Classifying and Characterizing University Maker Space Users: A Foundation
Morocz, Ricardo J
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The popularization of maker spaces in academic institutions has raised the question of how these unique learning environments are impacting the students. With the ultimate goal of understanding this impact, it is necessary to identify what type of individuals are taking advantage of these spaces and how users are utilizing the resources and equipment available. This thesis presents two studies with the objective of providing a better understanding of university maker space users and their behavior and activities. The first study describes the process of classifying and characterizing university maker space users and non-users. As part of a four-year longitudinal study, this thesis reports issues found within the first two semesters of data collection at Georgia Tech, and provides recommendations to ensure all the necessary data is being collected. One of the main contributions of this study is the development of a survey instrument capable of collecting the student’s level of involvement and participation in maker spaces. This survey was developed by combining survey design theory with the author’s experience and knowledge as a maker space user and student volunteer. It was then leveraged to compare students with high participation and, the more common, low participation students in terms of their engineering design self-efficacy evaluations. The participation level results were correlated with those of the engineering design self-efficacy. It showed that high participation students are more motivated and less anxious about performing engineering design related tasks than their low participation counterparts. Additional results show that there might be a migration of highly self-efficacious students from low to high participation as they progress in their academic career and encounter more opportunities to participate. While at this point the relationship found is only correlational, based on the previous findings there is reason to believe that higher motivation and lower self-efficacy may drive students to seek places that allow them to explore engineering design related activities like university maker spaces. This also suggests that there might be barriers in place that prevent students with lower motivation and higher anxiety from participating in maker spaces and further supports the concern about introducing barriers when studying these environments. To have a better understanding of university maker spaces as learning resources and quantify their impact on the users, it is important to understand to what extent and how these spaces are being used. Identifying the number of users that take advantage of these spaces and their characteristics could provide insight on the usage and inclusiveness of these environments. A pilot study was developed to assess the effectiveness of automatic people counters technology for maker space applications. This technology could allow researcher to collect traffic and usage data in a non-obtrusive manner, minimizing the introduction of unwanted barriers. The data collection methodology described leveraged the use of a video camera and automatic people counter technology to calculate the ratio of individual users and count data. This ratio allows one to estimate the number of users for any given day based on the automatically collected count data. The results from the pilot study show that, on average, users tend to enter the 3D Printing room associated with the university maker space two times a day, which is consistent with the expected use of a 3D printer. Moreover, the methodology was also used to identify the number of female users participating in the activities associated with this space. While this pilot study was limited due to a small sample size, the methodology developed for data collection and analysis proved to be promising for many different applications and objectives. Future studies will expand the sample size, calculate a more comprehensive user-to-count ratio, and leverage the methodology to capture other user characteristics.