An effective metacognitive strategy: learning by doing and explaining with a computer‐based Cognitive Tutor

TLDR

Self‑explanation is a proven metacognitive strategy, yet its application in real classrooms remains unclear. The study compared instructional treatments emphasizing self‑explanation to other treatments to assess their impact on student learning. The authors used a Cognitive Tutor, an intelligent software that guides learning by doing, to scaffold self‑explanation in classroom settings. Across two classroom experiments, students who explained their problem‑solving steps with the Cognitive Tutor achieved deeper understanding, better transfer, and more integrated knowledge, demonstrating that self‑explanation can be effectively scaled through simple computer‑based instruction.

Abstract

Abstract Recent studies have shown that self‐explanation is an effective metacognitive strategy, but how can it be leveraged to improve students' learning in actual classrooms? How do instructional treatments that emphasizes self‐explanation affect students' learning, as compared to other instructional treatments? We investigated whether self‐explanation can be scaffolded effectively in a classroom environment using a Cognitive Tutor, which is intelligent instructional software that supports guided learning by doing. In two classroom experiments, we found that students who explained their steps during problem‐solving practice with a Cognitive Tutor learned with greater understanding compared to students who did not explain steps. The explainers better explained their solutions steps and were more successful on transfer problems. We interpret these results as follows: By engaging in explanation, students acquired better‐integrated visual and verbal declarative knowledge and acquired less shallow procedural knowledge. The research demonstrates that the benefits of self‐explanation can be achieved in a relatively simple computer‐based approach that scales well for classroom use.