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2019 ASEE Annual Conference & Exposition

A Mixed Methods Analysis of Motivation Factors in Senior Capstone Design Courses

Presented at Best In DEED

This paper presents a continuation of prior research exploring the impact of motivation factors on performance in cornerstone and capstone design courses. The previous longitudinal study focused on a single cohort of 32 students, examining their motivation at two instances in time: their freshman cornerstone design course and their senior capstone design course. The results from the prior quantitative research revealed that student grades in their freshman cornerstone design course were impacted by their anxiety levels with significance. The student’s senior capstone design grades were determined by their intrinsic motivation, and the delta in their grade between their freshman and senior year was correlated to their freshman year anxiety and their residency.

An interesting finding from the quantitative survey was the student’s anxiety levels did not decrease significantly between their cornerstone and capstone design course. However, the student’s capstone design grades were not affected by their anxiety. This indicated that there was a paradigm shift in which the students no longer allowed their anxiety to dictate their performance in design courses. This prompted the authors to further explore the impact of motivation on the student’s performance in their senior capstone design courses. This study focuses on a cohort of 80 students, and uses data from two instances in time: their Fall and Spring senior capstone design course. The findings from the prior longitudinal study also impelled the authors to implement a qualitative survey to gain insight into the student’s perspective of their motivation. Both of the surveys measure five factors of student motivation: cognitive value, intrinsic value, self-regulation, self-efficacy, and test/presentation anxiety.

This paper presents quantitative and qualitative results to further explore the impact of student motivation on their performance in senior capstone design courses. The study also examines the student’s motivation factors with regard to their demographic information. This includes the student’s gender, age, residency (domestic or international), family income, and the highest degree attained by parents.

The results of the study indicate that the students’ fall, spring, and change in performance are all impacted by their intrinsic value, with the students’ spring performance being further exacerbated by their cognitive value. Their performance in the spring is also found to be closely related to their residency. Interestingly, the student’s intrinsic value actually dropped, with significance, between the beginning of the fall and end of the spring semester. The results of the qualitative study indicate that the students who were confident entering into senior capstone design identify necessary areas of improvement by the end of the two semester course.

Authors
  1. Elisabeth Kames Florida Institute of Technology [biography]

    Elisabeth Kames is a Visiting Assistant Professor at Florida Polytechnic University. Her research focuses on the impact of motivation on performance and persistence in mechanical engineering design courses. Elisabeth is an active member of ASEE, ASME, Tau Beta Pi Engineering Honor Society and Pi Tau Sigma International Mechanical Engineering Honor Society.

  2. Devanshi Shah Florida Institute of Technology [biography]

    Devanshi Shah is currently a PhD student in Engineering at the University of Georgia, under the advisement of Dr. Beshoy Morkos. She received her masters degree in Mechanical Engineering from Florida Institute of Technology in 2019. She graduated with her bachelors in Mechanical Engineering from India in 2016. Her research area is focused in design engineering and engineering education.

  3. Miss McKenzie Carol Clark
  4. Dr. Beshoy Morkos Florida Institute of Technology [biography]

    Beshoy Morkos is an associate professor in the Department of Mechanical and Civil Engineering at the Florida Institute of Technology where he directs the STRIDE Lab (SysTems Research on Intelligent Design and Engineering). His engineering design research focuses on developing computational representation and reasoning support for managing complex system design. The goal of Dr. Morkos’ research is to fundamentally reframe our understanding and utilization of system representations and computational reasoning capabilities to support the development of system models which help engineers and project planners intelligently make informed decisions at earlier stages of engineering design. On the engineering education front, Dr. Morkos’ research explores means to integrate innovation and entrepreneurship in engineering education through entrepreneurially-minded learning, improve persistence in engineering, address challenges in senior design education, and promote engineering education in international teams and settings. Dr. Morkos’ research is currently supported by the National Science Foundation (NSF), Kern Entrepreneurial Engineering Network (KEEN), and NASA JPL.
    Dr. Morkos received his Ph.D. from Clemson University in the Clemson Engineering Design and Applications Research (CEDAR) lab under Dr. Joshua Summers. In 2014, he was awarded the ASME CIE Dissertation of the year award for his doctoral research. He graduated with his B.S. and M.S in Mechanical Engineering in 2006 and 2008 from Clemson University and has worked on multiple sponsored projects funded by partners such as NASA, Michelin, and BMW. His past work experience include working at the BMW Information Technology Research Center (ITRC) as a Research Associate and Robert Bosch Corporation as a Manufacturing Engineer. Dr. Morkos was a postdoctoral researcher in the Department of Engineering & Science Education at Clemson University performing NSF funded research on engineering student motivation and its effects on persistence and the use of advanced technology in engineering classroom environments. Dr. Morkos’ research thrust include: design automation, design representations, computational reasoning, systems modeling, engineering education, design education, collaborative design, and data/knowledge management.

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