Explorations in the Parameter Space of a Model Fit to Individual Subjects' Strategies while Learning from Instructions
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Explorations in the Parameter Space of a Model Fit to Individual Subjects' Strategies while Learning from Instructions

Abstract

In earlier work, we presented results from an empirical study that examined subjects' learning and browsing strategies as they explained instructional materials to themselves that were contained in a hypertext-based in- structional environment. W e developed a Soar model that, through parameter manipulation, simulated the strategies of each individual subject in the study. In this paper, w e explore the parameters of these simula- tions and contribute several new results. First, we show that a relatively small proportion of strategies captured a large percentage of subjects' interaction behaviors, suggesting that subjects' approach to the learning task shared some underlying strategic commonalities. Sec- ond, w e show that lower performing subjects employed a high proportion of working memory intensive strate- gies, which m a y have partially accounted for their in- ferior performance. Third, clusters of subjects identi- fied through parameters analyses continued to exhibit similar behaviors during subsequent problem solving, suggesting that the clusters corresponded to genuine strategy classes. Furthermore, these clusters appeju-ed to represent general learning and browsing strategies that were, in some sense, adaptive to the task.

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