Research Initiation: Investigating Engineering Students Habits of Mind: A Case Study Approach

21世纪工程师面临的问题非常复杂，往往涉及与现有技术、社会需求、环境考虑等相关的相互竞争的要求。培养学生解决这些复杂问题的能力，需要超越教授技术技能的范围，考虑工程师的思维方式。这个项目是一项跨学科的研究，旨在刻画工科本科生的“思维习惯”，这是工科学生成为有效的问题解决者所需的思维方式，能够将这些技能转移到新的环境中。思维习惯的一个例子是在试图解决问题时愿意犯错，这种态度允许工程师成功地解决以前没有解决的问题。拥有这些思维习惯的学生将准备好解决像NAE大挑战中描述的那些复杂的问题。该项目将利用一个名为Rhea的教育网站(www.project trhea.org)以及作为工程学课程一部分获得的数据，来确定学生在参与解决问题时如何体验科学的思维习惯。为此，定性和定量研究方法将与机器学习技术相结合。这将通过跨学科伙伴关系来实现，其中将启动一项跨越边界的研究方案，以确定和验证新的研究方法以及形成性和终结性评估机制。这些努力将集中于利用机器学习技术，如自动数据聚类，加强定性和定量的教育研究和评估方法。这个项目的基本原理是：(A)它的成功完成将使研究团队能够开展跨学科的协作工作，其中Pi Boutin将开发教育研究方法方面的专业知识，Pi Magana将开发机器学习专业知识；(B)为探索性研究提供背景，用作未来申请资金以建立工程教育研究方法和评估计划的基线；(C)通过在工程背景下的科学思维习惯，应对在专业和未来工程师中培养终身学习文化的挑战；以及(D)开发新的方法，以表征和衡量工程教育中专业形成过程的不同方面。长期愿景是在机器学习辅助教育研究领域发展跨学科伙伴关系。

The problems facing engineers in the 21st century are highly complex, involving often competing requirements related to available technology, societal needs, environmental considerations, and others. Fostering the ability of students to solve these complex problems requires going beyond teaching technical skills to consider the way engineers think. This project is an interdisciplinary study that aims to characterize undergraduate engineering students' "habits of mind", which are modes of thinking required for engineering students to become effective problem solvers capable of transferring such skills to new contexts. An example of habit of mind is a willingness to make mistakes while trying to solve a problem, an attitude that allows engineers to successfully attack problems that were previously unsolved. Students who have these habits of mind will be prepared to address complex issues such as those described in the NAE Grand Challenges.The project will leverage an education website called Rhea (www.projectrhea.org) along with data acquired as part of an engineering course to identify how students experience scientific habits of mind as they engage in problem solving. To this end, qualitative and quantitative research methods enhanced with machine learning techniques will be combined. This will be accomplished through an interdisciplinary partnership in which a boundary spanning research program to identify and validate novel research methods and formative and summative assessment mechanisms will be initiated. The efforts will center on enhancing qualitative and quantitative educational research and assessment methods with machine learning techniques such as automatic data clustering. The rationale for this project is that its successful completion will (a) enable the research team to develop collaborative inter-disciplinary efforts where the PI Boutin will develop expertise in educational research methods and the co-PI Magana will develop expertise in machine learning; (b) provide a context for an exploratory study to be used as a baseline to apply for future funding to establish a program in engineering education research methods and assessment; (c) address challenges in cultivating a culture of lifelong learning among professional and future engineers via scientific habits of mind in an engineering context; and (d) develop new methods to characterize and measure different aspects of professional formation processes in engineering education. The long term vision is to develop an interdisciplinary partnership in the field of machine-learning-assisted education research.