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Sketching is a valuable skill for many engineering students to support the development of various auxiliary skills such as refined spatial visualization, problem-solving, idea generation, and communication. As the students engage in the engineering design process, sketching skills, along with the auxiliary skills, become valuable tools in future courses and continually in their careers. Explicitly teaching students to sketch is challenging given the instructor-to-student ratio. Hence, intelligent tutoring systems (ITS) are highly beneficial to students in this context to develop these skills actively rather than expect students to develop these skills independently through the needs of other courses. An ITS was introduced through this study to teach sketching skills to students in mechanical engineering courses. The basics of Two-point perspective sketching were the focus of the instruction material facilitated by this ITS. The tutoring platform provides individualized automatic feedback to students immediately after they complete a sketch to inform them of their performance and ultimately to enhance their sketching skill development. This study aims to understand the experiences of graduate and undergraduate mechanical engineering students from three institutions learning sketching through the ITS environment. Our study is guided by the following research questions: 1. What was the engineering student's experience in learning to sketch in an intelligent tutoring platform? 2. What are the strengths, weaknesses, and suggestions for improving the intelligent tutoring system? 3. What are the impacts of the intelligent tutoring System on the sketching self-efficacy of engineering students? In this study researchers collected data through surveys and semi-structured interviews. The participants were students enrolled in undergraduate and graduate Mechanical Engineering courses at three different institutions where they learned and practiced sketching using the ITS. This study helps us to understand the strengths and weaknesses of the ITS, along with suggestions for how to improve the software from an engineering student perspective. The user experiences of mechanical engineering students was valuable to understand if and how students are finding this particular ITS helpful in their academic lives. The results of the study will be useful to researchers and engineering education community working to develop educational software tools, to better understand the student expectations, and educators interested in identifying a way to incorporate sketching. [ABSTRACT FROM AUTHOR]

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 openended 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. [ABSTRACT FROM AUTHOR]

In this Lessons Learned paper, we explore the themes uncovered from a series of facilitated faculty discussions on moving their course back to face to face teaching after the switch to online. The Institute for Engineering Education and Innovation (IEEI) at Texas A&M University (TAMU) administrates over 100 faculty whose primary department appointments and teaching assignments are in either engineering or education. Over the last two years, IEEI hosted numerous conversations for faculty members to share experiences, research, and assessments of teaching successes and concerns as they changed instructional modalities, both with the initial move online and the subsequent move back face to face. From these conversations, faculty agree that some things during the move to online instruction, such as office hours, video archives of lectures, and some activities in break-out rooms appear to enhance student learning. Yet data showed that students believed the online experience was less desirable than face to face courses. Now that we have had a near complete semester where most classes were required to be held in the face to face mode, we are hosting conversations with faculty to understand the changes they are now making to their teaching because of the experiences from online instruction. The results will be shared as a "lightning talk". [ABSTRACT FROM AUTHOR]

This paper discusses the recognized need for an organization focused on serving students from underrepresented populations in the computing field at Texas A&M University, describing the formation of the Aggie Hispanics in Computing group. In 2016, the combined undergraduate and graduate Hispanic enrollment in computer science and computer engineering at Texas A&M University initially sat at 17.9% and then decreased to approximately 11.76% in 2021, with undergraduate Hispanic enrollment in computing reduced from almost 22% to under 15% in that same time frame. This significant shift in Hispanic student representation spurred the development of the Aggie Hispanics In Computing (AHIC) student organization to create a computing community that provides support around the shared experiences of being part of a minority group in an even less diverse discipline. The university, overall, was recently recognized as a Hispanic-Serving Institution, but the College of Engineering remains a predominately-white institution. This new organization is not the only Hispanic-serving organization at Texas A&M University; rather, it was designed to focus particularly on serving Hispanic students in the computer science and computer engineering disciplines at Texas A&M University. The organization was founded during the COVID-19 pandemic in 2020. Since then, AHIC has grown significantly in membership, financial support, and activities to increase the representation of Hispanic students within the computing disciplines at Texas A&M University. The organization has grown from 6 to over 50 members from various majors in the past year alone. AHIC has also received financial support from a multitude of companies, such as General Motors and Chevron. AHIC's overall goal is to create a supportive community for minorities in computing fields. This community has grown through hosting events that provide information and resources about professional career opportunities, technical workshops, mentoring programs, and participation in research groups. AHIC has also initiated several long-term initiatives, such as peer-teaching for introductory computer science courses. AHIC's promotion of career-guidance events (where company representatives and alumni provide advice for currently enrolled students) proved to be an effective strategy for recruiting members. The organization has also hosted seminars and workshops educating first-year students on new computing skills and the opportunities that a computer science and computer engineering degree can provide. [ABSTRACT FROM AUTHOR]

Sketching is a valuable skill in engineering for representing information, developing design ideas, and communicating technical and abstract information. It is an important means of developing spatial abilities which are predictive of success in STEM fields. While existing spatial ability tests are predictive of engineering visualization skills, they do not allow students to develop drawing skills through spatial exercises. The Object Assembly Sketching test examines sketching skills with object assembly tasks using mental imagery and mental rotation. This study focuses on the development and pilot testing of a new sketching skills test using object assembly exercises. We piloted the test in two sections of an undergraduate mechanical engineering design course. Inter-rater reliability of two raters scoring students sketches on eight criteria was acceptable across exercises, but low across criteria. Students scored highest on Representation Accuracy, Scale, and Symmetry, and exhibited complex understanding of perspective sketching. We intend to revise the rubric to score for aesthetics and make instructions more precise. [ABSTRACT FROM AUTHOR]

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 differentiate instruction or reteach challenging material. Especially in the case of large enrollment classes, homework assignments often evaluates if students have comprehended a concept through multiple-choice questions, which marking answers as right or wrong with little feedback, or by using online text-only systems. In these scenarios, however, the feedback is often a binary type (right or wrong) with limited constructive feedback to scaffold learning. Set in this context, 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 can allow 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. In focus groups, students believed the system enhanced their learning and increased engagement. This project will assess a range of engineering education research questions as well as have a broader impact through its positive impact on at-risk students. To observe the effectiveness of this system for this project, 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. [ABSTRACT FROM AUTHOR]

Research has often found that engineering drawing is a vital tool for communication, ideageneration, problem solving, and developing key skills such as spatial visualization. However, many curricula have replaced the bulk of instruction in hand sketching with Computer-Aided Design programs despite these known benefits of drawing. In recent years, an introduction to engineering visualization course at Georgia Institute of Technology has modified the portion of the class dedicated to hand-sketching using pedagogy commonly used in industrial design courses to develop students' sketching ability and visualization skills. This modified curriculum involves instruction on techniques such as sketching in both isometric and perspective spaces, shading, and ray-tracing. This paper observes the impacts of a modified curriculum in and engineering graphics course on students' ability to sketch, self-efficacy in engineering design, and spatial visualization skills. Impact was measured using pre- and post-course assessments and surveys. The pre-to-post comparisons of the groups of students taught using different methods showed equal improvements in the spatial visualization of the students. The improvements in sketching ability of the students in the modified perspective curricula were found to be significantly higher than the improvements experienced by students in the traditional curriculum. These findings suggest that the modified perspective sketching curriculum maintains critical spatial visualization skills, which are effectively taught with the traditional engineering curriculum, while also introducing an additional skill without requiring additional student time. These findings justify the need to continue improvement of the perspective-based curricula and to continue development of tools to aid in the instruction of these new skills. [ABSTRACT FROM AUTHOR]

Engineering students in large classes receive far too little feedback on hand-sketched Free Body Diagrams (FBDs.) Despite the importance of these diagrams for engineering education and practice, large class sizes often make providing detailed paper-based feedback impractical. Such issues are even further strained in MOOC classes. Relatively recently, computing technology has become powerful enough to enable instantaneous, detailed feedback on hand-sketched engineering diagrams. Researchers have recently developed the free "Mechanix¹" sketch recognition tutoring system for free body diagrams (FBDs) and trusses. The Mechanix online software provides instantaneous and detailed feedback using a natural sketch recognition engine2,3,4,5,6,7,8,9,10,11,12. This paper describes the process and results of piloting the Mechanix software at a primarily undergraduate university. The experimental group included 39 students in a Statics class, contrasted with a similar class of 34 as a control group. Students in the experimental group completed the Mechanix tutorial and then worked 4 truss problems with instantaneous and unlimited online feedback regarding the accuracy of their free-body diagram drawing and numerical answers. The control group used commonly available online software giving feedback only on numerical answers. Results included here include student attitudes towards technology, online homework scores, class test scores, and self-reported perceptions of the software efficacy. Both quantitative learning outcomes and qualitative comments from users suggest the software system is effective and has great potential if further developed. Students in the experimental group performed as well as the control group, more than 90% of students chose to use the new software (rather than opt-out), and most students self-reported a positive learning experience. [ABSTRACT FROM AUTHOR]