The current research follows our work to validate the original, 26-item MUSIC Model of Motivation Inventory with Engineering students (presented in our 2022 FIE paper), where we validated the MUSIC inventory except for one of the MUSIC factors (Interest) which had several items cross-load onto other factors. Since then, the original survey authors have published a 19-item short form of the MUSIC Inventory. Our work seeks to validate the MUSIC survey instrument in the first-year engineering program, Michigan Technological University. We believe the MUSIC inventory could be a valuable tool for engineering education. To date, utilization of the MUSIC inventory has included few studies in an engineering learning context. This lack of uptake may be due to a lack of validation studies for the MUSIC Inventory in engineering classrooms. We report our early steps to validate the MUSIC Inventory for engineering programs. Our sample differed from the original validation work in that our sample consisted of mostly first-year students, while Jones sampled across class standing. All of our participants were engineering majors enrolled in a common first-year program of required coursework. In contrast, Jones sampled students in general education courses from various disciplines. We followed Jones and Wilkens' methodology for validation, which included validating items, scoring, and factors using a variety of analyses. Results revealed small differences in means between the long and short-form factors, but effect sizes indicate that they are negligible. Independence between the 5-factor scores from the 19-item version were gauged by examining correlations among the factors scores. Our correlations were higher than those Jones and Wilkens reported, leading us to question the independence between the Usefulness and Interest scale scores. Finally, a confirmatory factor analysis revealed high correlations between some factor scores and a lower-than-desired GFI. Again, problems appeared to stem from the Interest factor of the MUSIC Inventory. To better understand the validity of the inventory within engineering education, we conducted a 4-factor confirmatory factor analysis removing the Interest factor items. All fit indices improved, with the GFI approaching the desired value (0.90) at 0.879, and all correlations fell below the desired r = 0.71 (except for the Empowerment/Usefulness correlation of r = 0.776). In summary, the problems we reported between the Interest and Usefulness scores when validating the 26-item MUSIC Inventory for engineering students in an earlier study continue to exist in the more recent 19-item form. Possible reasons are discussed. We recommend caution when interpreting the Interest factor using the MUSIC inventory within engineering classes or programs and suggest future research.

One of the "Grand Challenges in Engineering Education" is to engage students in their own learning. Student engagement is widely seen as a necessary component driving the success of active learning methodologies. The Music Model of academic motivation was developed as a means to make the human motivation literature accessible to instructors interested in improving courses to increase student motivation and engagement. The model has a reliable and validated survey instrument that assesses 5 components of academic motivation. The model has been applied in two contexts relevant to our current project: in course design and improvement to assess the impact of changes on student motivation and learning, and second, it is used to examine students’ motivational perceptions and their relationship to other learning-related constructs. MUSIC has been used in K12 through higher education, and across a variety of fields.In this Work in Progress report, we had two purposes: First, we sought to test the use of the Music Model in an engineering course, since little research has been conducted in engineering courses to date. Second, we sought set the stage for developing a community of practice focused on student engagement with a common and straightforward assessment methodology for the first-year engineering community. Our broad goal is thus to leverage the MUSIC components as one metric for gauging improvement of student engagement for our own first-year engineering program, then eventually a community wide tool for first-year engineering programs broadly. The MUSIC scale inventory data (n=221) was collected electronically in 3 sections of a first-year engineering course at a mid-western technological university. A confirmatory factor analysis replicated the 5-factor MUSIC Model. An ANOVA revealed no differences in student motivations between our three-course sections. This result validates our ability to offer similar experiences across sections and instructors within our first-year course. Multiple comparisons between factor scores demonstrated significantly higher motivation reported on both the caring and success factors as compared to the others. In addition, the interest motivation factor was significantly lower than all other factors. These findings demonstrate the utility of the Music Model within engineering education. We discuss future research to develop a process for instructors to understand the results and make formative decisions for future course iterations. Further, we suggest future research re-establishing the link between the various motivational factors and educational outcomes such as GPA, course grades, retention in STEM, etc. We propose that global events, such as the pandemic, may have resulted in changes in students’ priorities regarding education, thereby altering previous findings regarding the importance of specific motivational factors on educational outcomes.

This full paper, in the research to practice category, focuses on student preferences for online versus face-to-face instruction. Spring Semester, 2020 started as usual but proved to be anything but usual. Instead, in a seven-day turnaround, the first-year engineering program at Michigan Technological University moved from a face-to-face, highly interactive studio environment to a remote/synchronous environment. At the end of the semester, our University and many others across the United States conducted a short survey of undergraduate students on their preference of face-to-face versus online instruction. Results showed a strong preference for face-to-face instruction. However, to adequately consider the extensive ranges of approach in both umbrella terms (“face-to-face instruction” and “online instruction”), we need to unpack the surface results. This paper reports on a short survey given to second-semester students in our College of Engineering, First-Year Engineering Program, and students in the first-year course in Systems Engineering. The survey sought to gather student preferences for two variations of our instructional models in current use in our first-year program: (a) remote/synchronous instruction versus (b) a hybrid environment that included face-to-face instruction with mandatory masking and social distancing. Results showed that students, at worst, held preferences that were generally not statistically different in terms of preferences. The several exceptions that did show significance showed numerical differences that were not of practical importance, with one exception. The core takeaway from our study is that determining student preferences for “face-to-face instruction” versus “distance learning” needs to be unpacked to enable students to register reasoned judgments and set the stage for meaningful results.

Creativity has been studied extensively since 1956 when the NSF sponsored the first national research conference on creativity (Taylor, 1962). Within engineering education, one often hears the call for the development of creativity in engineering students. As part of the IDEAS grant (DUE-0836861), we examined the relationship between domain specific hypothetical challenges and opportunities, and engineering students' self-reported attitudes and behavioral intentions designed to measure creative self-efficacy in engineering. Our concept of creative self-efficacy in engineering was designed to assess one's confidence in their ability to be creative within the engineering domain. Using factors analysis procedures, we have identified four factors that appear to be novel indicators of creative self-efficacy in engineering: Cognitive Approaches, Cognitive Challenges, Cognitive Preparedness, and Impulsivity in problem solving.

For more than two decades researchers have addressed gender issues in engineering. Efforts such as Engineer Girl (www.engineergirl.org), a website dedicated to increasing young girls' interest in pursuing an engineering degree, address the engineering "diversity gap". Potential improvements exist for increasing the number of women pursuing engineering careers, such as increasing their engineering self-efficacy. Engineering self-efficacy refers to a person's belief that he or she can successfully navigate the engineering curriculum and eventually become a practicing engineer. It encompasses self-efficacy, feeling of inclusion, and outcome expectations. A longitudinal multi-institutional study conducted by Marra and Bogue indicated statistically significant differences for female engineering students with respect to the coping, mathematics, and self-efficacy subscales. The authors wish to explore whether institutional setting (urban vs. rural) accounts for differences in female students' engineering self-efficacy.

In 2000, we introduced an undergraduate engineering curricular option to serve as an alternative to the traditional two-semester senior capstone experience, and intended to better meet the needs of students and industry. Initially funded through NSF, this program offers teams of students from varied disciplines the opportunity to work for several years in a business-like setting solving real-world problems supplied by industry. This program has converted the traditional classroom into a multi-year, interdisciplinary, experiential learning environment, and the role of instructor from one who imparts knowledge to that of mentor, guiding students as they discover and apply knowledge. The program is now self-sustaining and successfully attracts and retains STEM-discipline students, making them more marketable to employers upon graduation. Under NSF's IEECI program, we undertook a study to determine whether participation in such a project-based learning environment, together with the redefined role of faculty mentors, are positively correlated to student education outcomes. One measurement tool used to capture student perceptions was a modified form of the Academic Pathways of People Learning Engineering Survey (APPLES), to look at contributors to students' persistence in engineering. In this paper, we will share the results of the APPLES survey component of our study as related to the faculty mentor role and project-based team learning environments.