Per definition, collaborative learning studies deal with the data of individuals nested in groups, and the influence of a specific learning setting on the collaborative process of learning. Most well-established statistical methods are not able to analyze such nested data adequately. Therefore, multilevel modelling (MLM) is an alternative and adequate statistical approach in CSCL research. MLM enables testing interactional effects of predictor variables varying within groups (for example, the activity of group members in a chat) and predictors varying between groups (for example, the group homogeneity created by group members’ prior knowledge). So it allows taking into account that an instruction, tool or learning environment has different but systematic effects on the members within the groups on the one hand and on the groups on the other hand. One caveat is the fact that MLM requires large sample sizes which are not provided in most CSCL research.
Syllabi and Slides
For a brief overview of Multilevel Analysis, watch the 3 minute introductory video featuring Ulrike Cress:
15 minutes about Multilevel Analysis featuring Ulrike Cress:
Interview with Ulrike Cress:
Watch the full webinar on Multilevel Analysis featuring Ulrike Cress:
Listen to the Multilevel Analysis webinar
- Cress, U. (2008). The need for considering multi-level analysis in CSCL research. An appeal for the use of more advanced statistical methods. International Journal of Computer-Supported Collaborative Learning, 3(1), 69-84. [Access Online] doi: 10.1007/s11412-007-9032-2
- Janssen, J., Cress, U., Erkens, G., & Kirschner, P. A. (2013). Multilevel analysis for the analysis of collaborative learning. In C. E. Hmelo-Silver, A. O’Donnell, C. A. Chinn, & C. Chan (Eds.), The International Handbook of Collaborative Learning (pp. 112-125). New York: Routledge.
Learning Scientists Who Have Researched This Topic
- Ulrike Cress
- Jeroen Janssen