Earlier quantitative studies in computer-supported collaborative learning identified 'Productive Failure' (Kapur, Cognition and Instruction 26(3):379-424, 2008) as a phenomenon in which students experiencing relative failures in their initial problem-solving efforts subsequently performed better than others who were in a condition not involving an initial failure. In this qualitative study, we examine the problem-solving dynamics of two dyads: a Productive Failure (PF) dyad who initially received a low-structured activity and a Non-Productive Failure (N-PF) dyad who initially received a high-structured activity. Both dyads then received an identical high-structured problem-solving activity. This process was repeated using multiple sets of problems, and this paper will discuss two sets. Interactions of the two dyads were logged. Data for this study included video conversations of the dyads, screen captures of their use of a computer model, and their submitted answers. Results indicated that initial struggle and failed attempts provided an opportunity to the PF dyad to expand their observation space and thus engage deeply with the computer model. Over-scripting proved to be detrimental in creation of a mutual meaning-making space for the N-PF dyad. This paper suggests that the relative success of the PF dyad might be viewed in terms of induction of reflective reasoning practices.
This paper presents a process-oriented case study of successes and failures in collaborative inquiry. The interactions of pairs were recorded and transcribed while they were engaged in learning activities, mediated by agent-based NetLogo electricity models. Transcripts of learner interactions were coded for engagements in science inquiry. The purpose of this paper is to articulate the dynamics of collaborative science inquiry approach resulting from varied scaffolding and consistent scaffolding in learning activities. Our findings indicate that students under a varied scaffolding approach were more deeply engaged in inquiry process and performed better on model-based explanations.
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