Informal learning in social networks during the COVID-19 pandemic: an empirical analysis

Ocaña, Mauro; Mejía, Rebeca; Larrea, Carolina; Analuisa Maiguashca, Jessica Carolina; Freire, Carla

http://hdl.handle.net/10400.8/6000

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Authors

Ocaña, Mauro

Mejía, Rebeca

Larrea, Carolina

Analuisa Maiguashca, Jessica Carolina

Freire, Carla

Abstract(s)

The use of the Internet and social networks have increased dramatically during the COVID quarantine mainly because several activities were moved online. In education, numerous stakeholders stayed at home and their academic plans were modified and adapted to an entire virtual environment. This was the case of a live event (Science Café) whose purpose was to disseminate knowledge through Facebook and YouTube. Thus, this study aimed at verifying if there was knowledge construction in social networks through user interactions by using 1,083 comments posted by the audience. Comments were coded according to validated frameworks for language taxonomy and collaborative knowledge construction. Results show that the predominant interaction is that in which viewers pose questions to speakers. Our analyses also revealed that attendees hardly reached the highest levels of knowledge construction through unguided interaction. Often, user interactions went beyond emotional expressions towards evaluation and therefore, could reach a higher level of knowledge construction. This study shows that social networks may offer informal spaces for deliberation and collaborative interaction with the potential to support learning if guided properly. This research aims to contribute empirical evidence to the growing body of literature that online interactions in informal environments may provide productive learning.

Keywords

Informal learning Knowledge construction Social network analysis Sociograms SNA COVID-19

URI

http://hdl.handle.net/10400.8/6000

Citation

Ocaña M., Mejía R., Larrea C., Analuisa C., Freire C. (2021) Informal Learning in Social Networks During the COVID-19 Pandemic: An Empirical Analysis. In: Botto-Tobar M., Cruz H., Díaz Cadena A. (eds) Artificial Intelligence, Computer and Software Engineering Advances. CIT 2020. Advances in Intelligent Systems and Computing, vol 1327. Springer, Cham. https://doi.org/10.1007/978-3-030-68083-1_30