Problem-solving is a critical skill in the workplace, but recent college graduates are often deficient in problem-solving skills. Introductory STEM courses present engineering students with well-structured problems with single-path solutions that do not prepare students with the problem-solving skills they will need to solve complex problems within authentic engineering contexts. When presented with complex problems in authentic contexts, engineering students find it difficult to transfer the scientific knowledge learned in their STEM courses to solve these integrated and ill structured problems. By integrating physics laboratories with engineering design problems, students are taught to apply physics principles to solve ill-structured and complex engineering problems. The integration of engineering design processes to physics labs is meant to help students transfer physics learning to engineering problems, as well as to transfer the design skills learned in their engineering courses to the physics lab. We hypothesize this integration will help students become better problem solvers when they go out to industry after graduation. The purpose of this study is to examine how students transfer their understanding of physics concepts to solve ill-structured engineering problems by means of an engineering design project in a physics laboratory. We use a case-study methodology to examine two cases and analyze the cases using a lens of co-regulated learning and transfer between physics and engineering contexts. Observations were conducted using transfer lenses. That is, we observed groups during the physics labs for evidence of transfer. The research question for this study was, to what extent do students relate physics concepts with concepts from other materials (classes) through an engineering design project incorporated in a physics laboratory? Teams were observed over the course of 6 weeks as they completed the second design challenge. The cases presented in this study were selected using observations from the lab instructors of the team’s work in the first design project. Two teams, one who performed well, and one that performed poorly, were selected to be observed to provide insight on how students use physics concepts to engage in the design process. The second design challenge asked students to design an eco-friendly way of delivering packages of food to an island located in the middle of the river, which is home to critically endangered species. They are given constraints that the solution cannot disrupt the habitat in any way, nor can the animals come into contact directly with humans or loud noises. Preliminary results indicate that both teams successfully demonstrated transfer between physics and engineering contexts, and integrated physics concepts from multiple labs to complete the design project. Teams that struggle seem to be less connected with the design process at the beginning of the project and are less organized. In contrast, teams that are successful demonstrate greater co-regulated learning (communication, reflection, etc.) and focus on making connections between the physics concepts and principles of engineering design from their engineering course work.
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Promoting Computational Thinking in Integrated Engineering Design and Physics Labs
Computational thinking has widely been recognized as a crucial skill for engineers engaged in problem-solving. Multidisciplinary learning environments such as integrated STEM courses are powerful spaces where computational thinking skills can be cultivated. However, it is not clear the best ways to integrate computational thinking instruction or how students develop computational thinking in those spaces. Thus, we wonder: To what extent does engaging students in integrated engineering design and physics labs impact their development of computational thinking? We have incorporated engineering design within a traditional introductory calculus-based physics lab to promote students’ conceptual understanding of physics while fostering scientific inquiry, mathematical modeling, engineering design, and computational thinking. Using a generic qualitative research approach, we explored the development of computational thinking for six teams when completing an engineering design challenge to propose an algorithm to remotely control an autonomous guided vehicle throughout a warehouse. Across five consecutive lab sessions, teams represented their algorithms using a flowchart, completing four iterations of their initial flowchart. 24 flowcharts were open coded for evidence of four computational thinking facets: decomposition, abstraction, algorithms, and debugging. Our results suggest that students’ initial flowcharts focused on decomposing the problem and abstracting aspects that teams initially found to be more relevant. After each iteration, teams refined their flowcharts using pattern recognition, algorithm design, efficiency, and debugging. The teams would benefit from having more feedback about their understanding of the problem, the relevant physics concepts, and the logic and efficiency of the flowcharts
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- Award ID(s):
- 2021389
- NSF-PAR ID:
- 10451275
- Date Published:
- Journal Name:
- ASEE Annual Conference & Exposition Proceedings
- Page Range / eLocation ID:
- https://peer.asee.org/43977
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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