sorting on a deeper, more meaningful level.6 Appliedresearch in engineering education has suggested that students strive to develop conceptualknowledge, but, unfortunately, do so at low cognitive levels. In a study of the learning effects ofa computer-based module on the topic of control systems10, the researchers found greater gains atlower cognitive levels of Bloom‟s taxonomy11 (Level 2: Comprehension; Level 3: Application) Page 22.1619.3than at higher levels (Level 4: Analysis; Level 6: Evaluation). Other research has identifiedmisconceptions held by engineering students regarding basic engineering concepts, like rate andenergy12, and concept
defined self-regulated learningas “learning that results from students‟ self-generated thoughts and behaviors that aresystematically oriented toward the attainment of their learning goals” (p. 125). In addition,Bandura9 showed that self-efficacy beliefs impact performance because these beliefs representpeople‟s perception of their capabilities to perform a task at designated levels. These researchershave provided empirical data on causal or correlational relationships between self-efficacy andepistemic beliefs and self-regulated behaviors and performance in subjects such as mathematics5,10 .During problem solving, students assess the difficulty of the task while disambiguating theimportant from irrelevant information. According to Jonassen11
furtherexpand the PBH implementation by increasing the number of project-based activities and makingthe PBH assignments a required course activity in the next semester and continue to evaluate thestudent performances. The preliminary data obtained in this study from the first round of PBHimplementation is encouraging considering these experiments were devised and completed bythe students using simple objects and items while in the middle of the COVID-19 pandemic. Theauthors will continue their efforts in improving the PBH implementation process in future studiesthrough the above mentioned measures to enhance student learning and student success rates inthe Dynamics course.References[1] S. A. Ambrose, M. W. Bridges, M. DiPietro, M. C. Lovett, and M. K
Figure 2. Pictures of plastic venturi sections for (a) Air flow and (b) Water flow.time to figure things out, although open-ended play with the hardware was encouraged. TheTA(s) and instructor were in the room to interact with the students.Venturi nozzle experimentThe first experiment implemented was based on flow through a venturi. The learning objectivestargeted for this experiment were • How fluid flow rates are measured. • How Conservation of Mass defines the relation between velocity and cross-sectional area in a conduit. • How the Bernoulli Equation defines the relation between pressure and velocity in a flowing fluid. • How experimental and ideal conditions differ.Students performed experiments with both
. Iterate Iteratea The difficulty of meeting the requirements will necessitate iteration.b The difficulty of manufacturing will necessitate iterative prototyping.c My team has minimal prototyping experience.3 For a high avg, use a virtual prototype; else, use physical models. Physical Virtuala Virtual prototype(s) will require less time than a physical one(s).b Virtual modeling will validate: physics, interfaces and/or requirements.c A CAD model is needed for analysis (FEA, CFD, etc.) or manufacture.d Time & budget allow pursuit of both virtual and physical prototypes
∫ h(v, k , c) ⋅ 8760 ⋅ v3 ⋅ dv (8) 0The best way to assimilate the aforementioned is to consider some example problems.Wind Energy ExamplesExample 1Find Vmode, Vmean, Vrmc , the power density available distribution, and the power extracted per m2for a wind turbine at a site which possesses a Weibull wind distribution with c = 15 m/s and k =1.5. The density is 1.225 kg/m3.Solution:A graphical representation of the Weibull distribution for k = 1.5 and c = 15 m/sec is presented inFigure 2. The mode, the most probable wind speed, occurs at 7.21 m/sec. The mean wind speedand the root-mean-cube speed are defined in Eqs. (2) and (4), respectively. The arithmetic forthis example is
for instructorsseeking one of the four desired outcomes to incorporate the appropriate activities in theircourses. Future publications, based on on-going work, will provide a comprehensive list ofexample activities to each of the four identified goals based on a survey of current courseofferings in US undergraduate engineering programs.References1. Agogino, A., Sheppard, S. and Oladipupo, A. “Making Connections to Engineering During the First Two Years”, Proceedings of Frontiers in Education Conference, 11-14, November, pp. 563-569, 1992.2. Burton, J. and White, D., “Selecting a Model for Freshman Engineering Design.” Journal of Engineering Education, pp. 327-332, July 1999.3. Barr, R., Schmidt, P., Krueger, T., and Twu, C-Y., “An
, Page 25.100.2students need to have an advisor from their respective program. The elements emphasized andadopted to promote project evaluation practices include periodic review of journal/log bookentries, presentations, periodic milestone reports, at least weekly meetings with the advisor(s),and the final design report.IntroductionDue to its culminating nature, the senior design project course is probably the most significantexperience of the undergraduate engineering students (1). In the process the students apply whatthey have learned in their undergraduate course work; develop their communication,interpersonal, project management, and design skills; and learn about the product developmentprocess. Students also get an understanding of the
: Messages for Improving Public Understanding of Engineering. Available: http://www.nap.edu/catalog.php?record_id=12187[3] S. M. Lord, "Work in Progress - Engineering students' disciplinary choices: Do race and gender matter?," in Proceedings of the IEEE/ASEE Frontiers in Education Conference, San Antonio, TX, 2009, pp. W1D-1 - W1D-2.[4] ASME Intl. (2013, March 30, 2013). About American Society of Mechanical Engineers - ASME. Available: https://www.asme.org/about-asme[5] B. L. Yoder. (2011, June 21, 2013). Engineering by the Numbers. Available: http://www.asee.org/papers- and-publications/publications/college-profiles/2011-profile-engineering-statistics.pdf[6] S. M. Lord, R. A. Layton, and M. W. Ohland
, Nashville, Tennessee, 2003.10 R. D. LaRoche, B. J. Hutchings, R. Muralikrishnan, “FlowLab: Computational Fluid Dynamics (CFD) Framework for Undergraduate Education”, Proc. 2002 ASEE Annual Conference & Exposition, June, Montreal, Quebec, CA, 2002.11 Appanaboyina, S. and Aung, K., , “Development of a VRML Application for Teaching Fluid Mechanics,” Proc. 2004 ASEE Annual Conference & Exposition, June, Salt Lake City, Utah, 2004.12 R. Jia, S. Xu, S. Gao, EL-S. Aziz, S. Esche, and C. Chassapis, “A Virtual Laboratory on Fluid Mechanics,” Proc. 2006 ASEE Annual Conference & Exposition, June, Chicago, Illinois, 2006.13 R. LaRoche, B. Hutchings, and R. Muralikrishnan, “FlowLab: Computational Fluid Dynamics (CFD) Framework for
a1 a1* h (s) ? - (s / p1 ) (s / p1* ) TRANSDUCER 100 |?203' |?3' Y CALIBRATION |?4' |?7' 10 |?32' |?42' 0
modeled as laminar flow with a density of 1060 kg/m3, the specific heat of3513 J/Kg-K, the thermal conductivity of 0.44 W/m-K, and a viscosity of 0.003 Kg/m-s. Theblood entered through the two branches of the inlet at 0.3 m/sec velocity and left through thelarge main branch of the artery outlet. Mesh sensitivity analysis revealed the optimum meshconfiguration with 139,202 elements and 27,309 nodes. The meshed artery is presented in Figure5. Figure 5: Depiction of fine mesh configurationBlood flow refers to the movement of blood through a vessel, tissue, or organ and is initiated bythe contraction of the ventricles of the heart. Ventricular contraction ejects blood into the majorarteries, resulting in flow from regions of
-power pulsed laser(s), sheet optic(s), digitalimager(s), and processing software. Figure 1. A simple laboratory PIV system consisting of a digital camera, a pulsed wave laser, sheet optics, and seeding particles. To date, each of these hardware/software technological problems have been individuallysolved. The modern availability of LED “laser” pointers, increasing quality of smartphonecameras, and improvements in smartphone processing speed now provide economical, safe, andaccessible illumination, imaging, and image processing capabilities for smartphone PIV.However, while open source algorithms exist for PIV [12], they a) do not port to modern mobiledevices and b) are not equipped with interfaces that guide
paper did notexamine actual measure of student learning and only reported their perception of learning. Futureresearch needs to specifically examine measures of student learning by using “methodologicallysophisticated, qualitative methods such as, interviews, journals entries, observations, and casestudies of particular students as alternatives to standardized objective tests or constructed caseanalysis tests” 8. Page 14.344.7References:1. Williams, S. M. "Putting Case-Based Instruction into Context: Examples from Legal and Medical Education." The Journal of Learning Sciences 2, no. 4 (1992): 367-427.2. Mayo, J. A. "Case-Based Instruction
1) improve individual learning, 2) improve team performance, and 3) would mostbenefit individual members within teams performing at a high level. To explore these hypotheseswe compared student performance across two semesters, one that utilized cooperative groups andthe second that utilized TBL.MethodsThis research was approved by the University of Kansas Human Research Protection Program.In Fall 2014, 59 students enrolled in the course which was taught in a flipped format (Beichner,2008) in an active-learning classroom and utilized cooperative groups. Each class meetingconsisted of: 1) a reading quiz, 2) lecture highlights, 3) example problem(s), and 4) group work.The instructional team consisted of the professor, two graduate teaching
in appropriate subsequent analyses. From the Y2 data, we observed no significantdifferences at the 95% confidence level (α = 0.05) between any student sections’cumulative pre-test score, and thus, we include this data in the appropriate analyses in Section 4. When looking atindividual parts of the pre-test, however, we did find a significant difference between section 1 andsection 4 on the Lab 5 portion of the pre-test (p-value = 0.04), with section 4 having a significantlylower average on this portion of the material. Because of this, student section 4’s data was omittedin the Lab 5 analyses for Y2. 4 Results 4.1 Educational Benefit from Course The first question we sought to answer was whether or not the students learned and
mechanical, electrical, and wireless communications components withfurther diversity within each discipline. For example, the mechanical system could includecomplex nonlinear dynamics in vibration harvesting, thermodynamics and heat transfer inthermal harvesting, and fluid dynamics in wind harvesting. Electrical components to condition,store, and deliver power to the load may be a mixture of analog and digital, whilecommunications may be performed in a number of frequency bands and network protocols. Aneducationally diverse team is therefore beneficial. One can envision a student team composed ofbiomedical, mechanical, electrical, and computer engineering students working on harvestingenergy of human walking in an everyday basis to power a user‟s
Professor in the Department of Engineering Mechanics at the U. S. Air Force Academy. He has published approximately 100 technical publications and generated approximately 2 million dollars of research finding. His current research interests include development of new design methodologies as well as methods for improving engineering education. Page 22.1350.1 c American Society for Engineering Education, 2011Studying Ideation in Engineering Design Education: Application to Highly Mobile RobotsIntroduction Developing innovative ideas as part of engineering design can be
AC 2008-1308: A VENTILATION SYSTEM CAPSTONE DESIGN PROJECTCharles Forsberg, Hofstra University Charles H. Forsberg is an Associate Professor of Engineering at Hofstra University, where he primarily teaches courses in the thermal/fluids area. He received a B. S. in Mechanical Engineering from the Polytechnic Institute of Brooklyn (now Polytechnic University), and an M. S. in Mechanical Engineering and Ph. D. from Columbia University. He is a Licensesd Professional Engineer in New York State. Page 13.129.1© American Society for Engineering Education, 2008 A Ventilation System
and Controls Laboratory while concurrently working on the NSF Engineering Education Grant. Page 11.479.1© American Society for Engineering Education, 2006 DEVELOPMENT OF VISUALIZATION TOOLS FOR RESPONSE OF 1ST AND 2ND ORDER DYNAMIC SYSTEMSAbstractStudents often enter a Dynamic Systems course with no real background or exposure to many ofthe concepts used to define “non-static” systems. The material is often a significant departurefrom the previous material covered, and the vernacular/terminology is very new and unfamiliarto the students. Nomenclature and concepts such as poles, zeros, s-plane, and others cause
skills they need to tackle that next topic.One challenge was motivating even the strongest students to prepare for the more open-endedportions of the exams. With such clear goals for the proficiency analyses, many of the beststudents over prepared for these problems, at the expense of the higher-order skill set. Studentsmay need coaching on how to balance their preparation and how to develop the higher orderskills.Works Cited[1] B. S. Bloom, Human Characteristics and School Learning, New York: McGraw-Hill, 1976.[2] B. S. Bloom, "The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring," Educational Researcher, vol. 13, no. 6, pp. 4-16, 1984.[3] T. R. Guskey, "Lessons of Mastery Learning
Paper ID #25824Advancd Design and Fabrication of Prosthetic and Medical DevicesDr. Gaffar Barakat Gailani, New York City College of Technology Dr. Gailani is an associate professor in the Dept. of Mechanical Engineering and Industrial Design Tech- nology. Received his Ph.d in Mechanical Engineering from the City University of New York in 2009. His research work is focused on poroelasticity and its application in biomechanics, additive manufactruring, and medical devices.Dr. Andy Zhang, New York City College of Technology Dr. Andy S. Zhang received his Ph.D. from the City University of New York in 1995. He is currently the
resource-basedindustries such as paper or in textile mills which were widely dispersed geographicallyaround the state, the textile mills in the more populous southern part of the state and thepaper mills in the north. In the 1960’s, however, these industries began a slow,precipitous decline which accelerated in the 1970’s and 1980’s. At the same time, newindustries, which required higher skill sets, began locating in the greater Portland area.These included such companies as National Semiconductor, Fairchild Semiconductor,Pratt and Whitney, Idexx Laboratories and other. These industries were interested inhaving a local institution which would not only provide educational opportunities fortheir employees but also would be a source of new engineers
S S irrev change the system’s state. 1 1 Q2 , out 1W2 , out 1 T Time is irrelevant. Equilibrium prevails at the E 2 E 1 E
influences (EQ) and understanding the rules underlying asystem (SQ), as it relates to this curriculum experience. Systemizing is defined as the drive andability to analyze the rules underlying a system, in order to predict its behavior and appears to becentral to the understanding of engineering. Empathizing is defined as both the interest andability to identify another's mental states and to respond to these with one of a range ofappropriate emotions.10The SQ-EQ model places these cognitive styles in tension and compares the relative strength ofthese styles within individuals as a predictor of their cognitive behavior. For example, S>E is anindividual that favors systemizing thinking over empathizing thinking, while E>S is anindividual that
attention cueingin animations. Computers & Education, 55(2), 681-691.[2] de Koning, B. B., Tabbers, H., Rikers, R. M. J. P., & Paas, F. (2009). Towards a frameworkfor attention cueing in instructional animations: Guidelines for research and design. EducationalPsychology Review, 21(2), 113-140.[3] de Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2007). Attention cueing asa means to enhance learning from an animation. Applied Cognitive Psychology. 21(6), 731-746.[4] de Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2010a). Attention guidancein learning from a complex animation: Seeing is understanding? Learning and Instruction, 20(2),111-122.[5] Mayer, R. E., Hegarty, M., Mayer, S., & Campbell, J
sufficient forfinding an equilibrium state. Press the Enter key or the Calculate button and the complete state iscalculated and displayed. The green background signifies input and the cyan backgroundindicates a computed property. The property symbols are also color coded – red for materialproperties such as the molar mass MM, blue for all thermodynamic properties, green for extrinsicproperties, and black for system properties34. Page 14.1187.6While the ideal gas table displays only hk (T ) , uk (T ) , and sk0 (T ) , the state daemon producesthe complete set of thermodynamic properties – v , u , h , s , g , and c p . To obtain mole basedvalues
communication skills.Presenting problem solutions in a clear and concise structured manner is an important skill for the studentto develop. This provides an opportunity to practice the language of the scientific method includingspecifying the knowns and unknowns, the assumptions and the applicable principle(s), the solution, aninterpretation of the results, and a discussion on the accuracy and the recommendations for further study.Working homework problems outside of class, whether done individually or in a study group, is also anopportunity for the student to engage in a self-evaluation of mastery of the concept and whether additionalhelp is required to develop the expected level of mastery. Learning is enhanced when homework is goal-directed
survey questionsprovides some insight into student perceptions. M E3 5 0 C o urs e End S urv e y Que s t i o ns 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 E1. M E3 50 imp ro ved my ab ilit y to und ers t and , d et ermine, and wo rk wit h p hys ical and thermal p ro p ert ies . E2 . M E3 50 imp ro ved my ab ilit y t o ap p ly a s ys t emat ic t ho ug ht p ro ces s to s o lve eng ineering p ro b lems . E3 . M E3 50 imp ro ved my ab ilit y to analyze t hermal s ys t ems b y ap p lying co ns ervatio n o
length of = 1 kg/m, and is initially at rest. If the weight of theoverhanging section of chain is sufficient to overcome friction, it causes the remainder of thechain to smoothly unwind from the drumThe axle diameter is 10 cm, and the width of the drum is 0.75 m. The gap between the drum andaxle is 2 mm wide, and is filled with a lubricant that has a viscosity of 0.5 kg/m-s. 1. Develop the governing differential equation for V(x), where V is the speed of the falling chain, and x is the length of chain that has unwound from the drum at any instant. 2. Obtain an analytical solution for V(x) by assuming that friction between the shaft and the drum is negligible. 3. Obtain a numerical solution for V(x) when x0 < x <