This paper describes results from a series of experimental studies to explore issues related to structuring productive group dynamics for collaborative learning using an adaptive support mechanism. The first study provides evidence in favor of the feasibility of the endeavor by demonstrating with a tightly controlled study that even without adaptive support, problem solving in pairs is significantly more effective for learning than problem solving alone. The results from a second study offer guidelines for strategic matching of students with learning partners. Furthermore, the results reveal specific areas for needed support. Based on the results from the second study, we present the design of an adaptive support mechanism, which we evaluate in a third study. The results from the third study provide evidence that certain aspects of our design for adaptive support in the form of strategic prompts are effective for manipulating student behavior in productive ways and for supporting learning. These results also motivate specific modifications to the original design.
This paper contributes to a theory-grounded methodological foundation for automatic collaborative learning process analysis. It does this by illustrating how insights from the social psychology and sociolinguistics of speech style provide a theoretical framework to inform the design of a computational model. The purpose of that model is to detect prevalence of an important group knowledge integration process in raw speech data. Specifically, this paper focuses on assessment of transactivity in dyadic discussions, where a transactive contribution is operationalized as one where reasoning is made explicit, and where that reasoning builds on a prior reasoning statement within the discussion. Transactive contributions can be either self-oriented, where the contribution builds on the speaker's own prior contribution, or other-oriented, where the contribution builds on a prior contribution of a conversational partner. Other-oriented transacts are particularly central to group knowledge integration processes. An unsupervised Dynamic Bayesian Network model motivated by concepts from Speech Accommodation Theory is presented and then evaluated on the task of estimating prevalence of other-oriented transacts in dyadic discussions. The evaluation demonstrates a significant positive correlation between an automatic measure of speech style accommodation and prevalence of other-oriented transacts (R = .36, p< .05).
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