Comparison of Learning Environments: All That Glitters

摘要

Aptitude-treatment interactions (ATI) refer to the covariation between learnercharacteristic and instructional treatment in relation to some outcome measure. To systematically test for ATI, I used an intelligent tutoring system instructing basic principles of electricity as a complex but controlled learning task. I created two instructional environments from this one tutor, differing only in feedback. In the rule-application environment, the system provided learners with relevant principles, and in the rule-induction environment, learners had to induce principles on their own. The learner characteristic examined in this paper was 'exploratory behavior,' a measure of on-line tool usage. I hypothesized that exploratory learners would learn faster and better if they had been assigned to the inductive environment and less exploratory learners would benefit from the more structured, application environment. Results showed significant aptitude-treatment interactions in the data, confirming the above hypotheses. Implications of these findings are discussed in relation to the design of intelligent tutoring systems.