The ability to solve complex and real-life problems is one of the key competencies in science education. Different studies analyzed the relationships between complex problem solving (CPS) and covariates such as intelligence, prior knowledge, and motivational constructs on a manifest level. Additionally, research findings indicate that intelligence and prior knowledge are substantial predictors of CPS. Due to the interconnections between covariates, the relationships between CPS and covariates are quite complex. Therefore, we propose a model which describes these relations by taking direct and indirect effects into account. All analyses are based on structural equation modeling. Results show that the proposed model represents the data with substantial goodness-of-fit statistics and explanation of variance. Intelligence, domain-specific prior knowledge, computer familiarity, and attendance in advanced chemistry courses are direct predictors of CPS, while interest and scientific self-concept show indirect effects.Peer Reviewe
