Promoting students’ ability to draw collaborative inferences from distributed information in group problem-solving: a training experiment

Abstract

By drawing collaborative inferences, groups can co-construct new solution-relevant knowledge from information initially distributed between individuals. Such collaborative inferences result in a true assembly bonus; however, in unsupported collaboration, they are much less frequent than inferences from undistributed information (Meier & Spada, 2007). In an experiment, n=36 dyads of university students were trained to apply specific collaborative inferencing strategies. Four training interventions (no training; reflected problem-solving; reflected problem-solving with strategy instruction; and reflected problem-solving with strategy instruction and guidance from a computerized inference tutoring tool) were implemented during a training phase and their effects tested during subsequent unsupported collaboration. Collaborative inferences were the least frequent type of inference to be drawn during both training and transfer. However, training significantly increased the number of inferences drawn. Specifically, dyads who had trained with the inference tutoring tool showed near optimal performance during unsupported collaboration on the transfer task.


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