product design, process selection, manufacturing system design, etc. affect the company's financial issues. To develop skills that extend the basic concepts to solve problems encountered in personal financial situations.The class involves lectures, quizzes, homework assignments, two midterm exams, in-classproblems, and a final exam. The course grade reflects the student performance in six quizzes(20%), two midterm exams (40%), in-class clicker questions (10%), and a final exam (30%). Theinstructor decided not to grade the homework assignments because these assignments proved tobe ineffective in enhancing students’ learning in previous semesters. The instructor noticed thatstudents would receive a high or perfect grade in the homework
nature. The final project report includes a section where the students areencouraged to reflect on the quality of their experience as it pertains to their understanding ofsystems engineering. Student surveys are also conducted in an effort to assess the impact of thecourse and elicit feedback on how the course may be improved.Previous Design Explorations in Engineering Education via Systems EngineeringCourses involving integration and testing of complex hardware systems are not new toengineering education. In 2012, faculty at St. Louis University reported on a systems engineeringcourse where students gained hands-on experience with the development of a small satellite.They claim, “It is very important to use real hardware for practicing the
finished their projects (see figures 2a and 2b). Participants were asked to reflect back tobefore the project began to rate their confidence on skills on a Likert scale, and then considertheir confidence at the conclusion of the project. In the future, a survey will be given to studentsat the first build session, and the same survey upon completion to measure competencies.A statistical analysis of the survey results was performed. For each category considered, the datawas first tested for normality. For normally distributed data sets, a paired t-test was used. For thedata that was not normal, the Wilcoxon R-S test was used to test for significance. A p-value lessthan 0.05 was considered statistically significant. Figure 2a: First part of survey
fact that SEEDS programs provide an immediate link to other underrepresented populationsin the Clark School of Engineering through LLCs and regular networking events.Regardless of the type of SEEDS program in which they participated (i.e., LLC, mentoring, orthe combination of LLC and mentoring), engineering undergraduates were more likely to beretained within engineering than peers who did not participate in SEEDS programming.Moreover, based on the study’s findings it appears that participation in the LLC programs (i.e.,Flexus and Virtus) in combination with the mentoring program may have the most positiveimplications for student retention. Reflected in the results, as a whole SEEDS students whoparticipated in the combination of living and
in fall 2016. The goal of the course was to providegraduate students who come with undergraduate degrees in engineering, plant sciences, or datasciences, with a common knowledge base in the area of predictive plant phenomics. The firstoffering of the course was successful, but areas for improvement were identified, and includebetter coherence between course topics and improved student assessment throughout the course.A revised course is now being planned for fall 2017.AcknowledgementsThis material is based upon work supported by the National Science Foundation under GrantNumber DGE-1545463. Any opinions, findings, and conclusions or recommendations expressedin this material are those of the authors and do not necessarily reflect the views of
material, withmany students viewing any given video multiple times. Students took advantage of the outcomebased assignments to progress at their chosen rate, with several students finishing the course oneor two weeks prior to the end of the term.IntroductionIf one is seeking information on the best teaching practices in higher education, or engineeringeducation, you do not need to go far to find a vast library of resources. Terms such as “activelearning”, “flipped classrooms”, “hybrid courses”, “reflective thinking”, “standards-based grading”,and others run through the literature (for examples see Felder et. al, 2011[1]). As an engineeringprofessor, I find the number of options and recommendations to be somewhat daunting. Myneeds are not for more
system to collect AssignmentReview data every fall and spring, but only on about 1/6 of our courses each semester. Wemodified the formal requirements for the Assignment Review to reflect this change. Theseprocedures are in effect for our 2016-2022 ABET cycle. With 1/3 of courses being reviewed each year (1/6 in fall, 1/6 in spring), the matchbetween assignments and targeted SOs are reviewed twice in the ABET accreditation cycle. Forthe Assignment Review assessment, the instructor is required to submit a copy of an assignment(+ solution) that targets each SO associated with his/her course. It is recommended that, ifpossible, one assignment be designed to target multiple student outcomes. This serves todiminish the volume of data collected
had observed from using TeachEngineering curricula;student engagement was commonly mentioned: “I am a physics teacher that has been looking for a more creative way to introduce vectors. My honors physics [students] are having a blast with the vector voyage activity!” “Students are engaged and thinking. That’s a definite plus!” “My students are engaged in critical thinking, they have a lot to say about it and my principal is impressed.” “My students have loved using this curriculum. They have been so engaged and excited. They meet me at the door asking what we’re going to do in science today!” “Students are more engaged and excited about learning. Their conversations reflect what they are
Technology,Inc., ABET) [3] agencies already have communication requirements. In addition, ABET intro-duced new language for 2016-17 requiring an ability to communicate effectively with a range ofaudiences [4], reflecting the fact that the communication demands of engineers are increasing inscope as well as intensity.Recent graduates likewise recognize the importance of communication in their professional lives.A recent study of graduate opinion places communication as fourth out of twelve ABET require-ments in terms of importance (related “teamwork” placed first) [5]. Nevertheless, graduates them-selves have emphasized communication as a weakness [6], with most feeling insufficiently pre-pared [1].1 These findings suggest that communication is one
anambiguous category. As such, it holds promise for insight into how engineers imagine the socialorder in which they operate as well as their own position in it. Our premise is twofold: that howengineers conceive of “the public” likely informs their conceptions of self, professional duty, andprofessional right, as well as engineering decisions, practices, and products; and that knowingwhat imaginaries of “the public” engineering education fosters is necessary for understanding theideologies that inform the critical but often elusive boundary that engineers raise between theirprofession and society. Our ultimate goal is to throw into relief the texture of this boundary:What social order might it promote? What values might it reflect? What interests
.., 2010) and that afemale scientist needed 64 more impact points than an identical male scientist to be seen asequally competent—which translates into three extra papers in Nature or Science or 20 in lessprestigious journals (Wenneras & Wold, 1997).A second mechanism that fuels Prove-It-Again bias is in-group favoritism: in-groups, but notout-groups, tend to get the benefit of the doubt (Brewer, 1999; Brewer & Gardner, 1996;Hewstone, 1990). The Prove-It-Again phenomenon also reflects stereotype expectancy(Hamilton & Rose, 1980), aka confirmation bias (Mahoney, 1977): we see what we expect tosee. Because low-competence stereotypes set expectations low, more evidence will be requiredof out-groups, as compared with in-groups, to persuade
, Lewin, and Piaget. The second reason is to emphasizethe central role that experience plays in the learning process.”19 Kolb aligns Lewin’s model ofaction research, Dewey’s model of learning, and Piaget’s model of cognitive development intohis own model of experiential learning that he described as “the process whereby knowledge iscreated through the transformation of experience.”Figure 1, utilizing a recast and critiqued version of Kolb’s experiential learning model fromBergsteiner, Avery, & Neumann, illustrates four ways of experiencing: Concrete Experience,Reflective Observation, Abstract Conceptualization, and Active Experimentation. 22 These fourways of experiencing iteratively interact with four distinct learning styles, Diverging
80 63 14 13 2015 206 154 24 63 2016* 94 86 20 19*In 2016, we recruited one math teacher who was suitably matched to a research project, but he failed to completethe program.Table 2 shows the diversity of the applicant pool demographics reflecting the diversity of theteachers in the Houston region.Table 2. Demographics of the RET applicant pool (2014-2016).Ethnicity % Gender %Asian 13% Female 64
for improving that must be recognized and interpreted this course?by the user. Comprehensive LCAs require large amounts of resources and time. Accurate datacollection Students is central to a reliable assessment and the value of an LCA is only as good as the dataused. LCAs Simpler are usually assignments performed and tutorials withsoftwares. on the LCA truncated boundaries to limit the amount of extraneousdata n/a implying a compromise for practicality. While LCAs offer insights into the environmental I think that really hitting us with the deep views and making us reflect more on big ideas and
Deputy Chairman of the Boar respectively of the Housing andBuilding National Research Center (HBRC) in Cairo, Egypt, for their partnership and continuoussupport of the program. Any opinions, findings, and conclusions or recommendations expressedin this material are those of the authors and do not necessarily reflect the views of the NSF. 16 APPENDIX AADVERTISING BROCHURES (2015 & 2016) 17 APPENDIX B SUMMARY OF REQUIREMENTS FOR STUDENT WORK PRODUCTSA. Summary of your personal experience (1-2 pages) Times New Roman (12) Single Space Brief Description of your research, including project title(s), team and mentors Brief
PT Work Family Other Students 104 3 33 38 33 Percentage 97.20% 2.80% 30.80% 35.50% 30.80% Table 5. Shows question 5 and the results of the answers received for question 5In addition to results shown in Table 5 reflecting what other commitments students have everyweek; an average of Work/Family/Other commitments was calculated with results showing anaverage of 52.1 hours committed to activities per student. Table 6 shown below displays thestudents’ preference by grouping the answers from question one into two groups. These twogroups being prefer and not prefer
. Unfortunately, no data for Physics IB were collected due toan error in the reporting system. Also, some students may not have reported scores if they weretoo low to receive credit. Thus, there is potentially a larger number of students who took thephysics advanced placement exams than the reported 45-50%.Table 3 shows the level of math and physics preparation of the 2015-2016 incoming class,reflecting the math they were placed into and their self-reporting of AP physics scores. The vastmajority of students test into multivariable calculus, with half self-reporting an AP physics scoreand half not reporting an AP physics score. The students placing into a lower level of math(differential or integral single variable calculus) were much more likely not to
Fig.14: Timer Control HMIIV. ResultsThis section summarizes results which demonstrate the functionality of the system and discussproject execution from a cost and schedule viewpoint.System PerformanceInitially, to test the integrity of the sensor, an RS232 terminal named Termite was utilized. Thisterminal allows the user to send and receive data from a sensor based upon written commands.When commands were sent to the sensor through the terminal, desired responses andmeasurements were received assuring that the sensor was in good working order and ready to beemployed. Further experimentation included the testing of the sensor in dry soil and wet soil toconfirm that the sensor’s measurement numbers are consistent and reflect nominal values
: built into the key program features were evaluation criteriathat efforts be “radically, suddenly, or completely new; producing fundamental, structuralchange; or going outside of or beyond existing norms and principles” [6]. With an innovativedepartment head or dean at the helm, change had to be rooted in engineering education research,a social science understanding of organizations, and a theoretical change framework that couldmove research to practice, with team composition reflecting this varied expertise. Facultydevelopment efforts, incorporation of professional practice, and a plan for scalability thatcountered anticipated obstacles had to be baked in to the original vision and project plan.With NSF investing relatively large amounts of
reflects less understanding thantalking about which first order pole is slowest.Question 2Question 2 is a bit of a philosophical one that probes the students understanding of the definitionof a transfer function. Students are given time domain expressions of the input and output of asystem and asked to find the transfer function. If students remember that a transfer function is theLaplace transform of the output divided by the Laplace transform of the input, this problemshould be fairly straight forward. Ideally, students will also remember the instructors’ preferencesthat a transfer function be given as a proper fraction with one polynomial of s in the numeratorand one polynomial in the denominator.Question 2 Problem Statement ∙ You are given a
, Engineering Education1. Introduction – Research to Practice PaperEngineering education, and especially computer science (CS) within that realm, is embeddedwithin science, technology, engineering and mathematics (STEM), but K12 classroom practicesdo not often reflect CS content due in part to teacher skill levels and an efficacy gap. CS can takeon many meanings, but at its core, it is the science of problem solving in a computationalcontext, and CS as a skill is challenging (Burrows, Borowczak, Slater, & Haynes, 2012). MostCS university programs prepare software engineers, and as such the subjects are entwined. Thedistinction between engineering and CS can be blurry if only examining the theory of CS insteadof the practical applications. This
responses. The nature of quantitativeresults consist of probabilities that reflect the students’ technology preferences and the variationanalysis of the programming preferences across different research questions. The results presentedin this paper help to determine and understand engineering students’ technology choices forsolving different calculus problems based on their technology education. The participants of thisInstitutional Review Board (IRB) approved research completed the third calculus course of a four-course calculus sequence. This article is a continuation of another IRB approved research that wasconducted by the researcher at a large Midwest U.S. institution.Key Words: Computer programming preference; Undergraduate education
Proceedings of the 45th ACM Technical Symposium on Computer Science Education (pp. 355-360). ACM.15 Exter, M., & Turnage, N. (2012). Exploring experienced professionals’ reflections on computing education. ACM Transactions on Computing Education (TOCE), 12(3), 12.16 Lethbridge, T. C. (2000). What knowledge is important to a software professional? Computer, 33(5), 44-50.17 Andriole, S. J. and Roberts, E. (2008). Technology curriculum for the early 21st century. Retrieved from http://cacm.acm.org/magazines/2008/7/5359-point-counterpoint- technology-curriculum-for-the-early-21st-century/fulltext 21Formal
& Lechuga, 2017; Trowler, 2014).Researching such learning communities involves a systematic exploration of many contextualaspects, including “the culture of the institution, the administrative hierarchy, students, faculty,and external constituencies” (Pasque & Lechuga, 2017, p. 2).The recent surge in ethnographic or participant-centered, qualitative research in higher educationaligns with an increased awareness that classrooms, programs, lectures, work sessions and thelike all operate within a system that is multilayered and often hierarchical (Bryk, Sebring,Allensworth, Easton, & Luppescu, 2010). As such, final scores or reflections may hint at thecomponents, activities, and resources most useful to, or constraining the
,” Academic Exchange Quarterly, 2007. 3. http://idea.ed.gov/explore 4. The State of Learning Disabilities, 3rd edition, 2013, National Center for Learning Disabilities. 5. “Academic accommodations for students with learning disabilities,” Disabilities, Opportunities, Internetworking, and Technology (DO-IT), University of Washington, 2012. 6. U.S. Department of Education, National Center for Education Statistics, 2016. 7. U.S. Department of Education, National Center for Education Statistics, 2011, Table 4. 8. “For your consideration… suggestions and reflections on teaching and learning,” University of North Carolina Center for Faculty Excellence, Nov. 2009. 9. Lyman, F. T. (1992). Think-Pair
towards interdisciplinary cooperation in the next phase, which isa long way from the initial state three years ago when we were not quite familiar with oneanother’s areas of expertise, it would be worthwhile to share our reflections on the journeywith other teachers.Context Over the course of 3-year-cooperation (from December 2013 to November 2016), threecurricular experiments were conducted consecutively in the Spring Semesters (i.e., Februaryto June) from 2014 to 2016 at National Taiwan University (NTU) in Taiwan. The firstexperiment was meant to be an initial try-out, embedding two 8-hour sessions of futuresthinking curriculum in an existing selective advanced CE course, with the purpose offamiliarizing engineering teachers with futures
, as well as publishing papers in conference or in journals.In summary, as depicted by the testimonials, the results of assessing the peer mentoring,undergraduate research output, and post-graduation placement, our NSF STEM program,coupled with well-designed support services, helps students successfully complete theirundergraduate studies and secure a bright future for themselves. Further longitudinal assessmentsare forthcoming.References[1] S. Agili, A. Morales, L. Null, J. Smith, and S. Vidalis, “Reflections on Experiences of a Successful STEM Scholarship Program for Underrepresented Groups,” Proceedings of 2015 ASEE Conference, Seattle, Washington June 14-June 17, 2015.[2] P. Hubel, “Student Satisfaction: An Examination of
entity along the lines of a maker lab? In somecases, existing facilities are rebranded, but in other cases, brand new spaces are created.The “origin story” of SCU’s maker lab is that of a new space that grew out of a desire for anenhanced level of accessibility to a broad set of tools of making. SCU’s School of Engineeringhas a well-equipped, maintained and managed suite of standard fabrication/assembly/test labs.These shops have been developed, operated, and maintained by individual departments, withpolicies and use reflecting decades of practice. Traditional shop use typically serves students ina single department once students reach a specific point in their program. While these shopsserve their traditional purpose well, they are not at all