El impacto de la pandemia de COVID-19 en los tweets de los profesores en EspañaNecesidades, intereses e implicaciones emocionales

  1. Olga Moreno-Fernández 1
  2. Alejandro Gómez-Camacho 1
  1. 1 Universidad de Sevilla, Spain
Revista:
Educación XX1: Revista de la Facultad de Educación

ISSN: 1139-613X 2174-5374

Año de publicación: 2023

Volumen: 26

Número: 2

Páginas: 185-208

Tipo: Artículo

Otras publicaciones en: Educación XX1: Revista de la Facultad de Educación

Resumen

La difusión del Covid-19 impuso el confinamiento de gran parte de la población mundial. Por este motivo, en España las clases presenciales se interrumpieron y no se reanudaron hasta septiembre de 2021. La situación obligó a los centros educativos a trasladar tanto la docencia como la comunicación entre el profesorado a un entorno digital, lo que favoreció un mayor uso de las redes sociales. Este trabajo realiza un estudio exploratorio de 30751 tweets extraídos de ocho hashtags educativos (#eduhora, #claustrovirtual, #SerProfeMola, #otraeducaciónesposible, #claustrotuitero, #profesquemolan, #orgullodocente, y #soymaestro) utilizados por la comunidad educativa de profesores en España. Se realiza un análisis semántico de contenidos que utiliza una metodología mixta basada en la minería de datos públicos y el análisis de sentimientos. El análisis de los datos proporcionó información novedosa sobre las necesidades, los intereses y las preocupaciones, así como las implicaciones emocionales que el profesorado expresó en la red social Twitter durante la transición a la enseñanza virtual. La situación de encierro se asoció con el aumento del contenido emocional en los tuits analizados en la muestra, independientemente de la polaridad positiva, negativa o neutra de los mismos. Los resultados muestran también que el profesorado en España utiliza las redes sociales para el desarrollo profesional y el apoyo emocional, y que esta tendencia ha aumentado después del Covid-19. El uso de la red social Twitter se vincula con el desarrollo profesional continuo en momentos de especial dificultad también en España, como ha sucedido en otros países. Las conclusiones del estudio ponen de manifiesto que el archivo histórico de Twitter es un recurso válido para el análisis de los sentimientos del profesorado en investigaciones longitudinales que incluyan el periodo de Covid-19.

Referencias bibliográficas

  • Arora, A., Chakraborty, P., Bhatia, M. P. S., & Mittal, P. (2021). Role of emotion in excessive use of Twitter during COVID-19 imposed lockdown in India. Journal of Technology in Behavioral Science, 6, 370-377. https://doi.org/10.1007/s41347-020-00174-3
  • Beardsley, M., Albó, L., & Aragón, P. (2021). Emergency education effects on teacher abilities and motivation to use digital technologies. British Journal of Educational Technology, 52(4), 1455-1477. https://doi.org/10.1111/bjet.13101
  • Carpenter, J. P., & Krutka, D. G. (2015). Engagement through microblogging: educator professional development via Twitter. Professional Development in Education, 41(4), 707-728. https://doi.org/10.1080/19415257.2014.939294
  • Carpenter, J. P., Trust, T., Kimmons, R., & Krutka, D. G. (2021). Sharing and self-promoting: An analysis of educator tweeting at the onset of the COVID-19 pandemic. Computers and Education Open, 2, Artículo 100038. https://doi.org/10.1016/j.caeo.2021.100038
  • Carpenter, J., Tani, T., Morrison, S., & Keane, J. (2020). Exploring the landscape of educator professional activity on Twitter: an analysis of 16 education-related Twitter hashtags. Professional Development in Education, 48, 784-805. https://doi.org/10.1080/19415257.2020.1752287
  • Chen, Y., Yuan, J., You, Q., & Luo, J. (2018). Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM. MM’18: Proceedings of the 26th ACM International Conference on Multimedia Seoul (pp. 117-125). Republic of Korea.
  • Del Olmo, E., & Arias, I. (2021). An empirical study with sketch engine on the syntactic-pragmatic interface for the identification of thematic structure in Spanish. Revista de Humanidades Digitales, 6, 129-150. https://doi.org/10.5944/rhd.vol.6.2021.30965
  • Fernández-Gavilanes, M., Juncal-Martínez, J., García-Méndez, S., Costa-Montenegro, E., & González-Castaño, F. J. (2018). Creating emoji lexica from unsupervised sentiment analysis of their descriptions. Expert Systems with Applications, 103(1), 74-91. https://doi.org/10.1016/j.eswa.2018.02.043
  • Firoozeh, N., Nazarenko, A., Alizon, F., & Daille, B. (2020). Keyword extraction: Issues and methods. Natural Language Engineering, 26(3), 259-291. https://doi.org/10.1017/S1351324919000457
  • Galvin, S., & Greenhow, C. (2020). Educational networking: A novel discipline for improved K-12 learning based on social networks. En A. Peña-Ayala (Ed.), Educational networking: A novel discipline for improved learning based on social networks (pp. 3-41). Springer.
  • Gao, F., & Li, L., (2017). Examining a one-hour synchronous chat in a microblogging-based professional development community. British Journal of Educational Technology, 48(2), 332-347. https://doi.org/10.1111/bjet.12384
  • Gee, J. P. (2017). Affinity spaces and 21st century learning. Educational Technology, 57(2), 27-31.
  • Gómez, M., & Journell, W. (2017). Professionality, preservice teachers, and Twitter. Journal of Technology and Teacher Education, 25(4), 377-412.
  • Greenhalgh, S. P., Rosenberg, J. M., & Russell, A. (2021). The influence of policy and context on teachers’ social media use. British Journal of Educational Technology, 52(5), 2020-2037. https://doi.org/10.1111/bjet.13096
  • Greenhalgh, S. P., Rosenberg, J. M., Willet, K. B. S., Koehler, M. J., & Akcaoglu, M. (2020). Identifying multiple learning spaces within a single teacher-focused Twitter hashtag. Computers & Education, 148, 103809. https://doi.org/10.1016/j.compedu.2020.103809
  • Greenhow C., Staudt Willet K. B., & Galvin S. (2021). Inquiring tweets want to know: #Edchat supports for #RemoteTeaching during COVID-19. British Journal of Educational Technology, 52(4), 1434-1454. https://doi.org/10.1111/bjet.13097
  • Harris, A. (2020). COVID-19–school leadership in crisis? Journal of Professional Capital and Community, 5(3/4), 321-326. https://doi.org/10.1108/JPCC-06-2020-0045
  • Harron, J., & Liu, S. (2022). Tweeting about teachers and COVID-19: An emotion and sentiment analysis approach. En E. Langran (Ed.), Society for Information Technology & Teacher Education International Conference (pp. 1502-1511). Association for the Advancement of Computing in Education (AACE).
  • Kimmons, R., & Veletsianos, R. (2018). Public internet data mining methods in instructional design, educational technology, and online learning research. TechTrends, 62, 492-500. https://doi.org/10.1007/s11528-018-0307-4
  • Li, X., Zhang, J., Du, Y., Zhu, J., Fan, Y., & Chen, X. (2022). A novel deep learning-based sentiment analysis method enhanced with emojis in microblog social networks. Enterprise Information Systems, 1-22. https://doi.org/10.1080/17517575.2022.2037160
  • Luo, T., Freeman, C., & Stefaniak, J. (2020). “Like, comment, and share” professional development through social media in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1659-1683. https://doi.org/10.1007/s11423-020-09790-5
  • Menon, A., Klein, E. J., Kollars, K., & Kleinhenz, A. L. W. (2020). Medical students are not essential workers: examining institutional responsibility during the COVID-19 pandemic. Academic Medicine, 95(8), 1149-1151. https://doi.org/10.1097/ACM.0000000000003478
  • Michaels, D., & Warners, G. R. (2020). Occupational Safety and Health Administration (OSHA) and worker safety during the COVID-19 pandemic. JAMA, 324(14), 1389-1390. https://doi.org/10.1001/jama.2020.16343
  • Neuendorf, K A. (2017). The content analysis guidebook. SAGE.
  • Nochumson, T.C. (2020). Elementary schoolteachers’ use of twitter: exploring the implications of learning through online social media. Professional Development in Education, 46(2), 306-323. https://doi.org/10.1080/19415257.2019.1585382
  • Osorio, S., Peña, A., & Espinoza-Valdez, A. (2021). Systematic literature review of sentiment analysis in the Spanish language. Data Technologies and Applications, 55(4), 461-479. https://doi.org/10.1108/DTA-09-2020-0200
  • Parrish, C. W., & Martin, W. G. (2022). Cognitively demanding tasks and the associated learning opportunities within the Math Twitter Blogosphere. International Journal of Mathematical Education in Science and Technology, 53(2), 364-402. https://doi.org/10.1080/0020739X.2020.1772388
  • Pokhrel, S., & Chhetri, R. (2021). A literature review on impact of COVID-19 pandemic on teaching and learning. Higher Education for the Future, 8(1), 133-141. https://doi.org/10.1177/2347631120983481
  • Quintana-Gómez, A. (2021). Análisis de los procesos de tratamiento de información en un estudio de análisis de sentimiento utilizando la tecnología de Google. Vivat Academia. Revista de Comunicación, 154, 41-55. http://doi.org/10.15178/va.2021.154.e1336
  • Rehm, M., Moukarzel, S., Daly, A. J., & Del Fresno, M. (2021). Exploring online social networks of school leaders in times of COVID-19. British Journal of Educational Technology, 52(4), 1414-1433. https://doi.org/10.1111/bjet.13099
  • Rosell-Aguilar, F. (2018). Twitter: A professional development and community of practice tool for teachers. Journal of Interactive Media in Education, 2018(1), 6. http://doi.org/10.5334/jime.452
  • Sailunaz, K., & Alhajj, R. (2019). Emotion and sentiment analysis from Twitter text. Journal of Computational Science, 36, Artículo 101003. https://doi.org/10.1016/j.jocs.2019.05.009
  • Scott, M. (1997). PC analysis of key words – And key key words. System, 25(2), 233-245. https://doi.org/10.1016/S0346-251X(97)00011-0
  • Semingson, P., & Kerns, W. (2020). Categorizing and leveraging hashtag-based efforts to #Keeplearning and #Keepteaching with remote learning due to COVID-19. En Proceedings of EdMedia + Innovate Learning (pp. 115–119). Association for the Advancement of Computing in Education (AACE).
  • Trust, T., Carpenter, J.P., Krutka, D.G., & Kimmons, R. (2020). #RemoteTeaching & #RemoteLearning: Educator tweeting during the COVID-19 pandemic. Journal of Technology and Teacher Education, 28(2), 151-159.
  • Trust, T., Krutka, D. G., & Carpenter, J. P. (2016). “Together we are better”: Professional learning networks for teachers. Computers & Education, 102, 15-34. https://doi.org/10.1016/j.compedu.2016.06.007
  • Willet, K. B. S. (2019). Revisiting how and why educators use Twitter: Tweet types and purposes in #Edchat. Journal of Research on Technology in Education, 51, 273-289. https://doi.org/10.1080/15391523.2019.1611507
  • Xing, W., & Gao, F. (2018). Exploring the relationship between online discourse and commitment in Twitter professional learning communities. Computers & Education, 126, 388-398. https://doi.org/10.1016/j.compedu.2018.08.010
  • Zhou, J., & Ye, J. (2020). Sentiment analysis in education research: a review of journal publications. Interactive Learning Environments, 1-13. https://doi.org/10.1080/10494820.2020.1826985
  • Zhou, M., & Mou, H. (2022). Tracking public opinion about online education over COVID‑19 in China. Education Tech Research Dev, 70, 1083-1104. https://doi.org/10.1007/s11423-022-10080-5