The Influence of Metacognitive Self-Regulation on Problem-Solving in Computer-Based Science Inquiry

Research from several quarters has shown that metacognitive monitoring and control are important skills for successful problem solving (e.g., Artzt & Armour-Thomas 1992; Carr & Jessup, 1997; Hmelo & Cote, 1996; Tobias & Everson, 1995). In particular, research has demonstrated that self-regulation and use of metacognitive skills predicts problem solving success as well as or better than traditional predictors of general ability such as achievement scores (Swanson, 1990). The purpose of this research study was to examine particular metacognitive monitoring and regulatory skills (Knowledge of Cognition, Objectivity, Problem Representation, Subtask Monitoring, and Evaluation) in the context of solving science problems within a computer-based learning environment. We also sought to replicate the results of Swanson’s (1990) study, which examined the interactions between problem solving outcomes for students categorized according to high and low levels of metacognition and high and low levels of aptitude. Overall, the total score for metacognitive monitoring and regulatory skills was a significant predictor of both Content Understanding and Problem Solving. Three of the five factors (Knowledge Of Cognition, Objectivity, & Problem Representation) were significant predictors of Content Understanding. In addition, four of five factors (Knowledge Of Cognition, Objectivity, Problem Representation, & Evaluation) were significant predictors of Problem Solving. Results also showed that those with High Metacognitive Self-Regulation compensated for Low Aptitude on both Content Understanding and Problem Solving measures. These results have several implications for researchers and classroom educators alike. Most researchers in the learning sciences agree that metacognition and self-regulation are important predictors of student success in school. The research briefly presented here helps to justify such claims by demonstrating that particular metacognitive self-regulation variables are important for success in both content understanding and problem solving. It is our hope that such research will eventually lead to the development of a model for problem-solving activities and materials that would foster metacognitive self-regulation. Background Metacognitive Self-Regulation Various definitions and models of self-regulated learning abound in the research literature (Bruning, Schraw and Ronning, 1995). Zimmerman (1989) captured the sweeping purview of self-regulated learning when he said, “Students can be described as self-regulated to