load of 20 MPa at the right end andsupported by a fixed support on the left. The plate has respectively the modulus of elasticity (E) andPoisson’s ratio (n) of 200 GPa and 0.32. To aid in meshing the part, ten partitions were created onthe model in Abaqus as shown in Figure 1(b). The partitions help in creating a finer mesh aroundthe hole and in the vicinity of the fillet where the stress concentrations occur. The meshed model ofthe part is provided in Figure 1(c) showing the axial stress contour exerted on the plate. A CPS8Rtype element (An 8-node biquadratic plane stress quadrilateral, reduced integration element) wasused in the analysis to produce the displayed results. Various tools in Abaqus allow the users toproduce and display the
results examine several factors influencing the success of a partnership, including differencein cumulative grade point average (GPA), gender balance, and work habits like starting projectsearly. After controlling for GPA, we observed an association between starting projects early andincreased performance on both exams and projects. The impact was greatest among those in thelowest GPA quartile, where an early start made the difference between an average final lettergrade of C+ (lowest early-start quartile) and B- (highest early-start quartile).1 Introduction and Related WorkAn important goal of group work in education is to increase student learning of course material.In computer science courses, group work often takes the form of pair
. A pilot study to assess the material was started in the Fall 2014semester in the Mechanical Engineering Department at Clemson University. The junior levelundergraduate course “Manufacturing Processes and their Application” (ME 3120) providesstudents an overview about common industry manufacturing processes. The instructor for thiscourse used a standard grading system of 90-100, A; 80-89, B; etc. Special emphasis wasgiven in developing the supplemental e-learning material in a manner that would help 3 students grasp the basic concepts while simultaneously practicing typical applications withina virtual industrial environment. For instance, the module for machining operations features afully
-Bass.6. Hahn, L.D. and C. Migotsky, Formative Classroom Observations for New Faculty. ASEE Conferences: Seattle, Washington.7. Furtak, E.M. My Writing Productivity Pipeline. 2016. 2017.8. Boice, R., Advice for New Faculty Members: Nihil Nimus. 2000, Needham Heights, MA: Allyn & Bacon.9. Allen, D., Getting Things Done: The Art of Stress-Free Productivity. 2001: Penguin Group.Appendix – SurveyDemographics 1. What is your current title? a. Lecturer b. Assistant Professor c. Associate Professor d. Professor e. Other 2. What is your tenure status? a. Tenure-track (or equivalent) b. Tenured (or equivalent) c. Non-tenure-track 3. If applicable, how many
one to what we need in the current project. We then make thenecessary changes to tailor the (old) code to our needs. We believe that this step will greatlyimprove our students’ performance and their test results.References[1] S. Brown and Z. Vranesic, Fundamentals of Digital Logic with VHDL Design, 3rd edition, McGraw Hill.[2] A. B. Marcovitz, Introduction to Logic Design, 3rd edition, McGraw Hill.[3] R. S. Sandige, M. L. Sandige, Fundamentals of Digital and Computer Design with VHDL, McGraw Hill.[4] F. Vahid, Digital Design with RTL Design, VHDL, and Verilog, 2nd edition, John Wiley & Sons.[5] J. F. Wakerly, Digital Design, Principles and Practices, 4th editon, Prentice Hall.[6] J. Pang 2015. “Active Learning in the
two groups of 30 constructionstudents: the first group included students with prior project time managementknowledge (Group A) whereas the second group consisted of students without any priorknowledge (Group B). The students’ data were captured and retrieved automaticallywithout any human interaction. A quantitative research method was used for analyzingthe data and a retrospective post- survey was conducted to obtain participants’perceptions of the application. The results indicated the effectiveness of PERFECT andsupported the expansion and further development of similar simulation applications.This type of evidence-based learning system not only enhances the validity andreliability of the application, but has a potential for incorporation
making Innovative materials materials presentation at at Maker space last week A B CFig. 2 (A) Some materials used in the course module based on a project, (B) Students wereworking on the project, and (C) Students were making “innovative materials” at Maker space. The content of “Experiential Manufacturing and Material Aesthetics” module (II) islisted in Table 2. This module focuses on the aesthetic of product and 3D manufacturingtechniques. Students were taught to sketch products beginning with a basic line drawing.Following with learning prospective principle and sketchily sculpturing
question about the absolute pressure in a tire that has 30 psi gauge pressure.This is posed as a clicker multiple choice question: “If the air pressure is 30 psi gauge, what is itin psi absolute?” The multiple choice responses were given as: (a) 15 psia, (b) 30 psia, or (c) 45psia. Students were given at least 30 seconds to respond during which time the instructor wassilent and students were encouraged to discuss with other students. After students submitted theirchoices, the results were displayed on the classroom projector screen as shown in Fig, 1. Thefigure shows that 22 students (or 29% of the class) selected (a), 19 selected (b), 35 selected (c) andone selected (d). The correct choice was (c) which is shown as a green bar in Fig.1.Figure 1
Navy’s Print the Fleet initiative that can be later replicated at other locations.The Workshop Structure and Learning Activities The first workshop was delivered to active duty sailors at the end of January 2017. In total, 15Creating the Fleet Maker workshops will be executed and assessed. Each workshop is designedfor 20 sailors. The content delivered in the workshop is focused on the following STEM areas: a)computer aided design, b) reverse engineering, c) additive manufacturing, d) solid mechanics andmaterial testing, and e) product lifecycle management and part retrieval. The following sub-sections will briefly outline these subject areas grouped under modules.Module: Dive into Printing The first module starts with a lecture session
. Mark is also researches empathy and mindfulness and its impact on gender participation in engineering education. He is a Lecturer in the School of Engineering at Stanford University and teaches the course ME310x Product Management and ME305 Statistics for Design Researchers. Mark has extensive background in consumer products management, having managed more than 50 con- sumer driven businesses over a 25-year career with The Procter & Gamble Company. In 2005, he joined Intuit, Inc. as Senior Vice President and Chief Marketing Officer and initiated a number of consumer package goods marketing best practices, introduced the use of competitive response modeling and ”on- the-fly” A|B testing program to qualify
Paper ID #19440Computational Curriculum for MatSE UndergraduatesAlina Kononov, University of Illinois, Urbana-Champaign Alina Kononov is a Ph.D. student in Physics and the computational teaching assistant in Materials Science and Engineering at the University of Illinois at Urbana-Champaign. She obtained her S.B. in Physics from the Massachusetts Institute of Technology. Her research in the Schleife Group uses time-dependent density functional theory to study charge transfer and secondary electron emission processes during ion irradiation of thin materials.Dr. Pascal Bellon, University of Illinois, Urbana-Champaign
. (2008). Understanding randomness and its impact on student learning: lessons learned from building the Biology Concept Inventory (BCI). CBE-Life Sciences Education, 7(2), 227-233. [4] Smith, M. K., Wood, W. B., & Knight, J. K. (2008). The genetics concept assessment: a new concept inventory for gauging student understanding of genetics. CBE-Life Sciences Education, 7(4), 422-430. [5] Midkiff, K. C., Litzinger, T. A., & Evans, D. L. (2001). Development of engineering thermodynamics concept inventory instruments. In Frontiers in Education Conference, 2001. 31st Annual (Vol. 2, pp. F2A- F23). IEEE. [6] Martin, J., Mitchell, J., & Newell, T. (2003, November). Development of a concept inventory
-pair-share teaching method was employed a lot throughout the class.Other pages, later on, in the course had major concepts, figures to explain the concepts and bothquantitative and qualitative exercises (Appendix A, Figure A-1 (B)). The notebook was color-coded to help students identify the different activities in it.Then, few PowerPoint slides were prepared to help student visualize what the instructor wasteaching. The slides included more visuals than text and were distributed to the students at theend of each day. Some slide samples are provided in Appendix A, Figure A-2.Since students had to implement the concepts they learned through small hands-on projects,supplemental material was prepared to guide them and help them through the
York University Tandon School of Engineering1,2. FeedbackInstruments produces process control laboratory modules for temperature control and mass flowrate control as shown in Figure 1. These modules use heat and mass as surrogates for chemicalreaction process control. Figure 1. Temperature and mass flow rate process control training systems3.In the absence of laboratory exercises that directly investigate process control of reactions, acommon approach in undergraduate chemical engineering courses is to focus on textbookproblems. Many undergraduate textbooks abstract away reactant and product names and insteaduse generic reagents4–9. For example, a usual problem formulation is A B C , which meansadding reactant A and reactant B to
district in the Midwestern United States. The lessons wereimplemented in the fall of the school year. All students participated in the integrated STEM unit,however not all students attended all lessons. Two of the lessons included pair work. Eight pairsof students who attended all four target lessons were included in this sample, sixteen totalstudents, including nine female and seven male students (See Table 2). All students names givenin Table 2 are pseudonyms.Table 2. Student demographics Partner A Partner B Pair Name Gender Name Gender 1 Allyson Female Amy Female 2 Brianna Female Bill Male 3 Carl Male Cathy Female 4 Darlene Female
many seconds) does it become possible to determine if a student will struggle. Asimple neural network is proposed which is used to jointly classify body language and predicttask performance. By modeling the input as both instances and sequences, a peak F Score of0.459 was obtained, after observing a student for just two seconds. Finally, an unsupervisedmethod yielded a model which could determine if a student would struggle after just 1 secondwith 59.9% accuracy.1 IntroductionIn this work, the role of machine learning for planning student intervention is investigated.Specifically, t his w ork a sks t wo q uestions: ( i) C an a s tudent’s s truggles b e p redicted basedon body language? (ii) How soon can these struggles be predicted
paired with anincorrect explanation, indicates the student guessed. This is identified as “Scenario 2”. Incorrect“yes” or “no’ responses with incorrect “why” responses indicates “no understanding” as isidentified as “Scenario 3”. Instances of misunderstanding, guessing and no understanding areidentified. Each part of the question is assigned a metric or maximum point total. The scores foreach part are summed and represent the total score for that question. Each question had a total of5 points, resulting in a total of 15 points for all three questions. For each of the pre- and post-instruction surveys, the following data is collected: • Individual question scores for each student – Parts A and B individually • Individual question scores
the sophomore level. EET 4550 has beentaught primarily to EET students at the senior level but also has been taught as asometime substitute for EECS students when EECS 4220 was not available.The course description as well as topics studied are listed below:EECS 4220 – Programmable Logic Controllers Catalog An introduction to programmable logic controllers (PLCs), process control algorithms, descriptions interfacing of sensors and other I/O devices, simulation and networking. Topics and Introduction to Relay Logic reading Introduction to PLC programming on the PC assignments The A-B instruction set The Siemens instruction set Hardware considerations Addressing
Paper ID #19184MAKER: Smart Multipurpose Drainage SystemDr. Hugh Jack P.E., Western Carolina University Dr. Jack is not the author. The abstract has been submitted on behalf of B. Joseph Britto, S. Gowri Shankar, B. Ganga Gowtham Prabhu - Kumaraguru College of Technology, Coimbatore, India. c American Society for Engineering Education, 2017 Smart Multipurpose Drainage SystemAuthorsB. Joseph Britto, S. Gowri Shankar, B. Ganga Gowtham PrabhuKumaraguru College of Technology, Coimbatore, IndiaAbstract The drainage systems are required to be monitored in order to maintain its
required to solve the problem. b) Identify the process type (batch, semi-batch, or continuous). The system is defined as the fluid reservoir. Therefore, this is a semi-batch process, since mass will leave the system but no mass enters the system. c) Use various resources to obtain the molecular weight, density, heat capacity, normal boiling point, and heat of vaporization for the two components in the liquid reservoir. Data obtained from webbook.nist.gov unless otherwise noted Molecular Weight Glycerol (A) = C3H8O3, MWA = 92 g/mol Propylene glycol (B) = C3H8O2, MWB = 76 g/mol Density ρA = 1.261 (g/cm3) (source: Properties of Gases and Liquids, 4th ed. Reid, Prausnitz, and Poling) ρB = 1.036 (g
sampling techniquesduring campus site visits (Patton, 2015).AcknowledgementsThe authors would like to acknowledge Dr. Kevin Fosnacht with the National Survey of StudentEngagement for assistance in providing the initial analysis of the data being used to validate theproposed model presented in this paper.ReferencesAllie, S., Armien, M. N., Burgoyne, N., Case, J. M., Collier-Reed, B. I., Craig, T. S., . . . Wolmarans, N. (2009). Learning as acquiring a discursive identity through participation in a community: improving student learning in engineering education. European Journal of Engineering Education, 34(4), 359-367. doi:10.1080/03043790902989457American Society for Engineering Education. (2014). Divisions: American Society
from engineering,chemistry, and biology) from various levels including freshmen through seniors (n=10). Thesestudents filled out surveys both before (“Group A : Before”) and after (“Group A : After”)performing the experiment. The second group, Group B are the engineers in the originalThermodynamics class who were surveyed approximately 9 months after having completed theexperiment (n=6). Responses from selected questions are included below. For free answerquestions, answers were categorized. Therefore, the data below represents aggregate data, notquoted responses.The first two questions were to define heat and temperature. All students correctly respondedthat temperature was a property of the material that represented the energy contained
vehicles faster and that other modes of transportationwere not necessarily considered in the designs. The instructor then refers to Grand Boulevard, astreet next to the summer camp site. There are three lanes on the northbound Grand Boulevard(Figure 1-a). The far right lane is an 8-foot-wide dedicated right-turn-only lane and appearsunderutilized (Figure 1-b). It appears that this lane could be retrofitted as a bike lane. Studentsare asked to propose a method for analyzing the impact of such a change. They are guided todetermine MOEs for the decision-making process and to identify the data needed to develop asimulation model of the street. a b N Figure 1: a
the analysis of the results.Materials and Methods This research began the summer of 2013 with the design and development of aninfrastructure that would support the use of a 3D printer for class projects. It was incorporated as partof the UNIV 1301 Foundations of Engineering classes (3 sections of the same class using the 3Dprinting technology) beginning the fall semester of 2014. The classes participating in this initial studyconsisted of similar enrollment numbers and demographics. Class A had twenty-four students andwas designated as the reference group and did not participate in the use of 3D printers in the class.The first class that did use 3D printers in the class (Class B) consisted of twenty-six students; thesecond class
. Libraries and resource sharing hubs are becoming moreimportant in the support of multi-curricular and multidisciplinary programs which are notactually housed in a particular department.References[1] Iowa Core, “Universal Constructs: Essential for 21st Century Success,”https://iowacore.gov/content/universal-constructs-essential-21st-century-success-0, 2017,(accessed February 16, 2017).[2] W. Richardson, Why School? How Education Must Change When Learning and Informationare Everywhere, Ted Conferences, 2012.[3] J. S. Brown, and D. Thomas, “Learning for a World of Constant Change,” in 7th GilonColloquium, Gilon, Switzerland, 2009.[4] T. W. Barrett, M. C. Pizzico, B. Levy, R. L. Nagel, J. S. Linsey, K. G. Talley, C. R. Forest,and W. C. Newstetter, “A
Collaborative Learning Space.The class consists of three main components: (a) reading assignments using the zyBooks onlineinteractive book platform [15], (b) 75 minutes in-class sessions held twice a week, and (c) a3-hour lab held weekly. Students are requested to complete a set of participation and challengequestions before every in-class session. These are automatically graded through the zyBooksplatform. The in-class time is structured as a sequence of active-learning tasks, and lecturing/demonstration periods. The administration of the activities is assisted by preceptors (teachingassistants and undergraduate learning assistants that have previously taken the course). A typicaldistribution of the instructors' and students’ activities during a 75
students fullycompleting both the pre- and post-tests. A Conceptions of Design Test (CDT) was used tocharacterize changes in learners’ prioritization and understanding of 20 design activities from“analyzing data” to “using creativity” (see Table 1). The instrument included three sets ofquestions: (a) given the list in Table 1 (in alphabetical order to reduce response bias) “select thefive most important and five least important concepts for producing a high quality design”, and(b) “for one of the five terms you marked as most important for producing a high quality, pleaseexplain why you believe it is important.” (c) “for one of the five terms you marked as leastimportant for producing a high quality, please explain why you believe it is not
of x, (b) Calculate 𝑍!" at 𝜆! /8 away from the load, (c)Calculate Γ! , (d) Calculate VSWR and (e) Calculate the transmitted power and reflected power as apercentage of incident power 𝑃!"Solution: (a) 𝑍! = 0, 𝑍! = 50 Ω. !! !!! Γ! = = -1 = 𝑒 !!"# => Γ! = 1 50 Ω 𝑍! !! !!! Φ = 180 ! !/! Applying this for 𝑉(𝑥) , we get ( 𝑉(𝑥) = 𝑉! (1 + Γ! )! − 4 𝑠𝑖𝑛! (𝛽𝑥
related tothe subject matter taught in statics, which the students would have taken previously. This waspurposefully done to examine the long-term retention of the content. Students were also asked toself-report their instructor from statics.Instructor A at University A began each lecture in statics by presenting a concept map ofconcepts relevant to that day’s lecture, highlighting how the new information would fit intoprevious content. This serves as an experimental group with regular exposure to concept maps asadvance organizers. Instructors at Universities B and C did not use concept maps as advanceorganizers and serve as a control group for comparison.Table 1: Overview of Research Participants
uncertainty in measurement (“GUM”).Students advance through a rotation of experiments that involve topics from mechanics, optics,electronics and quantum optics. The course follows a progressive structure by starting withconceptually simpler experiments designed to show the effects that the design of the experimentcan have on the final result and its uncertainty. These early labs allow students to focus onconcepts including Type A and Type B uncertainties; systematic errors; standard uncertainty andcombined standard uncertainty; coverage factor; and the propagation of uncertainty. Studentsalso begin to track uncertainty with a rudimentary uncertainty budget. For the rest of the course,the experiments become more open-ended and complex, and the