de los Á. (2020). Bienestar psicológico y estrés laboral en trabajadores no profesionales de la ciudad de Ambato, aplicando el cuestionario de salud general de Goldberg GHQ-12. Ciencia Digital, 4(1.1.), 32–44.
Zhong, S. H., Li, Y., Liu, Y., & Wang, Z. (2017). A computational investigation of learning behaviors in MOOCs. Computer Applications in Engineering Education, (March), 1–13.
Zimmerman, B. (1990). Self-Regulated Learning and Academic Achievement: An Overview. Educational Psychologist, 25(1), 3–17.
Awofala, A. O., Olabiyi, O. S., Awofala, A. A., Arigbabu, A. A., Fatade, A. O., & Udeani, U. N. (2019). Attitudes toward Computer, Computer Anxiety and Gender as determinants of Pre-service Science, Technology and Mathematics Teachers’ Computer Self-efficacy. Digital Education Review, (36), 51–67.
Barrera Erreyes, H. M., Barragán García, T. M., & Ortega Zurita, G. E. (2017). La realidad educativa ecuatoriana. Revista Iberoamericana de Educación, 75, 9–20.
Bisandu, D. B. (2016). Design Science Research Methodology in Computer Science and Information Systems. International Journal of Information Technology, (November 2016), 1–7.
Bledsoe, W. W. (1963). Informe de un proyecto de reconocimiento facial. February 23, 2020, https://archive.org/details/firstfacialrecognitionresearch/page/n3/mode/2up
Bogarín, A., Cerezo, R., & Romero, C. (2017). A survey on educational process mining. 17
Carretero Bermejo, R. (2015). Intelingencia emocional y sexismo: estudio del componente emocional del sexismo (UNED). https://ebookcentral.proquest.com/lib/uteclvtsp/ reader.action?docID=4775759&query=Goleman+%281995%29
Cerezo, R., Bogarín, A., Esteban, M., & Romero, C. (2020). Process mining for self-regulated learning assessment in e-learning. Journal of Computing in Higher Education, 32(1), 74–88.
Chango, W., Cerezo, R., & Romero, C. (2019). Predicting academic performance of university students from multi-sources data in blended learning. ACM International Conference Proceeding Series. Association for Computing Machinery.
Cifuentes, M. (2013). La influencia de la inteligencia emocional en el rendimiento matemático de alumnos de educación secundaria. Aplicación de un programa de intervención psicopedagógica de educación emocional. 388. https://repositorio.ucjc.edu/.../Tesis Doctoral_Ma Elisabet
Clarizia, F., Colace, F., De Santo, M., Lombardi, M., Pascale, F., & Pietrosanto, A. (2018). E-learning and sentiment analysis: A case study. ACM International Conference Proceeding Series, 111–118.
Cordero, J., & Aguilar, J. (2016). Reconocimiento multimodal de emociones humanas en un ambiente inteligente. (September), 107.
Dresch, A., Pacheco Lacersa, D., & Valle Antunes, A. (2018). Design-science research, A Method for Science and Technology Advancement. In Research Methods: Information, Systems, and Contexts: Second Edition.
Engeness, I., Nohr, M., Singh, A. B., & Mørch, A. (2019). Use of videos in the Information and Communication Technology Massive Open Online Course: Insights for learning and development of transformative digital agency with pre- and in-service teachers in Norway. Policy Futures in Education, 1–20.
Espinoza Freire, E. E., & Ricaldi Echevarríaz, M. L. (2018). El tutor en los entornos virtuales de aprendizaje. Universidad y Sociedad, 10(3), 201–210.
Estrada Villa, E. J., & Boude Figueredo, O. R. (2015). Hacia una Propuesta Para Evaluar Ambientes Virtuales de Aprendizaje en Educación Superior. Academia y Virtualidad, 8(2), 14.
Franco Moreno, Y. M. (2017). Rol del Tutor en el Contexto del Aprendizaje Virtual. Journal of Chemical Information and Modeling, 2(6), 270–285.
Greene, J. A., & Azevedo, R. (2010). The measurement of learners’ self-regulated cognitive and metacognitive processes while using computer-based learning environments. Educational Psychologist, 45(4), 203–209.
Harley, J. M., Bouchet, F., Hussain, M. S., Azevedo, R., & Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615–625.
Hernández Lalinde, J., Espinosa Castro, J., Peñaloza Tarazona, M., Fernández González, J., Chacón Rangel, J., Toloza Sierra, C., … Bermúdez Pirela, V. (2018). Sobre El Uso Adecuado Del Coeficiente De Correlación De Pearson: Definición, Propiedades Y Suposiciones. AVFT – Archivos Venezolanos de Farmacología y Terapéutica, 37(5).
Hernandez Sampieri, R., Fernandez Collado, C., & Baptista Lucio, M. del P. (2010). Metodología de la investigación. In J. Mares Chacón (Ed.), Metodología de la investigación (Quinta Ed.). http://www.casadellibro.com/libro-metodologia-de-la-investigacion-5-ed-incluye-cd-rom/9786071502919/1960006
Hew, K. F., & Brush, T. (2007). Integrating technology into K-12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55(3), 223–252.
Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45–58.
Khalfallah, J., & Ben Hadj Slama, J. (2019). The effect of emotional analysis on the improvement of experimental e-learning systems. Computer Applications in Engineering Education, 27(2), 303–318.
Kumar, J. A., Muniandy, B., & Wan Yahaya, W. A. J. (2019). Exploring the effects of emotional design and emotional intelligence in multimedia-based learning: an engineering educational perspective. New Review of Hypermedia and Multimedia, 25(1–2), 57–86.
León Porras, C. J. P. (2009). La inteligencia emocional en estudiantes de educación superior universitaria y no universitaria de Administración de Empresas. 16. https://ebookcentral.proquest.com/lib/uteclvtsp/detail.action?docID=3183493
López Munguía, O. (2008). La Inteligencia emocional y las estrategias de aprendizaje como predictores del rendimiento académico en estudiantes universitarios. http://cybertesis.unmsm.edu.pe/handle/cybertesis/615
Madrid Tamayo, T. (2019). El sistema educativo de Ecuador: un sistema, dos mundos. Revista Andina de Educación, 2(1), 8–17.
Mao, Q. R., Pan, X. Y., Zhan, Y. Z., & Shen, X. J. (2015). Using Kinect for real-time emotion recognition via facial expressions. Frontiers of Information Technology and Electronic Engineering, 16(4), 272–282.
Mega, C., Ronconi, L., & De Beni, R. (2014). What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic Achievement. Journal of Educational Psychology, 106(1), 121–131.
Molinari, G., Trannois, M., Tabard, A., & Lavoué, E. (2016). EMORE-L: An emotion reporting tool for distance learning. Actes de La 28ième Conférence Francophone Sur l’Interaction Homme-Machine, 167–176.
Asamblea Nacional. (2015). Reglamento General a la ley orgánica de educación intercultural. Registro Oficial, (4), 3–7.
Nikopoulou, R., Spyrou, E., Vernikos, I., & Mylonas, P. (2018). Emotion recognition from speech: A classroom experiment. ACM International Conference Proceeding Series, 104–105.
Picciano, A. G. (2013). Theories and Frameworks for Online Education : Seeking an Integrated Model. 166–190.
Ramachandiran, C. R., Mahmud, M. M., & Jomhari, N. (2019). A revolutionary approach in virtual learning: Usercentered kansei virtual agent. ACM International Conference Proceeding Series, 7–11.
Salazar P, C., & Castillo G, S. (2018). Fundamentos básicos de Estadística (Primera Ed). Quito.
Saneiro, M., Santos, O., Salmeron-Majadas, S., & Boticario, J. (2015). Towards using influence diagram on social-network based analysis for managing students’ collaborations. Computer-Supported Collaborative Learning Conference, CSCL, 2, 647–648.
Sautu, R. (2005). TODO es TEORÍA Objetivos y métodos de investigación (Lumiere). Buenos Aires.
Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273–1296.
Taruchaín Pozo, L. F., Freire Palacios, V. A., & Mayorga Álvarez, M. de los Á. (2020). Bienestar psicológico y estrés laboral en trabajadores no profesionales de la ciudad de Ambato, aplicando el cuestionario de salud general de Goldberg GHQ-12. Ciencia Digital, 4(1.1.), 32–44.
Zhong, S. H., Li, Y., Liu, Y., & Wang, Z. (2017). A computational investigation of learning behaviors in MOOCs. Computer Applications in Engineering Education, (March), 1–13.
Zimmerman, B. (1990). Self-Regulated Learning and Academic Achievement: An Overview. Educational Psychologist, 25(1), 3–17.