Prestige, Centrality, and Learning: A Social Network Analysis of an Online Class

Abstract This study explored relations between social network characteristics in an online graduate class and two learning outcomes: affective and cognitive learning. The social network analysis data were compiled by entering the number of one-to-one postings sent by each student to each other student in a course web site discussion space into a specially designed spreadsheet. Regression analysis revealed that both network prestige and network centrality were robust predictors of cognitive learning outcomes. Self-reported affective learning, however, was not related to network factors. Results illustrate the utility of social network analysis in understanding interaction and learning outcomes in online classes. Keywords: Online LearningSocial Network AnalysisCognitive LearningAffective LearningNetwork PrestigeNetwork Centrality An earlier version of this paper was presented as part of a panel presentation sponsored by the Human Communication and Technology Commission at the annual meeting of the National Communication Association, New Orleans, LA, November 2002. An earlier version of this paper was presented as part of a panel presentation sponsored by the Human Communication and Technology Commission at the annual meeting of the National Communication Association, New Orleans, LA, November 2002. Notes An earlier version of this paper was presented as part of a panel presentation sponsored by the Human Communication and Technology Commission at the annual meeting of the National Communication Association, New Orleans, LA, November 2002. Additional informationNotes on contributorsTracy C. Russo Tracy C. Russo (PhD, University of Kansas, 1995) is an assistant professor at the University of Kansas Joy Koesten Joy Koesten (PhD, University of Kansas, 2002) is a research assistant professor at the University of Kansas School of Medicine