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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 and 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. 

Freehand sketching is a powerful skill in engineering design [1, 2]. Freehand sketching empowers designers in the early stages of design to express ideas, communicate with stakeholders, and evaluate concepts at a rapid pace. However, teaching sketching in engineering education poses unique challenges for the classroom. Sketching in other domains is often taught in studio-style courses where instructors can provide personalized feedback on technique. This type of feedback is not possible in typical large entry-level engineering graphics courses. To address this problem, Sketchtivity was developed as an intelligent tutoring software to aid instructors in providing feedback on sketching. Using a tablet and smart pen, learners receive real-time personalized feedback on sketching practice. The main goals of this project are to improve sketching instruction methods, understand the educational efficacy of Sketchtivity, and work towards improving the feedback and content of Sketchtivity. 

Globally, universities have heavily invested in makerspaces. Purposeful investment however requires an understanding of how students use tools and how tools aid in engineering education. This paper utilizes a modularity analysis in combination with student surveys to analyze and understand the space as a network of student-tool interactions. The results show that a modularity analysis is able to identify the roles of different tool groupings in the space by measuring how well tool groups are connected within their own “module” and their connection to tools outside of their module. A highly connected tool in both categories is considered a hub that is critical to the network. Poorly connected tools indicate insignificance or under utilization. Makerspaces at two universities were investigated: School A with a full-time staff running the makerspace and School B run by student-volunteers. The results show that 3D printers and metal tools are hubs at School A and 3D printers, metal tools, and laser cutters are hubs at School B. School B was also found to have a higher overall interaction with all the tools in the space. The modularity analysis results are validated using two-semesters worth of student self-reported survey data. The results support the use of a modularity analysis as a way to analyze and visualize the complex network interactions occurring within a makerspace, which can support the improvement of current makerspaces and development of future makerspaces. 

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 increases 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. 

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 increases 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. 

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. 

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. 

Introductory engineering courses at large universities often number over a hundred students, while online classes can have even larger enrollments, significantly constraining instructors’ ability to provide feedback on homework, including the free-body diagrams (FBDs). Most online homework systems do not provide feedback on FBDs if the systems even allow the submission, and instructors often lack time or resources to provide this. A few systems have been developed that use a menu-based system allowing students to creative FBDs. There is a growing concern amongst engineering educators that student lacks critical sketching skills and the ability to idealize a real-world system as a free body diagram (FBD). A sketch-recognition based tutoring system, Mechanix, allows learners to hand-draw solutions just as they would with pencil and paper, while also providing iterative real-time personalized feedback. Sketch recognition algorithms use artificial intelligence to identify the shapes, their relationships, and other features of the sketched student drawing. Other AI algorithms then determine if and why a student’s work is incorrect, enabling the tutoring system to return immediate and iterative personalized feedback facilitating student learning that is otherwise not possible in large classes. Preliminary results using Mechanix, a sketch-based statics tutoring system built at Texas A&M University suggest that a sketch-based tutoring system increases homework motivation in struggling students and is as effective as paper-and-pencil-based homework for teaching method of joints truss analysis. The current project implements Mechanix at five different universities obtaining Pre/Post Concept Inventory, homework, and exam scores. It is compared against either the university's current online system or paper-based homework. Focus groups provide further insight into the students’ perceptions about the impact of Mechanix on their learning.