Computer-supported collaborative learning research considers learning as it occurs in complex situations, where human thought and action occurs in response to the environment (the task and social group) with respect to how complex contexts provide opportunities for integrating information from multiple sources (Clancey 1997; Greeno 1998; von Glaserfeld 1995). Accounting for changes or progressions in learning in such complex situations is difficult to tease apart due to the interdependency of the task, the group and the individual. The contributions in part two of this volume speak directly to this complexity by providing the readers with valuable examples of mixed method approaches to understanding the role of the individual learning as well as the group processing that occurs in CSCL research. Two chapters speak directly to the use of multilevel analyses (hierarchical linear modeling-HLM) as a way to accurately isolate statistically the contributions of the individual as well the group in CSCL research (Janssen, Erkens, Kanselaar, & Kirschner; Stylianou-Georgiou, Papanastasiou & Puntambekar). The other chapters speak directly to the temporal and sequential nature of knowledge development or argumentation, and describe data mining techniques and sequential pattern recognition as a step in describing changes in the group performance over time (Reimann, Yacef, & Kay; Jeong, Clark, Sampson, & Menekse). Jeong et al. discuss ways in which specific discourse acts may predict changes in argumentation. In the sections below I refer to some of the highlights of these chapters.
Lajoie, S. P. (2011). Is the Whole Greater than the Sum of Its Parts? Explaining the Role of Individual Learning and Group Processes in CSCL. In Analyzing Interactions in CSCL (pp. 235–243). Springer US. https://doi.org/10.1007/978-1-4419-7710-6_11