Board # 4 :The Role of Knowledge Structure, Knowledge Retention, and Misconceptions in Open-ended Biomedical Engineering Design Problems (Work in Progress)

Hannah Timbers Yssels; Marina Crowder; Ozcan Gulacar; Jennifer H. Choi

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Conference: 2017 ASEE Annual Conference & Exposition
Location: Columbus, Ohio
Publication Date: June 24, 2017
Start Date: June 24, 2017
End Date: June 28, 2017
Conference Session: Biomedical Division Poster Session
Tagged Division: Biomedical
Page Count: 5
DOI: 10.18260/1-2--27847
Permanent URL: https://peer.asee.org/27847
Download Count: 369

Paper Authors

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Hannah Timbers Yssels University of California, Davis

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Hannah Yssels is a third year biomedical engineering student at UC Davis, specializing in medical devices. She is currently a research assistant to Jennifer Choi, PhD, investigating problem solving performance and the development of design thinking skills in biomedical engineering. She has also assisted in the Heinrich Lab, researching the characterization of monocyte membrane protein populations. Hannah is a two-time finalist in the UC Davis Biomedical Engineering Society’s Make-a-Thon medical device design and prototyping competition.

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Marina Crowder University of California, Davis

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Marina Crowder is currently Teaching Faculty in the Department of Molecular and Cellular Biology at UC Davis. In addition to teaching core undergraduate courses, Marina is aimed at understanding how to better support the development students' problem-solving skills. She has interests in graduate student teaching professional development, effective supplemental instruction models at the upper-division level, and improving the success of transfer students in STEM. Prior to joining UC Davis, Marina taught at Laney Community College and was a postdoctoral fellow in the laboratory of Dr. Rebecca Heald in the Molecular and Cellular Biology Department at UC Berkeley. She received her doctoral degree in Biochemistry, Molecular, Cellular and Developmental Biology and B.S. degree in Genetics, both from UC Davis.

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Ozcan Gulacar University of California, Davis

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Dr. Gulacar has a Master’s degree in Physical Chemistry and a Ph.D. in Science Education. In the last 15 years, he has worked in settings including international high schools and doctorate granting institutions. He has designed and taught undergraduate/graduate chemistry and science education courses for a wide range of audiences. Due to his interest in investigating the effectiveness of different teaching methods and tools, he has received grants and established collaborations with colleagues from different fields and countries. Dr. Gulacar has developed and organized workshops about implementation of social constructivist methods and effective use of technological tools in science classrooms.

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Jennifer H. Choi University of California, Davis

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Jennifer Choi is currently a Lecturer with potential for security of employment (LPSOE) in the Department of Biomedical Engineering (BME) at UC Davis. In addition to teaching core undergraduate courses, Jennifer is aimed at integrating engineering design principles and hands-on experiences throughout the curriculum, and playing an active role in the senior design course. She has interests in engineering education, curricular innovation, as well as impacting the community through increased K-12 STEM awareness and education. Prior to joining UC Davis, Jennifer taught in the BME Department at Rutgers University, and was a postdoctoral fellow at Advanced Technologies and Regenerative Medicine, LLC. She received her doctoral degree in Biomedical Engineering from Tufts University, M.S. degree from Syracuse University, and B.S. degree from Cornell University.

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Abstract

The need to improve problem solving skills in STEM undergraduates has been widely reported1. This Works in Progress paper seeks to provide additional insight into the role of knowledge structure, knowledge retention, and misconceptions in solving open-ended biomedical engineering design problems. Correlations in problem solving performance to level of metacognitive awareness will also be assessed. As part of a larger multidisciplinary study, we seek to develop a model describing undergraduates’ STEM problem solving performance to serve as a tool to guide support of students’ problem solving skill development.

Current study participants are enrolled in a first year introductory biomedical engineering (BME) course aimed to introduce the field through BME specialization introductory lectures, prospective BME career guest lectures, and team-based hands-on design challenges. This two unit course consists of one 50 minute lecture and a 3 hour discussion session focused on engineering design each week. The majority of the 142 enrolled students are first quarter freshman engineering students, of which all BME students are required to take.

To gather a baseline of students’ design knowledge, the Comprehensive Assessment of Design Engineering Knowledge (CADEK) diagnostic test2 was administered to all students in the first week of class and will be administered during the last week of class. Upon consent, students were also asked to complete an online Metacognitive Awareness Inventory (MAI)3 during week 2. In addition to the CADEK and MAI, students will also answer an open ended design problem on their first quiz (in Week 5), in which ten high performing and ten low performing students will be identified and asked to participate in one hour think aloud interviews (TAInt). TAInt will be conducted in such a way to encourage participants to speak through their thought processes while asked to solve several open ended BME design problems. Following the TAInt, which will be held during week 7 and 8 of the quarter, participants will be asked to complete a follow up CADEK and participate in a second round of TAInt during the next quarter utilizing the same open ended problems. Level of knowledge retention will be assessed.

Several developed instruments will be utilized to analyze the open ended design problems from quiz 1 and transcribed TAInt. The COSINE (Coding System for Investigating Sub-problems and the Network) method in particular, is an in-depth analysis of the difficulties students have during the problem solving process4. In order to utilize the COSINE method, sub-problems, which will correlate to the specific steps of the engineering design process, will be assigned a code based on student performance in a particular task. Quantitative metrics will be developed based on resulting codes to gain insight into where and why students are unsuccessful. MAI and CADEK results will also be assessed and correlated to quantitative metrics generated. Assessing study participants over the course of the BME undergraduate curriculum will also provide insight into strengths and areas for improvement of design instruction across the curriculum.

Literature Cited 1 Saavedra, A. R.; Saavedra, J. E., Do colleges cultivate critical thinking, problem solving, writing and interpersonal skills? Economics of Education Review 2011, 30 (6), 1516-1526. 2 Okudan, G.; Ogot, M.; Gupta, S. Assessment of Learning and its Retention in the Engineering Design Classroom Part A: Instrument Development. American Society for Engineering Education Conference Proceedings 2007, AC 2007-2261. 3 Mulford, D. R.; Robinson, W. R., An Inventory for Alternate Conceptions among First-Semester General Chemistry Students. Journal of Chemical Education 2002, 79 (6), 739-744. 4 Gulacar, O.; Overton, T. L.; Bowman, C. R.; Fynewever, H., A novel code system for revealing sources of students' difficulties with stoichiometry. Chemistry Education Research and Practice 2013, 14 (4), 507-515.

Citation
Format

Yssels, H. T., & Crowder, M., & Gulacar, O., & Choi, J. H. (2017, June), Board # 4 :The Role of Knowledge Structure, Knowledge Retention, and Misconceptions in Open-ended Biomedical Engineering Design Problems (Work in Progress) Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--27847

TY - CPAPER
AB - The need to improve problem solving skills in STEM undergraduates has been widely reported1. This Works in Progress paper seeks to provide additional insight into the role of knowledge structure, knowledge retention, and misconceptions in solving open-ended biomedical engineering design problems. Correlations in problem solving performance to level of metacognitive awareness will also be assessed. As part of a larger multidisciplinary study, we seek to develop a model describing undergraduates’ STEM problem solving performance to serve as a tool to guide support of students’ problem solving skill development.

Current study participants are enrolled in a first year introductory biomedical engineering (BME) course aimed to introduce the field through BME specialization introductory lectures, prospective BME career guest lectures, and team-based hands-on design challenges. This two unit course consists of one 50 minute lecture and a 3 hour discussion session focused on engineering design each week. The majority of the 142 enrolled students are first quarter freshman engineering students, of which all BME students are required to take.

To gather a baseline of students’ design knowledge, the Comprehensive Assessment of Design Engineering Knowledge (CADEK) diagnostic test2 was administered to all students in the first week of class and will be administered during the last week of class. Upon consent, students were also asked to complete an online Metacognitive Awareness Inventory (MAI)3 during week 2. In addition to the CADEK and MAI, students will also answer an open ended design problem on their first quiz (in Week 5), in which ten high performing and ten low performing students will be identified and asked to participate in one hour think aloud interviews (TAInt). TAInt will be conducted in such a way to encourage participants to speak through their thought processes while asked to solve several open ended BME design problems. Following the TAInt, which will be held during week 7 and 8 of the quarter, participants will be asked to complete a follow up CADEK and participate in a second round of TAInt during the next quarter utilizing the same open ended problems. Level of knowledge retention will be assessed.

Several developed instruments will be utilized to analyze the open ended design problems from quiz 1 and transcribed TAInt. The COSINE (Coding System for Investigating Sub-problems and the Network) method in particular, is an in-depth analysis of the difficulties students have during the problem solving process4. In order to utilize the COSINE method, sub-problems, which will correlate to the specific steps of the engineering design process, will be assigned a code based on student performance in a particular task. Quantitative metrics will be developed based on resulting codes to gain insight into where and why students are unsuccessful. MAI and CADEK results will also be assessed and correlated to quantitative metrics generated. Assessing study participants over the course of the BME undergraduate curriculum will also provide insight into strengths and areas for improvement of design instruction across the curriculum.

Literature Cited
1 Saavedra, A. R.; Saavedra, J. E., Do colleges cultivate critical thinking, problem solving, writing and interpersonal skills? Economics of Education Review 2011, 30 (6), 1516-1526.
2 Okudan, G.; Ogot, M.; Gupta, S. Assessment of Learning and its Retention in the Engineering Design Classroom Part A: Instrument Development. American Society for Engineering Education Conference Proceedings 2007, AC 2007-2261.
3 Mulford, D. R.; Robinson, W. R., An Inventory for Alternate Conceptions among First-Semester General Chemistry Students. Journal of Chemical Education 2002, 79 (6), 739-744.
4 Gulacar, O.; Overton, T. L.; Bowman, C. R.; Fynewever, H., A novel code system for revealing sources of students&#39; difficulties with stoichiometry. Chemistry Education Research and Practice 2013, 14 (4), 507-515.

AU - Hannah Timbers Yssels
AU - Marina Crowder
AU - Ozcan Gulacar
AU - Jennifer H. Choi
CY - Columbus, Ohio
DA - 2017/06/24
PB - ASEE Conferences
TI - Board # 4 :The Role of Knowledge Structure, Knowledge Retention, and Misconceptions in Open-ended Biomedical Engineering Design Problems (Work in Progress)
UR - https://peer.asee.org/27847
DO - 10.18260/1-2--27847
ER -