a Mathematician and Computer Systems Analyst for the U. S. Department of Energy as well as more than 25 years of experience teaching mathematics, statistics, computer science, and first-year engineering courses in higher education institutions. Currently, she leads a team of faculty who are dedicated to providing first year engineering students with a high- quality, challenging, and engaging educational experience with the necessary advising, mentoring, and academic support to facilitate their transition to university life and to prepare them for success in their engineering discipline majors and future careers. American c Society for Engineering
, and A. S. Malik, “The influences of emotion on learning and memory,” Front. Psychol., vol. 8, no. 1454, 2017.[3] M. J. Riemer, “Integrating emotional intelligence into engineering education,” World Trans. Eng. Technol. Educ., vol. 2, no. 2, pp. 189–194, 2003.[4] D. Kim and B. K. Jesiek, “Work-in-Progress: Emotion and intuition in engineering students’ ethical decision-making and implications for engineering ethics education,” 2019.[5] A. Bandura, Self-Efficacy: The Exercise of Control. New York, NY: Freeman, 1997.[6] F. Pajares, “Self-efficacy in academic settings,” in American Educational Research Association, 1995.[7] D. W. McMillan and D. M. Chavis, “Sense of community: A definition and theory,” J
of the author(s) and do not necessarily reflect the views of the NationalScience Foundation (NSF). The authors also wish to thank Karen Clark, Research Assistant,Institute for Public Policy and Survey Research, Office for Survey Research at MSU for hertimely and efficient programming, survey administration, and data retrieval. We are alsoindebted to Mr. Timothy Hinds, the instructor of EGR 100, who has generously allowed us touse his class as a contact point for the CF program.Bibliography1. Seymour, Elaine and Nancy M. Hewitt (1997). Talking about Leaving: Why Undergraduates Leave the Sciences. Boulder, CO, Westview Press.2. Keller, J.M. (1983). Motivational design of instruction. Instructional-design theories and models: An
-495. 5. Lopez, F. G., & Brennan, K. A. (2000). Dynamic process underlying adult attachment organization: Toward an attachment theoretical perspective on the healthy and effective self. Journal of Counseling Psychology, 47(3), 283-300. 6. Bögels, S. M., & Brechman-Toussaint, M. L. (2006). Family issues in child anxiety: Attachment, family functioning, parental rearing and beliefs. Clinical Psychology Review, 26(7), 834-856. 7. Bebbington, P. E., Meltzer, H., Brugha, T. S., Farrell, M., Jenkins, R., & Ceresa, C. (2000). Unequal access and unmet need: neurotic disorders and the use of primary care services. Psychol Med, 30(6), 1359-1367 8. Knapp, J. R., & Karabenick, S
with all ofthe program outcomes, ABET does not define lifelong learning or provide guidelines forassessing achievement of lifelong learning skills. Besterfield-Sacre et al.[2] identified keyattributes of lifelong learning as part of an NSF-funded Action Agenda study (listed on theEngineering Education Assessment Methodologies and Curricula Innovation website[3]). Theseattributes included the ability to: ● demonstrate reading, writing, listening, and speaking skills; ● demonstrate an awareness of what needs to be learned; ● follow a learning plan; ● identify, retrieve, and organize information; ● understand and remember new information; ● demonstrate critical thinking skills; and, ● reflect on one‟s own
experience, living-learning communities, and persistence to graduation for students in science, technology, engineering, and mathematics programs.Michael Georgiopoulos, University of Central Florida Michael Georgiopoulos is a Professor in the UCF School of Electrical Engineering and Computer Science and the PI of the NSF-funded S-STEM program at UCF entitled the "Young Entrepreneur and Scholar(YES) Scholarship Program" as well as the NSF-funded STEP program entitled "EXCEL:UCF-STEP Pathways to STEM: From Promise to Prominence." Dr. Georgiopoulos' research interests lie in the areas of machine learning, neural networks, pattern recognition and applications in signal/image processing
El n er o e Mo ctri w ssi to c Po mi rs Design s Dynamics an Statistics Tr Transportation Research Vibrations & Resonance Materials
) Strategic Thinking (S)Achiever Activator Adaptability AnalyticalArranger Command Developer ContextBelief Communication Connectedness FuturisticConsistency Competition Empathy IdeationDeliberative Maximizer Harmony InputDiscipline Self-Assurance Includer IntellectionFocus Significance Individualization LearnerResponsibility Woo Positivity StrategicRestorative RelatorResultsThe data collected for this study come from the online survey Clifton’s StrengthsFinder®. Thetop five
. computer lab work and group exercises [25].Table 3. Description of categories within the Assessment Methods theme. Description Example Student reflections Students are asked to report A five-point scale was used to on their perceptions of the ask students about the course innovation(s), impacts of an engineering typically using Likert scales professor visiting precalculus and/or open response courses [17]. questions. Pre
students: one student reported low participation inboth projects and s/he attended the classes about half in person and half online, which mighthave contributed to the low participation. The other student reported low participation in thesecond project although s/he attended the classes fully in person and s/he reported fullparticipation in the first project. There was no data to explain the reason, but project 2 wasstudent-driven by the team leader who came up with that project topic. As instructors, we need toencourage all students to contribute to the final design and prototyping.Course ManagementA mixture of teaching modalities was used in this course, as explained in the Course Setupsection.Depending on the course content, such as for
[5] Mentzer, N. (2014). Team based engineering design thinking. Journal of Technology Education 25.2 (2014): 52-72.[6] Atman, C. J., Adams, R. S., Cardella, M. E., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: A comparison of students and expert practitioners. Journal of Engineering Education, 96(4), 359–379.[7] Schön, D. (1979). Generative metaphor: A perspective on problem-setting in social policy. In A. Ortony (Ed.), Metaphor and Society (pp. 254–283). Cambridge: Cambridge University Press.[8] Dorie, B. L., Cardella, M., & Svarovsky, G. N. (2014). Capturing the design thinking of young children interacting with a parent. 2014 ASEE Annual Conference &
have difficulty explaining what they did to others.Personality CharacteristicsA variety of personality assessment tools exist, and several can be easily accessed online. One ofthe most well-known ones is the Myers-Briggs Personality Type Indicator® (MBTI). The MBTIidentifies 16 different personality types founded on preferences in four major categories based onJung’s Theory of Psychological Types. MBTI results indicate whether a person tends to beextroverted (E) or introverted (I), sensing (S) or intuitive (N), thinking (T) or feeling (F), andjudging (J) or perceiving (P). Extroverted people focus on those around them, while introvertedpeople focus within themselves. Sensing people interpret information through facts and details
Empirical Study. Paper presented at the International Conference of Design Research Society.Anderson, E. (2003). A place on the corner (2nd Edition ed.). Chicago: University of Chicago Press.Atman, C. J., Adams, R. S., Cardella, M. E., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: A comparison of students and expert practitioners. Journal of Engineering Education, 96(4), 359-379.Brown, G. S., & Strange, C. (1981). The Relationship of Academic Major and Career Choice Status to Anxiety Among College Freshmen. Journal of Vocational Behavior, 19(3), 328-334. doi:10.1016/0001-8791(81)90067-1Crismond, D. P., & Adams, R. S. (2012). The Informed Design Teaching and Learning
., Falconer, K., Benford, R., Bloom, I., & Judson, E. (2000). Reformed Teaching Observation Protocol (RTOP): Training guide. (ACEPT Technical Report No. IN00-2). Tempe, AZ: Arizona Collaborative for Excellence in the Preparation of Teachers.[3] Judson, E. & Sawada D. (2002). “Tracking Transfer of Reform Methodology from Science and Math College Courses to the Teaching Style of Beginning Teachers of Grades 5-12,” Journal of Mathematics and Science: Collaborative Explorations, vol. 5, pp. 189-207.[4] Ross, L., Judson, E., Krause, S. J., Ankeny, C. J., Culbertson, R. J., & Hjelmstad, K. D. (2017, June). “Relationships between engineering faculty beliefs and classroom practices,” in 2017 Proceedings of the
uncovered several insightful findings related to first-year engineeringstudents' use of time. Future work should look at collecting data on a larger scale to determine ifany of the activity categories are significant predictors of success, such as GPA. Additionally,the development and use of a time tracking app and dashboard may allow for deeper findingsinto how students and potentially faculty can think about time spent outside the classroom.AcknowledgementsThis work was supported in part by NSF Grants#1447489 and #1444277. We would like to thankour informants for participating in the field studies reported here. Any opinions, findings, andconclusions or recommendations expressed in this material are those of the author(s) and do notnecessarily
is having difficulties in their process and step in to assist.Design challenges provide a safe environment for students to feel the pressure of working on achallenge problem with a tight timeline. However, the stakes are not so high that failure iscatastrophic. In addition, they see where they are failing and work to develop methods toanticipate failure conditions and avoid them. Further studies need to be performed to determineif students’ increase in skills and confidence transfer to their other design experience in theiracademic and professional careers.REFERENCES 1. ABET. (2000). ABET Engineering criteria 2000: criteria for accrediting programs in engineering in the United States. 2. Jamieson, L., Brophy, S., Houze, N
be acquired without the instructor’s presence. Table 1 : Schedule for a typical inverted class day Before Class In Class After Class preparation activity: short lecture finish application reading, video, tutorial, or assignments problem(s) activities prepare for next evaluation: online quiz or begin application class turned-in solution assignment(s
these three knowledge domains into first-yearprograms. The collection of qualitative data has brought tremendous insight into the studentexperience and is something we plan to expand. And while this current qualitative study did notmeasure and compare which learning activities helped the most with the development oftransdisciplinarity among students, our findings showed that first-year engineering students’conceptual schema and perspectives diverged and transformed through their engagement withthe courses’ learning activities [23].References[1] S. Ambrose and C. Amon, "Systematic Design of a First-Year Mechanical Engineering Course at Carnegie Mellon University," Journal of Engineering Education, vol. 86, no. 2, pp. 173-181, 1997
Concept % of students Moral(s) 45.8 Right 34.2 Others 28 Wrong 27.6 Values 26.7 Personal 22.7 Good 22.7 Work 22.2 Problem 21.3 Consequence(s) 18.7Hess (2018) identifies the
provided to the instructor. Student surveyresponses and course outcomes were combined using their student ID number, which was thenremoved. Only students that completed all study components were included in the analysis.The beginning of the semester surveys included student demographic information, a self-assessment of engineering skills, and the GRIT-S questionnaire [17]. The end of semester surveyincluded the intrinsic motivation activity perception questionnaire for computer programming, arepeat of the engineering skills assessment, rating for how much students felt different aspects ofthe course benefited them and additional questions about their perceptions of the self-directedproject. To determine students’ feelings on the aspects of the
Definition M Empowerment How in control a student feels about his or her own learning experience. U Usefulness How useful a student thinks course material is to them. S Success The student’s belief in their ability to do well in the course. I Interest How fun or interesting course material is to the student. C Caring Whether the student feels that course instructors are empathetic towards how they experience the courseThe constructs of the MUSIC model are geared towards course-level motivation. Collectively, they helpto tell a story about how
reverseengineering and imitation. Typical civil engineering problems are used to present theprogramming concepts. Especially in the instance of VBA, students learn how to combine theuse of spreadsheet functions with VBA code. The paper includes an overview of the course andexamples of the materials covered and the teaching techniques employed. General thoughts arealso presented about the directions in which programming education may be headed in the future.1.0 IntroductionCourses about computer programming have been part of undergraduate curricula for more thanhalf a century. For example, the electrical engineering department at CMU was teachingcomputer programming in FORTRAN in the late 1960’s as a way to introduce logical thinking(e.g., flow charts) and
Apr 2, 2014).(2) Purdue University. Data Digest 2013 - 2014 http://www.purdue.edu/datadigest/Students/studrilldowns (accessed May 9, 2014).(3) UIUC. IUIC Student Enrollment http://www.dmi.illinois.edu/stuenr/ (accessed Jan 1, 2014).(4) Institute of International Education. Open Doors Report 2013 http://www.iie.org/Who-We-Are/News-and- Events/Press-Center/Press-releases/2013/2013-11-11-Open-Doors-Data.(5) Altbach, P. G.; Knight, J. J. Stud. Int. Educ. 2007, 11, 290.(6) Wang, Y. Young Chinese Students ’ Teamwork Experiences In A UK Business School, PhD Thesis. University of Westminster, 2010.(7) Nassim, S. Z. The World is Knocking on our Doors : International Students and Support Services Programs
Paper ID #16190Enculturation of Diverse Students to the Engineering Practices through First-Year Engineering College ExperiencesDr. Jacques C. Richard, Texas A&M University Dr. Richard got his Ph. D. at Rensselaer Polytechnic Institute, 1989 & a B. S. at Boston University, 1984. He was at NASA Glenn, 1989-1995, taught at Northwestern for Fall 1995, worked at Argonne National Lab, 1996-1997, Chicago State, 1997-2002. Dr. Richard is a Sr. Lecturer & Research Associate in Aerospace Engineering @ Texas A&M since 1/03. His research is focused on computational plasma modeling using spectral and lattice
] ASME, "ASME Vision 2030 project: Drivers for Change Data Actions & Advocacy," ASME, New York2013.[3] A. Kirkpatrick, S. Danielson, and R. O. Warrington, "Reduction to Practice," Mechanical Engineering, vol. 134, pp. 38-39, Nov 2012.[4] A. Kirkpatrick, "ASME Vision 2030: Designing the Future of Mechanical Engineering Education," in Conference for Industry and Education Collaboration, Phoenix, AZ, 2013, pp. 1-38.[5] M. Prince, "Does active learning work? A review of the research," Journal of Engineering Education, vol. 93, pp. 223-231, Jul 2004.[6] S. Freeman, S. L. Eddy, M. McDonough, M. K. Smith, N. Okoroafor, H. Jordt, et al., "Active learning increases student performance in science
they made no use of the peer-mentors or they had little to no effect (‘NoDetermination’). Technique 2’s requirement that students make use of their assigned peer-mentorclearly, and unsurprisingly, forces them to establish some clear ‘consultant’ or ‘mentor’relationship. More importantly, the provided instructions for those interactions do admittedlyfocus on the project itself and steer those interactions towards a more transactional interaction,which explains the majority ‘consultant’ roles identified. Clearly without having interacted, theydo not even have a chance to promulgate a relationship at all, particularly one that goes beyondthe project and towards developing as a student and budding engineer. (a
persistence rates, CMICH is on the low end: 27% versus the range of 30% to 91% citedabove. With respect to technology programs, CMICH is most similar to Purdue (64%) and PSU(30%). However, with respect to size, ASU (74%) or PSU Surveying (76%) seems moreappropriate. In this sense, the lessons presented here fill a gap in the persistence literatureespecially in terms of young engineering programs.3. MethodsData were collected for six years in two forms: transcript information and brief in-class surveys.The six years correspond to twelve semesters: six fall semesters and six spring semesters. Here,a semester is referred as the academic year with a “F” or “S” for fall or spring; e.g., the lastsemester examined was the spring of the 2010-2011 year, or
data, labeling evidence and specific details of each theme in the data, and compared andreached consensus for any discrepancies. The frequencies with which each theme wasmentioned/represented were also counted and tabulated.The first theme is ‘Customer Involvement’. As “the end goal [of engineering design] is the creationof an artifact, product, system, or process that performs a function or functions to fulfill customerneed(s).” [27], it is very important to involve the customer throughout the process from needsanalysis to gaining feedback to ensure that the design solution fulfills customer need(s) and meetsor exceeds customer expectations. For this theme, when coding, data was categorized into threegroups: no mention of customer; some
did somelevel of mental manipulation of the object to get the answer. Guessing categorizes responses thatused the word ‘guess’ or explained that the student arrived at a conclusion by chance or withoutshowing evidence of deliberate reasoning. Guiding rule describes when the student(s) used astandard or criterion to judge which option is likely to be the answer, for example, studentresponses that involved the use of if-then logic (“if…then…”) or stating a specific criterion thatled to the answer (“whatever is…is the answer”). Intuition describes students’ responses inwhich the word “intuition” was used or the response showed that the student came to anunderstanding of the answer immediately without the need of conscious reasoning. Process
) -90 -135 -180 1 2 3 10 10 10 Frequency (rad/s) 1 Invivo=onalivesubject,asopposedtousingexcisedskinfortesting. 2 Boyeretal.,“Dynamicindentationonhumanskininvivo:ageingeffects.”Skin.Res.Tech.15(2009) AppendixB