Normalizing Computational Methods in the Undergraduate Physics Curriculum

该项目旨在通过加强本科生使用计算技能解决问题来服务于国家利益。传统的物理课程把培养学生运用数学方法作为解决物理问题的常规方法。然而，计算机硬件和软件的扩散和进步表明，受过训练使用计算机解决问题的学生可能能够解决比仅用数学方法更困难和复杂的问题。理想情况下，学生应该认为数学和计算方法是互补的，并明智地利用两者来解决问题。由于传统的物理课程已经强调数学方法，因此本项目的目标是培养物理专业学生使用计算机解决物理问题，并将计算解决问题视为解决问题的正常方式，就像传统上培养学生看待数学方法一样。物理学领域应该受益于将计算视为解决问题的自然方式的学生，因为这些学生将有信心解决更广泛的问题。此外，受过使用计算机解决科学问题培训的学生带来了额外的工具和理解来承担任务。 因此，他们应该发现自己在商业和工业就业做好了充分的准备。调查人员的目标是让物理学生看到计算方法作为一种正常的方式来处理物理问题，而不是一个不寻常的技术仅限于狭义的问题。通过修改物理课程，嵌入计算方法，研究人员假设，学生将获得信心，经常使用计算，并将发展判断哪些问题可以用分析方法解决，哪些问题需要数值方法，以及如何这些互补技术之间的相互作用可以是富有成效的。为了实现这一愿景，研究人员将在本科物理课程中的每一门课程中都注入计算方法。主要工作将包括：（1）发展、测试和改进用于将计算技术纳入整个课程的课程材料;（2）发展、改进和验证用于测量学生在计算方法方面的情感和认知发展的评估工具;（3）研究学生在解决问题方面的自我效能的发展。该项目将解决以下研究问题：在什么样的点在课程中做学生获得自我效能与各种计算技能？他们是在同一点上，还是独立地，对自己的分析和计算能力充满信心？自我效能感与成绩和其他外部因素的联系有多强？ 该项目由改善本科STEM教育（IUSE：EHR）计划资助，该计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to serve the national interest by strengthening undergraduate students' use of computational skills to solve problems. The traditional physics curriculum trains students to think of using mathematical methods as the normal way to solve physics problems. However, the proliferation of and advances in computer hardware and software suggest that students who are trained to use computers to solve problems may be able to tackle more difficult and sophisticated problems than they can with mathematical methods alone. Ideally, students should think of mathematical and computational methods as complementary and make judicious use of both to solve problems. Since the traditional physics curriculum already emphasizes mathematical methods, the goal of this project is to train physics majors to use computers to solve physics problems and to think of computational problem-solving as a normal way to approach problems, in the same way students have traditionally been trained to view mathematical methods. The field of physics should benefit from having students who see computation as a natural way to solve problems, because these students will be confident about tackling a wider range of problems. In addition, students who are trained to use computers to solve scientific problems bring additional tools and understanding to bear on tasks. As a result, they should find themselves well-prepared for employment in business and industry.The investigators aim to get physics students to see computational methods as a normal way to approach physics problems, instead of an unusual technique limited to narrowly defined problems. By modifying physics courses to embed computational methods, the investigators hypothesize that students will gain the confidence to use computation routinely and will develop judgment as to which problems may be solved analytically, which problems require numerical methods, and how the interplay between these complementary techniques can be fruitful. To achieve this vision, the investigators will infuse every course in the undergraduate physics curriculum with computational methods. The primary work will include: (1) developing, testing, and refining curricular materials used to incorporate computational techniques throughout the curriculum; (2) developing, refining, and validating assessment instruments used to measure students' affective and cognitive development with respect to computational methods; and (3) conducting research on students' development of self-efficacy in problem-solving as they progress through the curriculum. The project will address the following research questions: At what points in the curriculum do students gain self-efficacy with respect to various computational skills? Do they gain confidence in their analytical and computational skills at the same points, or independently? How strongly is self-efficacy tied to grades and other external factors? This project is funded by the Improving Undergraduate STEM Education (IUSE: EHR) program, which supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.