included additional data or their calculations in attacheddocument(s), but all aspects of their solution were required in the written document. To alignwith the model generalization principle, students received additional material property data inMEA Draft 2. 9 Table 2. QDSC MEA Implementation Sequence Completed Assignment by: Documen- Week Feedback Main Function or Task Indiv Team tation Due From idual Quantum Dot Introduction to Solar Cell equations
average by requiring its aircraft suppliers to design forthe variability observed among its pilots [3].While variability across humans is now acknowledged in aerospace engineering, other sources ofvariability are still mistreated. The standard practice in aerospace design is to quantify certainmaterial properties in terms of sample averages [5], a practice that has been in-use since at leastthe 1960’s [6]. This practice similarly ignores sources of variability, and exposes aircraftpassengers to elevated levels of risk.Figure 1. A rod with uniform cross-section, loaded in uniaxial tension. This image is relevant tothe example problem in this section, and was used to illustrate the rod design scenario consideredin Q7 for Study 1.To illustrate the
ofpractices described above in which participants are asked to look through the deck (or virtualdisplay) of practices to identify the three to six practices they feel are most emphasized or valuedand an additional three to six they perceived to be emphasized or valued in a given engineeringcontext. Participants are asked a series of follow up questions after these card sorts. Afterdiscussing the practices emphasized in the engineering context(s) in which they engaged,participants are asked to sort through the deck once again to identify the top practices theypersonally deem to be most important in addressing a complex problem in their field. In addition,participants are asked to reflect on the extent to which the valued aspects of engineering work
significantly more time to explore programming and manufactur-ing. In addition, the student(s) should be able to implement assembly design modifications rela-tively quickly or test several designs during the course of the semester. The only fabricated com-ponent of this CNC machine is the modular block itself. All other components can be readilypurchased in bulk from a retailor thus reducing the cost for organizations that plan to use this inthe classroom. The remainder of this section discusses the design, development and manufactureof the modular block.The modular block (figure 3 a-f) is designed as a two-part symmetric clamshell. Each part iscomprised of an internal face (figure 3 a-c) and an external face (figure 3 d-f). The internal faceof the
-emphasizing social and economicpillars. Furthermore, most instruction on sustainability, as reported in the literature, appears tofocus on teaching the engineering student to be an engineer who practices sustainabledevelopment rather than a consumer who has a role in sustainable practice. In part, thisemphasis on the engineer's role in sustainability is a result of the Accreditation Board forEngineering and Technology (ABET)'s mandate that engineering undergraduates complete theirdegrees having achieved student outcome (c): “...an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability
considerations for position abstract development, were provided to all applicants: Position Abstracts should begin by describing the authors’ current and planned research, then extend it to recommend approaches that improve the community’s shared understanding of DMTL. All Position Abstracts should address the following essential questions: I. Key Challenges: Which challenge(s) related to digitally-mediated team learning does this Position Abstract address? II. Maturity: Has the approach been implemented? Under what circumstances? What were the outcomes thus far (in terms of learning gains, student perception, etc.)? III. Research Direction: What is the promising research direction for this topic? IV. State-of-the-Art: Across the community
problem solvers from poor ones by their awareness of which strategies theyhave used and their knowledge of where they are in their thinking relative to the final solution[18].Such criticisms have led some to back away from the “teaching problem solving” approachemerging from the 1970’s [19–21]. In fact, Schön went as far to argue that there is no such thingas problem solving in the engineering profession as “no engineer has ever been given a problemto solve.” Schön’s contention is the value of engineers’ work is not found in their problemsolving abilities. Rather, the essential facet of engineers’ work is found in their “problemsetting.” Engineers make sense of a given messy world from which many factors need to beconsidered, organized, and
Increase the number of Introduction to engineers in the Engineering Increase SEET workforce Practice II (S) student motivation Industrial Mentors to study (IM) engineering at LETU Institutionalize
TypeThe Myers Briggs Type Indicator (MBTI) is similar to Felder-Silverman Learning Style, but islinked to personality preferences as shown in Table 2. MBTI includes four categories of how anindividual processes and evaluates information.29 The first category describes how a personinteracts with his or her environment. People who take initiative and gain energy frominteractions are known as Extroverts (E). Introverts (I), on the other hand prefer more of arelatively passive role and gain energy internally. The second category describes how a personprocesses information. People who process data with their senses are referred to as Sensors (S),and a person who sees where data is going in the future is called an iNtuitor (N). The Sensorversus
, and People of Color (BIPOC) in higher ed thrive. Dr. Z. is also a first-generation college graduate, child of immigrants, and a published author. He is a former McNair Scholar, National Academies of Sciences, Engineering, & Medicine-Ford Foundation Fellow, Herman B. Wells Graduate Fellow, International Counseling Psychologist, former Assistant Professor at the University of Kentucky, and current Post-Doctoral ©American Society for Engineering Education, 2024 Paper ID #41726 Research Scholar at the University of Pittsburgh. Dr. Z.’s research program focuses on examining the impact of
exploring if the different teaching strategiesare useful for students on offering a positive effect on personal performanceaccomplishments, vicarious learning, social persuasion, and physiological and affectivestates. Also, we are examining the fidelity of those instructors' teaching. Those analyseswill provide a deeper understanding of the efficacy of teaching techniques. In the future,we will present a summary of our findings combining all the results. Reference[1] J. Milord, F. Yu, S. Orton, L. Flores, and R. Marra, “Impact of COVID Transition to Remote Learning on Engineering Self-Efficacy and Outcome Expectations,” 2021 ASEE Virtual Annual Conference, Jul. 2021, Accessed: Feb. 04, 2022. [Online
graduating each year is shownin Figure 7. Over the four-year period presented, the graduation rate is fairly steady.Student Scholar DataAn additional source of data came from members of the STEM Scholars Program (SSP). Membersof the program were required to fill activity reports detailing their time use and academic progress(attendance, grades, etc.) The reports contained weekly data and were submitted monthly viaemail. In addition, students filled reports describing how they spent their stipend from grant. Themonthly data was complied, analyzed, translated to semester data, and reported to the S-STEMScholarship Reporting Site (www.s-stem.org).Key findings from the activity reports [5] include:• A common theme related to financial aspects of the
and satisfaction. The formative evaluation helps determinewhether project goals were met and what hampered their implementation. A summative reviewassessed this program's impact on student's professional abilities for global employment. TheGlobal Perspective Inventory [20] and Engineering Global Preparedness Index were used tocreate a survey (e.g., the belief that one can make a difference through engineering problem-solving). The evaluator used a Likert scale to poll students before and after IRES. The surveytool examined research skills and global perspective inventory professional skills. Research Skill Development - Pre v/s Post Survey Peer review and publication process Report writing and poster presentation Result
ofEngineering’s operating budget. Element F can be supported in the long term with endowmentfunds. The only concern at this time is Element E since it is a high cost program and we have notfound a permanent source of funding. We are evaluating if Element F can substitute for ElementE and how to strengthen the social and professional integration components in the remainingElements. Given the home locations for many of the at-risk students (e.g., Guam), Element Emay be the only way that they can catch-up academically.ReferencesJones, S., Naegele, Z., and VanDeGrift, T. (2014). Increasing Retention in Engineering andComputer Science with a Focus on Academically At-Risk First Year and Sophomore Students.American Society for Engineering Education Annual
.[4]. Guskey. T. (1986). Staff Development and the Process of Teacher Change, Educational Researcher, 15, 5-12.[5]. Joyce. B, Showers. B. (1988). Student Achievement through Staff Development, Longman.[6]. Garet. M. S., Porter. A. C., Desimone. L., Birman. B. F., Yoon. K. S. (2001). What Makes Professional Development Effective? Results from a National Sample of Teachers, American Educational Research Journal, 38(4), 915-945.[7]. Bransford. J., Brown. A., Cockings. R. (2000). How People Learn: Brain, Mind, Experience, and School, National Academy Press.[8]. Yoon. K. S., Garet. M., Birman. B., Jacobson. R. (2006) The Effects of Mathematics and Science Professional Development on Teachers’ Instructional
integrated approach to teacher professional development in stem. Journal of STEM Education: Innovations and Research, 13 (2), 69.[9] Bandura, A., (1997). Self-efficacy: The exercise of control: Macmillan.[10] Bandura, A. & Wessels, S., (1994). Self-efficacy.[11] Maddux, J.E., (1995). Self-efficacy theory. Self-efficacy, adaptation, and adjustment. Springer, 3-33.[12] Stohlmann, M., Moore, T.J. & Roehrig, G.H., (2012). Considerations for teaching integrated stem education. Journal of Pre-College Engineering Education Research (J- PEER), 2 (1), 4.[13] Shaughnessy, M.F., (2004). An interview with anita woolfolk: The educational psychology of teacher efficacy. Educational Psychology Review, 16 (2), 153-176.[14
. Walther, N. W. Sochacka, and N. N. Kellam, “Quality in interpretive engineering education research: Reflections on an example study,” J. Eng. Educ., vol. 102, no. 4, pp. 626–659, 2013.[2] C. E. Foor and S. E. Walden, “‘Imaginary Engineering’ or ‘Re-imagined Engineering’: Negotiating Gendered Identities in the Borderland of a College of Engineering,” NWSA J., vol. 21, no. 2, pp. 41–64, 2009.[3] R. M. Marra, K. a Rodgers, D. Shen, and B. Bogue, “Leaving Engineering: A Multi-Year Single Institution Study,” J. Eng. Educ., vol. 101, no. 1, pp. 6–27, 2012.[4] J. P. Gee, “Identity as an Analytic Lens for Research in Education,” Rev. Res. Educ., vol. 25, no. 1, pp. 99–125, 2000.[5] P. L. Horta, “Identity in Education
, which constantly collect data s thestudent plays the game. At several points within the game, the system adjusts the content to fit thestudent’s areas of difficulty. The game also offers support or prompts to encourage progresswithin the game. While the overarching problem is the same for every student, the path they taketo reach the solution will vary drastically.The proposed PING system combines techniques of statistical inference, cognitive psychology,education research, sensor informatics, and machine learning techniques to provide students apersonalized education process. The contextual problem-solving situation engages students,giving them incentives to succeed in their learning process while allowing them to both beentertained and move
concernsand better manage their life-work-study balance for the five cohorts that have been supported bythis NSF S-STEM program. Student demographics are summarized along with graduation rates.A description of the support activities is provided and their contribution to retaining students inengineering is discussed. The value of the financial support and ASPIRE related activities isassessed using a survey and student reflections. The paper concludes with lessons learnedthrough implementation of this program.BackgroundBeginning in fall 2012, the University of New Haven has offered financial support toacademically promising sophomore and junior engineering and computer science studentsthrough A Scholarship Program to Increase Retention in Engineering
Experiences on Students: An Overview of Current Literature." CUR Quarterly, Vol. 28, Issue 4 (Summer 2008), pp. 43-50.[4] Laursen, S., et al. Undergraduate Research in the Sciences: Engaging Students in Real Science. San Francisco: Jossey-Bass, 2010[5] Lopatto, D. Science in Solution: The Impact of Undergraduate Research on Student Learning. Tucson, AZ: Research Corporation for Science Advancement, 2009.[6] Taraban, R., and Blanton, R.L., Eds. Creating Effective Undergraduate Research Programs in Science: The Transformation from Student to Scientist. New York: Teachers College Press, 2008.[7] Russell, S.H., Hancock, M.P. and McCullough, J. "Benefits of Undergraduate Research Experiences" Science, Vol. 316
publications.Dr. Stephen Secules, University of Maryland, College Park Stephen received a PhD in education at the University of Maryland researching engineering education. He has a prior academic and professional background in engineering, having worked professionally as an acoustical engineer. He has taught an introduction to engineering to undergraduate engineers and to practicing K-12 teachers. Stephen’s research interests include equity, culture, and the sociocultural dimensions of engineering education.Prof. Shuvra Bhattacharyya, University of Maryland, USA, and Tampere University of Technology, Finland Shuvra S. Bhattacharyya is a Professor in the Department of Electrical and Computer Engineering at the University of
respect to effectively contributing to the research agenda. The evaluator askedparticipants to share their opinions on the conference sessions by indicating the name of aspecific session or sessions(s) in which they gained important new knowledge, insight, orunderstanding of research and/or practice in broadening participation in engineering (Woodruff &Li, 2017). As Figure 5 indicates, the largest percentage of participants felt that the culminatingsessions, Data Analysis and Concept Mapping, contributed to their new knowledge. TheConference Threads Breakout Session and Panel of Champions were also selected by a largepercentage of participants. These evaluations lead us to believe that the way in which wedesigned various sessions to build off
Washington in 1994 and a Ph.D. from the University of Washington in 2000.Dr. Gregory Mason, Seattle University Gregory S. Mason was born and raised in Spokane Washington. He received the B.S.M.E. degree from Gonzaga University in 1983, the M.S.M.E. degree in manufacturing automation from Georgia Institute of Technology in 1984 and the Ph.D. degree in mechanical engineering, specializing in multi-rate digital controls, from the University of Washington in 1992. He worked in a robotics lab for the Department of Defense for five years after receiving his M.S.M.E. He is currently an Associate Professor in the De- partment of Mechanical Engineering at Seattle University, Seattle, WA. His research interests are controls system
VPL system is universally accessible as it is developed for mostOperating Systems and current mobile devices. The rest of the paper is organized as follows: Section 2 is an overview of virtual tutoringsystems available in the literature. Section 3 summarizes the authors’ previous work6,7 on thedevelopment of VPL. The architecture of the Intelligent Tutoring System is presented in Section4. The paper is concluded in Section 5 with discussions and plans for the future.2. Virtual Tutoring Environment Computer-aided instruction (CAI) systems were introduced as early as 1960's as a means ofassisting students outside the classroom8. The first CAI programs were either computerizedversions of textbooks or drill and practice monitors9 that
. Further description of the“before” (traditional) and “after” (flipped) classroom experience for each course is needed inorder to provide a more complete picture of the true change in the learning environment.AcknowledgementThis material is based in part upon work supported by the National Science Foundation underGrant Number DUE-1245815. Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation.References[1] Berrett, D. (2012). How ‘Flipping’ the Classroom Can Improve the Traditional Lecture. The Chronicle of Higher Education. February 19.[2] Bishop, J.L., and Verleger, M.A. (2013). The Flipped Classroom: A Survey of
ire rt qu o VOLTA Host d? re upp S Server ta t sis ar e nc as Sm Client/Student Hardware for circuit implementation
/dbDetailForUser.do?id=3, .8. R. P. Ramachandran, K. D. Dahm, R. Nickel, R. Kozick, S. S. Shetty, L. Hong, S. H. Chin, R. Polikar and Y. Tang, ``Vertical Integration of Biometrics Across the Curriculum: Case Study of Speaker, Face and Iris Recognition”, IEEE Circuits and Systems Magazine, Vol. 14, No. 3, pp. 55—69, September 2014.9. J. A. Newell, H. L. Newell and K. D. Dahm, “Rubric Development and Inter- Rater Reliability Issues in Assessing Learning Outcomes”, Chemical Engineering Education, Summer 2002.10. J. A. Newell, H. L. Newell and K. D. Dahm, “Rubric Development for Assessment of Undergraduate Research: Evaluating Multidisciplinary Team Projects”, Chemical Engineering Education, 2003
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 engineering from the University of Louisville. Dr. Ralston teaches undergraduate engineering mathematics and is currently involved in educational research on the effective use of technology in engineering education, the incorpo- ration of critical thinking in undergraduate engineering education, and retention of engineering students. She leads a research group whose goal is to foster active interdisciplinary research which investigates learning and motivation and whose findings will inform the