Abstract or Table of Contents

This paper presents a quantitative assessment of the importance of adaptation to the learning environment as a component of the learning curve in performance data from a computer-based tutor. In Experiment 1, verbal protocols are used to investigate the nature of changes in low-level interactions that take place during learning with a computerized tutor called Stat Lady (Shute & Gluck, 1994). The data show consistent behavioral changes in the distribution of attention, which account for a substantial portion of the learning curve, independent of error rates. These changes primarily are decreases in the verbalization of on-screen text, although the elimination of interface confusion also contributes to the efficiency gain. Experiment 2 tests the generalizability of the results in a larger population of learners. It is shown that adaptation to the learning environment accounts for a comparable proportion of the learning curve in this new population. More than half of the learning curve could be accounted for by these changes in low-level interactions. These results suggest that more accurate learning models should include a representation of increasing knowledge of the instructional environment as the model interacts with that environment. An ACT-R (Anderson & Lebiere, 1998) model is provided that reproduces the qualitative and quantitative data from the verbal protocol participants. The model reproduces these behaviors via (1) the acquisition of declarative knowledge for the structure of the problem scenarios, and (2) subsymbolic procedural tuning for more efficient goal completion.