A Multivocal Process Analysis of Social Positioning in Study Groups
This chapter compares two multidimensional analyses of the PLTL Chemistry dataset, which each include a cognitive, relational, and motivational dimension. These multidimensional analyses serve to highlight the ways in which the complementary perspectives
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A Multivocal Process Analysis of Social Positioning in Study Groups Iris Howley, Elijah Mayfield, Carolyn Penstein Rosé, and Jan-Willem Strijbos
Introduction One advantage as well as challenge of multivocal approaches to analysis of collaborative learning interactions is that it reveals the ways in which our individual operationalizations of complex constructs are limited. In bringing together analyses from multiple perspectives addressing similar issues with the same dataset, our eyes are opened to the richness and complexity of how these constructs are manifest in language. In this chapter, we compare two multidimensional approaches to assessing collaborative learning processes, which are based on a similar theoretical foundation and sound superficially similar. However, when a line by line comparison is made between the specific codings, we find interesting differences that serve to highlight subtle nuances in the operationalization of these theories. We are left with a deeper appreciation for the difficulty of our task as analysts to capture the intricacies of the ways in which collaborative processes are displayed through the language that we see. The scope of the analytical work we present in this chapter is defined by our theoretical assumptions regarding formative assessment of collaborative learning interactions (Howley, Mayfield, & Rosé, 2013; Strijbos, 2011). Specifically, we assume that collaborative learning processes are an integration of three orthogonal dimensions, namely, cognitive, relational, and motivational. Furthermore, we assume that each dimension can be operationalized as a set of mutually exclusive codes, each of which is defined at the level of an individual contribution to a conversation. Thus, assessments within each dimension are performed by analyzing sequences or distributions of these codes. In this chapter, we focus specifically on distributional analyses. Overall, the purpose of the analysis could be considered broadly to be that I. Howley (*) • E. Mayfield • C.P. Rosé Carnegie Mellon University, Pittsburgh, PA, USA e-mail: [email protected] J.-W. Strijbos Ludwig-Maximilians-Univerity Munich, Munich, Germany D.D. Suthers et al. (eds.), Productive Multivocality in the Analysis of Group Interactions, Computer-Supported Collaborative Learning Series 16, DOI 10.1007/978-1-4614-8960-3_11, © Springer Science+Business Media New York 2013
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of identifying the discourse contributions that support or hamper the unfolding collaborative learning process. Automatic assessment of collaborative learning in real time using such approaches can be used to trigger support for collaborative learning in a context sensitive way, for example conversational agents triggered by detection of an attempt at an explanation that prompts other group members to respond with their evaluation (see (Kumar & Rosé, 2011) for a review of such context sensitive support techniques). Formative assessment of collaborative learning processes can also be used to measure progress within iterativ
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