Research Article
Asynchronous Assistance: A Social Network Analysis of Influencing Peer Interactions in PeerWise
Tomas Shields,
Geraldine Gray
,
Barry J. Ryan
Shields T, Gray G, Ryan BJ. Asynchronous assistance: A social network analysis of influencing peer interactions in PeerWise. . 2020;1(1):43-52. doi: 10.12973/ejmse.1.1.43
Shields, T., Gray, G., & Ryan, B. J. (2020). Asynchronous assistance: A social network analysis of influencing peer interactions in PeerWise. European Journal of Mathematics and Science Education, 1(1), 43-52. https://doi.org/10.12973/ejmse.1.1.43
Shields Tomas, Geraldine Gray, and Barry J. Ryan. "Asynchronous Assistance: A Social Network Analysis of Influencing Peer Interactions in PeerWise," European Journal of Mathematics and Science Education 1, no. 1 (2020): 43-52. https://doi.org/10.12973/ejmse.1.1.43
Shields, T Gray, G & Ryan, B 2020, 'Asynchronous assistance: A social network analysis of influencing peer interactions in PeerWise', European Journal of Mathematics and Science Education, vol. 1, no. 1, pp. 43-52. Shields, Tomas et al. "Asynchronous Assistance: A Social Network Analysis of Influencing Peer Interactions in PeerWise." European Journal of Mathematics and Science Education, vol. 1, no. 1, 2020, pp. 43-52, https://doi.org/10.12973/ejmse.1.1.43.
Abstract
This mixed methods, investigative case study explored student patterns of use within the online PeerWise platform to identify the most influencing activities and to build a model capable of predicting performance based on these influencing activities. Peerwise is designed to facilitate student peer-to-peer engagement through creating, answering and ranking multiple choice questions; this study sought to understand the relationship between student engagement in Peerwise and learning performance. To address the research question, various usage metrics were explored, visualized and modelled, using social network analysis with Gephi, Tableau and Python. These findings were subsequently analyzed in light of the qualitative survey data gathered. The most significant activity metrics were evaluated leading to rich data visualisations and identified the activities that influenced academic performance in this study. The alignment of the key qualitative and quantitative findings converged on answering questions as having the greatest positive impact on learner performance. Furthermore, from a quantitative perspective the Average Comment Length and Average Explanation Length correlated positively with superior academic performance. Qualitatively, the motivating nature of PeerWise community also engaged learners. The key limitation of the size of the data set within the investigative case study suggests further research, with additional student cohorts as part of an action research paradigm, to broaden these findings.
Keywords: Learning analytics, social network analysis, PeerWise, peer-to-peer learning.
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