Enhancing Learning of Science Categories Through Guidance of Psychological Models of Classification

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A ubiquitous component of science education is learning the key categories of each target domain. This project, a multidisciplinary collaboration of cognitive scientists, geologists, and science education researchers at Indiana University and Washington University, will take basic research findings on human learning and attempt to develop diagnostic tools that can be matched with instructional technique to facilitate the learning of scientific classifications. Rock categorization will be used as the example target domain because it provides challenges that are representative of scientific classification learning more generally, but the training insights that are generated should be applicable across multiple scientific domains. A critical practical issue in education research concerns how to explore the vast space of possible instructional variations. A key advantage of the project is that the researchers will delimit that space by testing specific hypotheses, drawn from successful formal models of human classification learning combined with principles from the training literature, about how content should be delivered to optimize learning of scientific classifications. The researchers will derive novice and expert representations of rock classifications, including the dimensions they attend to. This work bridges laboratory-based mathematical modeling research with more applied research: Instruction using real rocks in authentic learning situations will be contrasted with instruction delivered over computers. Principles that the researchers discover in comparisons of experts and novices should be useful in the development of diagnostic tools for future applications in the classroom and the field. The project fits centrally into the EHR Core Research (ECR) program goal of conducted funamental research and building enduring research foundations for STEM learning.The studies will entail fundamental scaling work to derive psychological similarity representations for the rock stimuli. Derivation of these representations is a prerequisite for rigorous application of the models of classification that will guide the subsequent empirical training studies. These representations will also provide important insights concerning the major psychological dimensions along which the rock stimuli are organized as well as how the rock category distributions are configured in the multidimensional similarity space. These student representations will be contrasted with those derived from expert geologists. It is highly likely that the experts will have learned to focus attention on dimensions that are far mor ediagnostic than those used by the students. Empirical investigations of these different multidimensional solutions should yield important information regarding fundamental parameters for how most efficiently to support students' learning of the rock categories. These include identifying: i) the optimal training instances to support learning and generalization, ii) the optimal sequencing of these training instances, and iii) the preferred training density for particular subtypes of hierarchically organized category distributions.