Detecting Changes in Math Strategy Use During Learning

Caitlin TenisonCarnegie Mellon University, Pittsburgh, PA, USA
John AndersonCarnegie Mellon University

Abstract

The ability to accurately assess math problem solving strategy is an important part of understanding the effects of practice. Unfortunately the measures researchers trust are often unreliable and ill suited for studying the effects of practice. In the current study we are interested in identifying intermediary strategies that emerge as people switch from computational to retrieval strategies. To build a more accurate assessment of strategy we combine latency, neural evidence, and verbal reports using a mixture model. We compare the model’s predictions of strategy use with concurrent assessments collected during the problem solving. The results suggest that while participants consider a partial computation-retrieval strategy, distinct from pure computation, our model finds no evidence of such a partial state; however, distinction is found between early and well-practiced retrieval. These results suggest a discrepancy between the distinctions people make when reporting strategy use and the distinctions in the cognitive processes underlying strategy use.

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Detecting Changes in Math Strategy Use During Learning (544 KB)



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