with others about this. • Is this continuum helpful for conversations or interventions? • Are the categories described in a way that creates a defense (is that bad)? • Are these categories, steps, or orientations (I have used these interchangeably)? • Do I have the necessary disciplinary background to develop this (who should help me)? • How does this relate to engineering and engineering education? References [1] Fortney, B.S., Morrison, D., Rodriguez, A.J. Upadhvav, B. (2019) “Equity in science teacher education: towardan expanded definition” Cultural Studies of Science Education 14: 259. https://doi.org/10.1007/s11422-019-09943-w[2] Ridgeway, M. L., (2019) “Against the grain: science education researchers and social
the followingstandards.CCSS.MATH.CONTENT.6.SP.B.5 [6]: Summarize numerical data sets in relation to theircontext, such as by: ● CCSS.MATH.CONTENT.6.SP.B.5.A: Reporting the number of observations. ● CCSS.MATH.CONTENT.6.SP.B.5.B: Describing the nature of the attribute under investigation, including how it was measured and its units of measurement. ● CCSS.MATH.CONTENT.6.SP.B.5.D: Relating the choice of measures of center and variability to the shape of the data distribution and the context in which the data were gathered.ISTE Empowered Learner [7] ● 1c: Students use technology to seek feedback that informs and improves their practice and to demonstrate their learning in a variety of ways.ISTE Computational Thinker
,” J. Eng. Ed., 94(1), 87-101.Stern, F., Xing, T., Yarbrough, D.B., Rothmayer, A., Rajagopalan, G., Prakashotta, S., Caughey,D., Bhaskaran, R., Smith, S., Hutchings B., and Moeykens, S. (2006). “Hands-on CFDeducational interface for engineering courses and laboratories,” J. Eng. Ed., 95(1), 63–83.Yoder, B.L. (2009). “Engineering by the numbers.” American Society for EngineeringEducation.‹https://www.asee.org/papers-and-publications/publications/college-profiles/09EngineeringbytheNumbersPart1.pdf› (May 15, 2019).Yoder, B.L. (2017). “Engineering by the numbers.” American Society for EngineeringEducation.‹https://www.asee.org/documents/papers-and-publications/publications/college-profiles/2017-Engineering-by-Numbers-Engineering-Statistics.pdf
://keenwarehouseprod.blob.core.windows.net/keen- downloads/KEEN_Framework_spread.pdf.5. J. B. Hylton, D. Mikesell, J.-D. Yoder, and H. Leblanc, “Working to Instill the Entrepreneurial Mindset Across the Curriculum,” Entrepreneurship Education and Pedagogy, vol. 3, no. 1, pp. 86–106, 2019.6. N. Sattele, K.M. Kecskemety, and K.A. Parris, “Analysis of the Entrepreneurial Mindset Elements in Established First-year Engineering Labs: Analysis Process and Lessons Learned and Changes for the Future,” Proceedings of the American Society for Engineering Education, 2019.7. M.T. Azim and A.H. Al-Kahtani, “Entrepreneurship Education, and Training: A Survey of Literature” Life Science Journal, vol. 11, no. 1s, 2014.8. A. R. Peterfreund, E. Costache, H. L. Chen, S. K
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.[31] Chambers, David Wade. "Stereotypic images of the scientist: The Draw‐a‐Scientist Test." Science education 67, no. 2 (1983): 255-265.[32] NVivo Software, qsrinternational.com, Retrieved from https://www.qsrinternational.com/nvivo/nvivo-products[33] V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qualitative Research in Psychology, vol. 3, ed. 2, pp. 77-101, 2006.[34] B. Glaser and A. Strauss, The discovery of grounded theory: Strategies for qualitative 10research, Aldine Publishing Company, 1967. 11
engineering departments require theirstudents to see engineering ethics in their coursework and (b) how much engineeringdepartments may offer regarding engineering ethics. For this we present a basic descriptivestatistical analysis. The results we present have implications for engineering ethics educators andadministrators working at various levels in the entire undergraduate engineering educationecosystem.Introduction Ethical decision-making is an essential element of engineering practice. Engineersoccupy myriad roles in society that enable them potentially to impact the lives of thousands ofpeople. Without an understanding of how to identify and make better and worse decisions,engineers could make detrimental decisions that register a
Not URM 483 86.4 % FGC FGC 79 14.1 % status Not FGC 476 85.2 % Total 559 100 %4.2 Industry and business unit-where are they working?Our data allow us to say a few things about where these early-career graduates are working,which gives a hint on the future development of the engineering workforce. These findingsalso complement data on the overall engineering workforce presented in the report onUnderstanding the Educational and Career Pathways of Engineers by the National Academyof Engineering [1]. a) b)Figure
engineering classes. The templates were created to helpstudents see exactly where to place steps. They were designed to fit on a class set of 9” x 12” dryerase whiteboards. This learning tool can be used for class warm-ups and hands-on activities forstudents. It can also be scaled down to fit in an interactive notebook. (a) One given (b) Two given Figure 1 Dimensional Analysis Templates - 3D printed Before the activity, the graphic in Figure 2 shows how shapes can be used to ‘cancel out’undesired shapes will be handed out to students to serve as a quick warm-up tool to helpillustrate the process of dimensional analysis. Teachers can have students look for patterns anddiscover that
SUMMER 2020 VOLUME 8 ISSUE 2ADVANCES IN ENGINEERING EDUCATIONUndergraduate Cross-Class Research Projects for Deep Learningin Engineering Education Figure 3. Elements of the Autonomous Vehicle UCCRP: Tasks and Required Skills. Figure 4. Elements of the Autonomous Vehicle UCCRP: (a) Tasks and KI Courses, (b) break down of tasks.SUMMER 2020 VOLUME 8 ISSUE 2 11 ADVANCES IN ENGINEERING EDUCATION Undergraduate Cross-Class Research Projects for Deep Learning in Engineering Education Second, the vehicle dynamics and control requires
SUMMER 2020 VOLUME 8 ISSUE 2ADVANCES IN ENGINEERING EDUCATIONUndergraduate Cross-Class Research Projects for Deep Learningin Engineering Education Figure 3. Elements of the Autonomous Vehicle UCCRP: Tasks and Required Skills. Figure 4. Elements of the Autonomous Vehicle UCCRP: (a) Tasks and KI Courses, (b) break down of tasks.SUMMER 2020 VOLUME 8 ISSUE 2 11 ADVANCES IN ENGINEERING EDUCATION Undergraduate Cross-Class Research Projects for Deep Learning in Engineering Education Second, the vehicle dynamics and control requires
- The 5 Deadly Diseases 1984," An Encyclopedia Britannica Film, 1984. [Online]. Available: https://www.youtube.com/watch?v=ehMAwIHGN0Y. [Accessed 8 January 2020].[49] T. Zywicki, "The Auto Bailout and the Rule of Law," National Affairs, no. 43, 2011.[50] D. D. Barlett and J. B. Steele, American: What Went Wrong?, Kansas City: A Universal Press Syndicate Company, 1992.[51] R. Khol, "GM struggles to repair the damage done by Mr. Lopez," American Machinist, p. 5, 1995.[52] G. S. Vasilash, "Save Now. Pay Later. Will success spoil General Motors," Production, pp. 9-10, 1993.[53] R. S. Kaplan, "Conceptual Framework of the Balanced Scorecard," Harvard Business School, Cambridge , 2010.[54] B. Ralph and T. Jordan, "BANKRUPTCY
. 62, no. 1, pp. 27-42, Mar. 2019.[6] V. B. Mansilla, “Assessing Student Work at Disciplinary Crossroads”, Change: The Magazine of Higher Learning, vol. 37, no. 1, pp. 14-21, Jan. 2005.[7] V. B. Mansilla and et. al., “Quality Assessment in Interdisciplinary Research and Education”, Research Evaluation, vol. 15, no. 1, pp. 69-74, Apr. 2006.[8] R. K. Yin, Case Study Research: Design and Methods, Applied Social Research Methods Series, 5th ed., Los Angeles, CA: Sage Publications, 2013.[9] S. B. Merriam, Qualitative Research: A Guide to Design and Implementation, San Francisco, CA: Jossey-Bass, 2009.[10] D. Wicks, “The Coding Manual for Qualitative Researchers”, Qualitative Research in Organizations and
research. International Journal of Listening. 22 (2), 141-151.[4] Trevelyan, J. 2014. The Making of an Expert Engineer. CRC Press.[5] Crumpton-Young, L. Pamela McCauley-Bush, L Rabelo, K Meza, A Ferreras, B. Rodriguez, A. Millan, D. Miranda, M. Kelarestani, 2010, “Engineering leadership development programs: a look at what is needed and what is being done.” Journal of STEM Education, 11 (3/4), 10-21[6] Wikoff, K., J. Friauf, H. Tran, S. Reyer, O. Petersen. 2004. Evaluating the communication component of an engineering curriculum: A case study. American Society for Engineering Education (ASEE) Annual Conference & Exposition, Session 2004-2532, 8 pp.[7] American Society of Civil Engineers (ASCE). 2019. Civil Engineering Body
Paper ID #29286Wisdom through Adversity: Situated Leadership Learning of EngineeringLeadersDr. Andrea Chan, Troost Institute for Leadership Education in Engineering (ILead) Andrea Chan is a Research Associate at the Troost Institute for Leadership Education in Engineering | University of TorontoDr. Cindy Rottmann, University of Toronto Cindy Rottmann is the Associate Director of Research at the Troost Institute for Leadership Education in Engineering, University of Toronto. Her research interests include engineering leadership in university and workplace settings as well as ethics and equity in engineering education.Dr
Engineering Programs to First-Year Engineering,” Ph.D. dissertation, Dept. Eng. Educa. Purdue Univ., West Lafayette, IN, 2014.[14] M.-C. Hsu, “Undergraduate engineering students’ experiences of interdisciplinary learning: a phenomenographic perspective,” Ph.D. dissertation, Dept. Eng. Educa. Purdue Univ., West Lafayette, IN, 2015.[15] C. B. Zoltowski, W. Oakes, and M. Cardella, “Students’ Ways of Experiencing Human-Centered Design,” J. Eng. Educ., vol. 101, no. 1, pp. 28–59, 2012.[16] A. Magana and S. Brophy, “Instructors’ intended learning outcomes for using computational simulations as learning tools,” J. Eng. Educ., vol. 101, no. 2, pp. 220–243, 2012.[17] E. Dringenberg, “A phenomenographic analysis of first
them from other prior generations [p. 11]. Millennials are 28% more likely to focus on business impact. Millennials are 71% more likely to focus on teamwork Millennials are 22% more likely to focus on a culture of connection.Non-Millennial definitions of inclusion are centered on traditional Part A to Part B precedentsand initiatives: Non-Millennials are 28% more likely to focus on fairness of opportunity. Non-Millennials are 31% more likely to focus on equity. Non-Millennials are 26% more likely to focus on integration. Non-Millennials are 28% more likely to focus on acceptance and tolerance.Moving beyond previous definitions of diversity and inclusion recognizes that most diversity andinclusion
Paper ID #30255WIP: First-year Engineering Students’ Study Strategies and TheirAcademic PerformanceAhmed Ashraf Butt, Purdue University, West Lafayette Ahmed Ashraf Butt is a doctoral student at the School of Engineering Education, Purdue University. He is currently working as a research assistant on the CourseMIRROR project funded by the Institute of Education Sciences (IES). He is interested in designing educational tools and exploring their impact on enhancing students’ learning experiences. Before Purdue University, Ahmed has worked as a lecturer for two years at the University of Lahore, Pakistan. Additionally, he has
Paper ID #30188Understanding Design, Tolerating Ambiguity, and Developing Middle SchoolDesign Based LessonsDesign Based LessonsProf. Reagan Curtis, West Virginia University Reagan Curtis, Ph.D., is Professor of Learning Sciences and Human Development and founding direc- tor of the Program Evaluation and Research Center at West Virginia University. He pursues a diverse research agenda including areas of interest in (a) the development of mathematical and scientific knowl- edge across the lifespan, (b) online delivery methods and pedagogical approaches to university instruc- tion, and (c) research methodology, program
(items 16-46 on the AWE LAESE survey), including the original twenty-one 7-point Likert scale questions, plus the ten 7-point Likert scale questions asking “to what extent doyou agree.” The LAESE subscales include: (1) Engineering career expectations, (2)Engineering self-efficacy 1, (3) Engineering self-efficacy 2, (4) Feeling of inclusion, (5) Copingself-efficacy, and (6) Math outcomes efficacy. The two subscales measuring “engineering self-efficacy” are differentiated in what they seek to measure as follows: (1) The “Engineering self-efficacy 1” subscale measures a student’s perception of his or her ability to earn an A or B inmath, physics, and engineering courses and succeed in an engineering curriculum while notgiving up participation in
, thenobservers share their thoughts and summary report. During the post-observation discussion,observers also attempt to make connections to the weekly seminars and the excellent teachervisits. The observation form and self-reflection sheet used are available in Appendix B andAppendix C respectively.Collection of Early Student FeedbackProgram participants collect informal early feedback from their students as a way to improvetheir instruction before the semester is over. The early feedback is reviewed with program staffduring the observation process. New faculty are not provided a single form to utilize for earlyfeedback, but the rationale and example feedback forms are covered in one of the weeklyseminars. Faculty then create their own form based on
) explains this seemingly contradictory result. Figure 3 shows a large number of studentspassed with a score in the 21-23 range in their first year, while a large number of students scoredin the 24-30 range in their final year. This result leads to a higher average test score in the finalyear, despite the greater number of students who failed the test in the final year. (a) First and final year test scores (b) Difference in entrance/exit scores Figure 1. Box plots of test scores, taken in the first year and final year of an engineering degree program. First Year 87.5% 12.5% Final Year
University. She teaches elementary science methods and secondary science and mathematics methods courses with emphasis on multicultural education and equity pedagogies. Her research interests include both formal and informal STEM education, with specialization in the integration of engineering and computer science into science education through preservice and inservice educator development.Dr. Stacie I Ringleb, Old Dominion University Stacie Ringleb is an associate professor in the Department of Mechanical and Aerospace Engineering at Old Dominion University. Dr. Ringleb received a B.S. in biomedical engineering from Case Western Re- serve University in 1997, a M.S.E. from Temple University in Mechanical Engineering in 1999
placement, career progression, and leadershipresponsibilities as compared similar graduates not in the leadership program. In addition, followup work will aim at better understanding where improvements can be made within the leadershipdevelopment curriculum.ReferencesABET (2020) Criteria for Accrediting Engineering Programs. Retrieved from: https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting- engineering-programs-2020-2021/.Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21, 1086–1120.Avolio, B. J., Reichard, R. J., Hannah, S. T., Walumbwa, F. O., & Chan, A. (2009). A metanalytic review of
Paper ID #30807The Pitt STRIVE Program: Adopting Evidence-Based Principles ”TheMeyerhoff and PROMISE Way”Ms. Deanna Christine Easley Sinex, University of Pittsburgh Deanna C.E.Sinex is a Bioengineering Ph.D. candidate at the University of Pittsburgh. She earned her B.S. in Mechanical Engineering from the University of Maryland, Baltimore County. Her research involves the development and application of engineering concepts and active learning techniques in clinical and institutional learning environments to help improve the literacy of fundamental, yet critical aspects of health.Dr. Mary E. Besterfield-Sacre, University of
questions atdifferent cognitive levels of the Bloom’s taxonomy aligned to the course and specificlearning outcomes spread across various continuous assessment tests (CAT). A sampleassessment framework for a typical course is shown in TABLE I. TABLE I: Assessment Framework of a Typical Course End Semester: Evaluation Type CAT 1 Outcomes 1 to 3 CAT 2 Outcomes 1 to 4 CAT 3 Outcomes 1 to 5 Outcomes 1 to 5 Section (marks) A (1) B (3) C (10) A (1) B (3) C (10) A (1) B (3
? interactive: A) cell culture game7. Adherent cultures should be passaged at what phase? (p<0.05, paired t-test, n=49 students)8. To encourage cell growth, what are the conditions inside of an and B) plasmid google form (p<0.05, incubator? paired t-test, n=47). Error bars9. Why is sodium bicarbonate added to cell culture media? represent standard deviation.10. What is confluency?Plasmid design assessments (multiple choice – choices were not included to conserve space):1. What is a small circular piece of DNA (often found in bacterial cells) called?2. You want to cut a specific region of DNA and insert your gene of interest. What is the site where you want
settings. In this paper, we describe the waysacademic contexts have shaped and re-shaped the study of the S-STEM projects, particularlyregarding a) quantitative student comparisons and b) patterns of 2 to 4-year transfer.Students under study exhibit various markers of systemic oppression by income, citizenshipstatus, gender, ethnicity, and race, indicating a need to consider intersectionality and socialjustice aims in any comparative data analysis. In addition, the institutions, nearly all designated“Hispanic-Serving Institutions,” vary in institutional infrastructure, leading to differing access tostudent level data and comparison data. While it is tempting to quantitatively compare S-STEMstudents’ course outcomes and time-to-degree directly to
. We then rearrange the orders that the chapters ofEthernet and Optical Communications are introduced as following: a. Introduction to fundamentals of networking b. Local-area networks c. Ethernet d. Optical principles and optical communication systems e. Fiber-optic structure and waveguiding principles f. Optical transmitters and receivers (including photodetectors and amplifiers) g. Passive optical networks h. 40Gb/s and 100Gb/s Networks/EthernetWavelength division multiplexing (WDM) concept is introduced in the “passive opticalnetworks” section to prepare students ready for introduction to 40Gb/s and 100Gb/s Ethernet.We then introduce the 40Gb/s and 100Gb/s Ethernet through an example that uses 4 x
episode of discourse is coded as containinga design decision if students (a) delineate the design alternatives and (b) provide justifications fortheir choices. Episodes that misses either of these components are not coded as a design decision.Consider the following example: A: When calibrating the y value, we are not going to worry about if the user is raising their hand or what? B: Yeah, I don’t think we have to consider that. I can go ask her [the teaching assistant]. [B came back after talking to the teaching assistant] We don’t have to consider regular day to day movements. Just walking. E: Just walking, okay, cool. It’s + or – 150 and anything greater than that is a step. That is what we got