methodological paradigm.Such an exercise can further help us develop some contextual knowledge that will prepare us toconduct qualitative research in Chinese engineering classrooms.Reference[1] B. M. Olds, B. M. Moskal, and R. L. Miller, “Assessment in Engineering Education: Evolution, Approaches and Future Collaborations,” Journal of Engineering Education, vol. 94, no. 1, pp. 13– 25, Jan. 2005, doi: 10.1002/j.2168-9830.2005.tb00826.x.[2] A. W. Astin, Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. Rowman & Littlefield Publishers, 2012.[3] D.-M. Duşe and C. Duse, Engineering education in a highly globalised world. 2008.[4] S. O. Shaposhnikov and E. Yu. Yatkina
research includes exploring a) how integrating holistic, socio-culturally responsive practices and His- panic/Latine cultural assets and values into educational success strategies influences Hispanic/Latine stu- dents’ sense of belonging in engineering and b) how Hispanics/Latines experience values conflicts in engineering and then navigate/reconcile those conflicts, as students or professionals.Dr. Peter Golding, University of Texas, El Paso Professor in the Department of Engineering and Leadership at UTEP. ©American Society for Engineering Education, 2023Piloting a socio-culturally responsive peer-mentoring program to promote HLX+ students’ sense of belonging in engineering education: Lessons
/white Spring 2022 3/8 4/7 6/5 Fall 2022 4/5 6/3 5/4Example reading, coding sessions,and homeworkHere is an example of how lecture-style and coding sessions werecombined to understand the importanceof blood vessel radii in determiningvascular resistance and blood flow.Lecture: Students read chapters basedon vascular resistance and Poiseuille’slaw. Class discussions focused on (a)how resistance, viscosity, and radiuschange in disease and (b) how Figure 1: Class homework to simulate steady, meanPoiseuille is used clinically. Coding: blood flow. Baseline
not shown in the table.Mentor students’ majors included chemical, Table 1. Mentor Student Demographicenvironmental, and mechanical engineering, and Black Hispanic Whitefinance. College freshman to senior students Female 1 1 5participated in our program as mentors. Male 1 0 2Our high school mentee student sample is drawn from two majority-minority high schools fromTuscaloosa County (School A) and Hale County (School B, located in Black Belt) districtsrespectively, approximately 60 and 25 students from each school for the program participation.While
both the pre- and post- survey. The last two questionsof the survey asked gender identity and age. Gender identity options included (a) man, (b) woman, (c)non-binary, (d) prefer not to answer, and a write in option. Students participating identified as 50% menand 50% women. Average age of the student respondents was 16.8 ± 1.5 years.Definitions of a soft robot In the free response section of the survey, participants were asked “What is asoft robot?”. Overall, students had reasonable ideas about what soft robots were and their uniquefeatures compared to traditional robots. Table 1 shows a summary of pre- and post- survey responses forthis question. While in the post survey, no one answered “I don’t know”, it is important to note that 4
Paper ID #37538Work In Progress: A Teamwork Training Model to Promote the Developmentof Teaming Skills in Chemical Engineering Students.Dr. Carlos Landaverde-Alvarado, University of Texas, Austin Carlos Landaverde-Alvarado is an Assistant Professor of Instruction in the McKetta Department of Chem- ical Engineering at the University of Texas at Austin. He received his B. S. in Chemical Engineering from Universidad Centroamericana ”Jose Simeon Ca˜nas” (UCA) in El Salvador, and obtained his M. Eng. and Ph.D. degrees in Chemical Engineering at Virginia Polytechnic Institute and State University (Virginia Tech). He then pursued
,” Group Organ. Manag., vol. 44, no. 1, pp. 165–210, Feb. 2019, doi: 10.1177/1059601118776750.[7] F. R. C. de Wit, L. L. Greer, and K. A. Jehn, “The paradox of intragroup conflict: A meta- analysis.,” J. Appl. Psychol., vol. 97, no. 2, pp. 360–390, 2012, doi: 10.1037/a0024844.[8] D. A. Harrison and K. Klein, “What’s the Difference? Diversity Constructs as Separation, Variety, or Disparity in Organizations,” Acad. Manage. Rev., vol. 32, no. 4, pp. 1199–1228, 2007.[9] J. Field and N. Morgan-Klein, “Studenthood and identification: higher education as a liminal transitional space,” presented at the 40th Annual SCUTREA Conference, Jul. 2010. Accessed: May 27, 2021. [Online]. Available: http://dspace.stir.ac.uk/handle/1893/3221[10] B
present preliminary results on students’ preferred debugging strategies andcompare them with their learning gains during a programming course. We focus on answeringthe following questions: a) “Is there a difference between students’ preference for debuggingstrategies and their course achievements?”; b) “Is there a relationship between softwaredebugging tools and the conceptual understanding of debugging strategies?”This study was conducted during Fall of 2022 in a 16-week programming fundamentals II courseat a large public southwestern university. This semester, 328 students enrolled from variousengineering and computer science majors. The data was gathered from a debugging assignment,which is an open-ended questionnaire. The open-ended
solving systems of nonlinear equations are the numerical techniques used in the worked examples and case study. The template is designed to be used as an interactive textbook. Template 2: M3_PipeNetwork.mlx & J3_PipeNetwork.ipynb The case study in this MATLAB Live Script and Jupyter Notebook is adapted from Problem 8.11 from [45]. The case study models the behavior of water flowing through a pipe system. The template is designed to be used as in-class activity or case study for part “a” and as a homework problem for part “b” and part “c”. The case study involves solving a system of nonlinear equations using a built-in nonlinear equation solver.• Ordinary Differential Equations (ODEs): Initial Value
Paper ID #39724Development of Amphibious Water Sampling Rover for Mosquito ResearchviaCapstone projectDr. Byul Hur, Texas A&M University Dr. B. Hur received his B.S. degree in Electronics Engineering from Yonsei University, in Seoul, Korea, in 2000, and his M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Florida, Gainesville, FL, USA, in 2007 and 2011, respectively. In 2016, he joined the faculty of Texas A&M University, College Station, TX. USA, where he is currently an Assistant Professor. He worked as a postdoctoral associate from 2011 to 2016 at the University Florida
negative impact of the quizzes onthe final grades, with an average reduction of merely 1.1 points out of 100, with a standarddeviation of 2.27.Figure 1 (b) shows a distribution of difference (grade with quizzes minus grade without quizzes)in final grade comparison among individual students. The figure clearly demonstrates that aslightly negative impact was a result of a combination of some students experiencing lower scoreswith quizzes factored in while others achieved higher scores. Specifically, approximately 62% ofstudents experienced a decrease in their grades, with an average decline of 2.34 points out of 100.Conversely, the remaining 38% of students saw an improvement in their grades, with an averageincrease of 0.91 points out of 100.Figure
prompts; (b) provideguidelines for effectively coding these prompts to understand how students aredifferentially engaging with the intervention; and (c) evaluate the extent to which thequality of writing prompt completion is associated with changes in motivational beliefs in aYouTube role model intervention for community college engineering students. Resultsprovide guidelines for effectively developing and coding writing prompts that target a widerange of motivational beliefs. Further, findings show that there were no statisticallysignificant associations between the quality of writing prompts and any of the post-motivational beliefs. Implications for developing more effective interventions by analyzingstudents’ writing prompt responses are
). Research characteristics and patterns in engineering education: Content analysis 2000-2009. World Transactions on Engineering and Technology Education, 8(4), 462-470.Chou, P. N., & Chen, W. F. (2014). Global resources in engineering education: A content analysis of worldwide engineering education journals. International Journal of Engineering Education, 30(3), 701–710.Counsell, A., & Harlow, L. L. (2017). Reporting Practices and Use of Quantitative Methods in Canadian Journal Articles in Psychology. Canadian psychology, 58(2), 140–147. https://doi.org/10.1037/cap0000074Godwin, A., Benedict, B., Rohde, J., Thielmeyer, A., Perkins, H., Major, J., Clements, H., & Chen, Z. (2021). New epistemological
ScienceFoundation.References[1] A. Godwin, “The Development of a Measure of Engineering Identity,” presented at the 2016 ASEE Annual Conference & Exposition, Jun. 2016. Accessed: Sep. 24, 2021. [Online]. Available: https://peer.asee.org/the-development-of-a-measure-of-engineering-identity[2] B. E. Hughes, W. J. Schell, B. Tallman, R. Beigel, E. Annand, and M. Kwapisz, “Do I Think I’m an Engineer? Understanding the Impact of Engineering Identity on Retention,” presented at the 2019 ASEE Annual Conference & Exposition, Jun. 2019. Accessed: Feb. 10, 2023. [Online]. Available: https://peer.asee.org/do-i-think-i-m-an-engineer-understanding-the-impact-of-engineering-i dentity-on-retention[3] M. M. Camacho and S. M. Lord, The Borderlands of
., Kirkman, R., & Swann, J. L. (2010). The Engineering and Science Issues Test (ESIT): A discipline-specific approach to assessing moral judgment. Science and Engineering Ethics, 16(2), 387-407. https://doi.org/10.1007/s11948-009-9148-zChi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 6(3), 271-315. Retrieved from https://www.jstor.org/ stable/1466699Godecharle, S., Fieuws, S., Nemery, B., & Dierickx, K. (2018). Scientists still behaving badly? A survey within industry and universities. Science and Engineering Ethics, 24, 1697- 1717 https://doi.org/10.1007/s11948-017-9957-4Creswell, J. W., & Plano Clark, V. L. (2018). Designing
. Resultant implications,limitations, and revelations of these findings conclude this paper.1. The Formal Makerspace Course1.1 Course OverviewDuring the first-year at the J. B. Speed School of Engineering at the University of Louisville(UofL), all engineering students are required to take a course titled Engineering Methods, Tools,and Practice II (ENGR 111) [1-7]. The ultimate goal of ENGR 111 is to instruct students inapplication and integration of institutionally-identified fundamental engineering skills that areintroduced and practiced in the prerequisite Engineering Methods, Tools, and Practice I (ENGR110) course. Other notable general features of ENGR 111 include a formal (15,000 ft2) makerspacesetting that exclusively employs active learning
size and weight of the tester suited for an SEM chamber. We alsoinvestigated different methods for rearranging the components of the tester so that the minimumoverall size be achieved. The micro-fatigue tester can be broken down into four main 2subsystems: The mechanical system, the electronics system, the SEM port interface, and thedigital control system interface. Collectively, these subsystems use the input provided by the userin tandem with the output from the sensors, which measure the stress and strain applied to thesample, in order to continuously adjust the required motor output. Figure 1 shows a) final designCAD model, b) various
Vries, "Biomimicry design thinking education: a base-line exercise in preconceptions of biological analogies," International Journal of Technology and Design Education, vol. 31, no. 4, pp. 797-814, 2021/09/01 2021, doi: 10.1007/s10798-020-09574-1.[6] https://biomimicry.org/what-is-biomimicry/[7] T. B. Kashdan et al., "The five-dimensional curiosity scale: Capturing the bandwidth of curiosity and identifying four unique subgroups of curious people," Journal of Research in Personality, vol. 73, pp. 130-149, 2018/04/01/ 2018, doi: https://doi.org/10.1016/j.jrp.2017.11.011. AppendicesAppendix I. Summary of project activities and related timelines. Activity
this, we quantify thecomplexity of the example problem as 26. We could choose to use other network centralitymeasures and an investigation into their suitability will be conducted in the future. Thehorizontal shear equation computation node is the most “central” to the computation, with adegree centrality of 5. Figure 3a-d: (a) Digraph of the correct solution. Steps to the two-part correct solution start at the "reaction forces" node. Solid circles show target nodes for achieving the two-part solution to the problem. (b) Student 1’s solution with solid and dotted circles showing parts of the solution achieved and unachieved, respectively. (c-d) Student 2’s and 3’s solutions, respectively, with dotted circles showing both
thinking: Part I,” Design and Culture, vol. 3, no. 3, pp. 285-306, Nov. 2011.[5] L. Carlgren, I. Rauth, and M. Elmquist, “Framing design thinking: The concept in idea andenactment,” Creativity and Innovation Management, vol. 25, no. 1, pp. 38-57, Mar. 2016.[6] R. Razzouk and V. Shute, “What is design thinking and why is it important?,” Review ofEducational Research, vol. 82, no. 3, pp. 330-348, Sep. 2012.[7] L. Bosman, “From doing to thinking: Developing the entrepreneurial mindset throughscaffold assignments and self-regulated learning reflection,” Open Education Studies, vol. 1, no.1, pp. 106-121, Jul. 2019.[8] J.H.L. Koh, C.S. Chai, B. Wong, and H.Y. Hong, “Design thinking and education,” SpringerSingapore, pp. 1-15, 2015.[9] A. Scheer, C
253 600Students were asked to self-report their GPA. GPA was based on a scale of 4, with an “A” being a4.00, a “B” being a 3.00, a “C” being a 2.00, a “D” being a 1.00, and an “S” being a 0.00. Someclasses also used a “+” or “–” system. A “+” adds 0.33 to the base grade, while a “-” subtracts0.33. For example, a “B+” would quantitatively be a 3.33 (3.00 + 0.33), while a “B-” would be a2.77 (3.00 - 0.33).Data was gathered on students’ expected majors. Out of a total of 600 students, 311 (51.8%) weremechanical and/or aerospace engineering students, 114 (19.0%) were civil and/or environmentalengineering students, 102 (17.0%) were biomedical engineering students and 73 (12.2%) studentshad other majors. This data can be seen in Figure 2
attributes: (a) the necessary technical knowledge, skills, and abilities to work intheir chosen field, (b) an appreciation for how all kinds of diversity strengthen engineering andcomputer science as disciplines, (c) knowledge of how to act in inclusive ways and createinclusive environments within their fields, and (d) consideration of diverse populations who areimpacted by their professional practice [4]. The thermodynamics activity focused on helpingdevelop attributes a, b, and d.Both the quick and long student reflection pieces showed that students understood the overalleffect group composition can have on the design process, helping reiterate attribute (b). In thequick reflection, 73% of students indicated that group composition mattered when
ed., vol. 2. pp. 241-263, 2011.[6] E. L. Thile and G. E. Matt, “The ethnic mentor undergraduate program: A brief description and preliminary findings, ” Journal of Multicultural Counseling and Development, 23rd ed., vol. 2., pp. 116-126, 1995.[7] T. D. Ennis, J. B. Milford, J. F. Sullivan, B. A. Myers, and D. Knight, “GoldShirt transitional program: first-year results and lessons learned on creating engineering capacity and expanding diversity” ASEE Annual Conference & Exposition, pp. 22.754.1-22.754.15, 2011.[8] M. Mattjik and M. Sanders, “Work in progress: a study on motivation in teams using self determination theory,” ASEE Virtual Annual Conference, pp. 1-9, 2020.[9] L. Tsui, “Effective
definition highlights the depth and complexity of successful mentoring. After a close review of theliterature, we opted for sticking to [31]’s identification of 4 latent variables that were validated by [32] in 2009 forthe College Student Mentoring Scale. The variables underlying the mentor-protégé relationship at the collegiatelevel involve (a) Psychological and Emotional support, (b) Degree and Career Support, (c) Academic SubjectKnowledge Support, and (d) the Existence of a Role Model. While more testing is needed to validate theseconstructs in a variety of settings, it provides an important starting point for a contextually sensitive mentoringstudy. A definition with this level of theoretical specificity can be helpful for assessing program
. Griffith and H. Frank, "Surveying the Safety Culture of Academic Laboratories," Journal of College Science Teaching, vol. 50, no. 2, pp. 18-26, 2020.[8] Y. Yang, G. Reniers, G. Chen and F. Goerlandt, "A bibliometric review of laboratory safety in universities," Safety Science, vol. 120, pp. 14-24, 2019.[9] K. A. McGarry, K. R. Hurley, K. A. Volp, I. M. Hill, B. A. Merritt, K. L. Peterson, P. A. Rudd, N. C. Erickson, L. A. Seiler, P. Gupta, F. S. Bates and W. B. Tolman, "Student Involvement in Improving the Culture of Safety in Academic Laboratories," Journal of Chemical Education, vol. 90, pp. 1414-1417, 2013.[10] I. O. Staehle, T. S. Chung, A. Stopin, G. S. Vadehra, S. I. Hsieh, J. H. Gibson and M. A
effectiveness is used as one of the measures to evaluateinstruction quality [2]. In addition, learning effectiveness measures can also include an efficiencymeasure (i.e., time on task) [4, 10], which became an additional concept we explored in ourstudy.Influencing Factors of Learning Effectiveness in Online SettingsLiterature shows that learning effectiveness has been studied in three contexts: (a) when newteaching methods were introduced [6]; (b) when online or blended teaching was employed [7];and (c) when comparing different learning modes [4, 5]. The first two contexts suggest that usingalternative course delivery modes can provoke thinking and prompt studies on learningeffectiveness in different instructional settings. The instructional changes
teachers’ intentions tointegrate physical computing concepts in their future classes? Design and Implementation of the Professional DevelopmentRecruitment and selection of participantsThe PD was advertised to public school district STEM curriculum coordinators acrossPennsylvania through email, a STEM outreach center website from the state’s land-grantuniversity, and posts on state STEM education association social media pages. To participate,educators had to attend as a team from their school district, requiring two teachers: (a) anelementary educator teaching in grade four or five and (b) a middle school educator teaching in aSTEM-related area. These parameters were intentionally created because the workshop content,materials, and
Data Analysis Phase Step one: Propose (a) Remove prior to 2022. (a) Develop an electronic the first keywords. (b)Remove high school and college online Excel sheet. Step two: State studies. (b) Demographic criteria. (c)Remove preservice and teachers’ Information from Step three: Explore education. participants. and establish new (d) Inclusion criteria identify journals (c)Research type. specific keywords and their publishing
learning compared to a traditional strategy where students learn allof the needed concepts for the course before the start of the design-build portion of the course.References [1] D. C. Brandenburg and A. D. Ellinger, “The future: Just-in-time learning expectations and potential implications for human resource development,” Advances in Developing Human Resources, vol. 5, no. 3, pp. 308–320, 2003. [2] M. M. Lynch, Learning Online: A Guide to Success in the Virtual Classroom. USA: Routledge, 2004. [3] D. J. Frank, K. L. Kolotka, A. H. Phillips, M. Schulz, C. Rigney, A. B. Drown, R. G. Stricko, K. A. Harper, and R. J. Freuler, “Developing and improving a multi-element first-year engineering cornerstone autonomous robotics
. 5 References[1] S. Agarwal, J. H. Wendorff and A. Greiner, "Use of electrospinning technique for biomedical applications," Polymer, vol. 49, pp. 5603-5621, 2008.[2] R. S. Bhattarai, R. D. Bachu, S. H. Boddu and S. Bhaduri, "Biomedical Applications of Electrospun Nanofibers: Drug and Nanoparticle Delivery," Pharmaceutics, vol. 11, no. 5, 2019.[3] N. Bhardwaj and S. B. Kundu, "Electrospinning: A fascinating fiber fabrication technique," Biotechnology Advances, vol. 28, pp. 325-347, 2010.[4] J. Berglund, "The Real World," IEEE Pulse, pp. 46-49, 2015.[5] R. A. Linsenmeier, "What makes a Biomedical Engineer," IEEE Engineering in Medicine and Biology Magazine