Paper ID #44227Project-based learning via creation and testing of a silicone venous valvemodelMatthew S Ballard, Utah Valley University Dr. Ballard is an Associate Professor of Mechanical Engineering at Utah Valley University. He earned his B.S. in Mechanical Engineering from Brigham Young University and his M.S. and Ph.D. in Mechanical Engineering from the Georgia Institute of Technology. Dr. Ballard teaches primarily in the areas of fluid and thermal sciences, and his research focuses on biofluid mechanics, design of microfluidic devices and applied aerodynamics.Taten McConahay, Utah Valley University Taten McConahay
tothe vital nature of the Statics course itself to education for future engineers, it was beneficial toprepare a systematic review, providing an objective summary of the current research landscapeof Statics interventions.Categorization of Course InterventionThe intervention categories we considered fall under a set of three intervention frameworks:Harackiewicz and Prinski (2018)’s motivational interventions, Donker et al. (2014)’s learningstrategy interventions, and Borrego et al. (2013)’s practice and/or research-based instructionalstrategy (PRBIS) interventions.Harackiewicz and Prinski (2018) revised and evaluated psychology-driven interventionspresented two decades before its publication. It condensed the research landscape up until
follow a similar set of rules.In general, any property that needs to be “accounted” for during a process would lend itself wellto be represented visually. A summary of such properties, their definition, and the courses thatthey are encountered in presented in Table 1. Before proceeding though, it is important toestablish the generalized accounting principle and define some nomenclature that will be usedthroughout the rest of this work.Table 1 Properties that can be accounted for, their definition, and course(s) in which they primar-ily appear — Ek , Ep , Usys , Eother are kinetic, potential, internal, and other sources of energy in thesystem; s is entropy per unit mass; T0 and P0 are the dead state (thermodynamic term) temperatureand pressure
concept maps while solvingproblems in class and on exams. All students included some form of the concept map on thepersonal reference sheets permitted for exams; however, only two students appeared to havemodified the map shared by the instructor. Future work will include student-developed conceptmaps, as well as self-reported data from student surveys on the effectiveness of concept maps inthe heat transfer course.References [1] Greitzer, E., & Soderholm, D. H., & Darmofal, D., & Brodeur, D. (2002, June), Enhancing Conceptual Understanding With Concept Maps And Concept Questions Paper presented at 2002 Annual Conference, Montreal, Canada. 10.18260/1-2—11019 [2] Moore, J. P., & Pierce, R. S., & Williams, C. B
theyare more capable of performing a task. In this vein, constructive feedback plays a crucial role indeveloping strong self-efficacy beliefs. The fourth source of self-efficacy beliefs is emotionalarousal. Emotional arousal, that happens during challenging situations, can also help peopleinform themselves of their expectations of self-efficacy. High levels of emotional arousal canhamper an individual’s performance by increasing anxiety and stress.3. Research Question(s)This type of research, called sequential explanatory mixed-methods research, is practical in itsapproach. The research questions play a crucial role in guiding and shaping the entire process,including choosing the research design, determining the sample size, and selecting
educational technology to plan, prepare, and deliver robotics lessons tofifth graders at a local school. The meeting times for the two courses were scheduled to overlapfor 75 minutes a week, allowing the engineering and education students to work collaborativelyduring multiple class sessions. Each team comprised one or two engineering student(s), onepreservice teacher, and one or two fifth grader(s). The teams engaged in the followingcollaborative activities over the course of the semester: ● Training phase. The first two collaborative sessions involved engineering students and preservice teachers meeting in a classroom on campus and partnering in teams to: ○ train with the Hummingbird BitTM hardware (e.g. sensors, servo motors) and
= 4responses; normalized to a 5-point scale). LOs data are presented in Figure 2. The seven LOsincluded:1. Apply 3D modeling principles to design your soft robot prototype (3D Model).2. Demonstrate one or more actuation principles used in soft robots (Demo Actuate).3. Integrate your actuation principle in a soft robot prototype (Proto Actuate).4. Develop learning activities associated with your soft robot design (Learning Activity).5. Develop learning outcomes associated with your soft robot learning activities (Learning Outcome).6. Explain the scientific principle(s) behind your design's actuation mechanism (Explain Actuate).7. Design a soft robot prototype using soft Figure 2. Averaged survey responses to LOs questions materials
to focus on course specific knowledge when preparing, and not memorizing trig identities... I know I prefer when this basic information is given, so I do not stress about remembering math, and rather can focus on the important content of the course I am preparing for.Q4: How do you go about making your equation sheet(s), when student-created equationsheets are to be used? Please describe your process for selection of information and how youorganize it.This question was an open-response question, investigating what students’ processes are forproducing equation sheets when they are making up their own. The student responses were asfollows: Student 1: If I know absolutely nothing about what will be on the test, I go through
credit in the modifiedproblems (Understand, Analyze, and Evaluate). * represents a significant difference at 95%confidence (p < 0.05).ExamThe final exam was comprehensive, consisting of problems on various topics covered over theduration of the semester, including viscous flow in pipes. Since the final exam was scheduled ona different day for each section, the exam problems (all at the Apply level) were different butdesigned to be at a similar difficulty level. The average score of the problem(s) covering thefocused topic was compared and has been shown in Figure 4. There is no significant differencebetween the exam scores of the two student sections. This finding is consistent with the result ofthe formative assessment (Figure 3A). Both
integrating and applying this information cohesively for a specific task. This limitation is evident in Steps 8 and 9 of ChatGPT’s solution, which redundantly recapitulate prior results, ultimately culminating in Step 10’s provision of a wholly incorrect conclusion—a mere !"# *, - $% repetition of information from Step 5 𝑎 = *' . It is evident that ChatGPT failed to resolve this problem, yielding a result that appears far- )./ fetched, with 𝑎& incorrectly equated to
change (reverse scored) 32. I like to work on problems that have clear solutions (reverse scored) 33. I prefer tasks that are well-defined (reverse scored) 34. I tend not to do something when I am unsure of the outcome (reverse scored)Aim and SignificanceThis research demonstrates the implementation of Problem-Based Learning (PBL) in Statics andDynamics courses within the Mechanical Engineering program, typically taught during freshmanand junior years, respectively. The primary purpose of this endeavor is to address the challengesencountered by students in their initial year of engineering studies. Condoor, S., et al. [8],highlighted the difficulties students encounter when embarking on the Statics course, often the firstengineering
Rose-Hulman Institute of Technology in 2006. Matthew received his doctorate from Clemson University in 2011 in Mechanical Engineering, focused primarily on automotive contDr. Sean Tolman P.E., Utah Valley University Sean S. Tolman is an Associate Professor in the Mechanical Engineering Program at Utah Valley University in Orem, UT. He earned his BSME degree at Brigham Young University in 2002 and a MSME degree from the University of Utah in 2008 before returning toAmanda C Bordelon, Utah Valley University Amanda Bordelon, PhD, P.E. joined Utah Valley University’s faculty in the new Civil Engineering program in August 2018. She has all of her degrees in Civil and Environmental Engineering emphasized in
descriptive statistics, and t-tests were performed to compareresponses from the midterm survey to responses from the end of term survey. The quantitativeresults from questions Q1-Q4 are shown in Figures 2–5, and the responses to the open-endedquestion Q5 are discussed below. (a) Responses (b) Statistics (p = 0.195).Figure 2: Responses to Q1: “The specifications grading scheme helps me learn in this course.”In (b), the red line indicates the median, the blue circle indicates the mean, the top and bottomedges of the box indicate the 25th and 75th percentiles, and the whiskers extend to data points notconsidered to be outliers. Outliers, if they exist, are plotted as red +’s. Responses from the
utilized to tackle thisever-growing issue due to its ability learn and classify complex data. AI can be described as twomain subfields: machine learning (ML) and deep learning (DL). ML leverages labeled data tobuild models for predicting labels on unlabeled data. DL relies on extensive unlabeled datasets touncover underlying patterns within the dataset. On the other hand, knowledge-based modelingand simulation (M&S) techniques utilize known models to generate data for the analysis of newand existing designs. M&S works well for simple systems but becomes increasingly difficult formore complex systems. The difficulty comes from the uncertainties associated with each addedvariable being modeled. To bridge the gap between AI and M&S, the
Director for the Industrial Assessment Center at Boise State University. He served as the Faculty in Residence for the Engineering and Innovation Living Learning Community (2014 - 2021). He was the inaugural Faculty Associate for Mobile Learning and the Faculty Associate for Accessibility and Universal Design for Learning. He was the recipient of the Foundation Excellence Award, David S. Taylor Service to Students Award and Golden Apple Award from Boise State University. He was also the recipient of 2023 National Outstanding Teacher Award, ASEE PNW Outstanding Teaching Award, ASEE Mechanical Engineering division’s Outstanding New Educator Award and several course design awards. He serves as the campus representative and
the list of parameters. If one of units appears only once, then reduce it to a more basic unit. For example, W = J/s. 4. Calculate the number of expected dimensionless parameters (π groups), in the problem: k = (n - j) 5. Identify the repeating parameters that has one of the units. Avoid choosing dependent or independent variables as repeating parameters (such as x, r, t, etc, in heat transfer problems). 6. At each step choose one of the repeating parameters to eliminate one of the units.The following shows the steps taken in obtaining the dimensionless parameters for Example 6,using the functional replacement method. 1. Listing the parameters in the problem and counting their total number, n 𝑘𝑔
their ability to learn the ma-terial, apply the material they have learned, and how well they believe they will perform in the Figure 5: User testing flow chart.learning activity. The full list of questions in the affective assessment is provided in Appendix B.The cognitive assessment consists of five multiple-choice questions focusing on technical aspectsof AFM imaging and identifying sources of common image artifacts.In the lab session, once it was confirmed that each student had completed the pre-lab, they wererandomly assigned to either the simulation cohort or the traditional paper (control) cohort. Stu-dents in the paper cohort did not have access to the simulation and were instead provided withimage(s
their perspectives on the project.“I feel like it’s valuable because it really gets you to work with those who you think you’d neverwork with. Although, working with an education student has shown me ways that an engineerlike myself would have never done. I think working with such different people is good because itshows how these two different professions can work together even though they know little tonothing about each other’s majors.”Acknowledgment This material is based upon work supported by the National Science Foundation underGrants #1821658 and #1908743. Any opinions, findings, and conclusions, or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science
+C2 t2+D3 t3 [deg] (1)Therefore, the angular velocity is, Equation 2: θ̇(𝑡)= B1 +2 C2 t+3 D3 t2 [deg/s] (2)The polynomial generates joint’s control variables between initial time t0 and final time tf.The initial and final values for position θ(t) and velocity θ̇(𝑡) are, Equation 3:θ(t0)= θ0 ; θ̇(t0) = 𝜔0; θ(tf ) = θf ; θ̇(tf) = 𝜔f (3)In biomedical area for rehabilitation applications, the initial and final angular velocities are zerosince a smooth motion is desired for patient’s elbow.θ0 = 0 deg ; 𝜔0 = 0 deg/s; θf= 90 deg; 𝜔f = 0 deg/s
. Finelli and T. Harding, "Suggestions For Establishing Centers For Engineering Education," in ASEE Annual Conference, Montreal, 2002 .[5] L. Bosman and S. Fernhaber, "Applying authentic learning through cultivation of the entrepreneurial mindset in the engineering classroom," Education Sciences, vol. 9, no. 1, p. 7, 2019.[6] L. Bosman and S. Fernhaber, Teaching the Entrepreneurial Mindset to Engineers, Cham: Springer, 2017.[7] N. M. Anid, S. H. Billis and M. A. Panero, "Entrepreneurship and Technology Innovation Center: Bringing Together Industry, Faculty, and Students," in ASEE Annual Conference & Exposition, Atlanta, 2013 .[8] T. Mason, "Impacts Of Entrepreneurship Centers And Programs On The Preparation Of
.[6] B. B. Smith, Y. S. Park, L. Ross, S. J. Krause, Y. Chen, J. A. Middleton, E. Judson, R. J. Culbertson, C. J. Ankeny, K. D. Hjelmstad, and C. Y. Yan, "Faculty characteristics that influence student performance in the first two years of engineering," in ASEE Annu. Conf. & Expo., New Orleans, LA, 2016.[7] S. J. Dooley, "Designing a reference training course and cultivating a community of practice: Utilizing the LMS for staff training and development," in ASEE Annu. Conf. & Expo., Tampa, FL, 2019.[8] C. Hodges, S. Moore, B. Lockee, T. Trust, and A. Bond, “The difference between emergency remote teaching and online learning,” EDUCAUSE Review. Mar. 27, 2020. [Online]. Available: https
for bringing deep and complex insights into fields - but relies onthe capacity for reflexivity and connection of their reflection on experience to a field’s existing knowledgebase [22]. It is not, nor is it meant to be immediately generalizable - acknowledging and leveraging itsgrounding in one’s own experience to garner unique insights.Facilitated, sometimes called collaborative, autoethnography is a version of autoethnography in which theprocess is supported by someone with either/both expertise in ethnographic research or research in thefield in question [14], [15]. In facilitated autoethnography, the outside researchers (usually calledfacilitators) and participant(s) (participant-researchers) interact to aid in the elicitation of
’ work for term project:Team A2 chose the Tesla Model S body for their design. They modeled the body and chassis andperformed a drop test in SolidWorks to show how the vehicle chassis will react to a collision asshown in figures 1 and 2. The impact velocity was set at 130 m/s to show the extremes that thechassis would undergo while hitting a vehicle or obstruction head on at high speeds. Theyperformed the simulation for front, side and rear impacts.They also performed an aerodynamics study with speed that was set was 40 mph as a baselineand results are shown in figure 3.For integrating AI, the team chose Obstacle Avoidance and Automated Parking. Then, the teamreferenced MathWorks’ Obstacle Avoidance Using Adaptive Model Predictive Control
from Dynamics of MachinesFigure 1a represents a problem from dynamics of machines course in a form suitable for in-person exams. The problem shows a four-bar mechanism with given dimensions, and the angularvelocity of the crank. The solution includes drawing a scaled position diagram and a scaledvelocity polygon. For the four-bar linkage, assume that ω2 = 4 rad/s cw. Write the appropriate vector equations and solve them using vector polygons determining, when θ4 = 53o: A (a) θ2, θ2 (b) vC, ω3, ω4. 2 B 3 C
strategies, and give basic bearing life calculations. In practice, roller element bearingsare manufactured in a wide number of variations that are intended for specific conditions of useand specific mounting geometry. Engineering guides from the major bearing vendors containextensive information on proper mounting, allowable loads, and load types, sealing andlubrication, and allowable environment. In practice, engineers use these guides to learn thedetails involved in selection and application of roller bearings. The author contends thateffective use of vendor-supplied engineering guides is a significant skill in engineering practice,in addition to the fundamentals of Machine Design, and is a big missing piece in many curricula.Since the 1950’s, a
courseelectives are mentioned from first year through fourth year to show how course curriculumsbuild on MSE concepts throughout the program. The non- MSE courses mentioned areindicated as such, and all courses discussed are within either the civil, mechanical, and/orelectrical undergraduate engineering program(s).2 MSE Connections with Different Engineering CoursesPNW Civil, mechanical, and electrical engineering students must all complete ENGR18600First Years Seminars for Engineers and ENGR19000 Elementary Engineering Design firstyear in their respective program [15, 16,17]. The rationale is that there are concepts and ideasapplicable to all engineering fields. It is important that first-year students learn thefundamentals of engineering shared by all
depend on the flow type. The current problem is transient, incompressible, laminar, and isothermal flow whose physics is governed by Eq. (1) and (2). Also the fluid properties (density and viscosity), initial conditions (the initial velocity field of a fluid domain), and boundary conditions need to be prescribed. The boundary conditions used for the current problem are given in Fig. 4. The velocity of 2.8 × 10−4 m/s at the inlet and zero velocity at the cylinder surface were assigned. The slip condition was used at the top and bottom of the domain and zero stress was assigned to the outflow. Figure 4: Boundary conditions• Step 4. Discretize the governing equations to obtain solutions. In FEM, the governing
, to verify if balancing solution is satisfactory by comparing the vibration amplitude V2 to the original amplitude vibration Vo.In the following example, the virtual rotor kit (VRK) is required to be balanced when it runs at1000 RPM constant speed, and the “original” unbalance configuration is unknown. The tests andsolution will be based on the coefficient influence method. Similar to a “real” rotor test rig,constrains must be addressed when the solution is implemented. To add any trial weight to theVRK ‘s correction plane the constrains shown in yellow in the setup window in Figure 2 have tobe taken into account.Step 1: Select the configuration, rotor speed and output as shown in Figure 2, and collect data.The second window (VRK Lab) is
, 2018.[2] D. Clark and R. Talbert, Grading for Growth: A Guide to Alternative Grading Practices That Promote Authentic Learning and Student Engagement in Higher Education, 1st ed., vol. 1. London: Routledge, 2023. doi: 10.4324/9781003445043.[3] R. Butler, “Task-involving and ego-involving properties of evaluation: Effects of different feedback conditions on motivational perceptions, interest, and performance,” J. Educ. Psychol., vol. 79, no. 4, pp. 474–482, 1987, doi: 10.1037/0022-0663.79.4.474.[4] R. Lynch and J. Hennessy, “Learning to earn? The role of performance grades in higher education,” Stud. High. Educ., vol. 42, no. 9, pp. 1750–1763, Sep. 2017, doi: 10.1080/03075079.2015.1124850.[5] S. D. Blum, A. Kohn, and T
5discussions not only provided insight into the individual scoring process but also enhanced overall scores.After such discussions, and careful re-evaluation of select concept maps, a general consensus wasreached. The new score(s) assigned represented a shared understanding and agreement on the gradingcriteria. At some discussions, the variations were solved through compromise and mutual understanding.The collaborative evaluation and consensus process were vital in maintaining the credibility andreliability of the grading process. It allowed for a well balanced perspective on the concept maps and theirquality, since no two individuals are the same.ResultsThe first activity for root finding methods led to interesting results in the student concept maps