Various forms of relational processing have been linked to cognitive capacity measures, such as working memory and fluid intelligence. However, previous work has not established the extent to which different forms of relational processing reflect common factors, nor whether individual differences in cognitive style also contribute to variations in relational reasoning. The current study took an individual-differences approach to investigate the prerequisites for relational processing. In two studies, college students completed a battery of standardized tests of individual differences related to fluid intelligence and cognitive style, as well as a series of experimental tasks that require relational reasoning. Moderate correlations were obtained between relational processing and measures of cognitive capacity. Questionnaire measures of cognitive style generally did not improve predictions of relational processing beyond the influence of measures of cognitive capacity.

Analogy is a powerful tool for fostering conceptual understanding and transfer in STEM and other fields. Well‐constructed analogical comparisons focus attention on the causal‐relational structure of STEM concepts, and provide a powerful capability to draw inferences based on a well‐understood source domain that can be applied to a novel target domain. However, analogy must be applied with consideration to students' prior knowledge and cognitive resources. We briefly review theoretical and empirical support for incorporating analogy into education, and recommend five general principles to guide its application so as to maximize the potential benefits. For analogies to be effective, instructors should use well‐understood source analogs and explain correspondences fully; use visuospatial and verbal supports to emphasize shared structure among analogs; discuss the alignment between semantic and formal representations; reduce extraneous cognitive load imposed by analogical comparison; and encourage generation of inferences when students have some proficiency with the material. These principles can be applied flexibly to topics in a wide variety of domains.

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Using multiple representations is an important part of learning and problem-solving in science, technology, engineering and mathematics fields. For students to acquire flexible knowledge of representations, they must attend to the structural information within each representation and practice making relational connections between representations. Most studies so far have only attempted to help students connect between multiple representations in the lab or short-term classroom interventions, with the intervention largely separated from students' authentic learning. The present study developed a representation-mapping intervention designed to help students interpret, coordinate, and eventually translate across multiple representations. We integrated the intervention into an online textbook being used in a college course, allowing us to study its impact in a real course over an extended period of time. The findings of this study support the efficacy of the representation-mapping intervention for facilitating learning and shed light on how to implement and refine such interventions in authentic learning contexts. Public Significance StatementThe study advances the idea that explicit representation-mapping can facilitate students' learning and transfer of statistics concepts. The findings provide important insights into college students' real learning behaviors and outcomes in an online environment. The method used in this study also guides the implementation of future theory-based interventions in authentic learning contexts.