The Relation Between Cognitive and Metacognitive Strategic Processing During a Science Simulation

Background: This investigation was designed to uncover the relations between students' cognitive and metacognitive strategies used during a complex climate simulation. While cognitive strategy use during science inquiry has been studied, the factors related to this strategy use, such as concurrent metacognition, prior knowledge, and prior interest, have not been investigated in a multidimensional fashion.

 Aims: This study addressed current issues in strategy research by examining not only how metacognitive, surface-level, and deep-level strategies influence performance, but also how these strategies related to each other during a contextually relevant science simulation.

 Sample: The sample for this study consisted of 70 undergraduates from a mid-sized Southeastern university in the United States. These participants were recruited from both physical and life science (e.g., biology) and education majors to obtain a sample with variance in terms of their prior knowledge, interest, and strategy use. 

Methods: Participants completed measures of prior knowledge and interest about global climate change. Then, they were asked to engage in an online climate simulator for up to 30 min while thinking aloud. Finally, participants were asked to answer three outcome questions about global climate change. 

Results: Results indicated a poor fit for the statistical model of the frequency and level of processing predicting performance. However, a statistical model that independently examined the influence of metacognitive monitoring and control of cognitive strategies showed a very strong relation between the metacognitive and cognitive strategies. Finally, smallest space analysis results provided evidence that strategy use may be better captured in a multidimensional fashion, particularly with attention paid towards the combination of strategies employed. 

Conclusions: Conclusions drawn from the evidence point to the need for more dynamic, multidimensional models of strategic processing that account for the patterns of optimal and non-optimal strategy use. Additionally, analyses that can capture these complex patterns need to be further explored.