Abstract
Peer interaction has been found to be conducive to learning in many settings. Knowledge co-construction (KCC) has been proposed as one explanatory mechanism. However, KCC is a theoretical construct that is too abstract to guide the development of instructional software that can support peer interaction. In this study, we present an extensive analysis of a corpus of peer dialogs that we collected in the domain of introductory Computer Science. We show that the notion of task initiative shifts correlates with both KCC and learning. Speakers take task initiative when they contribute new content that advances problem solving and that is not invited by their partner; if initiative shifts between the partners, it indicates they both contribute to problem solving. We found that task initiative shifts occur more frequently within KCC episodes than outside. In addition, task initiative shifts within KCC episodes correlate with learning for low pre-testers, and total task initiative shifts correlate with learning for high pre-testers. As recognizing task initiative shifts does not require as much deep knowledge as recognizing KCC, task initiative shifts as an indicator of productive collaboration are potentially easier to model in instructional software that simulates a peer.
Highlights
Knowledge Co-Construction (KCC) actions: The number of utterances, code changes and drawing actions that occurred during KCC episodes
Further analyses showed that this result holds robustly for the dyad, and individually for low pre-test subjects; among high pre-test subjects, task initiative shifts correlate with post-test score
For this group of subjects, the total number of task initiative shifts, those contained within KCC episodes, correlate with learning
Summary
A vast body of research has found that peer interactions can promote learning for one or more participants, especially dyads in cross-age settings (Doise, Mugny, & Perret-Clermont, 1975; P. Cohen, Kulik, & Kulik, 1982; Britz, Dixon, & McLaughlin, 1989; Brown & Palincsar, 1989; Rekrut, 1992; Topping, 2005; Roscoe & Chi, 2007; Asterhan & Schwartz, 2009; Topping et al, 2011; Asterhan, 2013; Bowman-Perrott et al, 2013; Kuhn, 2015). Some students dominate the conversation (Asterhan & Schwartz, 2009), produce proposals that are unrelated to what has been discussed so far (Barron, 2003), and ignore or offhandedly reject their peers’ proposals (Barron, 2003). In response to these observations, an important line of inquiry from the cognitive and learning sciences is, which mechanisms are at work during productive peer interactions? In response to these observations, an important line of inquiry from the cognitive and learning sciences is, which mechanisms are at work during productive peer interactions? A more pragmatic question, from an educational technology perspective, is, to what extent and how can such mechanisms be operationally modelled, and supported, in a computer-based, collaborative learning environment?
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