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Buildup of speaking skills in an online learning community: a network-analytic exploration

Rasoul Shafipour (), Raiyan Abdul Baten, Md Kamrul Hasan, Gourab Ghoshal, Gonzalo Mateos and Mohammed Ehsan Hoque
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Rasoul Shafipour: University of Rochester
Raiyan Abdul Baten: University of Rochester
Md Kamrul Hasan: University of Rochester
Gourab Ghoshal: University of Rochester
Gonzalo Mateos: University of Rochester
Mohammed Ehsan Hoque: University of Rochester

Palgrave Communications, 2018, vol. 4, issue 1, 1-10

Abstract: Abstract Studies in learning communities have consistently found evidence that peer-interactions contribute to students’ performance outcomes. A particularly important competence in the modern context is the ability to communicate ideas effectively. One metric of this is speaking, which is an important skill in professional and casual settings. In this study, we explore peer-interaction effects in online networks on speaking skill development. In particular, we present an evidence for gradual buildup of skills in a small-group setting that has not been reported in the literature. Evaluating the development of such skills requires studying objective evidence, for which purpose, we introduce a novel dataset of six online communities consisting of 158 participants focusing on improving their speaking skills. They video-record speeches for 5 prompts in 10 days and exchange comments and performance-ratings with their peers. We ask (i) whether the participants’ ratings are affected by their interaction patterns with peers, and (ii) whether there is any gradual buildup of speaking skills in the communities towards homogeneity. To analyze the data, we employ tools from the emerging field of Graph Signal Processing (GSP). GSP enjoys a distinction from Social Network Analysis in that the latter is concerned primarily with the connection structures of graphs, while the former studies signals on top of graphs. We study the performance ratings of the participants as graph signals atop underlying interaction topologies. Total variation analysis of the graph signals show that the participants’ rating differences decrease with time (slope = −0.04, p

Date: 2018
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