“coopertition” not only during build season andcompetition season but all the time. Veteran teams reach out to and support rookie teamsnot only with materials but with knowledge and guidance. This paper presents some ofthe knowledge that rookie teams return to the FIRST community.MethodologyFive rookie teams from the 2016 season willingly provided their lessons learned for thispaper. (See Appendix.) The teams were all from North Carolina and had submitted thelessons learned at the end of the season as fulfillment of the requirements for the ArgosyFoundation Rookie Grant. The author had hypothesized that the lessons learned wouldbe focused more on learning mechanical aspects; such as for each wheel, one must ordera wheel hub which can be a ½” hex, a 3/8
Paper ID #10375Developing a Minor Program in Nuclear Science and EngineeringDr. Masoud Naghedolfeizi, Fort Valley State University Dr. Masoud Naghedolfeizi is a senior professor in the Department of Mathematics and Computer Science at Fort Valley State University. His academic background includes a B.S. in Mechanical Engineering with minor in instrumentation and control, an M.S. in Metallurgical Engineering, and M.S. and Ph.D. in Nuclear Engineering. Dr. Naghedolfeizi’s research interests include instrumentation and measurement systems, control systems, applied artificial intelligence, information processing, and
ABET and the data collection and analysis. v. A PI Asset Library is posted with ample examples on how to integrate PI into a course, how to collect data, and how to analyze collected data. This material is available for all faculty and staff, full-time and part-time. vi. The Department has automated end to end ABET workflow.These efforts have greatly reduced the burden on faculty who have historically scrambledfor data collection and submission on time. The practice described above has helpedtransform a once tedious and ad hoc process to predictable and routine activities.Participation by all faculty improved dramatically and process automation has significantlydecreased the perception of
-shelf engineering ethics textbooks, produce a mix of factors thatmay result in the common finding that students often become measurably less ethical as theyprogress through their undergraduate career [9], [10].In response to this, the College of Engineering at Boise State University is taking advantage ofsystemic curricular change efforts made possible by an NSF sponsored RED grant(Revolutionizing Engineering and Computer Science Departments) to its Department ofComputer Science [11]–[17], and adapting innovations from that project to other engineeringdepartments. This manuscript describes efforts in the Department of Mechanical and BiomedicalEngineering and Micron School of Materials Science and Engineering. These efforts
solutions that facilitate onlineeducation by offering tools for attending classes, accessing study materials, delivering content,and tracking teaching progress across different locations and time zones. Dillenbourg et al. [16]argued that VLE is not simply a trendy phrase used to describe educational software solutions.Instead, they define VLEs as planned spaces, either informational or social, where educationalinteractions happen not only as a form of distance learning but also to improve activities in aclass. In VLEs, students play an active role in constructing the virtual space that can berepresented in various forms, ranging from text-based platforms to fully immersive 3D worlds.VLEs bring together different technologies and pedagogical
practicing engineer in industry, and holds a B.S. in mechanical engineering, an M.S. in environmental engineering, and a Ph.D. in chemical engineering; all from the University of Connecticut. His current research efforts focus on increasing our knowledge of physical and chemical processes for enabling sustainable design of engineered systems including water treatment and wastewater treatment systems.Ms. Paula Quinn, Worcester Polytechnic Institute Through her role as Associate Director for the Center for Project-Based Learning at Worcester Polytechnic Institute, Paula Quinn works to improve student learning in higher education by supporting faculty and staff at WPI and at other institutions to advance work on project
continue to collect research data in subsequent cohorts in (cur-rently) Spring 2021 and (upcoming) Fall 2021 sections, our early studentresponses show that new design has improved overall course reviews, whileachieving curriculum guideline goals for common computer organization andarchitecture course design. In addition, course materials that include coreknowledge areas (KAs) have been kept intact, and student feedback showsthat they understand each KA at comparable levels to classical computerorganization and architecture course content.2 MethodIn typical computer organization and/or computer architecture courses,knowledge areas are composed of the following concepts [1]: • Digital logic • Digital systems • Machine level
AC 2012-4711: IMPLEMENTING ENGINEERING-BASED LEARNING INBOSTON ARTS ACADEMY HIGH SCHOOL STEM COURSESDr. Ibrahim F. Zeid, Northeastern University Ibrahim Zaid is a professor of mechanical, industrial, and manufacturing engineering at Northeastern Uni- versity. He received his Ph.D. degree from the University of Akron. Zeid has an international background. He received his B.S. (with highest honor) and M.S. from Cairo University in Egypt. He has received var- ious honors and awards both in Egypt and the United States. He is the recipient of both the Northeastern Excellence in Teaching Award and the SAE Ralph R. Teetor National Educational Award.Mr. Ramiro g Gonzalez, Boston Arts Academy High School Ramiro Gonzalez is
engineering practitioners.Curriculum for an engineering major consists of foundation courses in engineering, science andmath, mezzanine coursework consisting of a focus on technical engineering content knowledge,and capstone courses pulling this material together and often applied to example engineeringprojects. Course sequences chain together, building on the relevance and complexity of thesubject matter. While active-learning techniques can take shape in any classroom learningexperience, project-based learning pervade the capstone experience1. Project-based learning2focuses pedagogical efforts on open-ended, authentic problem solving. The basis of manycapstone engineering design courses are engineering challenges undertaken on behalf of a third
gaps in the engineering educational literature:1) to bridge research in engineering students’ beliefs and emotions with theory, and 2) toexplore an interdisciplinary approach to understanding how engineering students engage intheir courses using salivary cortisol.Theoretical frameworks The field is gravitating towards an understanding and acceptance of the roles thatmotivation, beliefs, and emotions play in engineering education. 4, 15 However, theory-basedempirical research is much needed in order to tease apart the mechanisms of how eachpsychological construct is associated with another. In the current study, we used twomotivational frameworks: Control-Value Theory (CVT) 16, 17 and Future Time PerspectiveTheory (FTPT). 18 CVT addresses
forsophomores through seniors, with some data on the characteristic measures of a student as theyenter college (SAT scores, GPA, skills in math and science) and demographics. These are large,extensive detailed studies that informed our work.The vast amount of data from the P2P study is used in a number of ways. Knight16 used clusteranalysis to group skills of senior mechanical and chemical engineers. The professional skillsidentified by the students as strengths were leadership, teamwork, communication and contextualawareness, demonstrating that our graduates are likely bringing these desired skills to their futurework, and our programs are accomplishing this. In a study that asked undergraduates their viewon excellence in engineering education17, the
working on involves chemical deicers that are used onconcrete pavements. The project initially involved writing a standard, but after its reviewit was determined that further research regarding de-icing chemicals was necessary. Acomplete analysis of different materials is currently being completed, which will result ina decision of the best product to use. This project has allowed me to use skills that Ilearned in my mechanical design classes relating to the five-step engineering problemsolving method and decision making.I am also currently working on a project that pertains to convenience store buildings. I amcompleting an investigation regarding the cost savings expected regarding lumber,masonry piers, foundation cell grout, and bond beams
Mechanical Engineering and Education at Tufts University. Her research efforts at at the Center for Engineering Education and Outreach focus on supporting discourse and design practices of engineering learners from all backgrounds and at all levels.Geling Xu, Tufts Center for Engineering Education and Outreach Geling Xu is a Ph.D. student in STEM Education at Tufts University and a research assistant at Tufts Center for Engineering Education and Outreach. She is interested in K-12 STEM Education, AI Education, MakerSpace, LEGO Education, and curriculum design.Dr. Michael CassidyDr. Ethan E Danahy, Tufts University Dr. Ethan Danahy is a Research Associate Professor at the Center for Engineering Education and Outreach (CEEO
to model-based system design. It requireseach team of students to use analytic and numerical models to develop a procedure by which alarge slingshot-type device (provided) could accurately launch a softball to hit a series of targetsplaced 75 to 200 feet away. The launch process is partitioned into three parts: transfer of energyfrom a stored-mechanical-energy device to the ball, the dynamics of the flight of the ball withdrag, and the planning and coordination of activities on the ground, as illustrated in the followingschematic. The students are tasked with developing a numerical simulation based upon ananalytic model for the propulsion and flight dynamics, and then use the simulation results todevelop range tables for use in the field
, and balance of GPA’s across the groups. Twograduate students also were in the class, and placed in a single team of 2. The assessmentdata reported here is for the undergraduates only. The in-class groups were self-selected,but generally were the same as the home assignment groups.3. Description of the ModuleThe module consists of an instructor’s guide and PowerPoint slides for three 50-minuteclasses. The slides include both lecture material and in-class cooperative learning exercises.The instructors guide includes learning objectives, justification for the module, prerequisitesby topic, a description of the classes, home assignments from Dorf and Bishop6, and a list ofreferences on cooperative learning.The objectives of the module are: At
. Final and midterm examinations were required. EVE 445L is scheduled for a threehour period one day per week and student’s that successfully complete the course receive onesemester hour of credit towards graduation. The popular laboratory manual authored by Jenkinset al. 4 was a required text for the course.Prior to each laboratory experience, students were assigned a chapter to read from their text orprovided with an instructor-generated handout. As a result, student participants were equippedwith associated background materials as well as with the experimental methods and goals to beachieved during each laboratory session. In this format, students were only learning laboratoryand data analysis techniques. Minimal experimental design was
solving has been presented.Beginning students regularly experience frustration and anxiety because they have not yetlearned to organize newly learned material systematically. The extensive use of GUI toolsprovides convenient user interaction and continuous visual feedback concerning current statusand further required solution steps. The GUI software environment structures the solutionprocess and offers step-by-step guidance, yet requires the student to make all choices. The Page 6.170.10 Proceedings of the 2001 American Society for Engineering Education Annual Conference & ExpositionCopyright 2001, American Society for Engineering
22.530.10possible responses for each question.The remaining paragraphs in this section present the survey results for each question. Since thefocus of this paper is the EET session, there are no comments here on responses for the IndustrialTechnology (IT) and Mechanical Engineering Technology (MET) sessions.The first question asked about the level of technical information (see Figure 12). Three of theteachers thought it was about right for the EET session. One thought there was too muchtechnical material, but did not leave a comment to explain (question 10). Given the overallresponse, no changes to the level of technical detail are planned if this project is used again. Figure 12: Question 1 resultsThe second question was
math derivation steps. Our assessment echoed the your ideas and learning to express them clearly” [1]recently reported mechanical reasoning and sensemaking resultsin the use of AI in physics pedagogy, with Computer Vision toemphasis spatial reasoning which plays an essential role in the III. SECOND LEVEL IMPLEMENTATIONproblem solving of rotational motion in daily engineering and To improve conceptual understanding, the followingtechnology examples. Extending heat transfer coverage in pedagogy was deployed. An information pedagogy ofPhysics One to support diffusion models Generative AI is converting assessment in multiple choice question format to andiscussed
professional development activities for science teachers should provideopportunities for learning and various tools/techniques for both self reflection and collegialreflection 5,6. A collegial community is developed where the participants are providedopportunities for interaction and information exchange, such as interactive seminars on learningand teaching7. Led by faculty in the TAMU University’s College of Education and HumanDevelopment, the interactive seminars expose the teachers to leading edge ‘culture and learning’research discussions.Based on their engineering research experience, each teacher prepares instructional materials andhands-on learning activities/projects to integrate into their classroom8. The faculty mentorparticipation in this
basic math course in calculus that included derivatives and integrals 3) Introduce a course in strength of materials and statics which was calculus-based 4) Introduce a course in basic operations research and simulation 5) Include a capstone courseAfter trying out these changes for a period of over 4 years from 2006 to 2010, it was felt thateven with the new changes the program was not attracting the number of students needed tomake it sustainable as the number of enrolled students as of Fall 2010 stood at 34. The programwas flagged for reevaluation in Fall 2010 and was given the following three options [1]: 1) Restructure the program by combining it with one or more other campus programs 2) Actively investigate collaboration
the emotional dimension, while learning strategies such as rehearsal,summarizing and elaboration to retain, organize and understand the material as elements of thecognitive domain.Engaging students who are not interested in STEM is a challenge. Strategies that are grounded intheories of engagement therefore need to be devised. Active learning is one such strategy. Ifproperly designed, active learning environments promotes engagement in all the three dimensions(behavioral, cognitive and emotional) resulting in reducing absenteeism, boosting cooperativelearning skills, and improving academic performance [12]. Students are motivated to learn whenthey perceive that “1) they are empowered, 2) the content is useful, 3) they can be successful, 4
was utilized6. Here, results based on approximately 7,000 responses fromgraduating seniors at 58 engineering schools are available. The information includes a tabulationof the highest and lowest score for various Engineering majors. These include: Aerospace,Bioengineering, Chemical, Civil/Construction, Computer/Computer Science, Electrical/Electronic,Engineering Management, Environmental, Industrial, Materials, and Mechanical/MechanicsEngineering.Specifically, as illustrated in Table 5, the students at Lamar University rate ABET outcomes “a”through “k” with a higher score compared to those students included in the EBI benchmarkingdata. In particular, the following attributes are among those showing the largest difference incomposite scores
utilized6. Here, results based on approximately 7,000 responses fromgraduating seniors at 58 engineering schools are available. The information includes a tabulationof the highest and lowest score for various engineering majors. These include: Aerospace,Bioengineering, Chemical, Civil/Construction, Computer/Computer Science,Electrical/Electronic, Engineering Management, Industrial, Materials, andMechanical/Mechanics Engineering.II. Engineering CriteriaABET, the recognized accreditor for college and university programs in engineering, technology,computing, and applied science, is a federation of 31 professional and technical societiesrepresenting these fields. It is also involved in international activities, including the WashingtonAccord, and
, the description of thefailure does not need to be overly cumbersome, as it will detract from the positive aspects of theengineer’s capabilities8 and the value to the owner of the engineer’s “lessons-learned” onanother project.IX. Soil Mechanics and FoundationsStudents are instructed to treat the submission of a retaining wall design assignment as adesign/build project proposal. Proposers must design the wall in accordance with specifiedagency guidelines. One of the agency guidelines prohibits certain types of proprietary retainingwall products when used in a corrosive soil environment. The student engineer is designing andsubmitting prices for his/her company’s proprietary wall system. A last minute addendum to thedesign criteria package
statistical Signal processing. Adaptive filters have found use in many and diverse fieldssuch as communications, control, radar, and sonar, and seismology, etc. I started with random variables andstochastic processes. Then I discussed Wiener filters, Least mean square algorithm, least squares andrecursive least squares signal processing. Second series of lecture consists of multirate processingtechniques which allow data to be processed at more than one sampling rate and have made possible suchnovel applications as single bit ADCs and DACs, oversampled digital filtering, which are exploited in anumber of modern digital systems, including for example familiar compact disc player. The materials inthis series have been extended to include polyphase DSP
Paper ID #9209Integrating Sustainability Engineering into Second-Year CompositionDr. Connie Gomez, Galveston College Dr. Gomez received her Ph.D. in mechanical engineering from Drexel University in Philadelphia, PA. She has worked in the areas of computer aided tissue engineering and sustainability at the University of Texas at El Paso. She is currently a member of Galveston College in Galveston, TX, where she is developing a new engineering program.Ms. Leslie Braniger, Galveston College Page 24.778.1
the gas adsorption in solving the overall mass balancefor conventional PSA. However, this term is significant for Ultra-Rapid PSA owing to its morerapid adsorption rate and the superior properties of the adsorbent material, such as smalleradsorbent particle size, the monolith structure of the adsorption bed, and the active binder thatholds the small adsorbent particles together. For Ultra-Rapid PSA, the absorption term is soimportant that its presence in the total mass balance is essential. The magnitude of the group N3indicates that the adsorption equilibrium can be described by a simple linear relationship for bothconventional as well as Ultra-Rapid PSA.This example indicates the power of scaling analysis as a systematic method for
separate course forpre-EE freshmen, ELEN1200 Introduction to Electrical Engineering based on the Infinity Projectcurriculum.ELEN1200 is a two-credit lab/lecture course that meets twice weekly--a one-hour lecture sessionfollowed by a three-hour lab. The laboratory is critical to the course and involves a set of well-designed experiments intended to introduce the student to major aspects of electrical engineeringstudy. One mechanism for this is the use of the Hyperception Visual Application Builder (VAB)software. The VAB uses a methodology of developing DSP algorithms and systems graphicallyby simply connecting functional components together with a mouse. A user only needs to choosethe desired functions, place them onto a worksheet, select their
): Using Mobile Devices to Improve Student Interest in and Perceptions of Qualitative method 3 Learning Fluid Mechanics via Hands-on Flow Visualization and Experimentation Engineering Identity Qualitative method 3 During Phases 1 and 2, each participant was asked to submit bi-monthly quick reflection online. In all projects, in addition