Simplifying complexity: Analyzing students' models of biological systems

这个研究项目回应了生物科学不断变化的本质，生物科学变得越来越少碎片化，越来越关注复杂相互作用系统的模型。研究人员将研究一个框架，他们假设这个框架非常适合于帮助学生思考从亚细胞遗传过程到进化和生态的生物跨度中的复杂系统。所选择的框架是结构-行为-功能理论（SBF），在该理论中，系统由其结构的组成元素、这些组件执行的过程以及系统的总体功能或目的来表示。该框架起源于人工智能文献，作为对设计系统（如电路）进行推理的有效策略。在这项研究中，它将被应用于诸如基因、进化和生态等主题的推理。研究人员将在密歇根州立大学生命科学专业和非专业的几门生物学入门课程的背景下，将SBF理论作为一种教学法和评估系统。学生将创建、完善和推理他们所研究主题的自己的模型的图解表示，研究者将构建软件来自动编码这些表示和它们在课程中变化的方式。大约800名学生的作业将被分析，以寻找学生在教学重点上的推理变化，跟踪学习轨迹随时间的变化。评估方法将通过将分数与其他形式的平行评估相关联来验证，例如多项选择题，简答题和图表题。总体而言，研究人员将开发和评估一个分析学生理解的框架，提出促进系统思维和元认知的策略，并推进复杂系统学习的研究。该项目的创新之处在于将SBF理论应用于大学生物学入门课程的教学、评估和分析，并采用软件工具对学生模型和相关推理进行自动化分析。

This research project responds to the changing nature of biological science, which has become less fragmented and more focused on models of complex interacting systems. The investigators will study a framework that they hypothesize is well-suited to facilitating students thinking about complex systems across the biological span from sub-cellular genetic processes to evolution and ecology. The selected framework is Structure-Behavior-Function Theory (SBF), in which a system is represented by the component elements of its structure, the processes that these components perform, and the overall function or purpose of the system. The framework originated in the Artificial Intelligence literature, as an effective strategy for reasoning about designed systems such as electrical circuits. In this research, it will be applied to reasoning about topics such as genes, evolution, and ecology. The investigators will use SBF theory as a pedagogy as a well as an assessment system, in the context of several courses in introductory biology at Michigan State University for both life science majors and non-majors. Students will create, refine, and reason with diagrammatic representations of their own models of the topics they study, and investigators will build software to automate the coding of such representations and the ways they change over the course. The work of approximately 800 students will be analyzed to look for shifts in student reasoning at key points in the instruction, tracking learning trajectories over time. The assessment method will be validated by correlating the scores with those on other forms of parallel assessment, such as multiple-choice, short-answer, and diagrammatic questions. Overall, the investigators will develop and evaluate a framework for analyzing student understanding, propose strategies for promoting systems thinking and metacognition, and advance research on learning about complex systems. The project is innovative in its application of SBF theory to the pedagogy, assessment, and analysis of introductory biology college courses, and also in its adaptation of software tools to automate the analysis of students models and associated reasoning.