How Components of Metacognitive Self - Regulation Affect Problem - Solving Bruce

First, we sought to examine the effects of metacognitive self-regulation on problem solving across three conditions— an interactive, computer-based treatment condition, a non-interactive computer-based alternative treatment condition, and a control condition. Second, we sought to investigate which of five components of metacognitive self-regulation were important for scientific problem solving. We hypothesized that overall metacognitive self-regulation and its various components would predict success at content understanding and problem solving and that the treatment condition would be more effective in promoting learning outcomes than either the alternative treatment or control conditions. Overall, 12 hierarchical linear models were produced. Results indicated that students in the treatment condition demonstrated significantly more Content Understanding and Problem Solving skill than students in the alternative and control classrooms. In regards to the treatment condition, of the five IMSR components, only Problem Representation was a significant predictor for success at Content Understanding. In contrast, within the alternative condition, students' Problem Representation had a significant inverse influence on Content Understanding. In terms of Problem Solving, Knowledge of Cognition and Problem Representation were found to be significant predictors. These findings are especially noteworthy for science education and inquiry-based education. In particular, results indicate that metacognitive and self-regulatory constructs are important in teaching problem solving. Being able to identify and delineate these constructs further should allow our educational research and teacher professional development teams to begin providing teachers with a set of tools and training resources to help them target student self-regulation in their classrooms.