Search for: All records

Makerspaces continue to be a part of many university engineering programs. More work is needed to understand their impacts and how makerspaces should be implemented to maximize their impact for all students. Many of the available approaches to ascertain impact are highly effective but excessively time-intensive, especially for continuous monitoring of a space. This paper presents the use of bipartite network analysis of weighted and unweighted matrices of student tool usage to determine modularity as an easy-to obtain metric to monitor space. To obtain the data needed, an end-of-the-semester survey asks students which tool they used in the space and how frequently. Data was collected in Spring 2021 and Spring 2022 as covid restrictions were being lifted, providing a data set where the modularity values should be changing. Prior work demonstrated unweighted modularity values as an effective tool for identifying changes in the health of a makerspace. Current work explores the inclusion of tool frequency use on the conclusion drawn from modularity analysis. Results show differing patterns of results between the weighted (includes frequency of use) and unweighted (only considers if a tool was used) modularity values. More work needs to explore the use of weighted bipartite network analysis and the benefits it may provide over the much simpler to obtain the unweighted analysis. Additional research is also needed on other methods to monitor the health of a makerspace and the benefits to all of its users. 

Makerspaces continue to be a part of many university engineering programs. More work is needed to understand their impacts and how makerspaces should be implemented to maximize their impact for all students. Many of the available approaches to ascertain impact are highly effective but excessively time-intensive, especially for continuous monitoring of a space. This paper presents the use of bipartite network analysis of weighted and unweighted matrices of student tool usage to determine modularity as an easy-to-obtain metric to monitor space. To obtain the data needed, an end-of-the-semester survey asks students which tool they used in the space and how frequently. Data was collected in Spring 2021 and Spring 2022 as covid restrictions were being lifted, providing a data set where the modularity values should be changing. Prior work demonstrated unweighted modularity values as an effective tool for identifying changes in the health of a makerspace. Current work explores the inclusion of tool frequency use on the conclusion drawn from modularity analysis. Results show differing patterns of results between the weighted (includes frequency of use) and unweighted (only considers if a tool was used) modularity values. More work needs to explore the use of weighted bipartite network analysis and the benefits it may provide over the much simpler to obtain the unweighted analysis. Additional research is also needed on other methods to monitor the health of a makerspace and the benefits to all of its users. 

Prior research emphasizes the benefits of university makerspaces, but overall, quantitative metrics to measure how a makerspace is doing have not been available. Drawing on an analogy to metrics used for the health of industrial ecosystems, this article evaluates changes during and after COVID-19 for two makerspaces. The COVID-19 pandemic disturbed normal life worldwide and campuses were closed. When students returned, campus life looked different, and COVID-19-related restrictions changed frequently. This study uses online surveys distributed to two university makerspaces with different restrictions. Building from the analysis of industrial ecosystems, the data were used to create bipartite network models with students and tools as the two interacting actor groups. Modularity, nestedness, and connectance metrics, which are frequently used in ecology for mutualistic ecosystems, quantified the changing usage patterns. This unique approach provides quantitative benchmarks to measure and compare makerspaces. The two makerspaces were found to have responded very differently to the disruption, though both saw a decline in overall usage and impact on students and the space’s health and had different recoveries. Network analysis is shown to be a valuable method to evaluate the functionality of makerspaces and identify if and how much they change, potentially serving as indicators of unseen issues. 

The circular economy (CE) is a resource system in which byproducts and traditional end-of-life resource flows are fed back into the system to reduce virgin resource use and waste production. Emerging technologies offer an exciting opportunity to support circular economy efforts, especially in the early design phase when opportunities for incorporating these technologies are relatively easy. Traditionally, however, the early design phase has access to very little data about resource flows which makes the introduction of new technologies difficult to do, especially with respect to market-related design decisions. In the later design stages, this data is easier to obtain but is met with increased inflexibility and costs that make these types of changes less common. This paper proposes the use of cyclicity, also known as spectral radius, and NS* minimal-data input metrics that can direct designers to options with the greatest theoretical impact on routing commonly wasted resources back into value circulation. Cyclicity is a metric commonly used in ecology to assess the existence and complexity of cycles, or material/energy pathways that can start and end at the same node, occurring in a system. The metric uses a topological adjacency matrix of resource flows between potential circular economy actors, modeled as a directional graph, and is calculated as the largest absolute eigenvalue of an adjacency matrix and can be a value of zero (no cycles), one (basic cycles), and any value larger than one (increasing presence and complexity of cycles). This study also evaluates actors making up the network as to whether they are part of a strong cycle, a weak component of a cycle, or are disconnected from a cycle, quantified with NS*. In a strong cycle, all actors feed into the cycle and the cycle feeds back into the actors. Actors that are weakly connected to a cycle do not contribute to a cyclic pathway. Disconnected actors are not connected to any actor participating in cycling. This paper conducts two case studies on these design tools. The first, a survey of 51 eco-industrial parks (EIPs) and 38 ecological food webs to compare the presence and complexity of cycles in industrial resource systems to ecological resource systems. The latter, food webs, are very effective at retaining value inside the system boundaries. The former, EIPs, were built in support of circular economy principles to use waste streams from one industry as resource streams for others. The analysis shows that 46 out of 51 EIPs had cyclicity values of one or greater and an average of 54% of actors in an EIP are strong. The food webs all have a cyclicity greater than one and an average of 79% of actors in a food web are strong. These results can help decision makers consider CE-supporting pathways earlier in the design process, increasing the likelihood that emerging technologies are incorporated to maximize their CE impact. The second case study explores an emerging technology, Brine Miners, and how cyclicity and NS* can be used to guide design decisions to impact the ability of this technology to aid in the creation of a circular economy. The exploration found that focusing on the creation of energy has the potential to add new actors to resource cycling and that diversifying the uses of byproducts creates more complex cycling within a hypothetical economy. 

Analyzing interactions between actors from a systems perspective yields valuable information about the overall system's form and function. When this is coupled with ecological modeling and analysis techniques, biological inspiration can also be applied to these systems. The diagnostic value of three metrics frequently used to study mutualistic biological ecosystems (nestedness, modularity, and connectance) is shown here using academic engineering makerspaces. Engineering students get hands‐on usage experience with tools for personal, class, and competition‐based projects in these spaces. COVID‐19 provides a unique study of university makerspaces, enabling the analysis of makerspace health through the known disturbance and resultant regulatory changes (implementation and return to normal operations). Nestedness, modularity, and connectance are shown to provide information on space functioning in a way that enables them to serve as heuristic diagnostics tools for system conditions. The makerspaces at two large R1 universities are analyzed across multiple semesters by modeling them as bipartite student‐tool interaction networks. The results visualize the predictive ability of these metrics, finding that the makerspaces tended to be structurally nested in any one semester, however when compared to a “normal” semester the restrictions are reflected via a higher modularity. The makerspace network case studies provide insight into the use and value of quantitative ecosystem structure and function indicators for monitoring similar human‐engineered interaction networks that are normally only tracked qualitatively. 

The growing popularity of progressive education pedagogies combined with the continued rise of the maker movement has propelled knowledge and interest in makerspaces across education. As a result, makerspaces have become a common sight on college campuses around the world. These spaces offer students a unique opportunity to apply the hard and soft skills learned in the classroom to projects with real consequences. Students learn to take ownership of their work and experiment and iterate until they are proud of their results. Through this process they grow in design self-efficacy, innovation, and collaboration skills. Makerspaces are a powerful tool in the hands of university professors, but not all students benefit from them equally. Many students still face real or perceived barriers to entry caused in part by a lack of comfort and confidence in the space. This study seeks to examine students’ sense of belonging at a university makerspace and determine how gender, major, study locations, and classes affect this sense. Online surveys were distributed to students who used the makerspace in Fall 2022 and Spring 2023. Students answered a series of Likert style questions about how they feel in the space and statistical tests were used to determine correlation and significance of the results. It was found that sense of belonging in the space overall was high, but that females, non-mechanical engineering majors, and students who did not study in the space reported statistically lower sense of comfort. Suggestions are given to makerspace administrators of how to address and avoid these gaps in belonging and make the space more inclusive and welcoming to all students. 

Academic makerspaces have continued to rise in popularity as research shows the diverse benefits they provide to students. More and more engineering curriculums are incorporating makerspaces and as such there is a need to better understand how their student users can best be served. Surveys administered to makerspace users at a public research university in the Southwest United States during Fall 2020, Spring 2021, Spring 2022, and Fall 2022 tracked student tool usage trends with academic career stages. The survey asked questions about prior experience, motivation, tool usage, and demographics. Analyzed results for mechanical engineering student users provide insight into how curriculum and class year affect the specific tools used and the percentage of students who used a particular tool. The survey results also create a bipartite network model of students and tools, mimicking plant-pollinator type mutualistic networks in ecology. The bipartite network models the student interactions with the tools and visualizes how students interact with the tools. This network modeling enables ecological network analysis techniques to identify key makerspace actors quantitatively. Ecological modularity, for example, identifies divisions in the student-tool makerspace network that highlight how students from different majors (here we investigate mechanical) utilize the makerspace's tools. Modularity is also able to identify “hub” tools in the space, defined as tools central to a student's interaction within the space, based on student-tool connectivity data. The analysis finds that tools commonly used for class by mechanical engineering students, such as the 3D printer or laser cutter, act as gateway tools that bring users into the space and help spark interest in the space's other tools. Using the combined insights from the survey results and the network analysis, ecological network metrics are shown here to be a promising route for informing makerspace policy, tool purchases, and curriculum development. The results can help ensure that the space is set up to give students the best learning opportunities.