and Lucas [15]. The study will be exploratory and the intervieweeswill be asked to give their personal perceptions of how they see the phenomenon and alsoregarding how and why they have developed those viewpoints.One week before the interview, the interviewees will receive the interview protocol, includingthe questions and short texts presenting the three contemporary challenges the informants aresupposed to reflect upon. The following questions will form the basis for the interview. 1. How do you think these challenges affect the development of your discipline and the educational program(s) you are involved in? 2. What do you expect the situation to be 10 years from now? 3. How do you prepare your students for the future with
). Any opinions, findings, and conclusions orrecommendations expressed in this material are those of the author and do not necessarily reflectthe views of the National Science Foundation.Bibliography[1] Hsieh, S. “Design of Remotely Accessible Automated Systems to Enhance Industrial Automation Education,” ASEE 2017 Annual Conference, June 25 - 28, Columbus, Ohio.[2] Grodotzki, J., Ortelt, T.R. and Tekkaya, A.E., 2018. Remote and Virtual Labs for Engineering Education 4.0: Achievements of the ELLI project at the TU Dortmund University. Procedia Manufacturing, 26, pp.1349-1360, 2018.[3] Bikas, H., Stavropoulos, P. and Chryssolouris, C., “Additive manufacturing methods and modeling approaches: A critical review,” Int. J. Adv. Manuf
emphasizing the understanding head loss.References [1] B. G. Southwell, J. J. Murphy, J. E. DeWaters and P. A. LeBaron, “Americans perceived and actual understanding of energy,” RTI Press, 2012. [2] J. E. DeWaters and S. E. Powers, “Energy literacy of secondary students in New York State (USA): A measure of knowledge, affect, and behavior.,” Energy Policy, vol. 39, no. 3, pp. 1699-1710, 2011. [3] M. Turner, F. Chris and P. Karl, “Development of an electric energy literacy survey,” in Energy and Sustainability Conference (IESC), 2014. [4] J. E. DeWaters and S. E. Powers, “Establishing measurement criteria for an energy literacy questionnaire,” The Journal of Environmental Education, vol. 44, no. 1, pp. 38
, etc.). Ideas to supportthese needs that have been discussed include (1) developing workshops that can provide moreclarity on the promotion process and even personalized feedback, (2) forming a mentoringnetwork through event(s), (3) developing resources that can support good mentors, and (4)having more informal networking events across the schools to support peer-mentoring. None ofthese ideas explicitly involve the creation of a formal mentor program at this point, which wouldrequire considerable resources. However, the ideas are all aligned with supporting mentorshipacross the institution and can serve as an intermediate step for eventually formalizing mentorshipacross the college of engineering. The master mentors are now in the process of
this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation. The authors also wish to thank Dr. Rebecca Bates, Dr. TamaraFloyd-Smith, Dr. Melani Plett, and Dr. Nanette Veilleux for their help in recruiting interviewparticipants for this project.References[1] S. Fayer, A. Lacey, and A. Watson, “Science, technology, engineering, and mathematics (STEM) occupations: past, present, and future : Spotlight on Statistics: U.S. Bureau of Labor Statistics,” U.S. Bureau of Labor and Statistics, Jan. 2017.[2] D. J. Nelson and C. J. Brammer, “A national analysis of minorities in science and engineering faculties at research universities,” Oklahoma University, Norman, Oklahoma, Jan
creations. In addition to everything else,this program allowed creative thinking and problem solving. Students that participated and thosewho attended the public polling session agreed that a program like the one described abovewould be largely beneficial. Most believed that the program would help positively influenceundergraduate engineers who are teetering on the edge of leaving the program. To take this ideafurther, one needs to implement a system that allows a larger group of participants withoutsacrificing any of the attributes which define it.References[1] M. Meyer and S Marx, “Engineering Dropouts: A Qualitative Examination of Why Undergraduates Leave Engineering,” Journal of Engineering Education, vol. 103, iss. 4, p
as a Learning Experience. What Research Says to the Teacher. ERIC, 1981. [2] A. Fernandez, C. Saviz, and J. S. Burmeister, “Homework as an outcome assessment: Relationships between homework and test performance,” 2006. [3] K. S. Jackson and M. D. Maughmer, “Promoting student success: Goodbye to graded homework and hello to homework quizzes,” in ASEE Annual Conference and Exposition, Conference Proceedings, 2017. [4] E. F. Gehringer and M. B. W. Peddycord III, “Teaching strategies when students have access to solution manuals,” age, vol. 23, p. 1, 2013. [5] J. Widmann et al., “Student use of author’s textbook solution manuals: Effect on student learning of mechanics fundamentals,” 2007. [6] L. Roecker, “Using oral
outside engineering about stayingin the program. Students from outside the major often express a combination of sympathy andrespect for engineering students, based on the perception that their majors are very difficult. Acouple of examples demonstrate what engineering students hear from their peers outside ofengineering: “Other students? Um. Yeah. That’s for sure. They definitely, you know say, oh she’s an engineering major. She has to study a lot so, you know, she can’t hang out with us too much.” S- Whenever I mention my major, people always go, ...tell me that they’re sorry. I- And this is people you mean other students or faculty or... S- No, they’re students. So, I feel like they’re…they’re…I feel like they kind of
practices in which stakeholder concernschanged actual engineering decisions and practices as exemplary CSR activities, rather thanthose that simply redistributed some of the economics earnings of industry to a broader array ofpeople. In so doing, we drew inspiration of Auld et al.’s distinction between “old” and “new”CSR, in which “old” CSR encompasses philanthropy (such as volunteering and charitabledonations like scholarships) and “new” CSR refers to activities that change core businesspractices to create social, economic, and environmental value for stakeholders as well ascompanies [4]. The question asking students to evaluate CSR practices as being excellent, okay,or not CSR therefore included a range of activities on the old to new scale, from
to 1950’s [1] researchers started to explore this technology. Simplyspeaking, computer vision deals with the technology that mimics the capabilitiesof a human (normal) vision system. Naturally, a normal human being is equippedwith sensors for five different sensing capabilities (vision, smell, taste, touch, andhear). These capabilities are controlled by the central nervous system (brain)allowing a human being to demonstrate intelligent behavior. By default, thevision system of a human being is three dimensional and it uses two eyes thatwork as sensors (detectors) to capture images. Earlier computer vision systemused only one camera along with the associated computational platform andsoftware and therefore, it dealt largely with two
semester of 2017, a local inventor (2nd author of this paper) needed some CADmodeling support. We adapted our curriculum and made it a priority to help meet this need. Wewere rewarded for it - students loved these service projects. The S-L project served as a link fromengineering theory to everyday objects people can touch and see. Along the process they learnedwhat they needed to learn - the CAD tools. It was a win-win situation. In the following sections,we will document these activities and share some ABET outcome assessment results.The Wrap Rack ProjectOur university’s motto is "To Seek to Learn is to Seek to Serve."1 Service-Learning (S-L) haslong been recognized as an effective way of achieving multiple student learning outcomes
a creative way tosolve a given problem without the conventional step by step laboratory procedure. Thischallenging experience provides students a taste (flavor) of real life engineering environmentand thus better prepares them for professional activities, while increasing their learning andcreativity.REFERENCES[1]. Abd.Rahman, Norliza & Kofli, Noorhisham & Takriff, Mohd & Abdullah, Siti. (2011). Comparative Study between Open Ended Laboratory and Traditional Laboratory. IEEE Global Engineering Education Conference, EDUCON 2011. 40 - 44. 10.1109/EDUCON.2011.5773110.[2]. Dr. Bridget M. Smyser, Kavin McCue. (2012). From Demonstration to Open-Ended Labs, Revitalizing a Measurement s and Analysis Course. ASEE Conference
the study reported inthis paper. In the future, we will use factor scores derived from factor analysis to evaluate themediation relationships between the variables in our study, and we will employ learning andmotivation theories to further explore these relationships.References[1] L. Tian, T. Yu, and E. S. Huebner, "Achievement goal orientations and adolescents’ subjective well-being in school: the mediating roles of academic social comparison directions," Teaching for understanding at university: Deep approaches and distinctive ways of thinking., vol. 8, p. 37, 2017.[2] M. V. Covington, "Goal theory, motivation, and school achievement: An integrative review," Annual review of psychology, vol. 51, no. 1, pp. 171-200
Potential sources of material include your own personal notes where you Present students with made a mistake, or a homework/exam inaccurate work (on a solution that introduces mistakes worth slide or handout) and pointing out have them take a few Make clear something is wrong on your notes on what is wrong, handout, to avoid confusing students Intentional 2-10 then follow up by calling who arrive late or aren't fully paying Mistake(s) minutes on students
one student smaller.Next, gruepr runs its genetic optimization algorithm, displaying its progress to the instructor. Allof the instructor’s chosen teaming options are used in the algorithm’s fitness function. After theoptimization algorithm operates for some time, the set of teams with the quantitatively highestscore is shown to the instructor. The instructor can choose to keep these teams, make minortweaks by swapping one or more pairs of students between teams, or get rid of the teams andrestart the optimization scheme from the beginning. If the instructor chooses to restart theoptimization, they may also choose to adjust the teaming options and/or team size(s) at that time.Since genetic algorithms are, in general, not guaranteed to find
introductory and advanced technical writing courses.Data-driven learningAs the educational marketplace expands, institutions of higher learning are experimenting withhow active learning increases student success. Freeman et al.’s meta-analysis of STEM educationstudies found that active learning significantly increased course grades over didactic methodsand was particularly effective in classes of 50 or less students. In contrast, students were 1.5times more likely to fail a course that lacked active learning strategies [1].The spectrum of active learning ranges from simple activities, such as writing minute papers orpausing for reflection, to more complex activities, such as hands-on technology and inquirylearning. Active learning is being promoted as
findings are that the integration of makingactivities into cornerstone courses provides a great resource to expose students to authenticengineering experiences that can help them be more prepared for their senior years inengineering school and for their future engineering careers.A limitation of this study is that there was only one team in group B. The interventions understudy were initiated in 2015, and a handful of students had registered for them as an elective atthe time. Hence, team B1 was the only team that was available to study whose members hadtaken the design courses under study in their first or second year of study. Still, B1 was thesource of a wealth of qualitative data.Another potential explanation for team B1’s performance is their
methods that best fit the metal(s) of interest in the sample matrix. Method development has also provided participating students the ability to learn the importance and relevance of optimizing analytical methodologies in order to confidently measure trace metals in a sample. Each project required unique sample preparation methods. For example, sample preparation in the "Buried Treasure" project (a collaborative project that involved engineering, art and history disciplines) included development of a digestion method as well as a non- destructive method to preserve archeological glass and ceramic artifacts. 2) Training Instrumentation training was an ongoing program throughout this project
. The contents, opinions, and recommendations expressed are those of the author(s) anddo not represent the views of the National Science Foundation. We would also like to thank ourparticipants for contributing their personal experiences to this research. References[1] O. Amsterdamska, “Demarcating epidemiology,” Sci., Technol., & Human Values, vol. 30, no. 1, pp. 17-51, Jan. 2005.[2] A. C. Barton, V. Johnson, and the students in Ms. Johnson’s Grade 8 science classes, “Truncating agency: Peer review and participatory research,” Res. in Sci. Edu., vol. 32, no. 2, pp. 191-214, Jun. 2002.[3] M. Eisenhart and L. Towne, “Contestation and change in national policy on
contributed to the overall ranking. The graph is essentially a stacked bar chart.An artifact that received all “1”s would be at one side of the chart, and an artifact that receivedall “5”s would be at the other. Ratings of “1” are shown as taller bars than lower ratings, so thatthe stacked bar for a higher-rated artifact is taller than other bars. We provide the rainbow chartas a web service for ranking-based systems. Here is an example of the visualization. Figure 1. Example of visualization of rankingsThe green bars represent a first-place ranking from one’s peers. They are taller than the (yellow)second-place ranking bars, or the bars for any other rank. In this case, the highest-ranked artifactreceived six first-place
National Science Foundation’s Division ofUndergraduate Education: Improving Undergraduate STEM Education (Grant Number: NSF-DUE-1712089). Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.ReferencesMacNell, L. (2015). What’s in a Name: Exposing Gender Bias in Student Ratings of Teaching. Innovative Higher Education, 40(4), 291–303. https://doi.org/10.1007/s10755-014-9313- 4Matz, R. L., Koester, B. P., Fiorini, S., Grom, G., Shepard, L., Stangor, C. G., … McKay, T. A. (2017). Patterns of Gendered Performance Differences in Large Introductory Courses at Five Research Universities. AERA Open, 3(4
. Accessed January 23, 2019.https://www.canr.msu.edu/news/does_your_community_have_a_tool_library2. Wang, F., Wang, W., Wilson, S., & Ahmed, N. 2016. The state of library makerspaces.International Journal of Librarianship, 1(1),2-16. https://doi.org/10.23974/ijol.2016.vol1.1.123. Berkeley Public Library. 2014. Tool Lending Library – a brief history. Accessed January 23,2019. https://www.berkeleypubliclibrary.org/locations/tool-lending-library/tool-lending-library-brief-history4. Tabor, N. 2013. Evaluating the success of tool-lending libraries and their contributions tocommunity sustainability. Undergraduate Student Thesis University of Nebraska - Lincoln.5. Ameli, N. 2017. Libraries of Things as a new form of sharing. Pushing the Sharing
steps, was the norm among participants. Figure 5 Example of a student’s circular design process concept mapClassifications and Learning TrajectoriesThe general coding scheme is based on a spectrum of students’ models of the design process.Steps in a student’s design thinking learning trajectory, from novice to expert, is demonstratedby, linear, circular, successive, iterative, interwoven, and affective concept maps, as illustrated inFigure 6 below. ? novice444444444444444444444444444444444444444expert Figure 6: Models of the Design Process as steps in a S Design Thinking Learning Trajectory; from novice to expert, (l-r), linear
under Grant No.EEC 1623105. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National ScienceFoundation.References[1] J. P. Lampi and T. Reynolds, "Connecting Practice & Research: From Tacit to Explicit Disciplinary Writing Instruction," Journal of Developmental Education, vol. 41, pp. 26- 28, 2018.[2] D. E. Gragson, J. P. Hagen, L. Diener, C. J. Nichols, L. F. Hanne, A. G. King, et al., "Developing technical writing skills in the physical chemistry laboratory: A progressive approach employing peer review," Journal of Chemical Education, vol. 87, pp. 62-65, 2010.[3] S. D. Loveland and S. D
College. Feedback from this group has been uniformly positive to these efforts to meetcurrent and emerging needs of business and industry. The College has been encouraged tocontinue to look to future needs and continue to develop programs that meet these needs throughcreative approaches to offering degree programs.References[1] World Economic Forum, “The Future of Jobs Report 2018,” World Economic forum, 2018. Available at http://www3.weforum.org/docs/WEF_Future_of_Jobs_2018.pdf.[2] J. Bughin, E. Hazan, S. Lund, P. Dahlstrom, A. Wiesinger, and A. Subramainian, “Skill Shift - Automation and The Future of the Workforce,” McKinsey Global Institute, McKinsey and Company, 2018. Available at https://www.mckinsey.com/featured-insights/future-of