A novice-expert study of modeling skills and knowledge structures about air quality

Abstract

This study compared modeling skills and knowledge structures of four groups as seen in their understanding of air quality. The four groups were: experts (atmospheric scientists), intermediates (upper-level graduate students in a different field), advanced novices (talented 11th and 12th graders), and novices (10th graders). It was found that when the levels of modeling skills were measured, for most skills there was a gradual increase across the spectrum from the novices to the advanced novices to the intermediates to the experts. The study found the experts used model-based reasoning, the intermediates and advanced novices used relation-based reasoning, and the novices used phenomena-based reasoning to anticipate conclusions. The experts and intermediates used more bi-variable relationships in experimental design and anticipated conclusions, but used more multiple-variable relationships in identifying relationships. By contrast, the advanced novices and novices mostly used bi-variable relationships in all modeling skills. Based on these findings, we suggest design principles for model-based teaching and learning such as designing learning activities to encourage model-based reasoning, scaffolding one’s modeling with multiple representations, testing models in authentic situations, and nurturing domain-specific knowledge during modeling.