Abstract University makerspaces have been touted as a possible avenue for improving student learning, engagement, retention, and creativity. As their popularity has increased worldwide, so has the amount of research investigating their establishment, management, and uses. There have, however, been very few studies that use empirical data to evaluate how these spaces are impacting the people using them. This study of three university makerspaces measures engineering design (ED) self-efficacy and how it is correlated with involvement in the makerspaces, along with student demographics. The three university makerspaces include a relatively new makerspace at a Hispanic-serving university in the southwestern US, makerspaces at an eastern liberal arts university with an engineering program that has been created within the last decade, and a makerspace at a large, research university in the southeast often considered to be one of the top programs in the US. Students at all three universities are surveyed to determine their involvement in their university's makerspace and how they perceive their own abilities in engineering design. The findings presented in this paper show a positive correlation between engineering design self-efficacy (EDSE) and involvement in academic makerspaces. Correlations are also seen between certain demographic factors and the percentage of students who choose to use the academic makerspace available to them. These findings provide crucial empirical evidence to the community on the self-efficacy of students who use makerspaces and provide support for universities to continue making these spaces available to their students.
more »
« less
Makerspace Participation: Which Students Return and Why?
Makerspaces are becoming increasingly common facilities in engineering departments and universities across the country. Facility stakeholders, including students, professors, and university administration, hold many assumptions about the benefits and importance of the spaces, but little research has been done to quantify student usage and to evaluate participation within these spaces. This is especially important to understand given the interdisciplinary and multipurpose nature of these facilities. In this paper, we seek to understand which undergraduate engineering students use the Makerspace and what factors influence their likelihood to return. In partnership with a Makerspace at a large, public institution in the Southwest, we analyzed nearly 29,500 sign-in entries from 4,230 unique participants. Log-in information from these students included an open-ended response to their reason for visiting the facility, which was coded into five categories. We provide descriptives by major of the students, who visited the Makerspace within a two-year period, as well as results of chi-square analyses to determine differences in use of the Makerspace and results of logistic regression to determine the probability of students’ return. Analysis of this data begins to uncover the ways in which undergraduate students engage with Makerspaces and illuminates differences in behavior between majors. Further research should investigate the reasons behind these patterns and possible barriers to entry.
more »
« less
- Award ID(s):
- 1636449
- NSF-PAR ID:
- 10100971
- Date Published:
- Journal Name:
- 2019 ASEE Annual Conference & Exposition, Tampa, FL
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Building upon our two years of research on the use of makerspaces in undergraduate engineering programs, we engaged in a large-scale data collection from students enrolled in undergraduate engineering preparation programs with affiliated makerspaces established for a minimum of three years. Using web searches, and other sources of information (e.g. references from other researchers or faculty members), we have identified 28 institutions that met our criteria. Working with a third party, we gathered over 574 responses from undergraduate engineering students with makerspace experiences spread across the 28 institutions. To gather our data, we created and validated an online survey with a combination of quantitative and qualitative items. We constructed a survey with subscales aligned with motivation to learn, growth mindset, learning goal orientation, knowledge of engineering as a profession, and belongingness and inclusion, as associated with work within makerspaces. We found significant positive correlations among the variables, positive levels of motivation, growth mindset, knowledge of engineering as a profession, and belongingness. We found differences in levels for gender, engineering majors, and student class standing. We discuss the implications for our findings in the context of undergraduate engineering student learning in makerspaces.more » « less
-
Building upon our two years of research on the use of makerspaces in undergraduate engineering programs, we engaged in a large-scale data collection from students enrolled in undergraduate engineering preparation programs with affiliated makerspaces established for a minimum of three years. Using web searches, and other sources of information (e.g. references from other researchers or faculty members), we have identified 28 institutions that met our criteria. Working with a third party, we gathered over 574 responses from undergraduate engineering students with makerspace experiences spread across the 28 institutions. To gather our data, we created and validated an online survey with a combination of quantitative and qualitative items. We constructed a survey with subscales aligned with motivation to learn, growth mindset, learning goal orientation, knowledge of engineering as a profession, and belongingness and inclusion, as associated with work within makerspaces. We found significant positive correlations among the variables, positive levels of motivation, growth mindset, knowledge of engineering as a profession, and belongingness. We found differences in levels for gender, engineering majors, and student class standing. We discuss the implications for our findings in the context of undergraduate engineering student learning in makerspaces.more » « less
-
Makerspaces have become a rather common structure within engineering education programs. The spaces are used in a wide range of configurations but are typically intended to facilitate student collaboration, communication, creativity, and critical thinking, essentially giving students the opportunity to learn 21st century skills and develop deeper understanding of the processes of engineering. Makerspace structure, layout, and use has been fairly well researched, yet the impact of makerspaces on student learning is understudied, somewhat per a lack of tools to measure student learning in these spaces. We developed a survey tool to assess undergraduate engineering students’ perceptions and learning in makerspaces, considering levels of students’ motivation, professional identity, engineering knowledge, and belongingness in the context of makerspaces. Our survey consists of multiple positively-phrased (supporting a condition) and some negatively-phrased (refuting a condition) survey items correlated to each of our four constructs. Our final survey contained 60 selected response items including demographic data. We vetted the instrument with an advisory panel for an additional level of validation and piloted the survey with undergraduate engineering students at two universities collecting completed responses from 196 participants. Our reliability analysis and additional statistical calculations revealed our tool was statistically sound and was effectively gathering the data we designed the instrument to measure.more » « less
-
There has been dramatic growth in the number of makerspaces at educational institutions. More research is needed to understand student interactions in these spaces and how these spaces should be designed to support student learning. This project uses network analysis techniques to study the network of activities in a makerspace that lead to successful student experiences. The proposed analyses will model a makerspace as a network of interactions between equipment, staff, and students. Results from this study will help educators to 1) identify and remove previously unknown hurdles for students who rarely use the space, 2) design an effective space using limited resources, 3) understand the impact of new equipment or staff, and 4) create learning opportunities such as workshops and curriculum integration that increase student learning. The new knowledge produced by this project may be useful for maximizing equipment and support infrastructure, and for guiding new equipment purchases. Thus, the results will support further development of effective makerspaces. This project hypothesizes that network-level analyses and metrics can provide valuable insights into student learning in makerspaces and will support what-if scenarios for proposed changes in spaces. Systems modeling and analysis have been used successfully to understand complex human and biological networks. In the context of makerspaces, this technique will provide measures of interaction between system components such as students, staff, and equipment. The analyses will identify the system components that are frequently used when students work in the makerspace over multiple visits. The project will allow for the comparison of makerspaces that have different levels of integration with the curriculum and methods of student introduction (pop-up classes, tours, extra-curricular competitions, advertising, and bring a friend). Demonstration of the effectiveness of the analyses in characterizing makerspaces and the ease of data collection will help support the use of this approach in future work that compares makerspaces nationwide. Current results explore the order in which students choose to learn and use the tools in the space, which tools/features are used most frequently and how the data from the daily entry/exit surveys compares to the end-of-semester self-reports. A key question in this research, especially for making it adoptable by other universities, is if end-of-semester, self-reported data is accurate enough to create informative, actionable guidance from the network models without requiring the daily tool usage data.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
