Capturing the Participation and Social Dimensions of Computer-Supported Collaborative Learning through Social Network Analysis: Which Method and Measures Matter?

Autor/inn/en	Saqr, Mohammed; Viberg, Olga; Vartiainen, Henriikka
Titel	Capturing the Participation and Social Dimensions of Computer-Supported Collaborative Learning through Social Network Analysis: Which Method and Measures Matter?
Quelle	In: International Journal of Computer-Supported Collaborative Learning, 15 (2020) 2, S.227-248 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Saqr, Mohammed)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1556-1607
DOI	10.1007/s11412-020-09322-6
Schlagwörter	Computer Assisted Instruction; Cooperative Learning; Social Networks; Network Analysis; Learning Analytics; Validity; Robustness (Statistics); Educational Indicators + Suchen Sie Ihr Suchwort? Computer based training; Computerunterstützter Unterricht; Kooperatives Lernen; Social network; Soziales Netzwerk; Netzplantechnik; Gültigkeit; Widerstandsfähigkeit; Educational indicato; Bildungsindikator
Abstract	The increasing use of digital learning tools and platforms in formal and informal learning settings has provided broad access to large amounts of learner data, the analysis of which has been aimed at understanding students' learning processes, improving learning outcomes, providing learner support as well as teaching. Presently, such data has been largely accessed from discussion forums in online learning management systems and has been further analyzed through the application of social network analysis (SNA). Nevertheless, the results of these analyses have not always been reproducible. Since such learning analytics (LA) methods rely on measurement as a first step of the process, the robustness of selected techniques for measuring collaborative learning activities is critical for the transparency, reproducibility and generalizability of the results. This paper presents findings from a study focusing on the validation of critical centrality measures frequently used in the fields of LA and SNA research. We examined how different network configurations (i.e., multigraph, weighted, and simplified) influence the reproducibility and robustness of centrality measures as indicators of student learning in CSCL settings. In particular, this research aims to contribute to the provision of robust and valid methods for measuring and better understanding of the participation and social dimensions of collaborative learning. The study was conducted based on a dataset of 12 university courses. The results show that multigraph configuration produces the most consistent and robust centrality measures. The findings also show that degree centralities calculated with the multigraph methods are reliable indicators for students' participatory efforts as well as a consistent predictor of their performance. Similarly, Eigenvector centrality was the most consistent centrality that reliably represented social dimension, regardless of the network configuration. This study offers guidance on the appropriate network representation as well as sound recommendations about how to reliably select the appropriate metrics for each dimension. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01