This chapter begins with an extensive examination of the various ways that adaptive expertise can be measured. Most of them have fairly well-known theoretical explanations, which are reviewed briefly. On the other hand, theoretical explanations are not easily found for one particularly valuable manifestation of adaptive expertise: acceleration of future learning. Acceleration of future learning is valuable because the growth of knowledge anticipated for the twenty-first-century demands that experts be able to learn new task domains quickly. That is, their training now should raise their learning rates later: It accelerates their future learning. We present a case study where accelerated future learning was achieved. The trick was to use an intelligent tutoring system that focused students on learning domain principles. Students in this condition of the experiment apparently realized that principles were more easily learned and more effective than problem schemas, analogies, and so forth. Thus, when given the freedom to choose their own learning strategy while learning a second task domain, they seem to have focused on the principles of the new task domain. This caused them to learn faster than the control group, who were not focused on principles during their instruction on the initial task domain. In short, the metacognitive learning strategy/policy of focusing on principles seems to have transferred from one domain (probability) to another (physics), thus causing accelerated future learning of the second task domain (physics).
ASJC Scopus subject areas
- Computer Science(all)