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

科学教育的一个普遍组成部分是学习每个目标领域的关键类别。该项目是印第安纳大学和华盛顿大学的认知科学家、地质学家和科学教育研究人员的多学科合作，将采用人类学习的基础研究成果，并尝试开发可与教学技术相匹配的诊断工具，以促进科学分类的学习。岩石分类将被用作示例目标领域，因为它提供了更普遍地代表科学分类学习的挑战，但生成的训练见解应该适用于多个科学领域。如何探索教学变化的广阔空间是教育研究中的一个重要实践问题。该项目的一个关键优势是，研究人员将通过测试特定的假设来划定空间，这些假设来自成功的人类分类学习的正式模型，结合训练文献中的原则，关于如何交付内容以优化科学分类的学习。研究人员将得出新手和专家对岩石分类的表述，包括他们所关注的维度。这项工作将基于实验室的数学建模研究与更多的应用研究联系起来：在真实的学习情境中使用真实岩石的教学将与通过计算机提供的教学进行对比。研究人员在专家和新手的比较中发现的原则应该对未来在课堂和现场应用的诊断工具的开发有用。该项目符合EHR核心研究（ECR）计划的核心目标，即进行基础研究并为STEM学习建立持久的研究基础。该研究将需要基本的缩放工作，以获得岩石刺激的心理相似性表征。这些表征的推导是严格应用分类模型的先决条件，这将指导后续的实证训练研究。这些表征还将提供有关岩石刺激组织的主要心理维度以及岩石类别分布如何在多维相似空间中配置的重要见解。这些学生的陈述将与专家地质学家的陈述进行对比。专家们很可能已经学会将注意力集中在远比学生们使用的更具有诊断性的维度上。对这些不同的多维解决方案的实证研究应该会产生关于如何最有效地支持学生学习岩石类别的基本参数的重要信息。这些包括识别：i)支持学习和泛化的最佳训练实例，ii)这些训练实例的最佳排序，以及iii)分层组织类别分布的特定子类型的首选训练密度。

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.