Social Relationship and Content Analyses in an Online Class Community under Covid-19
DOI:
https://doi.org/10.54097/jceim.v11i1.10478Keywords:
Social network, Online interaction, Social network analysis, Latent Dirichlet allocation, Pearson correlationAbstract
Clarifying the blog’s background and successfully grasping its main subject are essential components of any examination of the blogging community. There are numerous previous researches on how to evaluate blogs and comments to discover the connections and content in a social group. This work seeks to focus on blog comments based on students’ relationships and conduct in a class community and to complement prior research techniques by emphasizing the importance of blog community analysis under Covid-19. It also provides an evaluation of the participation and performance results. We mainly focus on analyzing comments as the interaction among students and discuss the similarity in some highly performance students. With the help of social network analysis for online course student behavior, some tendency related to student behavior in this group has been shown in the experiment results by bias analysis, Pearson correlation, and Latent Dirichlet allocation, which serve as the data processing tools in this experiment.
References
Haushofer, J., Metcalf, C.J.E.: Which interventions work best in a pandemic? Science 368(6495), 1063–1065 (2020).
Walker, P.G., Whittaker, C., Watson, O.J., Baguelin, M., Winskill, P., Hamlet, A., Djafaara, B.A., Cucunub´a, Z., Olivera Mesa, D., Green, W., et al.: The impact of covid-19 and strategies for mitigation and suppression in low-and middle-income countries. Science 369(6502), 413–422 (2020).
Santos, H.: Covid-19 lockdown effects on student grades of a university engineering course: A psychometric study. IEEE Transactions on Education (2021).
Drury, J., Carter, H., Cocking, C., Ntontis, E., Tekin Guven, S., Amlˆot, R.: Facilitating collective psychosocial resilience in the public in emergencies: Twelve recommendations based on the social identity approach. Frontiers in public health 7, 141 (2019).
Vercellone-Smith, P., Jablokow, K., Friedel, C.: Characterizing communication networks in a web-based classroom: Cognitive styles and linguistic behavior of self-organizing groups in online discussions. Computers & Education 59(2), 222–235 (2012).
Biggs, N., Lloyd, E.K., Wilson, R.J.: Graph Theory, 1736-1936. Oxford University Press (1986).
Hagberg, A., Swart, P., S Chult, D.: Exploring network structure, dynamics, and function using NetworkX (2008).
James, L.R., Demaree, R.G., Wolf, G.: Estimating within-group interrater reliability with and without response bias. Journal of applied psychology 69(1), 85 (1984).
Hinton, A.: Understanding context: Environment, language, and information architecture.” O’Reilly Media, Inc.” (2014).
Chowdhary, K.: Natural language processing. Fundamentals of artificial intelligence, 603–649 (2020).
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).
Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research 50, 723–762 (2014).
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of machine Learning research 3(Jan), 993–1022 (2003)
Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson Correlation Coefficient, Springer (2009).
Downloads
Published
Issue
Section
How to Cite
