individual LED positioned at two distances to observer. Narrow and Analyse the calculated far-field angle with respect to the Wide Angle experimental variables. LEDs Compare the experimental far-field angles with the expected values documented in the component datasheets. Theorise why the measurements were made while pulsing the LEDs. Reflect on the accuracy of the calculated far-field angle and the changes in light intensity with angle as observed by eye. Compare the linearity of response of the optical sensor and the human eye as the optical power emitted by an LED
morecomparative analysis of what experiences are the most beneficial.AcknowledgementsThis work was supported in part by NSF Grant#EEC-1424444. We would like to thank ourinformants 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 reflect the views of the National Science Foundation.References1. ABET. (2011). Criteria for Accrediting Engineering Programs – Program Outcomes and Assessment. Baltimore, MD: Accreditation Board for Engineering and Technology.2. ASEE (2012). Innovation with Impact: Creating a Culture for Scholarly and Systematic Innovation in Engineering Education. Leah H. Jamieson and Jack R
the implications and interconnectionsbetween key terms and concepts linked to a topic. In this paper, we have present results based onthe “thought bubbles” approach for ‘Cybersecurity (for Networked Systems)’ course and‘Program Design for Engineers’ course. However, the proposed approach can be implemented inany other courses in a straightforward manner.AcknowledgementsThis work is supported in part by the US National Science Foundation (NSF) under Grant CNS1405670. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the author(s) and do not necessarily reflect the views of the Foundation. The authorswould like to thank the students who participated in the feedback process for different coursesand
Feedback: A Learning Theory Perspective, Educational Research Review 9, 1-15.5. Quinton, S., and Smallbone, T. (2010) Feeding Forward: Using Feedback to Promote Student Reflection and Learning–A Teaching Model, Innovations in Education and Teaching International 47, 125-135.6. Narciss, S. (2008) Feedback Strategies for Interactive Learning Tasks, Handbook of Research on Educational Communications and Technology 3, 125-144.7. Creasy, M. A. (2015) Data Extraction from Web-Based Learning Management Systems, In Illinois-Indiana ASEE Conference, Forty Wayne, Indiana.8. Creasy, M. A. (2014) Hybrid Class Experiences: Flipping Mechanics Courses and Homework Feedback, In ASEE Illinois/Indiana Section
, Indianapolis, IN.• Salzman, N., & Ohland, M. W. (2013). Precollege Engineering Participation among First-Year Engineering Students. Presented at the 5th First Year Engineering Experience (FYEE) Conference. Pittsburg, PA. Acknowledgements The authors would also like to acknowledge the support of the National Science Foundation (EEC Grant # 1550961). Any opinions, findings, conclusions, or recommendations do not necessarily reflect the views of the National Science Foundation. The authors would also like to thank Dr. Cathleen Barczys Simons and Dr. Stephen Hoffman for assistance with data collection and analysis for this project. References 1. Carr, R. L., Bennett, L. D. & Strobel, J. Engineering in
approximately 50 students spread across two classes, grade 12advanced placement physics at an elite private school with close affiliations to a local university.The response rate was over 50%, with a respondent sample of 27 students. There was anunderrepresentation of female students in the population, approximately 20%, but over 50% ofthe respondents in the sample were female (14 female and 13 male respondents). This was idealfor studying gendered perceptions, but in itself may reflect some gendered perceptions of theimportance of this area of research. If male students were not inclined to take the survey asseriously as female students, that could affect their answers. This was indicated in at least acouple of the responses. One male student with low
experience needs to blend into thecontext aware content. In addition, measurable goals and objectives that are challenge enough tostudents need to be counted.Standards. Standards of electronics and computer science curriculum and program goals must beinstilled and reflected to the projects. We need to consider the ABET requirement for thecomputer curriculum and program expectations. Overall, projects need to be developed byourselves to be authentic to students and local community. The research study is supported byNSF DUE program requirements are considered.In addition to the above project design issues, PBL carries the characteristics of pedagogicalunderpinnings.Innovation. Mobile computing is a new emerging area in computer science. Research
Curriculum with Coherent ThemeAbstractA design engineer uses math to solve real-world problems. To that end, traditional mechanicalengineering curricula teach modeling and analysis skills in a set of specific, often decades-old,courses. This regiment of courses give the student the skill set needed to be an engineer, but is alltoo often insufficient at teaching that student how to use that skill set. That is, the student is ill-prepared to bring those multidisciplinary skills together to solve problems, to actually be anengineer.A new curriculum strategy is proposed in which at least one course each semester reflects theconcepts of model-based design. Therefore, the engineering student becomes progressivelymature in applying his or her
and further developed in MaC II. One possibleavenue is the development of undergraduate STEM degree programs as alternatives to traditionaldiscipline majors. These might mirror the growth of Computational Science and Engineering programsover the past 10 – 15 years, and are likely to be reflected in the growth of Data-Enabled Science andEngineering in the next several years. A key question is the extent to which mathematical modeling istreated as a stand-alone “course” or whether it should be integrated as the Modeling across theCurriculum title suggests. Coordinating the fundamental mathematics, computation, statistics and sciencecontent to support application in a wide range of STEM fields may have strong appeal to potentialstudents.The 2.5
leads from the rest of the system. Much of this work was completed by my partner for the project, and fellow project manager for the second phase of the project, Ross Buttrum. The tasks required of the EET group overall were reflective of the necessary skills of both phases of the project. In this case it involved going through a selection process to properly choose and implement an electrical system in a 3-D printer that was comparable to a printer currently in the ET building of IUPUI. Secondarily our managerial responsibilities became more stringent in the second phase of the project because there was a new group of MET students inheriting the project, as the MET degree only
this paper are those ofthe authors and do not necessarily reflect the views of the National Science FoundationReferences:(1) Yawson, R. M. An epistemological framework for nanoscience and nanotechnology literacy. Int J Technol Des Educ 2012, 22, 297-310.(2) Resources: Courses Browse Visually. https://nanohub.org/resources/courses (accessed May 25, 2014.(3) Veety, E. N.; Ozturk, M. C.; Escuti, M.; Muth, J.; Misra, V. In Tilte, Indianapolis, Indiana2014(4) Rodgers, K. J.; Kong, Y.; Diefes-Dux, H. A.; Madhavan, K. In Tilte2014.(5) Schlosser, P.; Trott, B.; Tomasko, D.; Clingan, P.; Allam, Y.; Merrill, J. In Tilte, Chicago, Illinois2006.(6) Abernathy, S. M.; Carruthers, B. E.; Presley, K. F.; Clingan, P. A. In Tilte, San Antonio, Texas2012.(7
performance data via a midterm or final examination was not included heresince it would only be reflective of one component of the project (truss analysis) and not of theothers (computer and oral presentation skills, CAD, data analysis, and engineering design).Table 2. Online survey instrument Statement ID Statement Scale Skills SE 1 I can perform experiments independently. 1-6 Skills SE 2 I can analyze data resulting from experiments. 1-6 Skills SE 3 I can communicate results of experiments in written form. 1-6 Skills SE 4 I can solve problems using a computer. 1-6 Skills SE
. Describe future research directions 7A. Outline ‘next steps’ or future work 7B. Suggest methodological improvements 8. Engage in learning 8A. Appropriately connect/use course concepts in the investigation process 8B. Identify/reflect on “lessons learned” 8C. Manage time and resources effectively to complete the investigationIn problem analysis, the student displays the ability to: 1. Define the problem 1A. State the problem in their own words 1B. Identify primary problem goal(s) 1C. Characterize the type of problem and the type of solution sought 1D. Represent the problem visually (e.g., free body diagram, circuit schematic) 1E. Identify known information 1F. Recognize
appropriate distribution channels that will reachK-12 teachers, while bearing in mind ways to measure impact via altmetrics. One factor inselecting the distribution channel was the matter of timing. The faculty member wished to createan ongoing outreach but particularly wished to focus on the summer months, when K-12 teachershave some time to reflectively consider pedagogical practices. Because time was not a limitingfactor, but ongoing relationships were a factor in the strategy, it was clear that a socialnetworking site would have assets that a site such as a microblogging site such as Twitter wouldnot have. Ultimately, it was determined that LinkedIn would be the best social network to publicize theavailability of the tools due to the presence of
process of testing and refining the design. The testing isconducted in a small arena similar to that used in the competition. A reasonable lunar simulantwas created using fly ash, sand, and gravel. The original test pit was roughly 15 feet long, 10 feetwide and covered with one foot of simulant. Recently, it was modified to have an area that isapproximately three feet deep to allow testing of a system designed to dig icy regolith—regolithmore than one foot below the surface. This area contains regolith with larger rocks to moreaccurately reflect the icy regolith used in NASA’s competition arena. The pit dimensions aresufficient for the creation of a small obstacle course to test the drivability of the robot in the lunarregolith. In addition, the
operated during the development of one ofits objectives i.e. the development of outreach programmes. We shall use Stokols et al.working model13 of transdisciplinary scientific collaboration to benchmark our collaborativeeffort. This model is favoured as it has been extensively cited14. We will also reflect on ourability to collaborate between a disparate body of researchers as we wish to add to the debateon international collaborations; particularly as transdisciplinary networks, that span majorgeographic and cultural boundaries, are extremely complex10. As general literature points to anumber of factors that inhibit the development of successful collaborations such as expertise,language, cultural values, belief and norms, management, time and
program performance data to support future efforts to assess the program’s progress towards its desired outcomes as well as to estimate the impact of the program on its target student populations.The formative evaluation assess: • Independent assessment of the collaborative program quality by different parties, such as students, UB professors and non-teaching administrators, • Students’ assessment of the courses that are taught by UB visiting professors at WUST campus, and • Students’ assessment of the program at UB.Summative evaluation will reflect: • The students’ performance evaluation for the courses which are taught by UB visiting professors at WUST, and • The evaluation of program outcomes in terms of
, twice the value of the ambientair. The soil temperatures do not reflect any influence by the system as the temperature neverdropped below 60°F on this Spring Day. The few dips in temperature were purely for testingpurposes to see if the unit was functioning as designed. These data points have allowed us toconclusively evaluate the overall collector system efficiencies in the following graph in Figure 6. Figure 5: Solar temperature data Figure 6: Collector system efficiencyStudent Learning Experience for Green Energy ManufacturingFor the past years, the focus has shifted towards incorporating renewable energy manufacturingtopics in the senior design project course. In the first senior
Traditional path t102 Sig. Mean Mean (2-tailed) (SD) (SD)Engr. Self-Efficacy 0-6 4.43 5.00 3.16 .002Design confidence 0-100 56.38 72.69 2.19 .044Expect. of success 0-100 60.03 75.30 2.12 .049 Table 1: students’ differences reflected in pre-surveyThe post-survey was conducted at the end of the semester. Datasets from 49 students wereinvolved in the pre- and post-surveys analyses and 89.4% of them were males. There were nosignificant differences between the students who finished the post-survey and
Agree Average RankingFigure 4 – Results of Part II post survey at both institutionsConclusionsTwo problem-based learning modules were developed for an introductory, junior level soilmechanics/geotechnical engineering course. The first module was delivered at one institution,and the second module was delivered at two institutions. The instructors made generalobservations to assess the effectiveness of the modules with regard to comprehension and used aseries of pre and post surveys to assess the effect of the modules on student attitude towards soilmechanics and geotechnical engineering. The following conclusions are drawn from theinstructors’ reflections on the PBL delivery and from the results of the
CurtisShannon, Christopher Easley, William Josephson, and Joni Lakin; and current or formerstudents Alex Kelly, Shannon McGee, Alexander Haywood, Amber Hubbard, Rachel Bostic,Shannon Bales, the officers in Auburn University’s SHPE chapter, and Jessica Cooper. BrennenReece is acknowledged for producing the Youtube videos related to MSP outreach. Outreachcoordinators Mary Lou Ewald and Jessica Taylor are acknowledged for their ongoing efforts.The specific modules and activities highlighted in this paper were funded by NSF EPS-1158862and DRL-1102997. Any opinions, findings, and conclusions or recommendations expressed inthis paper are those of the author and do not necessarily reflect the views of the National ScienceFoundation.References:1. Nguyen
1 and the following are major definitions of assessment instruments that were embeddedinto the course: Project Journal: The maintenance of a bound design project journal is a requirement of the course by each team member. Teamwork (Peer-assessed): At least twice in the semester students are requested to complete a written evaluation of team members’ performance. Project Portfolio: This is an ongoing maintenance of a project portfolio. Records of team meetings, and updated plans for upcoming work are maintained in the portfolio, and are reviewed in project meetings with the instructor and industry’s sponsor. Standard contents of the portfolio reflects all proceedings of the team work on the
are those of the authorsand do not necessarily reflect the views of the National Science Foundation. The authors alsowould like to acknowledge the effort from Ms. Caroline Liron, Dr. Matthew Verleger, whohelped conduct the project in their classes, Dr. James Pembridge who offered suggestions on theproject design and implementation, and the support from the Institution Research at Embry-Riddle Aeronautical University who conducted and collected the survey data for this project.Bibliography1. Bualuan, R. (2006). Teaching Computer Programming Skills to First-year Engineering Students Using Fun Animation in MATLAB,” Paper presented at the 2006 American Society for Engineering Education Annual Conference & Exposition, Chicago, IL.2
methodology will not only improve students’ learningbut will also offer low-cost and flexible training platform necessary for 21st century students.Even though AUC is a preferable type of feedback compared to KCR, it is more complex andtherefore expensive to develop. Instructional designers are often interested in efficiency. It mightbe expected that the additional steps necessary for AUC would require more study time.References [1] Nahvi, M. (1996). Dynamics of student-computer interaction in a simulation environment: Reflections on curricular issues. Proceedings of the IEEE Frontiers in Education, USA, 1383-1386. [2] Hsieh, S., & Hsieh, P.Y. (2004). Integrating virtual learning system for programmable logic controller
scope isinterdisciplinary including design, development and research. The research paper is relevantto Chi Xu’s Ph.D. dissertation. Furthermore, the information is also used in a graduate levelpublic works engineering and management class that is offered each fall semester. Thismakes it relevant to the theme of the ASEE Graduate Studies Division.IntroductionThe solar energy is an ideal energy can gain from the sun, as a type of renewable energy, solarenergy has its advantage: widespread, low contamination and flexibility. High concentratedphotovoltaics is new solar technology which can produce electricity cost-effectively. Byusing a reflection system to concentrate solar radiation can decrease cost and increase theefficiency. HCPV uses cooling
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