Studying participation networks in collaboration using mixed methods
Jorrín-Abellán, Iván M.
Marcos, José Antonio
Contributor(s)Group of Intelligent and Cooperative Systems / Education,Media, Informatics and Culture (GSIC/EMIC) ; University of Valladolid
Faculty of Education ; UNIVERSIDAD DE VALLADOLID
EU Commision with TELL (e-Learning programme) EAC/61/03/GR009
Kaleidoscope NoE (IST programme)
and TIC-2002-04258-C03-02 and TSI2005-08225-C07-04 by the Spanish Ministry of Science and Technology)
social network analysis
mixed evaluation methods
[INFO.EIAH] Computer Science [cs]/Technology for Human Learning
Full recordShow full item record
AbstractThe pre-print version can be see in http://ijcscl.org/?go=contents&article=18 .
This paper describes the application of a mixed-evaluation method, published elsewhere, to three different learning scenarios. The method defines how to combine social network analysis with qualitative and quantitative analysis in order to study participatory aspects of learning in CSCL contexts. The three case studies include a course-long, blended learning experience evaluated as the course develops; a course-long, distance learning experience evaluated at the end of the course; and a synchronous experience of a few hours duration. These scenarios show that the analysis techniques and data collection and processing tools are flexible enough to be applied in different conditions. In particular, SAMSA, a tool that processes interaction data to allow social network analysis, is useful with different types of interactions (indirect asynchronous or direct synchronous interactions) and different data representations. Furthermore, the predefined types of social networks and indexes selected are shown to be appropriate for measuring structural aspects of interaction in these CSCL scenarios. These elements are usable and their results comprehensible by education practitioners. Finally, the experiments show that the mixed-evaluation method and its computational tools allow researchers to efficiently achieve a deeper and more reliable evaluation through complementarity and the triangulation of different data sources. The three experiments described show the particular benefits of each of the data sources and analysis techniques
DOI : 10.1007/s11412-006-8705-6