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
things in nature (e.g., butterflies, rocks) Page 26.1552.5 star Observed or studied stars and other astronomical objects group Participated in science groups/clubs/camps comp Participated in science/math competition(s) nonfic Read/Watched non-fiction science Abbreviation Reported Interest/Experience scifi Read/Watched science fiction game Played computer/video games prog Wrote computer programs or designed web pages talk Talked with friends or family about scienceResults and
based learning environment. She was previously an engineering education postdoctoral fellow at Wake Forest University supporting curriculum development around ethics/character education.Dr. Diana Bairaktarova, Virginia Tech Dr. Diana Bairaktarova is an Assistant Professor in the Department of Engineering Education at Virginia Tech. Through real-world engineering applications, Dr. Bairaktarovaˆa C™s experiential learning research spans from engineering to psychology to learning ©American Society for Engineering Education, 2023 Empathy and mindfulness in design education: A literature review to explore a relationshipAbstractLearning to design in undergraduate
included adoption of contextualculturally relevant teaching practices, recognizing indigenous worldviews, respecting communityand family, and supporting indigenous knowledge systems.MethodologyKhan et al. established a process for conducting a systematic literature review: [6] (1) frame thequestion, (2) identify relevant work, (3) assess study quality, (4) create a summary, and (5)interpret findings. We have framed the question in the previous section. Khan et al.’s final twosteps, summary and interpretation, are found in the Results and Discussion sections below.In addition to following the Khan et al. methodology, we also observed the guidelines found inthe PRISMA 2020 statement, [7] specifically the paper and abstract checklists. Figure 1 is
] S. Brunhaver, R. Korte, S. Barley, and S. Sheppard, “Bridging the gaps between engineering education and practice,” in Engineering in a Global Economy, R. Freeman and H. Salzman, Eds. Chicago: Chicago University Press, 2018, pp. 129–165.[3] C. Carrico, K. Winters, S. Brunhaver, and H. M. Matusovich, “The pathways taken by early career professionals and the factors that contribute to pathway choices,” Proceedings of the 2012 American Society for Engineering Education Annual Conference & Exposition, San Antonio, TX., June 2012.[4] C. J. Atman, S. D. Sheppard, J. Turns, R. S. Adams, L. N. Fleming, R. Stevens, R. A. Streveler, K. A. Smith, R. L. Miller, L. J. Leifer, K. Yasuhara, and D. Lund
asked to reflect on their experiences using the followingquestion:How often have you been in courses where some educational technology tools, especiallymobile applications, have been used? Tell us something about your experience. a. Please state the name of the application(s) or other technology tools (e.g., Clicker, CATME, Socrative, Any quiz software, etc.). b. What you liked about that application(s) and why? c. What you didn’t like and why? d. Were those applications academically relevant? If yes, why, if no, why not?Data AnalysisThe study focuses on exploring the students’ perceptions of using educational technology toolsin postsecondary STEM classrooms. To inform our study, we employed
to my 2.81 1.38transfer.I spoke to former transfer students to gain insight about their adjustment experiences. 2.63 1.38Scale: 1-Strongly disagree, 2-Disagree, 3-Neither agree nor disagree, 4-Agree, 5-Strongly agree; Meansare of weighted data. 1 Participants in co-enrollment program(s) were exempt from this survey item.Table 2. Perceptions about the "transfer process" while students were enrolled at [SI] Construct Sub-items Mean Std. Error (N = 1024)1 of Mean
for an intensive planningand analysis session. All of the focus groups have been transcribed and where possible, thespeakers have been identified so that textual analysis can be made by branch of service andmajor, among other things. The transcripts have been uploaded into Atlas.ti (a qualitative dataanalysis software program) and speakers will be identified with their salient characteristics asthey reported on their pre-qualification surveys. As analysis progresses, this will allow theresearch team to, for example, compare experiences and responses of Navy veterans to Armyveterans or mechanical engineering students to electrical engineering students.Preliminary Focus Group FindingsFrom: C. E. Brawner, C. Mobley, J. B Main, S. M. Lord, M. M
Davis S. Lewis Associate Professor in the Georgia Tech School of Aerospace Engineering Page 26.1129.1 c American Society for Engineering Education, 2015 Managing and Exchanging Knowledge Underlying Aerospace Engineering Design DecisionsIntroductionThe engineering design process is a complex, iterative process through which individuals andteams solve ill-defined, multidisciplinary problems by integrating domain-based technicalknowledge.1,2 Aerospace engineering integrates technical components from many differentdisciplines, such as aerodynamics, combustion, avionics
the object to learn about the different parts of theobject. The current supplemental videos provide a high-level view of the concepts, but theycould be split into smaller chunks or more targeted concepts/misconceptions to help the students.For future work, our team is focusing on developing the baseline VR/AR tool on normalsurfaces, as illustrated in this paper, the supplemental video, and the next integration of theenvironment and the video. We plan to pilot the tool in summer and fall classes this year.References[1] S. A. Sorby, N. Veurink, and S. Streiner, “Does spatial skills instruction improve STEM outcomes? The answer is ‘yes,’” Learn Individ Differ, vol. 67, pp. 209–222, Oct. 2018, doi: 10.1016/j.lindif.2018.09.001.[2] S
tailoredinterventions and resources to foster an environment conducive to profound transformation foreach student, addressing students' specific needs based on their current category oftransformation and facilitating their transition to the profound transformation category.References[1] M. A. Hutchison‐Green, D. K. Follman, and G. M. Bodner, "Providing a voice: Qualitativeinvestigation of the impact of a first‐year engineering experience on students' efficacy beliefs,"Journal of Engineering Education, vol. 97, no. 2, pp. 177-190, 2008.[2] S. S. Courter, S. B. Millar, and L. Lyons, "From the students’ point of view: Experiences infreshman engineering design course," Journal of Engineering Education, vol. 87, no. 3, pp. 283-288, 1998. [Online]. Available: https
, quizzes (fixed-choice questions from the original workbook), and the software should be madeavailable to students on the university LMS.References[1] I. M. Smith, Spatial ability: its educational and social significance. San Diego, Calif.: R.R. Knapp, 1964.[2] D. L. Shea, D. Lubinski, and C. P. Benbow, “Importance of assessing spatial ability in intellectually talented young adolescents: A 20-year longitudinal study,” Journal of Educational Psychology, vol. 93, no. 3, pp. 604–614, 2001.[3] M. Kozhevnikov, M. A. Motes, and M. Hegarty, “Spatial Visualization in Physics Problem Solving,” Cognitive Science, vol. 31, no. 4, pp. 549–579, 2007, doi: https://doi.org/10.1080/15326900701399897.[4] S. Y. Yoon and E. L. Mann, “Exploring
with the search quadcopter. The sensors and technologies used on the rescuequadcopter are similar to that of the search quadcopter. The main difference was that an electropermanent magnet is utilized in this system to hold and release the rescue package to be deliveredto the survivor (s).Figure 10 shows the collision avoidance system being tested for the search quadcopter. The firstflight test was conducted by hovering the quadcopter roughly 3 feet above the ground andactivating the altitude hold flight mode. The copter was then slowly pitched forward towards awall until the safety zone was breached and the Arduino took over the pitch control.The students presented their work both at student conferences and a professional conference.20
National Center for Women in Information Tech- nology (NCWIT) and, in that role, advises computer science and engineering departments on diversifying their undergraduate student population. She remains an active researcher, including studying academic policies, gender and ethnicity issues, transfers, and matriculation models with MIDFIELD as well as student veterans in engineering. Her evaluation work includes evaluating teamwork models, statewide pre-college math initiatives, teacher and faculty professional development programs, and S-STEM pro- grams.Dr. Joyce B. Main, Purdue University, West Lafayette (College of Engineering) Joyce B. Main is Assistant Professor of Engineering Education at Purdue University. She
on Education, Vol. 48, No. 3, pp. 462–471, August 2005. 3. R. W. Ives, B. L. Bonney and D. M. Etter, “Effect of Image Compression on Iris Recognition”, IEEE Instrumentation and Measurement Technology Conference, Ottawa, Canada, May 17—19, 2005. 4. S. Cotter, “Laboratory Exercises for an Undergraduate Biometric Signal Processing Course”, ASEE Annual Conference and Exposition, Louisville, Kentucky, June 2010. 5. S. Cotter, “Assessing the Impact of a Biometrics Course on Students’ Digital Signal Processing Knowledge”, ASEE Annual Conference and Exposition, Vancouver, Canada, June 2011. 6. S. Cotter and A. Pease, “Incorporating Biometrics Technology into a Sophomore Level
students in Texas. Students accumulate transfer student capital, or knowledge about thetransfer process, at sending institutions (i.e., the place(s) where students begin their degreepaths), receiving institutions (i.e., the final degree-granting institution), and potentially from non-institutional sources. The development of transfer student capital may come from experiencesrelated to learning and study skills, course learning, perceptions of the transfer process, academicadvising and counseling, and experiences with faculty. Upon arriving at the receiving institution,students must adjust to the new environment academically, socially, and psychologically, all ofwhich may influence a variety of educational outcomes. Figure 1
other contexts were not considered.• The research should incorporate at least one significant finding related to the discrimination encountered by Asian engineering students, even if this is not the primary research question the study aims to address. After refining the search criteria, we identified nine studies. These studies arelisted in Table 1.Table 1Selected Studies 1 Bahnson, M., Hope, E., Satterfield, D., Alexander, A., Briggs, A., Allam, L., & Kirn, A. (2022). Students’ Experiences of Discrimination in Engineering Doctoral Education. 2022 ASEE Annual Conference & Exposition. https://peer.asee.org/41006.pdf 2 Lee, M. J., Collins, J. D., Harwood, S. A., Mendenhall, R., & Huntt, M. B
, pp. 151–185, 2011.[6] Elementary science teachers’ sense-making with learning to implement engineering design and its impact on students’ science achievement[7] C. M. Cunningham and G. J. Kelly, “Epistemic Practices of Engineering for Education,” Science Education, vol 1010, no. 3, pp. 486–505, 2017.[8] T. J. Moore, A. W. Glancy, K. M. Tank, J. A. Kersten, K. A. Smith, and M. S. Stohlmann, “A Framework for Quality K-12 Engineering Education: Research and Development,” Journal of Pre-College Engineering Education Research (J-PEER), vol. 4, no. 1, 2014.[9] American Society for Engineering Education and Advancing Excellence in P12 Engineering Education. Framework for P-12 Engineering Learning, 2020
. Raghavan serves as a Professor and Associate Dean of Research and Graduate Studies at Embry Rid- dle Aeronautical University. Her research interests are in the areas of Mechanics of aerospace structures and materials. She joined UCF in Fall 2008 after completing her doctoral studies at Purdue University, Indiana, School of Aeronautics and Astronautics in the area of Structures & Materials. She obtained her M.S., Aeronautical Engineering in Structures at ISAE-SUPAERO, Toulouse, France where she also worked with Messier Bugatti in Velizy, Paris (S-92 wheels and brakes testing). Prior to this, she com- pleted her B.Eng in Mechanical Engineering at Nanyang Technological University, Singapore. She has 7 years of
essential that this work is done intandem, as it would be unethical to recruit women into an environment that is known tosystemically disadvantage them. Though chemical engineering has made great strides in genderparity compared to other engineering disciplines, the results of this study reinforce the idea thatdiversity is not the same as equity.References [1] NSF. Bachelor’s degrees awarded to women, by field, citizenship, and race/ethnicity: Women, minorities, and persons with disabilities in science and engineering, 2008. [2] C. E. Brawner, S. M. Lord, and M. W. Ohland, Undergraduate women in chemical engineering: Exploring why they come. ASEE Conference Proceedings, 2011. [3] J. Trapani and K. Hale, “Higher education in science and
to and survive in unwelcoming, toxic,and systemically oppressive computing environments, the aforementioned activities (and thoseof the greater Alliance) shift this focus to ensure that staff, educators, and administrators have thetools necessary to address and remove systemic barriers to student success in computing.References[1] S. Zweben and B. Bizot, “2020 Taulbee Survey,” 2020. [Online]. Available: https://cra.org/wp-content/uploads/2021/05/2020-CRA-Taulbee-Survey.pdf[2] M. Broussard, Artificial Unintelligence. The MIT Press, 2018. Accessed: Dec. 21, 2020. [Online]. Available: https://mitpress.mit.edu/books/artificial-unintelligence[3] R. Benjamin, Race After Technology: Abolitionist Tools for the New Jim Code, 1st edition
) only report result for the 'sweet-spot' factorsalong one or two dimensions (e.g., student educational history⸺ quizzes, assignment, andexams; demographic features⸺ sex, age, marital status, state) [1-2], (b) are carried out withdiverse and fragmented factors using dissimilar machine learners making their results difficult tocompare [3]. Towards this end, the paper exploits all the attributes (i.e., sixty-seven attributes)over ten dimensions (listed in Table 1) using five machine learning algorithms. The Objective ofthe work-in-progress (WIP) is two-fold: (i)To leverage machine learning to identify the factorsthat are the best predictor of an at-risk student(s) in a programming course and (ii) Compare theperformance of the machine learner(s
accepted responses forseveral weeks.Results and DiscussionImpacts of the AIChE Education Division’s VCP program on the delivery of chemicalengineering courses during the COVID-19 pandemic were wide-ranging. After a web-basedinterest form was circulated to attendees and other members of the AIChE community,respondents answered whether they would like to participate in a VCP, to identify course(s) theywere teaching, and to indicate their willingness and ability to lead/moderate a VCP. Within oneweek, 88 faculty members filled out the form, and the communities began to materialize. Thetotal number of interested participants continued to grow through the semester and into thefollowing semester. From March 2020 to December 2020, 191 participants from
follow-on group. It would providevaluable experience to the students if more clients could be recruited from the community.AcknowledgementsThe authors would like to thank the following ME students who participated in this project: Arlint,A., Durbin, T., Hayes, T.S., Jefferson, S., Jewett, S., Maltbie, J., Mihalec, B., Milne, S., Richards,T., Ward, M., and Willard, J..References[1] R. H. Todd, S. P. Magleby, C. D. Sorensen, B. R. Swan, and D.K. Anthony. “A survey ofcapstone engineering courses in North American,” Journal of Engineering Education, vol. 84,pp.165-174, April 1995.[2] A. J. Dutson, R. H. Todd, S. P. Magleby, and C. D. Sorensen. “A review of literature onteaching engineering design through project-oriented capstone courses,” Journal
authors and do not necessarily reflect the views of the National ScienceFoundation.References1. Committee on Equal Opportunities in Science and Engineering, “Broadening participation in America’s STEM workforce: 2011–2012 biennial report to Congress,” National Science Foundation, Arlington, VA, 2014. Retrieved from https://www.nsf.gov/od/oia/activities/ceose/reports/Full_2011- 2012_CEOSE_Report_to_Congress_Final_03-04-2014.pdf2. S. Hurtado, K. Eagan, and M. Chang, “Degrees of success: Bachelor’s degree completion rates among initial STEM majors,” Higher Education Research Institute at UCLA, 2010.3. M. Ong, C. Wright, L. Espinosa, and G. Orfield, “Inside the double bind: A synthesis of empirical research on undergraduate and graduate
. Michalsky, “Peer mentoring in mathematics: Effects on self- efficacy and achievement” Journal of Educational Psychology, 109(6), 767-778, 2017. Dual-Form Mentoring Model: Near-peer mentoring 4. C.M. Eddy & K.A. Hogan, “Peer mentoring in a university first-year science course: impact on academic performance and perceived experiences.” Journal of College Science Teaching, 49(2), 38-44, 2019. combined with reverse mentorship was employed. Near- 5. D. Yomtov, S. Plunkett, & R. Efrat “Can Peer Mentors Improve First-Year Experiences of University Students?” Journal
Paper ID #38028Board 145: Possible Relations between Self-Efficacy, SociodemographicCharacteristics, Dropout and Performance of Freshman Students inEngineering CoursesDr. Cristiane Maria Barra Da Matta, Instituto Mau´a de Tecnologia Master’s degree in Food Engineering at the Instituto Mau´a de Tecnologia and PhD in Psychology at the Universidade Metodista de S˜ao Paulo (2019). Assistant professor and coordinator of the Student Support Program (since 2007) at Instituto Mau´a de Tecnologia. It investigates themes of School and Educational Psychology: academic experiences, self-efficacy, school performance and dropout in
selection that utilized a measurement of a student’s adult mentor supportnetwork, reasoning that if the student had adequate circle of adult backers, then they were morethan likely to persevere and successfully complete higher education. The researchers earned an NSF S-STEM grant in 2016 to study the effects of mentornetwork connectedness on collegiate STEM field persistence. Students from low SESbackgrounds who had expressed an interest in STEM majors and were given admission intoexploratory studies were selected as the target pool of participants. These students have becomeknown colloquially as ‘Rising Scholars’ (RS) [7] [8]. Twenty-one admitted students wereselected through a process designed to quantize and measure the quality of a