University Applied Physics Laboratory (JHU/APL). His research interests include robotic manipulation, computer vision and motion capture, applications of and extensions to additive manufacturing, mechanism design and characterization, continuum manipulators, redundant mechanisms, and modular systems.Dr. John S DonnalDr. Carl E. Wick Sr., United States Naval Academy Dr. Carl Wick is currently a Professional Lecturer with the Biomedical Engineering Department of the George Washington University where he provides technical assistance and advice to capstone project students. Previously he was associated with the U.S. Na ©American Society for Engineering Education, 2023 The ScorBot
Jenna L. Gorlewicz received her B.S. in mechanical engineering from Southern Illinois University Ed- wardsville in 2008, before pursuing her PhD in mechanical engineering at Vanderbilt University, where she worked in the Medical and Electromechanical DesignDr. Sridhar S. Condoor, Saint Louis University Professor with a demonstrated history of working in the design innovation and technology entrepreneur- ship areas. Skilled in Innovation Management, Applied Research & Product Design, Entrepreneurship, and Training Next Generation Innovators and Entrepreneurs. ©American Society for Engineering Education, 2023The HapConnect: Teaching about Haptics and Inclusive Design with Modular
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
Paper ID #36976Building High-Level Environmental Behavior into HBCU EngineeringDr. John T. Solomon, Tuskegee University John T Solomon is an Associate Professor in the Mechanical Engineering department at Tuskegee Uni- versity, Alabama. He received Ph.D. in Mechanical Engineering from Florida State University, USA in 2010. His research interests include high-speed flow control, microscale flow diagnostics, and engineering education.Sadegh Poozesh, Tuskegee UniversityHang Song, Auburn UniversityDr. Karen S. McNeal, Auburn University Dr. McNeal conducts research in geoscience education investigating how people think and
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
, S. J. Mallo, S. O. Ismaila, J. O. Dada, S. Aderounmu, ... & E. Oyetunji. “Engineering students' virtual learning challenges during covid-19 pandemic lockdown: A case study.” In 2020 IFEES World Engineering Education Forum-Global Engineering Deans Council (WEEF-GEDC), pp. 1-5. IEEE. 2020.[4] A. Dworak. “United States university enrollment numbers during the COVID-19 pandemic recession.” Perspectives on the New Normal: Post COVID19, vol. 67, 2020.[5] E. Belanger, C. Bartels, & J. She. “Challenges and Strategies in Remote Design Collaboration During Pandemic: A Case Study in Engineering Education.” In International Design Engineering Technical Conferences and Computers and Information in
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
adjusted because the liftload cells counteract the moment created by the drag force due to how static mechanics work. Figure 4. Statics Diagram for Drag Moment AdjustmentUsing the statics illustrated in Figure 4, the adjustments for the lift force are [3]: 𝐹 , , =𝐹 , , −𝐹 ∗ (Eq. 1) 𝐹 , , =𝐹 , , +𝐹 ∗ (Eq. 2)Physical Models The test bed used is a Hampton H-6910 Wind tunnel with a test section of 23 in x 8 in x 8in. A cylinder of 1.6 in diameter was tested, as shown in Figure 5, to find the lift and drag forcesacting on a 3D-printed body. This was done with a flow velocity of 10.06 m/s, considering laminarflow conditions
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
startup and casting safety protocols aspart of her M.S. project.Referencesi R. W. Heckel, W. W. Milligan, C. L Nassaralla, J. Pilling, M. R. Plichta, “Benefits of CapstoneDesign Courses in Materials Education,” Science and Technology of Polymers and AdvancedMaterials, P. N. Prasad, J. E. Mark, S. H. Kandil, Z. H. Kafafi, (eds), Springer, Boston, MA., 1998.https://doi.org/10.1007/978-1-4899-0112-5_75ii M. Schaefer, “Use of Casting Simulation and Rapid Prototyping in an Undergraduate Course inManufacturing Processes,” ASEE Annual Conference & Exposition, 2016.iii K. Molyet, “Providing a Meaningful Lab Experience for a Manufacturing Processes Course,”(,” ASEE IL-IN Section Conference, 2019. https://docs.https://docs.lib.purdue.edu/aseeil
providing just-in-time feedback. The subsequent lecture, then, expands on the workshopexperience and formally presents the week’s learning goal(s).The impact of this course redesign is measured by analyzing and systematically scoring students’final project deliverables in the course. The scoring rubric, which we describe later, used for thisstudy is based on the four mechanical design practices derived from Salehi’s STEMproblem-solving practices [14].MethodsAs we outlined in the paper we submitted to ASEE in 2022 [15], the Fall and Winter offerings ofthe ’21-’22 academic year were used as the control condition for this study (see figure 2). TheSpring offering of that academic year was the pilot for the developed intervention, and the ’22-’23Fall
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
technique more rigorously. Figure 1: Design 1 layout and dimensions in inchesOnce modeled, SolidWorks Flow simulation was used for all CFD simulations for the design. AllCFD simulations for design 1 used an input fluid velocity of 9.8 ft/s and an output ofenvironmental pressure. Simulations were made using water as the fluid passing through thevalve. Fifty flow lines were used at the entrance of each simulation. The simulation results,shown in Figure 2, concluded that there was a great flow through the complex geometry of thechannel. The advantage of first simulating the design is that there is a higher chance of successand predictability when fabricated. Figure 2: CFD simulation results using SolidWorks Flow
=XGtable&output_vie w=data&include_graphs=true. Retrieved February 28, 2023.5. BLS me outlook website. https://www.bls.gov/ooh/architecture-and- engineering/mechanical-engineers.htm. Retrieved February 28, 2023.6. “LinkedIn article. https://en.wikipedia.org/wiki/LinkedIn. Retrieved February 28, 2023.7. [Marklines] https://www.marklines.com/en/global/usa. Retrieved February 28, 2023.8. [SDSMT career fair] https://www.sdsmt.edu/CareerFair/9. Atman, C. J., Sheppard, S. D., Turns, J., Adams R. S, Fleming, L. N., Stevens, R., Streveler, R. A., Smith, K. A., Miller, R. L., Leifer, L. J., Yasuhara, K., and Lund, D.. Enabling Engineering Student Success: The Final Report for the Center for the Advancement of Engineering Education
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
resources: funding and team A Organizing course notes (if available) A S: studentPlanning collaborators Consultation with open education S A librarians Defining the scope and audience A L: open Selecting open license, open platform S L A education and book style librarians Creating book style guide A L
Guidelines (WCAG) 2.1 Level AA standard. Recentwork [1] developed accessibility standards for textually describing images, figures, graphs,animations, and other visual elements for a series of interactive web native mechanicalengineering textbooks [22]-[23]. These new standards include: (i) alt text that balances precisionwith conciseness; (ii) structuring alt text to initially capture key information, then incrementallyadding in finer details; (iii) well-defined procedures for describing specific, yet common visualelements (e.g., phase diagrams, phase transformation plots, T-s and p-v diagrams, andtime-response plots); and (iv) alt text for animated visual elements that fully describe all dynamicprocesses and intermediate movements. Conveying
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
) levels to ensure that every student seeking anengineering degree is afforded the necessary support systems to complete degree requirements.Future WorkFuture work of this study includes associating the impact of grades with the socioeconomic factorsidentified by Bandura which include racial gaps, school sector, school environment, and familyconditions. A survey was created and administered in the Fall of 2022 with a cohort of studentsenrolled in a Rigid Dynamics course. Specifically, students were asked about the non-academicfactors that affect their academic performance such as family responsibilities, employment, andfinancial issues. The data is under review, and more will be collected in the Spring 2023.REFERENCES[1] Abdi, H. M., Bageri, S
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
tosynthesize knowledge across multiple domains, modes of inquiry, historical periods, andperspectives, as well as the ability to identify linkages between existing knowledge and newinformation. Individuals who engage in integrative thinking are able to transfer knowledgewithin and beyond their current contexts. We collected two things to assess the above objectivesbroadly for the University: 1. Student scores on the element(s) of the assignment aligned with integrative thinking (scored using the rubric developed in collaboration with faculty teaching integrative thinking courses provided in Appendix A). 2. Students’ perceptions of their own integrative thinking skills collected via a survey administered by OPA later in 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
, reflectionassignments could be updated to urge students to reflect more on how the service learning andinterdisciplinary components affected their overall performance in the project and the requisitecontent knowledge that came from the project.Future studies can examine students’ motivations regarding interdisciplinary projects and howthey relate to teamwork effectiveness. Future work can also examine the effects of theinterdisciplinary project on the students’ teamwork effectiveness skills over the course of severalsemesters.AcknowledgmentThis material is based upon work supported by the National Science Foundation under Grants#1821658 and #1908743. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s
/s and moves it through the front nozzle. If there is 1 kg/s of air brought in and the air moves at a rate of 10 m/s of air brought in and the air moves at a rate of 10 m/s through the nozzle, how much energy is required to through the nozzle, how much energy is required to run run the fan? It can be assumed that you are holding the the fan? It can be assumed that you are holding the dryer horizontally, and atmospheric pressure occurs dryer horizontally, and atmospheric pressure occurs throughout. throughout. 4. If you wanted to increase the speed of the air exiting 5. If you wanted to increase the speed of the air exiting the hair dryer, how would you change 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