Simplifying complexity: Analyzing students' models of biological systems

简化复杂性：分析学生的生物系统模型

• 批准号: 0910278
• 负责人: Tammy Long
• 金额: $ 74.5万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0910278&HistoricalAwards=false
• 关键词: Simplifying; complexity; Analyzing; students; models

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.

该研究项目响应生物科学不断变化的性质，生物科学已变得不那么分散，更专注于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帮助学生思考从亚细胞遗传过程到进化和生态学的生物学跨度中的复杂系统。所选择的框架是结构-行为-功能理论（SBF），其中系统由其结构的组成元素、这些组成部分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，作为一种有效的策略来推理设计的系统，如电路。在这项研究中，它将被应用于基因，进化和生态学等主题的推理。研究人员将使用SBF理论作为教学法以及评估系统，在密歇根州立大学为生命科学专业和非专业学生开设的几门生物学入门课程中。学生将创建，完善，并与他们研究的主题的自己的模型的图形表示的原因，和调查人员将建立软件，以自动化这些表示的编码和他们在课程中改变的方式。大约800名学生的工作将被分析，以寻找学生在教学中的关键点推理的变化，随着时间的推移跟踪学习轨迹。评估方法将通过将分数与其他形式的平行评估（如多项选择、简答和图解问题）的分数相关联来验证。总的来说，研究人员将开发和评估一个框架，用于分析学生的理解，提出促进系统思维和元认知的策略，并推进关于学习复杂系统的研究。该项目在将SBF理论应用于生物学导论课程的教学法，评估和分析方面具有创新性，并且还采用了软件工具来自动分析学生模型和相关推理。