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Abstract Drawing upon Bourdieu’s conceptualization of habitus, this ethnographic study explores the cultural bases guiding engineering makerspaces at a public university in the United States. Students carry forms of capital that impact their entry into these learning spaces, over time becoming disciplined in the “game” of makerspaces as they accumulate capital through everyday talk and storytelling. Communication constructs the makerspace habitus as students (1) move from outsider to insider as they acquire forms of capital; (2) negotiate a habitus characterized by tensions of access vs. exclusivity; (3) learn to use the vocabularies of innovation and creativity; and (4) cultivate supportive making communities. Findings point to the critical role of intentional communication and space design in cultivating inclusive makerspace cultures.

In online or large in-person course sections, instructors often adopt an online homework tool to alleviate the burden of grading. While these systems can quickly tell students whether they got a problem correct for a multiple-choice or numeric answer, they are unable to provide feedback on students’ free body diagrams. As the process of sketching a free body diagram correctly is a foundational skill to solving engineering problems, the loss of feedback to the students in this area is a detriment to students. To address the need for rapid feedback on students’ free body diagram sketching, the research team developed an online, sketch-recognition system called Mechanix. This system allows students to sketch free body diagrams, including for trusses, and receive instant feedback on their sketches. The sketching feedback is ungraded. After the students have a correct sketch, they are then able to enter in the numeric answers for the problem and submit those for a grade. Thereby, the platform offers the grading convenience of other online homework systems but also helps the students develop their free body diagram sketching skills. To assess the efficacy of this experimental system, standard concept inventories were administered pre- and post-semester for both experimental andmore »control groups. The unfamiliarity or difficulty of some advanced problems in the Statics Concept Inventory, however, appeared to discourage students, and many would stop putting in any effort after a few problems that were especially challenging to solve. This effect was especially pronounced with the Construction majors versus the Mechanical Engineering majors in the test group. To address this tendency and therefore collect more complete pre- and post-semester concept inventory data, the research group worked on reordering the Statics Concept Inventory questions from more familiar to more challenging, based upon the past performance of the initial students taking the survey. This paper describes the process and results of the effort to reorder this instrument in order to increase Construction student participation and, therefore, the researchers’ ability to measure the impact of the Mechanix system.« less

It is challenging to effectively educate in large classes with students from a multitude of backgrounds. Many introductory engineering courses in universities have hundreds of students, and some online classes are even larger. Instructors in these circumstances often turn to online homework systems, which help greatly reduce the grading burden; however, they come at the cost of reducing the quality of feedback that students receive. Since online systems typically can only automatically grade multiple choice or numeric answer questions, students predominately do not receive feedback on the critical skill of sketching free-body diagrams (FBD). An online, sketch-recognition based tutoring system called Mechanix requires students to draw free-body diagrams for introductory statics courses in addition to grading their final answers. Students receive feedback about their diagrams that would otherwise be difficult for instructors to provide in large classes. Additionally, Mechanix can grade open-ended truss design problems with an indeterminate number of solutions. Mechanix has been in use for over six semesters at five different universities by over 1000 students to study its effectiveness. Students used Mechanix for one to three homework assignments covering free-body diagrams, static truss analysis, and truss design for an open-ended problem. Preliminary results suggest the system increasesmore »homework engagement and effort for students who are struggling and is as effective as other homework systems for teaching statics. Focus groups showed students enjoyed using Mechanix and that it helped their learning process.« less

It is challenging to effectively educate in large classes with students from a multitude of backgrounds. Many introductory engineering courses in universities have hundreds of students, and some online classes are even larger. Instructors in these circumstances often turn to online homework systems, which help greatly reduce the grading burden; however, they come at the cost of reducing the quality of feedback that students receive. Since online systems typically can only automatically grade multiple choice or numeric answer questions, students predominately do not receive feedback on the critical skill of sketching free-body diagrams (FBD). An online, sketch-recognition based tutoring system called Mechanix requires students to draw free-body diagrams for introductory statics courses in addition to grading their final answers. Students receive feedback about their diagrams that would otherwise be difficult for instructors to provide in large classes. Additionally, Mechanix can grade open-ended truss design problems with an indeterminate number of solutions. Mechanix has been in use for over six semesters at five different universities by over 1000 students to study its effectiveness. Students used Mechanix for one to three homework assignments covering free-body diagrams, static truss analysis, and truss design for an open-ended problem. Preliminary results suggest the system increasesmore »homework engagement and effort for students who are struggling and is as effective as other homework systems for teaching statics. Focus groups showed students enjoyed using Mechanix and that it helped their learning process.« less

The Maker Movement has led to a boom in academic makerspace development over the past 15 years. Academic makerspaces—which are those located on community college and university campuses—enable students to engage in solving challenges that are meaningful to them, while uniting students of varied expertise levels to learn from one another. Using a typology of learning developed through in-depth phenomenologically based interviews (PBI) with 35 students, this study investigates how student learning differs at two Universities with differing amounts of making integrated into the curriculum. Big City U offers a large program with traditional engineering degrees, while Comprehensive U offers a smaller program with a single design-oriented B.S. in Engineering. Interviews were coded using a previously developed learning typology and categories of learning were compared across institutions to identify similarities and differences in experiences. Preliminary findings show students are gaining comparable content knowledge, cultural knowledge, and ingenuity, but Comprehensive U students are more self-aware and learn through relationships with others more than students at Big City U.

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 biologicalmore »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.« less

Sketching free body diagrams is an essential skill that students learn in introductory physics and engineering classes; however, university class sizes are growing and often have hundreds of students in a single class. This situation creates a grading challenge for instructors as there is simply not enough time nor resources to provide adequate feedback on every problem. We have developed a web-based application called Mechanix to provide automated real-time feedback on hand-drawn free body diagrams for students. The system is driven by novel sketch recognition algorithms developed for recognizing and comparing trusses, general shapes, and arrows in diagrams. We have discovered students perform as well as paper homework or other online homework systems which only check the final answer through deployment to five universities with 450 students completing homework on the system over the 2018 and 2019 school years. Mechanix has reduced the amount of manual grading required for instructors in those courses while ensuring students can correctly draw the free body diagram.