material are those of the author(s) and do not necessarily reflect the views of the NSF. References[1] M. F. Fox, “Women and men faculty in academic science and engineering: Social- organizational indicators and implications,” American Behavioral Scientist, vol. 53, no. 7, pp. 997–101, 2010.[2] M. Sabharwal and E. A. Corley, "Faculty job satisfaction across gender and discipline," The Social Science Journal vol. 46, no. 3, pp. 539-556, September, 2009.[3] Bureau of Labor Statistics, U. S. Department of Labor, Occupational Outlook Handbook, Postsecondary Teachers, on the Internet at https://www.bls.gov/ooh/education-training-and- library/postsecondary-teachers.htm
thatacademic preparation is typically not one of the main reasons for attrition 4,5. In other words, moststudents who leave academia choose to leave because of their own personal decision, not becausethey failed qualifying exams or are doing poorly in their courses 5–7. Indeed, Barnes et al.’s 8,9studies of graduate attrition showed that the attributions that professors give for their students thatleave are different than the rationale that the corresponding non-completing students give forleaving. The misalignment, misunderstanding, or attribution bias that may exist (from both parties)is worthy of study and is likely due to the issues that have arisen with sampling a sensitivepopulation.Further, most attrition literature takes a sociological view of
otheractivities. By practicing what you teach, you can efficiently accomplish the teaching,scholarship, and service goals necessary for promotion and tenure and have a fruitful andenjoyable career. Reference List[1] R. Brent, R. Felder, S. Rajala, J. Gilligan and G. Lee, "New faculty 101: an orientation to theprofession [engineering teacher training]," 31st Annual Frontiers in Education Conference.Impact on Engineering and Science Education. Conference Proceedings (Cat. No.01CH37193),Reno, NV, 2001, pp. S3B-1-3 vol.3. doi: 10.1109/FIE.2001.964046 [Accessed Jan. 11, 2018].[2] C. Lucas, J. Murry, “Teaching: Lectures and Discussion,” in New Faculty. New York:Palgrave Macmillan, 2011, pp. 39-63.[3] J. Pedersen, G
participated in this six-week nanotechnology summer research program in 2015 and who then integratednanotechnology into the classroom over the 2015-2016 academic school year. Second, we reportobservational data from five teachers’ nano-lessons by using a modified version of the ScienceTeacher Inquiry Rubric (STIR).5 Third, using the Student Attitudes toward STEM (S-STEM)survey,6 we present changes in these teachers’ students’ attitudes towards STEM, as well aschanges in students’ perceptions of their own 21st century skills. Lastly, we report changes instudents’ reported interests in 12 STEM careers.Table 1. Overview of Research Evaluation Questions and Methods Research Evaluation Questions Method Participant Q1
responsibilities for the design challengeInstructional Design Agents RoleWhat is the role of an instructional design agent? The instructional design agent’s role can bedefined as the set of responsibilities and activities that fall within an agent’s intended purpose,which when viewed holistically, demarcate its position or part to play within the designchallenge.In light of this definition, we turn to three points of consideration needed to develop this role:what design intelligence will the agent(s) embody, what specific types of roles will the designagent(s) assume and how many design agents should be employed.We discuss the design intelligence agents embody first, as this has implications for the
less mechanics concepts involvedwith cross sections while ENGT Strength of Materials course has mainly 2D orthogonal views ofstructural cross sections, thereby losing all depth cues associated with the 3D structures. Thisfinding is contradictory to the result from a previous study carried out by the same author(s)[1].The previous study found a significant positive correlation (ρ = 0.552 at p = 0.01) between SBSTscores of mechanical engineering students and their performance in the Mechanics of Materials(MOM) course. It is noted that the engineering students’ performances in MOM in the previousstudy was measured by using the MOM concept inventory [22], a survey consisting of 23conceptual understanding questions, not the final course grades as
group as a senior engineer, and later brought his real-world expertise back into the classroom at Purdue University Calumet. He is currently a Clinical Associate Professor at the University of Illinois at Chicago where he enjoys success in teaching and education research.Prof. Jeremiah Abiade c American Society for Engineering Education, 2019 Execution Details and Assessment Results of a Summer Bridge Program for First-year Engineering StudentsAbstractThis paper reports the execution details and the summary assessment of a Summer Bridge Program(SBP) that is a part of an ongoing National Science Foundation (NSF) Scholarships in Science,Technology, Engineering, and Math (S-STEM
planned.AcknowledgementsThis work was performed with support from the U.S. National Science Foundation (award #1757659).References[1] K. Evans and F. Reeder, A Human Capital Crisis in Cybersecurity: Technical Proficiency Matters. Washington, DC: Center for Strategic & International Studies, 2010.[2] Cyber Seek, “Cybersecurity Supply/Demand Heat Map,” Cyber Seek Website, 2019. [Online]. Available: https://www.cyberseek.org/heatmap.html. [Accessed: 03-Feb-2019].[3] J. Mirkovic, A. Tabor, S. Woo, and P. Pusey, “Engaging Novices in Cybersecurity Competitions: A Vision and Lessons Learned at {ACM} Tapia 2015.” 2015.[4] R. S. Cheung, J. P. Cohen, H. Z. Lo, F. Elia, and V. Carrillo-Marquez, “Effectiveness of Cybersecurity Competitions,” in
Paper ID #25415Faculty Embrace Collaborative Learning Techniques: Sustaining Pedagogi-cal ChangeMrs. Teresa Lee Tinnell, University of Louisville Terri Tinnell is a Curriculum and Instruction PhD student and Graduate Research Assistant at the Univer- sity of Louisville. Her research interests include interdisciplinary faculty development, STEM identity, and retention of engineering students through career.Dr. Patricia A. Ralston, University of Louisville Dr. Patricia A. S. Ralston is Professor and Chair of the Department of Engineering Fundamentals at the University of Louisville. She received her B.S., MEng, and PhD
quickassessment of student engineering identity and promote understanding of the relationshipbetween student engineering identity and persistence in engineering. The brief quantitativemeasure of engineering identity used in this study has the potential to be utilized in programs andinterventions developed to improve retention rates in engineering programs, especially in thosewith larger numbers of participants. The findings presented are part of a larger project supportedby the NSF under Grant No. 1504741.References[1] S. Olson and D. G. Riordan, "Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics," Executive Office of the President, President’s Council of
Faculty Award for Excellence in Service-Learning. Dr. Vernaza does research in engineering education (active learning techniques) and high-strain deformation of materials. She is currently the PI of an NSF S-STEM.Dr. Saeed Tiari, Gannon UniversityDr. Scott Steinbrink, Gannon University Dr. Scott Steinbrink is an associate professor of Mechanical Engineering.Dr. Lin Zhao, Gannon University Lin Zhao received the Ph.D. degree in electrical engineering from the University of Western Ontario, London, ON, Canada in 2006. She received the B.Sc. and M.Sc. degrees in electrical engineering from Shandong University, Jinan, China, in 1993 and 1996 respectively. From 1996 to 2002, she was a Faculty Member with the School of
. This project focuses on the National Society of Black Engineers (NSBE)'s SummerEngineering Experiences for Kids (SEEK) program. This multi-partner project allows us toexpand and strengthen the experience, conduct research on the impact of the program, andconduct research on how such outreach programs might grow in sustainable manners. Our posterwill present a summary of the large-scale data collection that occurred during the summer of2018 at all 16 sites located across the US. We administered a variety of instruments to identifychanges in the children's STEM-related outcomes over the course of the SEEK experience. Tofurther operationalize the variation in organizational contexts across sites, we collected data fromparents and mentors. In the
thesedistinctions, we can transition students back to traditional representations after their conceptualknowledge is robust enough to guide them. Our themes of perceptually similar concepts,perceptually obscure concepts, and informationally incomplete representations suggest clearavenues for investigating what types of perceptual cues may hinder students’ ability to developor use appropriate conceptual knowledge. As engineers, we can use this knowledge to potentiallydesign new notations or new pedagogical techniques that can help students recognize andovercome the ways our notation may currently be failing to help students learn.References [1] S. Carey, “Knowledge acquisition: Enrichment or conceptual change?,” in The epigenesis of mind., S. Carey and
: http://www.dtic.mil2. Abyad, A. (2011). Intercultural leadership and communication in global business. Middle East Journal of Business, 6(2), p. 8-12. http://dx.doi.org/10.5742/mejb.2011.620263. Ali, S., & Green, P. (2012). Effective information technology (IT) governance mechanisms: An IT outsourcing perspective. Information Systems Frontiers, 14(2), 179-193. http://dx.doi.org/10.1007/s10796-009-9183-y.4. Al-Rodhan, N. R. F. (2006). Definitions of globalization: A comprehensive overview and a proposed definition. GCSP. P. 1-21. Retrieved January, 25, 2014 from www.sustainablehistory.com/articles/definitions-of- globalization.pdf5. AME Info.com (2012). The Ultimate Middle East business resource. Retrieved from
“yes” responsesorH0: p = 0.5 vs. Ha: p < 0.5 when the claim was that there was a majority of “no” responsesIn this case, p represents the overall proportion of “yes” responses when the results for all threesections were combined.In other cases where the response was a 1-5 Likert scale rating, the proportion of selected ratings(often 4’s and 5’s or 1’s) were compared for the three sections. In many instances, thedistribution of ratings for two sections were very similar (typically for the traditional lecture andhybrid sections) so the proportions were pooled and compared to the other section. For this test,the hypotheses were:H0: p1 – p2 = 0 (i.e., p1 = p2) vs. Ha: p1 – p2 > 0 (i.e., p1 > p2) when the claim is that theproportion for
science knowledge using real data. This fell to just 7.7% post-institute – with furtheropportunities to engage in hands-on research using emerging technology throughout the schoolyear.VIII. AcknowledgmentThis work has been made possible by the NSF EPSCoR Track III Award #1348266.IX. References1 National Center for Education Statistics. 1990–2009. Digest of Education Statistics. US Department of Education. nces.ed.gov/programs/digest/2 Wang, M.T., Eccles, J.S., &, S. (2013). Not Lack of Ability but More Choice: Individual and Gender Differences in Choice of Careers in Science, Technology, Engineering, and Mathematics Psychological Science May 2013 24: 770-775, first published on March 18, 20133
Paper ID #16560ASCENT - A Program Designed to Support STEM Students through Under-graduate Research and MentoringDr. Kumer Pial Das, Lamar University Dr. Kumer Pial Das is an Associate Professor of Statistics and the Director of the Office of Undergraduate Research at Lamar University in Beaumont, TX. He is the PI of a S-STEM program funded by NSF.B. D. Daniel, Lamar UniversityDr. Stefan Andrei, Lamar University Stefan Andrei received his B.S. in Computer Science (1994) and M.S. in Computer Science (1995) from Cuza University of Iasi, Romania, and a Ph.D. in Computer Science (2000) from Hamburg University, Germany. He was
(Phase 2)Once phase 1 is complete and the benchmarking team has been formed, the identification of thebenchmarking subject (i.e., focus of the benchmarking process) must be completed. During thisphase, it is important that: • The desired areas to be benchmarked are identified; • The number of areas is narrowed down to key areas that can realistically be impacted; • The importance of each area is determined based on priorities; and • The final benchmarking subject(s) are identified.Through multiple conversations between the assistant director of The Center and one of theauthors, several desired areas to be benchmarked were identified. In particular, the assistantdirector was interested in benchmarking interventions with regard to
Graphics, 6(1), 99-109.4. Leopold, C., Gorska, R. A., & Sorby, S. A. (2001). International experiences in developing the spatial visualization abilities of engineering students. Journal for Geometry and Graphics, 5(1), 81-91.5. Strong, S., & Smith, R. (2001). Spatial visualization: Fundamentals and trends in engineering graphics. Journal of Industrial Technology, 18(1), 1-6.6. Hsi, S., Linn, M. C., & Bell, J. E. (1997). The role of spatial reasoning in engineering and the design of spatial instruction. Journal of Engineering Education, 86(2), 151-158.7. Sorby, S. A. (2001). Improving the spatial visualization skills of engineering students: Impact on graphics performance and retention. Engineering Design
applied inthis case [6].Challenge-based Ocean Engineering Project (COEP)The top-level objectives of this challenge-based ocean engineering project were two-fold: (a)respond to a report of potential UXO sighting and search a rectangular area approximately 100feet by 75 feet with depths of water up to 40 feet for the potential UXO; (b) If potential UXOwas located, then (1) provide as precise of a geo-location as possible in order to enable theExplosive Ordnance Disposal (EOD) expert to respond to the exact location and (2) provide asmuch information as possible on the located object(s) to an EOD subject matter expert (SME) onshore. The goal of providing this information was to enable the SME to assess if the object waspotentially dangerous, not
) presentingmore information to users through clickable pop-out boxes. The last three characteristics focuson students’ active involvement, giving them a chance to organize their learning process bynavigating through modules, changing input parameters, and observing the outcomes. Dependingon the technical limitations and CSA objectives, different researchers have focused on differentaspects of the above-mentioned characteristics 6, 11, 12, 17, 18, 24.Pedagogical innovations in the instruction of engineering mechanicsBefore 1990’s, the main emphasis of educational research was on improving teaching styles,active learning, and facilitation of student conceptual understanding20. Developments incomputer graphics and web-based tools have reinforced these
overlaps were not expected to cause any biasin the results. Categories 3 and 4 were formed to understand how the information learned in anentry-level gatekeeper course such as mathematics was carried forward to an advanced levelcourse. Table 1. Grading scale used for questions in the categories 1-4 Grade Explanation 5 Displays excellent understanding of the new concept and the pre- requisite(s) 4 Knowledge of the pre-requisite concept(s) is satisfactory and correctly applies it to the current concept, but the solution is incomplete 3 Knowledge of the pre-requisite concept(s) is satisfactory, but its
Disagree) to 5 (Strongly Agree). Students scale scores on the iSTEMinstrument were produced by taking the mean response across items. Therefore, individual scorescould range from 1 to 5, with higher scores indicating higher iSTEM perceptions, the descriptivestatistics for this study is shown in table 1 in the results section.STEM clubs. Participants responded “Yes” (1) or “No” (0) to the question regarding theirinvolvement in extracurricular STEM clubs: “Do you participate in any Math, Science,Engineering, or Technology clubs inside or outside of school?” If the student indicated “Yes,”s/he was asked to specify the name of the STEM club, see descriptive statistics in table 1 inresults section
, open source, and reimbursement policies provideboth opportunities and challenges to the entrepreneur or innovator and a non-market strategy isneeded to address them.Throughout this process, innovators may need to interface with policymakers to obtain theoptimal benefit. In sum, moving a new technology from invention from discovery to launchrequires an innovation public policy strategy.What are the Key Elements of a Non-Market Strategy Development?As with all analysis methods, there are different ways to approach developing a non-marketstrategy development. The most-well known scholar in this field is David Baron, David S. andAnn M. Barlow Professor of Political Economy and Strategy, Emeritus at Stanford University.In his text, Business and the
jobs become computer based, workers willspend greater amounts of time on a computer. It is important that the Industrial Engineeringcurriculum stays current on such demographic changes and update individual coursesaccordingly. This paper demonstrates how relatively simple and low cost studies can beintroduced into a traditional ergonomics class and benefit the students.References1. Bureau of Labor Statistics (2005). Computer and Internet use at work in 2003. Washington, DC: U.S. Department of Labor, Bureau of Labor Statistics.2. Reuters 2008 http://www.reuters.com/article/2008/06/23/us-computers-statistics-idUSL23245254200806233. Epstein, R., Colford, S., Epstein, E., Loye, B. Walsh, M. (2012). The effects of feedback on computer
multiple times to investigatewhether any themes were present across numerous students in the study. This transcript reviewfocused on specific questions asked during the interview, primarily students’ personal interest(s), 2career aspiration(s), experience with engineering, and understanding of engineering. Analysiswas performed by capturing consistencies in the data relevant to the framework of this paper, andthen student characteristics were considered for any plausible explanations.Findings/Discussion The first theme that became apparent following the analysis of the data is the narrowcomprehension of engineers and engineering conveyed by
. She researches STEM learning with a focus on math learning and spatial representations. Ms. Bego is also assisting the Engineering Fundamentals Department in the Speed School in performing student retention research. She is particularly interested in interventions and teaching methods that allevi- ate working memory constraints and increase both learning retention and student retention in engineering. Ms. Bego is also a registered professional mechanical engineer in New York State.Dr. Patricia A. Ralston, University of Louisville Dr. Patricia A. S. Ralston is Professor and Chair of the Department of Engineering Fundamentals at the University of Louisville. She received her B.S., MEng, and PhD degrees in chemical
Rater 4 -0.91 -0.99 -1 -1.17 -1.25 -1.26 -1.43 -1.41 -1.46 -1.5 -1.51 -1.54 -1.71 -1.76 -1.82 -2 -2.01 -2.06 -2.5Figure 6. Calculation of the range of rater severity from FACET parameter estimationFurther diagnosis revealed some of the overarching areas of disagreement. For example, Table 2reveals statistically significant bias regarding how Rater 5 scored the first PROCESS item,“Identify the Problem,” and Rater 3’s rating of the second item, “Represent the problem.” Thescores the
. She holds a Ph.D. in Learning, Teaching, and Social Policy from Cornell University, and an Ed.M. in Administration, Planning, and Social Policy from the Harvard Graduate School of Education.Dr. Ebony Omotola McGee, Vanderbilt University Ebony McGee, associate professor of diversity and STEM education at Vanderbilt Universityˆa C™s Peabody College, investigates what it means to be racially marginalized in the context of learning and achieving in STEM higher education and industry. In partic ©American Society for Engineering Education, 2019 Development of the Persistence of Engineers in the Academy Survey (PEAS)AbstractThis paper reports the
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