into engineering education inthe early 1990’s and has since been a staple in introductory courses. Although many studies havebeen conducted in relation to product dissection, research has not been systematic, leaving us toquestion how variations in product dissection impact learning, creativity, or both for studentswhen used in the classroom. To fill this gap, our research group has conducted numerous studiesover the last four years in order to systematically investigate variations in deployment of productdissection in an engineering classroom. Using the findings from these studies, we havedeveloped a virtual product dissection module and deployed it in an introductory engineeringcourse. We provide recommendations for the use of product
perceptionsof doing engineering work, regardless of occupational title. We also believe that a sequentialregression model will show that engineering belief measures predict a significant proportion ofvariance in perceptions of having jobs “related to” engineering, over and above SCCT variables.AcknowledgementsThe authors would like to thank the Purdue University Davidson School of Engineering, whosePipeline Center funded this project. This work was also supported by the NSF (DGE-1333468).Any opinions, findings, and conclusions or recommendations expressed in this material are thoseof the author(s) and do not necessarily reflect the views of the National Science Foundation.References[1] E. Cech, “The Self-Expressive Edge of Occupational Sex Segregation
researcher, including studying academic policies, gender and ethnicity issues, transfers, and matriculation models with MIDFIELD as well as student veterans in engi- neering. Her evaluation work includes evaluating teamwork models, broadening participation initiatives, and S-STEM and LSAMP programs. c American Society for Engineering Education, 2019 Paper ID #25442Dr. Joyce B. Main, Purdue University-Main Campus, West Lafayette (College of Engineering) Joyce B. Main is Assistant Professor of Engineering Education at Purdue University. She holds a Ph.D. in Learning, Teaching, and Social Policy from Cornell
in the rejected heat by a shaded and unshaded condenser isdue to solar flux received by the condenser face area. Thus, to investigate the effects of a shadedcondenser on the COP of the refrigeration cycle, solar flux was skipped for the correlatingequations and compared to the normal case when solar flux is available.The improvement in the COP of the cycle was defined as:𝐼 (1)where the subscripts “s” and “u” stand for shaded and unshaded cases, respectively.To evaluate the COP of each case, equations (2) and (3) were used for shaded and unshadedCOP, respectively.𝐶𝑂𝑃 (2) ,𝐶𝑂𝑃
Paper ID #24774Project-based Robotics Courses for the Students of Mechanical EngineeringTechnologyDr. Zhou Zhang, New York City College of Technology Assistant Professor, Ph.D. Department of Mechanical Engineering Technology, CUNY New York City College of Technology, 186 Jay St, Brooklyn, NY 11201. Email: Zhzhang@citytech.cuny.eduDr. Andy Zhang, New York City College of Technology Dr. Andy S. Zhang received his Ph.D. from the City University of New York in 1995. He is currently the program director of a mechatronics project in the New York City College of Technology/CUNY. For the past 15 years, Dr. Zhang has been
makerspaces critically calls attention to the practices of makerspaceswhich may be inequitable. However, makerspace practitioners rarely engage or are engagedin this type of work. There is an opportunity to bring together the generous and the critical tosupport the design of more equitable university makerspaces.Different stakeholders within engineering education have different definitions of equitywhich are drawn from their lived experiences. The purpose of our framework is not to putforth a definition of equity we believe everyone should use, rather we believe the frameworkcan help us structure conversations on equity in makerspaces through a shared understanding.Against this backdrop, our research is informed by Vossoughi et al.’s definition
Administration (2012) from the University of Central Florida. Currently he is working on getting his Doctorate in Education - Measurement, Methodology, and Analysis track.Dr. Lisa Massi, University of Central Florida Dr. Lisa Massi is the Accreditation and Program Approval Specialist II for the College of Engineering & Computer Science at the University of Central Florida. She has been Co-PI of two NSF-funded S-STEM programs and program evaluator for three NSF-funded REU programs. Her research interests include factors that impact student persistence, professional identity development, and cultural identity in the STEM fields.Ms. Rachel Straney, University of Central Florida Rachel Straney is an Applications
Industrial Arts Education, Pennsylvania State University OSU faculty member since 1984 Currently in the STEM education program 2013 InterLin Ding, The Ohio State University Lin Ding, Ph.D., is an associate professor in the Department of Teaching and Learning at The Ohio State University. Dr. Dingˆa C™s scholarly interests lie in discipline-based STEM education research. His work includes theoretical and empirical investigation ©American Society for Engineering Education, 2019 Work-in-Progress: Inclusive Learning and Teaching Strategies or Effective Course Design? Constructing Significant Learning Experiences in Low and High Achieving
#12 and #15 seemed touse multiple browsers while accessing the OWLS. Student #12 opened live graph in one browserand LEWAS intro in another, while student #15, first opened two browsers, then opened fourbrowsers, which can be detected by the alternating colors in the graph. Fourth, most of the usersclosed their browser/s after completing the task, but students #7 and #8 kept their browsers openeven after their class. Moreover, student #7 seemed to go back and forth for using the OWLSbrowser between 3.30 and 3.40 pm. Fifth, there seems to be a frequent activity trend in whichstudents were accessing the system for the OWLS-based task. Students were mostly navigatingfrom the home page (grey color) to the watershed summary (dull green), to the
infrastructurerelated to the formal institutionalsupport to the change initiative. Instructional The support resources directed to enhance the faculty´s training pedagogical knowledge. Flexibility of The flexibility of timing, content and sequence of the instruction. Curriculum Time
, number of engineering courses taken and studentclassification (freshman, sophomore, etc.) in addition to student demographics and engineeringmajor. Analyzing these connections, if any, may be of great interest to researchers and practitionersattempting to affect positive change in engineering students’ affective domains.References[1] Y. Tang, R. University, S. Shetty, T. S. University, X. Chen, and R. University, “Interactive VirtualReality Games to Teaching Circuit Analysis with Metacognitive and Problem-Solving Strategies,”presented at the ASEE Annual Conference & Exposition, Vancouver, BC, June,, 2011.[2] H. Khalil and M. Ebner, “MOOCs Completion Rates and Possible Methods to Improve Retention - ALiterature Review,” In Proceedings of
Statistics [8], first-generation college students were characterizedas students’ whose parents did not have postsecondary educational experience. Another studystated, “first-generation college students include students whose parents may have some college,postsecondary certificate(s), or associate’s degree, but not a bachelor’s degree” and this definitionclosely aligns with the definition set forth by the Federal TRiO program (i.e., outreach and studentservice programs created to serve students from disadvantaged backgrounds) [9, p. 8]. There areinconsistencies and numerous ways in defining first-generation college students, so much so thatWhitley et al. [10] found at least six different definitions. However, regardless of how first-generation
through undergraduate education. This frame is visually represented inFigure 2. Figure 2 Visual Representation of Relationships between Local Standards, National Directives, Higher Education Outcomes and Literature Synthesized for Engineering Epistemic Frame The epistemic frame elements are skills(S), knowledge(K), identity(I), values(V), andepistemology(E), and have been coded as such for analysis. Each parent code (S,K,I,V,E) has aset of sub-codes that allow for macro and micro analysis. The nomenclature for each code isparentcode.subcode, for example k.localknowledge represents the sub-code localknowledgeunder the parent code K. (but indicated in lowercase). Figure 2 shows how sub-codes
description, methodology and results are presentedin the following sections.Description of the Senior Design ProjectIn this senior design project, students should minimize the energy consumption of an industrialrobot without changing its planned task defined by manufacturers. The LR Mate 200iD/4S R-30iB Fanuc industrial robot [13] was employed in the research study defined in this project. Thisrobot is shown in Fig. 1 and has 6 axes, with 550 mm reach area. The motion range of Joints 1 to6 of this robot is 340°, 230°, 402°, 380°, 240°, and 720°, respectively. The maximum speed ofJoints 1 to 6 is also 460°/s, 460°/s, 520°/s, 560°/s, 240°/s, 720°/s, respectively. The maximumpayload capacity of this robot is 4 kg. The ultimate goal is to develop MATLAB
and places it for assembly 3) Robot 3 assembles the cap on the markerworking of multiple robots controlled safely with the PLC. Three teams work on three differentrobots to program individual tasks.The color of the markers, blue, red and pink are chosen in the increasing order of contrast. Thebelt being black in color makes it difficult for the robot to detect the dark colors such as blue.The students have to adjust the environment lighting and create enough brightness for the camerato detect the blue contrast. The caps are placed in the search region of robot 3 and the openmarkers are placed in the region of robot 2. The robot 2’s vision system detects the markersposition and orientation in ascending order of contrast (blue, red and pink
focuses on policy and regulatory issues related to developing efficient and low-carbon energy sources [21]–[24].Future WorkAs we move into Year 2 of the project, we plan to develop the learning objectives and coursematerials for the energy course to be offered in Spring 2020. We will explore opportunities forhands-on student engagement with data analysis techniques, innovative homework problems, andlab activities. We will conduct assessment and evaluation to determine the impact of CSPs andmake improvements for the next offering of the course in Spring 2021.References[1] G. D. Hoople, J. A. Mejia, D. A. Chen, and S. M. Lord, “Reimagining Energy: Deconstructing Traditional Engineering Silos Using Culturally Sustaining Pedagogies
role," Psychological science, vol. 24, no. 9, p. 1831, 2013.[7] Y. Maeda, & Yoon, S. Y., "A meta-analysis on gender differences in mental rotation ability measured by the Purdue spatial visualization tests: Visualization of rotations (PSVT: R)," Educational Psychology Review, vol. 25, no. 1, pp. 69-94, 2013.[8] C. L. Miller, Bertoline, G. R., "Spatial Visualization Research and Theories: Their Importance in the Development of an Engineering and Technical Design Graphics Curriculum Mode," Engineering Design Graphics Journal, vol. 55, no. 3, pp. 5-14, 1991.[9] L. L. Thurstone, "Primary mental abilities," Science (New York, N.Y.), vol. 108, no. 2813, p. 585, 1948.[10] E. Towle, J. Mann, B. Kinsey, E. J. O
platforms, which extend or compliment the LMS features and allow the instructorto provide their desired feedback. This paper summarizes the features of eight additional toolsthat can be used to expand feedback and assignments in engineering courses.References[1] M. D. Svinicki, and W. J. McKeachie, McKeachie's Teaching Tips: Strategies, Research, and Theory for College and University Teachers: Wadsworth Cengage Learning, 2014.[2] S. Navaee, “Application Of Technology In Engineering Education,” Portland, Oregon, 2005.[3] G. M. Nicholls, W. J. Schell, IV, and N. Lewis, “Best Practices for Using Algorithmic Calculated Questions via a Course Learning Management System,” New Orleans, Louisiana, 2016.[4] A. Jones
being measured. The EGCI aims to measureunderstanding in engineering graphics concepts; thus, unrelated constructs that should not beassociated with an EGCI construct should not have a significant correlation with performance onthe instrument. For examples, high performance on the EGCI should correlate with performancein solid modeling courses or other courses requiring an understanding of engineering graphicsconcepts such as machine design or production design, that require the creation or reading oftechnical drawings, but perhaps not with performance in history or philosophy classes for thesame participants. Works Cited[1] Sadowski, M., & Sorby, S. (2014). (2014). Defining concepts for an
retrieval from a T-s chart. Following the lecture, a group activity wasconducted to assess student comfort with paper-based property charts for property retrieval.When surveyed, student opinion was highly favorable towards the use of videos for instruction,review, and the visual approach. The direct outcome of the control and treatment activitiesshowed statistically significant advantage (p-value 0.038) of this approach. Students displayedadequate competence in solving water property problems using property charts. The results alsoshowed how the use of property charts reinforces the thermodynamic fundamentals, as opposedto the use of online databases or the steam tables. The implementation yielded a marked decreasein lecture time dedicated to
module is 1x1x1 ft3. Note that the module is completely enclosed to avoid any stray light,particularly during dark I-V measurements. The front lead and side cover plates can be removedeasily as they are magnetically attached. The developed hardware setup and the softwaretechnology is currently being assessed for a provisional patent application. The author(s) intend todemonstrate the functionality of a smaller prototype version of the online lab module at the ASEEmeeting.Figure 4. Photographs of the fabricated remote lab module (v1.0) with the front lead open – (a) LEDs off, (b) LED array operating at 40% intensity, and (c) LED array operating at full intensity.Multiple of these modules can be used together to perform complex experiments
that affords different levels of analysis that can be used to triangulatefindings. By doing so, the validity and reliability of the recommendations and implications canbe strengthened through maximum information and perspective, corroboration of data, andreduction of bias [18, 19]. Such methods might be used to clarify complex social, cultural, and/orpolitical phenomena [20] such as the lack of diversity in particular engineering fields.References[1] S. Cheryan, S. A. Ziegler, A. K. Montoya, and L. Jiang, “Why are some STEM fields moregender balanced than others?” Psychol Bull, vol.143, no.1, pp.1-135, Jan. 2017.[2] C. E. Foor, S. E. Walden. and D. A. Trytten, “I wish that I belonged more in this wholeengineering group: Achieving individual
company has successfully launched semi-autonomous vehicles (Model S and Model X) to Indian market, while Google’s Self-Driving Car(SDC) has been in development since the last decade (Figure 6). Google and Tesla differ fromeach other in the approach they take towards building self-driving cars. Their differences aremainly in two areas, computer vision technology and human car control.Google embraced the LIDAR (Light Detection and Ranging) technology [23], now a de factostandard for autonomous vehicles to form a 3D model of the world around the car (Figure 7).LIDAR is used to determine the size and distance of all things around the car in anycircumstance or situation. However, LIDAR has its own challenges: LIDAR is expensive and proves
somegaps in the current research that can lead to the development of novel research questions. Thesequestions will inform future research that will contribute to the body of knowledge available onthe role of makerspaces in engineering education.References[1] D. Dougherty, Free to Make: How the Maker Movement Is Changing Our Schools, Our Jobs, and Our Minds. Berkeley: North Atlantic Books, 2016.[2] L. Martin, “The Promise of the Maker Movement for Education,” J. Pre-College Eng. Educ. Res., vol. 5, no. 1, 2015.[3] E. R. Halverson and K. Sheridan, “The Maker Movement in Education,” Harv. Educ. Rev., vol. 84, no. 4, pp. 495–504, 2014.[4] K. A. Smith, S. D. Sheppard, D. W. Johnson, and R. T. Johnson, “Pedagogies of
, and veteran undergraduates in engineering. c American Society for Engineering Education, 2019 The Methodological Promise of ‘Narrative Inquiry’ for Exploring Student Veteran and Service Member Experience as ‘People in Relation’AbstractStudent veterans and service members (SVSM) represent a significant, yet vastly underutilized,human resource for strengthening and diversifying the nation’s science, technology, engineering,and mathematics (STEM) workforce. It is estimated that, by the year 2020, over 5 million post9/11 service members will have transitioned out of the U. S. Armed Forces. Yet, despiteadvanced technical skills and training and access to unprecedented levels of educational benefits,today’s
towards a teacher-led model and empower partner organziations to interactwith each other outside of university mediation.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.1657263. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the authors and do not necessarily reflect the views of the National ScienceFoundation.References[1] H. M. Matusovich, R. A. Streveler, and R. L. Miller, “Why Do Students Choose Engineering? A Qualitative, Longitudinal Investigation of Students’ Motivational Values,” Journal of Engineering Education, vol. 99, no. 4, pp. 289–303, Oct. 2010.[2] S. L. R. Bennett, “Contextual Affordances of Rural Appalachian
Paper ID #27132Impact of Research Experience Programs on National and International Un-dergraduate Engineering StudentsDr. Jacques C. Richard, Texas A&M University Dr. Richard got his Ph. D. at Rensselaer Polytechnic Institute, 1989 & a B. S. at Boston University, 1984. He was at NASA Glenn, 1989-1995, worked at Argonne National Lab, 1996-1997, taught at Chicago State University, 1997-2002. Dr. Richard is a Sr. Lecturer & Research Associate in Aerospace Engi- neering @ Texas A&M since 1/03. His research is focused on computational plasma modeling using spectral and lattice Boltzmann methods for studying
processes are always high and at stake. Thus, an engineer’s responsibility on livingup to the ethical standards and conduct have extensive risk. In this regard, educating the future engineeringworkforce (educational policy making) and establishing effective up-to-date policy making in theoperational aspects of engineering profession (professional policy making) are two important pillars ofsustaining the knowledge and practice of ethics in engineering profession.Regarding the professional policy making, US National Society of Professional Engineers (NSPE)establishes the code(s) of ethics for professional engineering guidance and compliance [3]. NSPE requiresengineers to perform under a standard of professional behavior that requires adherence to the
technical content outcomes. Figure 8 shows the distribution ofthese assessments. ABET evaluation criteria covered within thermodynamics included a rangeof topics, including evaluation of information, environmental / political / scientific policies,writing and communications, and safety. In addition, 19 institutions focus solely on technicalcontent within their course(s).Figure 8: ABET outcomes assessed through chemical engineering thermodynamics.Process and SettingUnsurprisingly, all thermodynamics courses report using class / lecture time (Figure 9).Laboratories were only reported for two programs, explaining the small number of lab reportsseen in Figure 10.Figure 9: Types of instructional settings used by thermodynamics coursesIn terms of