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Characterizing Student Work while Solving Ill-Defined Statics Problems in Groups

  • SUNY Buffalo

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Engineering problems are ill-defined, require assumptions, and have multiple unique solutions. Although most industry engineers solve ill-defined problems in groups, students typically only practice this in engineering design courses. Our research aims to expand these experiences to engineering science courses. Currently, most engineering science courses assign 'classic' textbook problems, where they are given certain physical parameters of a system, and are told to calculate an unknown value. Ill-defined modeling problems provide students with more opportunities to utilize engineering judgment when compared to traditional textbook problems, and when these problems are solved in a group setting, it is both a better representation of how engineering is performed in the industry, and can help students better understand the class concepts. This paper examines groups of students solving an ill-defined modeling task that asks students to design a portable pool lift. When working in a group, students have the opportunity to help each other understand what was taught in class, along with the ability to push back on other students' ideas. This will prepare students for their future career, lead to knowledge creation and help solidify concepts taught in class. This full paper analyzes data (approximately 15.5 hours) that was collected in the form of recordings of zoom meetings of two groups that were tasked with solving an ill-defined modeling problem in a second year statics course. Using comparative coding, we categorized how students spent time when working in their group. Results show students alternate between negotiating tasks, comparing assumptions, and aiding each other in understanding course concepts. Implications of this work include forming a better understanding of how students make decisions, judgments and build knowledge when working together on an ill-defined modeling problem. Similarly, the results may assist professors in iterating on assignment design to further engage students in knowledge creating and engineering judgment practices.

Original languageEnglish
JournalASEE Annual Conference and Exposition, Conference Proceedings
StatePublished - Jun 25 2023
Event2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023 - Baltimore, United States
Duration: Jun 25 2023Jun 28 2023

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