Analysis of Social Networks and Video Conferencing Systems in the Educational Context Through Data Science
Abstract
The aim of this mixed research is to analyse the perceptions of university students about the use of social networks and video conferencing systems during the COVID-19 post-pandemic through data science. The participants are 103 students of the Faculty of Sciences at the National Autonomous University of Mexico. The results of the deep learning algorithm indicate that social networks and video conferencing systems positively impact student autonomy and the exchange of ideas. The random forest algorithm facilitated the creation of the models on these tools considering the characteristics of the participants. Social networks facilitate use of multimedia resources, publication of school content and review of information. Likewise, video conferencing systems facilitate the realisation of classes in virtual modality through recordings and interaction between the educator and students. In conclusion, the use of social networks and video conferencing systems favour the planning and execution of new school activities at home and in the classroom.
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References
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