Factors that explain simulation-based inquiry performance in physics learning

Abstract Evidence on which cognitive and dispositional factors jointly predict inquiry performance, overall and by facet, remains limited. Identifying these predictors is key for designing guidance suited to different learners and tasks. Drawing on the Scientific Discovery as Dual Search (SDDS) framework, we examined four cognitive factors - content knowledge, control-of-variables strategy (CVS), functional thinking, and general cognitive ability - together with four dispositional factors - interest in physics, physics self-concept, personality, and task-specific current motivation - and perceived intrinsic and extraneous cognitive load. Data from 232 young adults across six online sessions included assessments of these factors and a far-transfer Cartesian diver task capturing seven inquiry facets. Hierarchical multiple regression analyses showed that cognitive factors explained about 50% of the variance in inquiry performance, with CVS and functional thinking emerging as the strongest predictors. Dispositional factors and cognitive load contributed little, except for self-concept and extraneous load. Cognitive factors also predicted specific inquiry facets: functional thinking, for example, was particularly relevant for reasoning about variable associations (SDDS relation space) based on interpreting data representations (SDDS experiment space). Latent profile analysis identified three learner profiles differing in combinations of cognitive factors, illustrating how these constellations affect inquiry performance. These profiles highlight the importance of tailored guidance and suggest that instructional strategies should align with learners’ cognitive profiles and task demands. The findings clarify inquiry learning’s cognitive underpinnings and inform the design of adaptive inquiry-based environments in physics and beyond.