This study investigated how participant’s specificity in shar-ing of information in collaborative problem solving was criti-cal to them reaching a successful shared perspective. We ana-lyzed participants’ communication strategies in a collaborativetask designed to make finding common ground challenging.We set out to better understand the difference between suc-cessful and unsuccessful collaborations by conducting a cog-nitive task analysis. From participants’ utterances, we inferredcognitive processes associated with repeating communicationmoves and coded those processes as if-then production rules.We thereby specified the communication strategies used duringinteractions and developed a production-rule model to explainwhether and how shared perspective developed or not. Ourcognitive task analysis indicated that although all collaboratingpairs described the objects they were seeing with a variety offeatures, the successful pairs were more specific in using com-binations of features. Quantitatively, we found significant cor-relations between frequency of combined feature statementsand success in sharing perspectives.

We have been refining a cognitive model, written in ACT-R, of student performance in early algebra problem solving. "Early algebra" refers to a class of problems and competencies at the boundary between arithmetic and algebra. Our empirical studies in this domain establish a striking contrast between students' difficulties with symbolic algebra and their relative success with certain kinds of "intuitive" algebraic reasoning. To better understand this contrast, we analyzed student solutions to identify the strategies and errors exhibited and then set out to account for this detailed process data with the utility-based choice mechanism of ACT-R. Our first model contained production mles for explicitly selecting strategies and for making certain systematic errors or bugs. It provided a good quantitative fit to student performance data (R2=.90), however, it had two quahtative shortcomings: 1) the productions for strategy selection appeared to serve no computational purpose and 2) the model systematically underpredicted the frequency of non-trivial errors on more complex problems. We created a new model in which explicit strategy selection was eliminated (strategic behavior is emergent) and in which failure to fire a production (an implicit, non-buggy error) is an option at every model choice point. Compared to the first model, this model achieved an equivalent quantitative fit with fewer productions and without the systematic deviations from the error data. We consider the implications of implicit strategies and errors for instruction.

In this paper, we review the literature on the relation between solving nonspecific goal problems and learning. Research has shown that reduced goal-specificity facilitates learning of rules and principles of the target domain. Researchers have accounted for this effect using a cognitive load theory (Sweller, 1988) and a dual space theory of problem solving (Vollmeyer, Bums, & Holyoak, 1996). Other researchers have shown that learning can be both facilitated by nonspecific as well as specific goals and account for their findings using goal appropriateness theory (Miller, Lehman. & Koedinger, 1997). W e judge each theoretical account by evaluating their consistencies with unified theories of cognition and other empirical data. We note the shortcomings of the each theory and incorporate elements of each to explain all the data.

Retrieval practice of information through testing has been shown to improve learning. So has studying examples. In this paper, we address inconsistencies in the literature concerning which of these two approaches is best. We test the hypothesis that learning depends on what is being learned; whereas practice emphasizes memorization, studying examples allows for selectivity of encoding, resulting in different information being learned. Accordingly, we predicted that practice will improve learning in situations that emphasize memorization (such as learning facts or simple associations), whereas studying examples will improve learning in situations where there are multiple pieces of information available and selectivity is necessary (such as when learning skills or procedures). We report evidence from a laboratory study using naturalistic materials showing results consistent with these predictions.