Investigating Trajectories of Learning & Transfer of Problem Solving Expertise from Mathematics to Physics to Engineering

测量、建模和方法类别项目的研究人员正在研究理工科学生如何在其学术生涯的主要部分中建立解决问题的专业知识，以及他们如何在本科STEM课程中转移他们的知识和技能。他们在三年的课程中观察学生的问题，从数学开始，一直到物理导论，再到工程课程。这项为期三年的研究计划包括纵向和横向研究，在课堂和课外进行，涉及堪萨斯州立大学7个班级的3000名学生。研究小组正在研究与问题解决相关的以下变量：问题的结构性、复杂性、领域特异性和动态性特征；形式、组织和顺序的问题表示；领域知识、解决问题经验、推理能力和认识论成熟度的个体差异。定量数据和定性证据被用于研究这些变量。一个在线作业系统是由以前的NSF资助（DUE 0206923）创建的，将使大量学生能够对变量进行定量分析。来自代表性不足群体的受试者数据将被分析，并与较大的学生群体进行比较。本项目的成果有望促进对本科教育中STEM教与学的认识和理解。

The investigators of the project Measurement, Modeling, and Methods Category are studying how science and engineering students build towards problem solving expertise throughout a major part of their academic careers and how they transfer their knowledge and skills across undergraduate STEM courses. They are observing students' problem across 3 years of courses starting with mathematics and continuing through introduction to physics to engineering courses. The three year research plan consists of longitudinal and cross-sectional studies that take place both in-class and out-of-class and involve 3000 students through seven classes at Kansas State University. The research team is studying the following variables associated with problem solving: the problem features of structuredness, complexity, domain specificity, and dynamicity; problem representation of form, organization, and sequencing; and individual differences of domain knowledge, problem solving experience, reasoning skills, and epistemological maturity. Quantitative data and qualitative evidence are being used to study the variables. An on-line homework system created with previous NSF funding (DUE 0206923) will enable quantitative analysis of the variables with large numbers of students.The data of subjects from underrepresented groups will be analyzed and compared to the larger groups of students. Results of this project are expected to advance the knowledge and understanding of STEM teaching and learning in undergraduate education.