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Discovery Learning Experiments in a New Machine Design Laboratory
Author(s) -
Mark Nagurka,
Fernando Rodriguez Anton
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--19453
Subject(s) - computer science , spark (programming language) , machine design , artificial intelligence , mechanical engineering , engineering , programming language
A new Machine Design Laboratory at Marquette University has been created to foster student exploration with hardware and real-world systems. The Laboratory incorporates areas for teaching and training, and has been designed to promote “hands-on” and “minds-on” learning. It reflects the spirit of transformational learning that is a theme in the College of Engineering. The goal was to create discovery learning oriented experiments for a required junior-level “Design of Machine Elements” course in mechanical engineering that would give students practical experiences and expose them to physical hardware, actual tools, and real-world design challenges. In the experiments students face a range of real-world tasks: identify and select components, measure parameters (dimensions, speed, force), distinguish between normal and used (worn) components and between proper and abnormal behavior, reverse engineer systems, and justify design choices. The experiments serve to motivate the theory and spark interest in the subject of machine design. This paper presents details of the experiments and summarizes student reactions and our experiences in the Machine Design Laboratory. In addition, the paper provides some insights for others who may wish to develop similar types of experiments. Introduction to Student-Centered Learning In traditional college teaching most class time is spent with a professor lecturing and students watching, listening, and writing. Students work individually and cooperation is generally discouraged. In contrast, in student-centered pedagogical methods the focus of activity is shifted from the teacher to the learners. Student-centered learning methods have been shown to have advantages relative to the classical teacher-centered approach in terms of a range of outcome metrics: short-term mastery, long-term retention, depth of understanding, critical thinking, creative problem-solving skills, positive attitudes toward the subject, and level of confidence in knowledge or skills. 1 Studentcentered learning methods include active, cooperative, collaborative, and inductive learning. □ Active learning is an instructional method that engages students in the learning process, in contrast with the usual lecture format where students passively receive information from an instructor. 3 In active learning methods students conduct meaningful learning activities; they think about and are connected to what they are doing. While this definition could include standard assignments such as homework, active learning most commonly refers to activities that are introduced in the classroom. The core elements of active learning are experiences that engage students. The more active the students are in the classroom, the more engaged they are in the learning process and the more they remember. Simulations of real experiences or “doing the real thing” involve students the most in the learning process and result in them remembering more of the underlying concepts to be learned. 4 □ Cooperative learning is a structured form of group work where students pursue common goals while being assessed individually. The most common model of cooperative learning includes five tenets: individual accountability, mutual interdependence, face-to-face interacP ge 23439.2 tion, appropriate practice of interpersonal skills, and regular self-assessment of team functioning. The focus is on cooperative incentives, rather than competition, to promote learning. □ Collaborative learning refers to an instructional method in which students work together in small groups toward a common goal. As such, collaborative learning encompasses all groupbased instructional methods, including cooperative learning. The core element of collaborative learning is the emphasis on student interactions, rather than on learning as a solitary activity. □ In inductive learning students are presented with challenges (questions or problems) and then allowed to learn the course material in the context of addressing the challenges. Inductive methods include inquiry-based learning, case-based instruction, problem-based learning, project-based learning, discovery learning, and just-in-time teaching. In problem-based learning students are introduced to relevant problems at the beginning of the instruction cycle to provide the context and motivation for the learning that follows. It is always active and usually cooperative or collaborative, and typically involves significant amounts of selfdirected learning on the part of the students. 5,6 Although some students may thrive more on one style than another, collectively these studentcentered methods capture the essence of transformational learning. These methods serve as the background and basis on which the experiments reported in this paper are predicated. Student-Centered Learning in the Machine Design Laboratory A new Machine Design Laboratory has been created in the College of Engineering at Marquette University. The 100m 2 Laboratory incorporates areas for teaching and training, and student-centered learning activities were specifically designed to foster student exploration with real-world hardware, machines, and physical systems. These activities promote “hands-on” and “minds-on” learning, and reflect the spirit of transformational learning that is a theme in the College of Engineering. The Laboratory is equipped with workbenches, tools, instruments, computers, data acquisition systems, and an assortment of machines and mechanical systems to enhance creative exploration and investigation. The machines and systems include motorcycle engine assemblies (engines and transmissions), bicycles (including a chainless bicycle and a custom front-wheeldrive, rear-wheel-steer bicycle), a go-kart chassis, a Machine Fault Simulator training station, and various other systems (industrial gearboxes and gear-motors, automotive transmission and differential, drill presses, etc.) The Machine Fault Simulator (SpectraQuest, Inc.), shown in Figure 1, is designed to create a range of common machinery conditions and faults, such as unbalance, misalignment, resonance, normal and worn bearing operation, normal and damaged gearbox operation, belt drive slippage and resonance, reciprocating mechanism operation, etc. The simulator can be preconfigured with components to present undesirable operating conditions, and then students are asked to identify the fault in a reverse-engineering approach. The Laboratory is located next to a modern machine shop where students have access to fabrication equipment, ranging from traditional (lathes, CNC milling machines, etc.) to rapid prototyping (3D printing) machines. The Laboratory is close to a Materials Testing Laboratory, where students can use Instron tensile testing machines and other equipment. P ge 23439.3 Discovery Learning Experiments A first priority was to develop discovery learning oriented experiments for a required juniorlevel “Design of Machine Elements” course in mechanical engineering. In the past, students in the course were taught primarily by traditional class lectures. They saw a few examples of actual machine components and demonstrations, and were not engaged in laboratory activities; the primary focus was on lecture concepts and virtual designs. A survey conducted by the authors revealed that this approach is widely adopted at most universities. Very few schools have a dedicated laboratory associated with their machine design course. Based on feedback from alumni, faculty, and industrial constituents, it was deemed essential to provide students in the course with more opportunities for practical experiences. In particular, there was a clear desire to have students in the course interact with physical hardware, use actual tools, and face real-world machine design challenges. Furthermore, it was important to embrace many of the student-centered learning methods described above in laboratory activities. A list of desired core competencies relevant to machine design was complied with the guidance of an Industrial Advisory Board (IAB) as well as input from several faculty members and mechanical engineering undergraduate and graduate students. New experiments were then created to intentionally immerse students in an environment where they would be forced to hone these core competency skills. These skills included the ability to identify machine components, know proper nomenclature, measure parameters (dimensions, speed, force), select components from catalogs for design challenges (understanding tradeoffs for performance, life, cost, etc.), distinguish between normal and used (worn) components, differentiate and predict proper and abnormal behavior, reverse engineer systems, develop engineering intuition, and communicate effectively in justifying design choices. These skills and others were identified by practicing engineers on the IAB as typically very weak and/or absent attributes of graduating engineers. Based on further input from IAB members, teaching assistants, former students, and faculty members, the initial experiments were refined and improved. By design, the experiments incorporate several student-centered learning methods including active, collaborative, and project-based learning. They provide students with experiential learning opportunities that are similar to, but a subset of, those associated with industrial co-op, internship, design and research experiences. Working in teams, students face directed and openended challenges, many of which initially can be daunting. These challenges offer significant potential for learning, resulting in student confidence in solving machine design problems. It was decided to replace the traditional laboratory report format for each experiment with two lab deliverables: (1) an end-of-lab session deliverable, which primarily poses qualitative challenges (for example, explain behavior) based on learning from lab experiences and activities, and (2) a post-la

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