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Freehand sketching equips engineers to rapidly represent ideas in the design process, but most engineering curriculums fall short of equipping students with adequate sketching skills. This paper is focused on methods to improve engineers’ sketching skill through type of instruction, length of instruction, and delivery of and feedback for assignments using Sketchtivity, an intelligent sketch-tutoring software. We answer several key questions for providing better sketching education for engineers. Does perspective training improve freehand drawing ability? Can an intelligent tutoring software improve education outcomes? And how much sketching instruction is necessary for engineers? Analyzing the changes in sketching skill from pre- to post-sketching instruction between different instruction types (n = 116), we found that perspective sketching instruction significantly improved freehand sketching ability compared to traditional engineering sketching methods. When comparing pre to post sketching skill of students using Sketchtivity (n = 135), there was no significant difference in improvement between students using the intelligent tutoring software and those that exclusively practiced on paper – both groups improved equally. However, completing sketching tasks on tablets did not hinder students’ skill development even when measured on paper. Future work will more directly explore the influence of Sketchtivity on sketching skill development. Additionally, we found that five weeks of sketching instruction greatly improves sketching skill compared to only three weeks of instruction (n = 108), but both approaches significantly improve sketching self-efficacy. These outcomes support more extensive sketching instruction in engineering classrooms, and changes in instruction type to promote more freehand sketching skills. 

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 within large universities often have annual enrollments exceeding several hundreds of students, while online classes have even larger enrollments. It is challenging to achieve differentiated instruction in classrooms with class sizes and student diversity of such great magnitude. In such classes, professors assess whether students have mastered a concept through multiple-choice questions, marking answers as right or wrong with little feedback, or using online text-only systems. However, in these scenarios the feedback is of a mostly binary nature (right or wrong) with limited constructive feedback to scaffold learning. A growing concern among engineering educators is that students are losing both the critical skill of sketched diagrams and the ability to take a real system and reduce it to an accurate but simplified free-body diagram (FBD). A sketch-recognition based tutoring system, called Mechanix, allows students 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. To observe the effectiveness of this system, it has been implemented into various courses at three universities, with two additional universities planning to use the system within the next year. Student knowledge is measured using Concept Inventories based in both Physics and Statics, common exam questions, and assignments turned in for class. 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. In focus groups, students believed the system enhanced their learning and increased engagement. Keywords: sketch recognition; intelligent user interfaces; physics education; engineering education 

Makerspaces have observed and speculated benefits for the students who frequent them. For example, previous studies have found that students who are involved in their campus’s makerspace tend to be more confident and less anxious when conducting engineering design tasks while gaining hands-on experience with machinery not obtained in their coursework. Recognizing the potential benefits of academic makerspaces, we aimed to capture what influences students to become involved in these spaces through a mixed-method study. A quantitative longitudinal study of students in a mechanical engineering program collected data on design self-efficacy, makerspace involvement, and user demographics through surveys conducted on freshmen, sophomores, and seniors. In this paper, the student responses from three semesters of freshmen level design classes are evaluated for involvement and self-efficacy based on whether or not a 3D modeling project requires the use of makerspace equipment. The study finds that students required to use the makerspace for the project were significantly more likely to become involved in the makerspace. These results inspired us to integrate a qualitative approach to examine how student involvement and exposure to the space are related. Using an in-depth phenomenologically based interviewing method, purposive sampling, and snowball sampling, six females, who have all made the conscious decision to engage in a university makerspace(s), participated in a three-series interview process. The interviews were transcribed and analyzed via emerging questions for categorical metrics and infographics of the student exposure and involvement in making and makerspaces. These findings are used to demonstrate 1) how students who do, or do not, seek out making activities may end up in the makerspace and 2) how student narratives resulting in high-makerspace involvement are impacted by prior experiences, classes, and friendships.