,studentsneedtounderstandhowtousethisknowledgeandPBLoffersawaytoshapehowstudentslearnandapplythisknowledgetocarefullycraftedproblemsintheclassroom.ItisthoughtthatPBLdoesthefollowing2:1.Developscriticalthinkingandcreativeskills.2.Improvesproblem-solvingskills.3.Increasesmotivation.4.Helpsstudentslearntotransferknowledgetonewsituations.Criticalthinkingandcreativeskillsrefer“totheabilitytoanalyze,synthesize,andevaluateinformation,aswellas,toapplythatinformationtoagivencontext.”3ThisistheheartandsoulofPBL. Figure1TraditionalvsProblem-basedLearning4TheProblem-basedLearningInitiative(PBLI)identifiessomegenericessentialsofPBL5:1.Studentsmusthavetheresponsibilityfortheirownlearning2.Problemsmustbeill-structuredandallowforfreeinquiry.3.Learningshouldcoverawiderangeofdisciplinesorsubjects.4.Collaborationisessential.5.Self-directedlearningmustbeappliedbacktotheproblem.6
CNCMilling, Manual G-coding and 3D printing assignments will be discussed. Students are asked tocut a plastic keychain with their own design using CAD/CAM software and CNC milling as afirst assignment. As a second assignment, they are asked to design and cut the various shapes ofslots in a piece of plastic without any CAD/CAM software (Manual G-coding). As a thirdassignment, students design and fabricate aluminum gusset. As a fourth assignment, Studentsbring their own 3D CAD model to 3D printer and fabricate their final semester project parts.Students are given maximum 4 weeks for each assignment and present their final products to aninstructor. In this paper, following topics will be explained and discussed. 1) History of the classprojects, 2
and neighboring reservations. understandings in relation to community needs. yinish yé Dinésh chįįn “I am robot”ContactKARMA - wiikarma.technology/contactDr. Robert Hayes - robert.hayes@tufts.eduExample CAD Lesson: Make Your Own JewelryLesson Overview★ Objective: Learning TinkerCAD and 3D printing basics through traditional Navajo craft★ Grade level: 3rd-8th grade★ Time to complete: < 1 hour for design, plus ~5-30 minutes per printed pieceExample Robotics Lesson: Count to 10 in Navajo Lesson Overview ★ Objective: Learning robotics and
framework for quantifying simulateddesign problem complexity, we present a metric of complexity, tractability 𝑻, supported by datafrom real student work on a simulated engineering design problem.TheoryEngineering Design EducationDesign is a critical part of the engineering profession [1], [2]. As a result, design is a centralfocus of engineering education in terms of teaching, learning, and assessment [3], [4]. In a recentstudy, Sheppard and others [5] interviewed faculty and students about the field of engineeringand concluded that design is the most critical component of engineering education. One facultymember asserted that “guiding students to learn ‘design thinking’ and the design process, socentral to professional practice, is the
order to identifykey differences between development and implementation that can impact adoption.PurposeThe purpose of this paper is to identify key differences in the attitudes and beliefs of instructorsbetween two material development workshops spaced approximately one year apart.MethodsWorkshopsTo date, two summer workshops have been held where instructors from the Pacific Northwesthave been invited to participate in the co-development of materials for a Mechanics of Materialscourse. A majority of instructors from year one returned during year two while five instructorsattended the workshop for the first time during year 2 (Table 1).Table 1. Comparison of participants from year 1 workshop and year 2 workshop
studentadoption of genAI for technical writing. Our study results showed that BME students adjustedtheir usage of GAI for technical writing after receiving a lecture on genAI prompting techniquesfor writing, editing, and assessing its efficacy. The students changed their usage of genAI indifferent ways and fell into two categories: 1) those who adopted it willingly and used it morefrequently, and 2) those who decided to abstain from using it at all. The latter group of studentsreported strong feelings for self-efficacy and to be independently proficient at technical writing.By examining the ways in which students adopt genAI for technical writing and the underlyingintentions, we hope to identify areas in curricula that may require greater emphasis. This
organizations have been cultivated and built-upon; website andproject management improvements have been initiated; new resources are standards-aligned;new collection organizations have been established; and NSF RET’s were continually supportedthrough webinars and conference sessions.Poster FocusThis paper and poster will focus on how Teach Engineering is beginning to create a communityof practice among K-12 educators through PD opportunities. Research has shown that whether informal or informal settings, K-12 teachers and influencers need to be trained to bring engineeringdesign into classrooms to increase students’ awareness of engineering, and ultimately, interest inand ability to pursue engineering careers [1]. Yet, many successful mathematics and
, computers with thousands processors were widely used for scientific research. Acomputer cluster consists of a number of computers to work as a system on computationalintensive tasks. Different processors are connected by network. Shared-memory or distributedmemory are dominate storage types for HPC cluster [1]. The advent of commodity highperformance processors, low-latency/high-bandwidth networks, software infrastructure anddevelopment tools facilitate the cluster to be widely used for climate modeling, disasterprediction, protein folding, oil and gas industry, and energy research [1, 2]. Currently China’sTianhe-2 is ranking No. 1 among all the super computers based on TOP500 project. Titan (OakRidge National Lab) and Sequoia (Lawrence Livermore
. Instead of a finalexam, a final presentation of Auto_Oil_ID is made by each team. The final presentation is donein PowerPoint with students encouraged to include video of their projects in action.EST104 Schedule of topics for fall 2015 Week 1-3 EXCEL with applications to Ohm’s Law and the Speed of Sound in air; designing an ultrasonic range finder. Week 4 Flowcharting and Procedural Programming Week 5-8 MATLAB – Programming a Stepper Motor in MATLAB Week 9-11 Spectroscopic ID of colored filters using a spectrometer and MATLAB Week 12 Spectroscopic ID of oils using visible light, a spectrometer, and MATLAB Week 13-14 Combining positioning by stepper-motor rotor with spectroscopic ID of oils
element is carried over to thecards problem. Students must decide if a set of cards matches set of cards made up of 6 standarddecks or has some non-standard distribution. The effective application of SPC methods will finda balance between 2 competing concerns: 1) sample enough to make a confident and correctdecision that your company is sending good parts to a customer and 2) don’t spend so much onSPC that there are no profits to be made. A similar balance is the goal for the cards wagerassignment, make a correct decision to gain some extra credit points but don’t spend so much onlooking at cards that the net is not worth the extra work.The assignment as given to the students is as follows: Suppose one weekend you are at the Bellagio Casino
the computing programs (CPEG, CS, and CPET) from the abovementioned three departments. Four faculty members from those three departments arecontributing to the project with their respective expertise. Worthwhile to mention is that the threedepartments also collaborated to obtain a HPC cluster through NSF Major ResearchInstrumentation (MRI) program. Page 26.652.3The three departments’ fall 2012 undergraduate enrollment, categorized by gender andracial/ethnic groups, is listed in Table 1. CPEG and CPET program enrollments are enclosed inparentheses beside their department enrollment. Of all the three programs, enrollment of womenis 25% and
engineering. Under the circumstance, teaching the logic programming in PLCs can beeasier if starting from texted languages. Therefore, the texted languages designated by theIEC61131-1 standard, such as Instruction List (IL) and Structured Text (ST), have been taught inthe first course, PLC Programming, at the college. After students have mastered the fundamentalknowledge needed for programming industrial automation devices, the Sequential FunctionChart (SFC), Ladder Diagram (LD) and Function Block Diagram (FCB) are taught in theadvanced course, Industrial Automation, where Siemens Step 7 PLCs have been used.Particularly, the SFC chart has been taught as an approach of developing a complex algorithm sothat students can first develop the pseudo code as
terms of volunteeringto answer questions was also used as an additional feedback. A sample data set is displayed in theResults and Discussions section.As the whole College, we have decided to transform the undergraduate learning experience in adisruptive fashion for the broadest impact. Mili and Bertoline (2014) defines eight major principlesfor such a transformation: 1. Students are encouraged to be autonomous. As opposed to faculty making all the decisions, students get actively involved in the learning process. 2. Learning is led by students. Faculty members serve as “mentors” who facilitate learning. 3. Students learn in an environment that is integrated; within context. Relevance is the key for involvement. 4. “Learning
well as differential equations (which is a co-requisite.) Since Matlab/Mathcadprograms are available in our computer laboratory, these are used throughout the course. Thetextbook by Kamen & Heck [1] is used for the course and students can access the accompanyingtextbook website. It should be noted that some class examples use both Mathcad and Matlab butthe textbook uses Matlab only. In our program we also have a senior-level elective course onfilters using the software tool called WFilter accompanying the textbook [2]. As engineeringeducators teaching undergraduate, first year graduate courses we are all aware that these toolsshould be used at the right time, right place to help student understanding and learning. Usuallya problem is
widely discussed. The National Academy of Engineering’sproject, “Changing the Conversation,” lays out effectively all the dimensions of this problem.1 It detailsthe major facets, from misinformed public perceptions of the field to the lack of diversity to engineers’poor communications practices to the global competitiveness concerns. And it offers solutions forpeople to test out in their own communications environments.In recognition of this “image” problem, colleges and universities have gone heavily into the business ofengineering outreach. “Engineering Outreach on Campus” is a report on the current state of theseoperations. Engineering outreach at colleges and universities takes many forms, with many differentkinds of activities, run at many
require batteries or maintenance [3]. They are small and haveindefinite lifetime.This research uses passive tags for RFID tagging. For this research we have used a passive tagmanufactured by Alien Technology®. This Gen 2 has been used throughout the whole experiment.(Take a picture of the RFID TAG)Again, the RFID reader used to perform the experiment is also manufactured by AlienTechnology®. Reader configuration is EPC Class 1. Model: ALR-9800. The objective of the EPCis to provide unique identification of physical objects. This is used to address and accessinformation about individual objects from the computer network, similar to the internet protocol(IP) address allows the computers to identify, organize and communicate with one another [2].The
TSE workshop Page 26.167.2participants observed industry professionals using the workshop techniques.Table 1. Agenda for Emerging Technologies and Technicians Workshop Wednesday, January 8, 2014 1:00 PM Welcome: CCET, SPC & FLATE 1:15 PM DFLC Sponsor & Lifecycle Overview 1:45 PM Lab & Hands-On Activities Orientation 2:30 – 5:00 Emerging Technologies & Technician PM Hands-On Project (sessions 1-2) 4:30 – 5:30 Optional: Fab Lab Mentoring & NGM PM Enterprise & Technician Models Thursday, January 9, 2014 8:00 AM Tour of TSE Industry 10:00 AM Break 10:15 AM Emerging
national ASEE teaching awards, and is internationally recognized in his primary research field.Dr. Temesgen Wondimu Aure, University of Cincinnati TEMESGEN W. AURE, Ph.D., is the STEM Program Coordinator working under Dr. Kukreti on the NSF Type 1 STEP and S-STEM Projects in the Department of Biomedical, Chemical and Environmen- tal Engineering at the University of Cincinnati (UC), Cincinnati, Ohio, USA. Temesgen joined UC as a graduate student in 2008 Fall and completed his doctoral degree in Civil Engineering in 2013. He started working on his current position at UC in January 2014. He plans, designs, evaluates and modifies pro- grams supported by the NSF Type 1 STEP and S-STEM Grants in the College of Engineering and
Cyber-Physical Systems AbstractThis paper outlines the Year 1 activities for a Research in Emerging Technologies for Teachingand Learning (RETTL) project about identifying threshold concepts in the field of cyber-physicalsystems (CPSs). Mastering threshold concepts, particularly in CPS design, leads to a transformedunderstanding of the subject and shifts students' identity within the context of the field. Given thecruciality of these concepts to a field, not just CPS, threshold concepts have been used to unpackstudent misconceptions and design the formative learning experiences necessary to master asubject's core ideas. In this project, we are developing a tabletop testbed for learning the
ContextManufacturing has historically been the economic engine of the Midwest. Globalization led tothe decline in traditional manufacturing. In recent years, there has been a resurgence ofmanufacturing activity in the Midwest [1, 2]. Supply chain pressures, national security threatsand shortages of microchips emerging during the coronavirus pandemic created the political willfor the U.S. to increase domestic manufacturing capability for microchips. Passage of P.L. 117-167 Creating Helpful Incentives to Produce Semiconductors and Science Act of 2022 – the“CHIPS Act” presented national goals to lead in the “research, development, manufacturing, andworkforce development” in semiconductors by catalyzing U.S. regional innovation andproviding workforce development
context-informed research measurement tool – a human-centered design (HCD)depth of thinking rubric that gauges undergraduate engineering students’ use of qualitative andquantitative data in a HCD task. The development of this rubric is part of a larger study that willintroduce qualitative methods training into an existing engineering curriculum so that studentsacquire both quantitative and qualitative skills (i.e., “mixed methods”). This mixed methodsapproach may better prepare engineering professionals for interdisciplinary work. There is abroad understanding that qualitative and mixed-methods approaches may be beneficial forengineering; however, there is a clear bias for favoring quantitative methods in the engineeringteaching curriculum [1
resources for pre-definedapplications between a sender and a receiver in an effort to assure a specific QoS level for thoseapplications 6.Network Utilization is the relationship between usage levels vs capacity. For example a 1 Mbpstransmission line experiencing an average usage of 500 Kbps is said to be 50 percent utilized 7.Network efficiency can be assessed by packet latency or the time it takes to deliver informationfrom sender to receiver 8. Networks will exhibit exponentially increasing latency, becoming lessefficient, as utilization approaches 60 to 70 percent.MethodologyThe concepts are studied using three questions evaluated pre-instruction and post-instruction.The terms “PRE” and “POST” are used in this discussion. Three, two part survey
LabVIEW Virtual Instrument into a HTMLfile are shown in Figure 1. These stages are briefly outlined below. (1) Initiating the publishing process by selecting the Web Publishing Tool from the Tools menus. (2) Indicating the location of the “VI” residing on the server in the Web Publishing Tool dialog Page 26.5.3 box as shown, so that this program can be accessed over the web. (3) Selecting an optional title, header, and footer for the HTML files to be generated. At this stage, the author is provided with an opportunity to view the HTML file before actually publishing it. (4) Indicating the location of the LabVIEW
and machine learning courses for undergraduate mechanicalengineering technology students.Keywords: Machine Learning, Property Prediction, Composite MaterialsIntroduction Modern engineering applications focus on designing novel materials with superiortailored properties, leveraging advancements in high-performance parallel computing, materialsscience, and numerical modeling. These advancements allow for the calculation of manyessential properties of materials, marking a significant shift in material science and engineering[1]. Material design comprises forward modeling problems, where the structure of a material isgiven and its properties are determined by physical laws, and inverse design problems, where thegoal is to generate a
putblished on MIT OpenCourseWare: SP.248 NEET Ways of Thinking and Defining real-world problems with the D.I.S. method.Dr. Gregory L. Long, Massachusetts Institute of Technology Gregory L. Long, PhD is currently the Lead Laboratory Instructor for NEET’s Autonomous Machines thread at the Massachusetts Institute of Technology. He has a broad range of engineering design, prototype fabrication, woodworking, and manufacturing experienceDr. M. Mehdi SalekDr. Amitava ’Babi’ Mitra, Massachusetts Institute of Technology Amitava ’Babi’ Mitra linkedin.com/in/babimitra|+1-617-324-8131 | babi@mit.edu Dr. Amitava ’Babi’ Mitra is the founding Executive Director of the New Engineering Education Transformation (NEET) program at MITSarah
). The QPserves as a career development roadmap, emphasizing self-regulated learning, ethical practices,and targeted action plans supported by reflective assessments. Moreover, experiential learningactivities within the PFE program foster a service orientation among students, significantly en-hancing their social agency, academic self-confidence, and critical thinking skills, all vital forengineering success [8][1].Initially, the QP framework relied on Excel sheets and Google Forms to collect data on students’qualification development plans. Over six years of data were refined to simplify implementa-tion and analysis of the QP. This led to the development of the QP App, a semi-automatedplatform enabling students to select action items, assign
implications of Wind energy Systems; (d) Benefits of solar energyand implications of Solar energy systems; (e) Benefits of Green Energy and Green EnergyManufacturing.In order to map a network at the conclusion of the semester based on the discussions, posts, andcomments observed in the group by both the students and moderators, Netvizz® a data extractiontool was used to extract data from Facebook group. This data was then imported into Gephi®software, an interactive network visualization platform. Figure 1 illustrates the raw networkextracted from the Facebook group. To better visualize, understand and see interactions in thenetwork, social network analysis tools available in Gephi were used which resulted in thenetwork illustrated in figure 2
the majority agreed that the format was effective in their learning.Additional results from comparing the two courses, as well as examples of student-generatedmaterials are presented and discussed in context of the overall research aim.Introduction: Engineering students face increasingly complex problems whose solutions often requireinterdisciplinary teams and significant interaction with diverse stakeholders [1-6]. Exploringcontemporary issues in society within engineering classrooms may help prepare students forthese challenges. One contemporary issue with significant engineering considerations is theadvancement and proliferation of hydraulic fractured oil/gas well stimulation, or “fracking” [7].Fracking has substantially increased
. For example, patternrecognition techniques can be applied to musical signals in order to classify a musical artist11. Infact, this form of engineering technology has been implemented into phone applications, e.g.SoundHound, to in order to identify popular music. Since music can be analyzed as a timedomain signal, it provides a natural medium for exploring time domain signal analysis12. Otherresearch has used music as inspiration for improving engineering control systems13. One of themost appealing aspects of integrating music and engineering education is the direct analogiesbetween concepts, examples of which are offered in Table 1. These analogies can serve to eitherapply knowledge of music to better understand engineering, or vice versa
career-relatedcapabilities. For instance, the work by Felder, et al, describe instructional methods relevant todeveloping the critical skills required of modern engineering graduates.1–3 As well, Feisel andRosa’s work describe the functional role of laboratories in engineering education, including theability to meaningfully assess the objectives set forth by EC2000 in the laboratory setting.4 Dym,et al, describe the role of design in the engineering curriculum and explore project-based learningas a method for developing these engineering capabilities.5 And, Prince and Felder describeinductive teaching and learning methods that include both problem- and project-based learningapproaches.6This manuscript compliments that body of research by