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Towards the Use of the MUSIC Inventory for Measuring Engineering Student Engagement

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Conference

2022 First-Year Engineering Experience

Location

East Lansing, Michigan

Publication Date

July 31, 2022

Start Date

July 31, 2022

End Date

August 2, 2022

Conference Session

Technical Session T1A

Tagged Topic

Full Papers

Page Count

7

Permanent URL

https://peer.asee.org/42250

Download Count

30

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Paper Authors

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Susan L. Amato-Henderson Michigan Technological University

biography

Jon Sticklen Michigan Technological University

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Jon Sticklen is an Associate Professor with the Engineering Fundamentals Department (EF) and Affiliated Faculty with the Department of Cognitive and Learning Sciences (CLS). He served as Chair of EF from 2014-2020, leading a successful effort to design and implement a rejuvenated first-year engineering program serving approximately 1000 matriculating students per year. His research interests have focused on AI/Knowledge-based Systems: in particular, the "task-specific" approach to knowledge elucidation and system building, function-based reasoning, explanation of reasoning. External support included NSF/CISE, DARPA, GE Aircraft, Boeing; Higher Education Research, Early STEM Education: in particular PBL, inverted classroom approaches, cooperative learning, distance education. External support included NSF/CISE, NSF/DUE. His current research foci include pedagogy of on-line learning, learning at the confluence of Human Factors, systems thinking mindset, and AI-based knowledge representation.

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Abstract

One of the "Grand Challenges in Engineering Education" is to engage students in their own learning. According to Vest (Vest, 2008), then president of the National Academy of Engineering, engineering education must focus on the environment in which students learn. While the content is changing at an amazing pace, facilitating a learning environment that fosters student ideas, inspiration, and empowerment will be critical in the 21st century. “Students are driven by passion, curiosity, engagement, and dreams.” (Vest, page 236). We need students who are technically and creatively able to solve the challenges of tomorrow. The MUSIC model of Academic Motivation was developed to help instructors apply motivation research to the design of instruction by providing an organizational framework of current motivation principles. There is strong evidence linking student motivation to student engagement. (Nayir, 2017) But what constitutes “student engagement”? The MUSIC model of academic motivation was developed as a means to pull together a plethora of literature focused on human motivation in a manner that would make core results from the literature on student motivation accessible to educational researchers at large through a validated instrument for the construct of student engagement. (Jones, 2009; Jones & Skaggs, 2012). The MUSIC model was implemented by developing a survey instrument (The MUSIC Inventory), now validated fairly extensively, containing five subscales: eMpowerment, Usefulness, Success, Interest, Caring. In Fall Semester, 2021, we gave the MUSIC Inventory to 220 first-year engineering students at as a first step towards utilizing the MUSIC Inventory as an assessment tool for “student engagement”. The results from our first use is described in a Frontiers in Education 2022 conference and is now in the draft paper stage, the abstract having been accepted. One unexpected result was that we found the five subscales of the MUSIC Inventory collapsed to four subscales when subjected to re-factor analysis. There are a number of possible causes for this variation: our population was engineering students, our university is somehow different in some way affecting the results, or perhaps the students in our study were made up of post-COVID students (students whose high school years were distinctly different because of COVID). In this report to the FYEE community we discuss our second use of the MUSIC Inventory in Spring, 2022. In this deployment of the MUSIC Inventory, we configured our study to include pre- and post- data to help to better understand the collapse of the five sub-scale version of the MUSIC Inventory to a four sub-scale result when refactored in our initial study. We note that there is a small but growing literature that supports the fact-based differences of the post-COVID students versus the pre-COVID students. Most of the MUSIC Inventory validation studies (all to our knowledge) were conducted on pre-COVID students. Engagement is a key factor in student success. The Engineering Education community needs a trusted instrument to objectify student motivation-fired engagement. We are on that path, as will be reported in this paper. Backing Literature Jones, B. D. (2009). Motivating students to engage in learning: the MUSIC model of academic motivation. International Journal of Teaching and Learning in Higher Education, 21(2), 272-285. Jones, B. D., & Skaggs, G. (2012). Validation of the MUSIC Model of Academic Motivation Inventory: A measure of students’ motivation in college courses. Paper presented at the Research presented at the International Conference on Motivation. Nayir, F. (2017). The Relationship between Student Motivation and Class Engagement Levels. Eurasian Journal of Educational Research, 71, 59-78. Vest, C. M. (2008). Context and challenge for twenty-first century engineering education. Journal of Engineering education, 97(3), 235-236.

Amato-Henderson, S. L., & Sticklen, J. (2022, July), Towards the Use of the MUSIC Inventory for Measuring Engineering Student Engagement Paper presented at 2022 First-Year Engineering Experience, East Lansing, Michigan. https://peer.asee.org/42250

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