Combining qualitative evaluation and social network analysis for the study of classroom social interactions

• Published in: Computers and education 2003-12, Vol.41 (4), p.353-368
• Main Authors: Martı́nez, A., Dimitriadis, Y., Rubia, B., Gómez, E., de la Fuente, P.
• Format: Article
• Language: English
• Subjects: Computer Science; Computers; Cooperative/collaborative learning; Data Analysis; Evaluation methodologies; Interpersonal Relationship; Learning communities; Network Analysis; Post-secondary education; Qualitative Research; Research Methodology; Social Networks; Teaching Methods; Technology for Human Learning
• ISSN: 0360-1315; 1873-782X
• DOI: 10.1016/j.compedu.2003.06.001

Studying and evaluating real experiences that promote active and collaborative learning is a crucial field in CSCL. Major issues that remain unsolved deal with the merging of qualitative and quantitative methods and data, especially in educational settings that involve both physical and computer-supported collaboration. In this paper we present a mixed evaluation method that combines traditional sources of data with computer logs, and integrates quantitative statistics, qualitative data analysis and social network analysis in an overall interpretative approach. Several computer tools have been developed to assist in this process, integrated with generic software for qualitative analysis. The evaluation method and tools have been incrementally applied and validated in the context of an educational and research project that has been going on during the last three years. The use of the method is illustrated in this paper by an example consisting of the evaluation of a particular category within this project. The proposed method and tools aim at giving an answer to the need of innovative techniques for the study of new forms of interaction emerging in CSCL; at increasing the efficiency of the traditionally demanding qualitative methods, so that they can be used by teachers in curriculum-based experiences; and at the definition of a set of guidelines for bridging different data sources and analysis perspectives.