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Board 129: Analyzing Student Learning Level for the Authentic Learning Assignment "Design Your Own Problem" Using Bloom’s Taxonomy

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2023 ASEE Annual Conference & Exposition


Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

Mechanical Engineering Division (MECH) Poster Session

Tagged Division

Mechanical Engineering Division (MECH)

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Paper Authors

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Elisa Koolman University of Texas at Austin


Boni Frances Yraguen Georgia Institute of Technology Orcid 16x16

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Boni Yraguen is a PhD student at Georgia Tech. Her dissertation work is in the field of combustion/thermo./fluids. She studies a novel diesel injection strategy: Ducted Fuel Injection (DFI), which is used to drastically decrease soot emissions during diesel combustion. In addition to her thesis work, Boni is passionate about engineering education. She has led and participated in various educational studies on the impact of student reflections, authentic learning assignments, ad the use of technology in the classroom. Boni hopes to pursue a career in academia with a focus on teaching and engineering education.

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Roxanne Moore Georgia Institute of Technology

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Roxanne Moore is currently a Research Engineer at Georgia Tech with appointments in the school of Mechanical Engineering and the Center for Education Integrating Mathematics, Science, and Computing (CEISMC). She is involved with engineering education inno

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Katherine Fu Georgia Institute of Technology

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Dr. Kate Fu is the Jay and Cynthia Ihlenfeld Associate Professor of Mechanical Engineering at the University of Wisconsin-Madison. From 2014 to 2021, she was an Assistant and Associate Professor of Mechanical Engineering at Georgia Institute of Technology. Prior to these appointments, she was a Postdoctoral Fellow at Massachusetts Institute of Technology and Singapore University of Technology and Design (SUTD). In May 2012, she completed her Ph.D. in Mechanical Engineering at Carnegie Mellon University. She received her M.S. in Mechanical Engineering from Carnegie Mellon in 2009, and her B.S. in Mechanical Engineering from Brown University in 2007. Her work has focused on studying the engineering design process through cognitive studies, and extending those findings to the development of methods and tools to facilitate more effective and inspired design and innovation. Dr. Fu is a recipient of the NSF CAREER Award, the ASME Design Theory and Methodology Young Investigator Award, the ASME Atlanta Section 2015 Early Career Engineer of the Year Award, and was an Achievement Rewards For College Scientists (ARCS) Foundation Scholar.

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This evidence-based practice poster will assess the impact of the authentic learning assignment ‘Design Your Own Problem’ (DYOP) on student learning levels as compared to typical assessments, such as quizzes, in a fluid mechanics course.

Upper-level engineering courses with a heavy emphasis on theoretical knowledge require students to understand and apply new, unintuitive co­­ncepts. Fluid Mechanics and other upper-level engineering courses rely upon a student’s prior knowledge of basic engineering principles and abstract understanding of mathematical concepts to comfortably approach new problems in this field. It is one of the first courses in which more abstract concepts are given physical and applicable meanings. As such, this course is a critical opportunity to teach higher-order engineering skills, such as problem definition, problem simplification, modeling, and solution analysis. These skills are required to solve ill-structured or open-ended engineering problems, which are most problems an engineer will face during their career. Yet, the vast majority of the problems students are assigned are well-structured or close-ended problems. There is a need to scaffold student learning from simple, well-posed textbook problems to more open-ended problems requiring higher levels of critical thinking. One possible strategy is to use authentic learning assignments in upper-level lecture-based engineering courses.

The purpose of this study is to evaluate the difference in levels of learning or critical thinking employed by students while solving typical quiz problems compared to those employed while completing this authentic learning assignment. The authentic learning assignment Design Your Own Problem (DYOP) challenges students to choose a specific sub-topic of fluid mechanics and research, define, and solve a real-world engineering problem within that domain. In this assignment, students are required to simplify a real situation by analyzing which portions of the problem are significant, identifying and selecting necessary values and input parameters, and choosing and evaluating their solution strategy. To measure levels of learning achieved, student reflections are coded for levels of Bloom’s taxonomy. Bloom’s taxonomy categorized levels of problem-solving and learning in a tiered system, the bottom of which is the most basic level of learning and the highest is the most complex and elevated level of learning. Students are assigned one-page reflections after completing their quizzes and DYOP assignments. The students are prompted to focus on their thought processes while completing each assessment. In this study, we analyze post-assessment reflections written by 54 students in a fluid mechanics course. Based on preliminary analyses, evidence suggests that students achieve higher levels of Bloom’s taxonomy during the DYOP as compared to traditional assignments.

Koolman, E., & Yraguen, B. F., & Moore, R., & Fu, K. (2023, June), Board 129: Analyzing Student Learning Level for the Authentic Learning Assignment "Design Your Own Problem" Using Bloom’s Taxonomy Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42440

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