AC 2011-1050: COMPUTATIONAL EXPERTISE IN ENGINEERING: ALIGN-ING WORKFORCE COMPUTING NEEDS WITH COMPUTER SCIENCECONCEPTS.Claudia Elena Vergara, Michigan State University Claudia Elena Vergara. PhD Purdue University. Fields of expertise: Plant Biology and STEM Education Research. Dr. Vergara is a Postdoctoral Fellow at the Center for Engineering Education Research (CEER) at Michigan State University. Her research interest is in STEM education through research projects on instructional design, implementation and assessment of student learning, aimed to improve science, engi- neering and technology education.Mark Urban-Lurain, Michigan State University Director of Instructional Technology Research & Development
Professor at North Carolina A&T State University. His research in- terest include the implication of cloud computing technology on teaching and learning environments for underserved student populations.Dr. Tony E. Graham I, North Carolina A&T State University Tony E. Graham is Associate Professor, with a D.Eng. in civil engineering, May 2002, Morgan State Uni- versity, Baltimore, Md., USA; a master’s of architecture, May 1998, Morgan State University, Baltimore, Md.; and a bachelor’s of science in architectural engineering, May 1979, North Carolina Agricultural and Technical State University, Greensboro, N.C. Graham’s research interests are infrastructure engineering, geographic information system, and building
Do’s and Don’ts related to industry engineering adjuncts.Do’s:Do provide industry adjuncts with the resources they need such as room keys, parking stickers, employee IDs, computer login information, learning resource management system (e.g., Blackboard, Desire2Learn, or WebCT) instructions, classroom technology instructions, passwords for the copy machine, phone numbers, etc.Do provide industry adjuncts with relevant schedule information such as dates for breaks, final exams, school holidays, when grades are due, etc.Do help industry adjuncts prepare course syllabi by providing appropriate institution and department policies and any specific formatting requirements.Depending on the course, do give industry
- University faculty/staff Community needs Corporations Engineering Mentors and students Community Mentors EPICS – Service-Learning Community Needs Page 22.1285.4Figure2: University Model for Corporate Engagement through Service-LearningA nationally recognized model for engineering service-learning is the EPICS Program. EPICSwas initiated in the School of Electrical and Computer Engineering at Purdue University in Fall1995 by
feedback we consider this a step in the rightpedagogical direction.We do believe that both of the actors in this study were right; the company representatives didnot consider Scrum especially advantageous for mechatronics development but the studentsdid. Our conclusion is therefore that Scrum is advantageous for learning mechatronicsproduct development but not necessarily for doing mechatronics product development. To beable to show the advantages of doing mechatronics product development we need to study thesubject further.References[1] B. Boehm, Get ready for agile methods, with care, Computer, 35 (2002), pp. 64-69.[2] http://www.agilemanifesto.org/principles.html, 2012-03-11.[3] C. Larman, V. R. Basili, Iterative and Incremental Development: A
in the areas of integration of computation in engineering curricula and in developing comprehensive strategies to retain early engineering students. She is active nationally and internationally in engineering accreditation and is a Fellow of ABET and of the AIChE. Page 25.645.1 c American Society for Engineering Education, 2012 Fostering Industry Engagement in the Co-Curricular Aspects of an Engineering Living-Learning ProgramIntroductionThe CoRe (Cornerstone Engineering / Residential Experience) living-learning program atMichigan State University (MSU) entails
strategies to measure performance against expected outcomes but not necessarilytowards applications to life‟s uses or individuals‟ preferences. In their book “Creative ProblemSolving” Edward and Monika Lumsdaine11 present many stimulating contrasts between ourinformation-based edu-system and creative learning. Education is a system that defines what andhow-much one should learn and from what sources; teaching focused, not learning focused. In aparallel sense, one historical perspective delving into the dichotomy of the engineeringpractitioner as contrasted with the educators‟ predilections that teach engineering was offered in1993 by Hazen and Ladesic.12 They tracked the changes of the aero-engineering curriculum fromthe 1920‟s forward to 1995
AC 2011-1421: CHALLENGES FACING GRADUATING ENGINEERS INTHEIR TRANSITION FROM COLLEGE TO CAREERHoda Baytiyeh, The American University of Beirut Hoda Baytiyeh is a computer engineer. She has earned a Ph.D. in Instructional Technology from The University of Tennessee, Knoxville. She is currently an assistant professor in the Education Department at The American University of Beirut. Her research interests include Engineering Education, ubiquitous computing using Open Source Software, and online learning communities.Mohamad K. Naja, The Lebanese University Mohamad Naja has earned his M.S. and Ph. D. in Civil Engineering from Michigan State University at East Lansing. He is currently an associate professor in the Civil
. 8. J. M. Chang, “Bridging to Practice in Computer Engineering Education,” Proceedings of 1998 International Conference on Engineering Education, pp. 300-302, Aug. 17-20, 1998.9. ENTC 352 Introduction to Mixed-Signal Test and Measurement Class Reference Manual, Texas A&M University, 2010.10. M. Prince, “Does Active Learning Work? A Review of the Research,” J. Engr. Education, 93(3), 223-231, 2004.11. C. Eugene, “How to teach at the university level through an active learning approach? Consequences for teaching basic electrical measurements,” Measurement, 39(10), pp. 936-946, December 2006. Page 22.965.10
projects encourage andexcite them about what they are learning in the classroom. Projects like this state of the art beltmonitoring system require applied research in electronics, mechanics, computer science, andmining. The PLC driven display can notify the operator when maintenance is needed saving thecompany a substantial amount of money. Similar to the bottling plant example, this systemmonitors weaknesses in the belt and detects rips, tears and delays. This is an applied researchproject with worldwide potential, that affords students an invaluable experience and opens theireyes to the many the possibilities in their new careers.One ELET - ARAP student described the experience as a “golden opportunity” and hoped that itwould “open the door for
, Page 25.69.5computer engineering and computer science. The devices that have been developed haveundergone extensive animal testing and are showing promising results.Stakeholder ImpactThe four major stakeholders for the program are our student interns, the client companies, theuniversity, and the regional innovation community. More than 880 student interns haveparticipated in the program. They have benefited from the program in several ways including thetechnical experience, a well-paid part-time job, gaining insights into the context in whichengineering is practiced, and an introduction to the norms of behavior within the practice of theengineering profession. Surveys of the student interns suggest that the experiences have beenwell regarded
AC 2011-1822: INDUSTRY EXPERIENCE AND PERSPECTIVE: A SUR-VEY OF ADVICE BRIGHAM YOUNG UNIVERSITY CAPSTONE ALUMNISHARE WITH INCOMING STUDENTSTaylor Halverson, Brigham Young University Taylor Halverson earned a double major PhD at Indiana University in Instructional Technology and design and Judaism and Christianity in antiquity. He earned Master’s degrees from Indiana University and Yale University. His Bachelor’s degree was earned at BYU. Dr. Halverson spent several years working for Cisco in Silicon Valley where he designed creative learning experiences for thousands of customer service agents spread across the globe. Dr. Halverson currently works as a Teaching and Learning Consultant at BYU, assisting faculty
joining CU-ICAR and Altair, Dr. Schmueser worked as a research engineer at Battelle Memorial Institute in Columbus, Ohio and as a senior staff engineer at General Motors Research Labs in Warren, Michigan. He was also an Adjunct Professor in the Mechanical Engineering Department at Wayne State University in Detroit, Michigan, 1993-2007. Dr. Schmueser has over 30 years experience in light-weight materi- als design, vehicle optimal structural design, and computer-aided-engineering instruction. He currently serves on the Board of Directors of the College-Industry Partnership Division of the American Society of Engineering Education.Johnell Brooks, Clemson UniversityMr. Shayne Kelly McConomy, Clemson University Shayne K
Memorial Institute in Columbus, Ohio and as a senior staff engineer at General Mo- tors Research Labs in Warren, Michigan. More recently, he was an Adjunct Professor in the Mechanical Engineering Department at Wayne State University in Detroit, Michigan and University Business Devel- opment Manager-US for Altair Inc. in Troy, Michigan. Dr. Schmueser has over 30 years experience in light-weight materials design, vehicle optimal structural design, and computer-aided-engineering in- struction. He currently serves as Past Chair of the College-Industry Partnership Division of the American Society of Engineering Education.Johnell O. Brooks, Clemson University Johnell Brooks is a Human Factors Psychologist. She was heavily
approaches, many scholars began to look past contextualsimilarities and started exploring what role the mind plays in transfer. Deemed the “cognitive”approaches, these transfer theories considered the ways that short-term, long-term, and sensorymemory relate to the transfer of learning (Mestre, 2005). Interestingly, the rise of these approaches corresponded with the growing use of computers.This becomes evident in approaches such as the Information-Processing Model, which likens themind to a computer. This model suggests that learning situations prompt the mind to take ininformation, process it, and store it to apply at a later time (Leberman et al., 2006). TheMetacognition model, on the other hand, focused on the ways in which learners
Q 8. I know how computing is useful. Q 9. I want to find out more about computing 0 1 2 3 4 5 Fig 7. Pre and Post survey comparison for 2011 Summer CampStudents’ answers to every question showed the positive effect of the camp other than the secondand the last one. The answer to second question can be explained as more students learned on thesubject more they realized how complex it is which shows the depth of their understanding. Theanswer to the last question was probably due to fatigue of over exposure in a fairly short time(about two days
, professionalism, initiative, dependability and reliability, adaptability and flexibility, and lifelong learning. 2) Tier 2 includes Academic Competencies which focus on reading, writing, math, science, communication, critical and analytical thinking, and basic computer skills. 3) Tier 3 includes Workplace Competencies such as business fundamentals, teamwork, customer focus, scheduling and coordinating, creative thinking and problem solving, recording or examining information, working with tools and technology, personal health and safety, and sustainable practices that meet the needs of future generations. 4) Tier 4 are Industry-Wide Technical Competencies and include manufacturing process design and development
to a theory of instruction. In order to achieve concept attainment, Brunerbelieves three simultaneous processes need to occur: (1) the acquisition of new information aboutthe technical system and the diagnostic practice, (2) the application of the new knowledge to thecase, and (3) verification of results (learner created diagnostic visual map) with an expert or otherfeedback cues.Another cognitive and instructional theory employed is Butcher and Sumner’s work onself-directed learning and sensemaking. In this case, learners use a self-paced computer-basedtraining program to acquire a content overview of technical systems and diagnostic strategies.These learners then engage in deep-thinking to process and apply this new knowledge to create
, the evolution of the educationsystem resulted in limited access and limited accommodation for underprivilegedpopulations. In this paper we provide case studies that illustrate how students that areeconomically disadvantaged and students with atypical learning styles have suffered themost. We then describe how the application of digital technology, in particular we explorethe Internet of Things (IoT), is making inclusion possible and affordable.The case studies will show that the evolution of the education system, driven by economicefficiency, has resulted in two types of exclusion. Without technology, the “affordable”education system has been delivering programs designed for a limited range of learningstyles, and only to those students and
-set in order to achieve such the lofty, but attainable, goal of "successful engineer." Most importantly, I will demonstrate my ability to learn and to manage through various projects, internships and research experiences that I have participated in, in the past. Specifically, I will describe the impacts of the following experiences: my undergraduate research at UW; my six month internship at [Firm x]; my various extra-curricular activities; and my projects and coursework for my computer engineering degree at the UW. I focus on the following skills, which I have found to be very important to employers and my co-workers and peers: • Multitasking • Documenting Your
. Course or Event Term Deliverables Introduction to Databases Spring- • A manual with ten laboratory experiments Course Summer 2013 Calculus and Math Fall 2012 • Assignments Practicum • Homework • Final report with statistics of success New course: Data Spring • Homework assignments Analysis and Statistical 2013 • Mid-term project Learning Course • Final paper Math Foundation of Fall 2012 • Online lecture materials related to linear Computer Science Course
emergence of powerful frameworksfor Deep Learning Neural Networks (DNN) such as Keras, Caffe, TensorFlow, Torch, Theano,and others requires less and less proficiency in programming languages, as the development tasksinvolve more configuration than programming. Another example is the emergence of powerfulcomputing platforms such as CUDA, the related Open Computing Language (“OpenCL”), FieldProgrammable Gate Arrays (“FPGA”s), and hardware accelerators targeted to meet the stringentrequirements of autonomous driving. All of this makes it challenging to design and offercompelling educational and training programs on autonomous driving. It is also difficult tobalance the breadth and depth of foundational courses such as programming languages
arrangement. Education 3.0 did not constitute much of a paradigm shift. The advent of automation meant that the education system now could do the same thing they were doing butfaster and more efficiently. College professors who wrote on chalk boards switched theirlectures over to electronic presentations, engineering drawing skills were replaced by CAD, andcalculators and computers replaced slide rules. But there was hardly any change in theparadigm. Classrooms have remained teacher centric, learning in classrooms is overwhelminglya passive exercise, and standardization of curriculum and testing continues to remain the order ofthe day. Driven by the needs of Industry 4.0 and associated speed of technological change,conversation has started in many
. Page 25.778.1 c American Society for Engineering Education, 2012Infusing the Curriculum with Cutting-Edge Technologies through Partnerships with IndustryAbstractTo ensure that curricula and course content reflect both academic and industrystandards the School of Engineering and Computing Sciences (SoECS) at NYITbelieves that course content must include elements of contextual teaching andlearning (CTL) which emphasizes the relationship of course content to real-lifesituations1,2. It is expected that CTL which incorporates 1. hands-on activities 2. work-based learning experiences and 3. project-based learningwill engage today’s students more thoroughly than the traditionallecture
communites31.‖ This perspective does not view knowledge asunimportant, but sees it as arising from the goal-directed activity that people engage in withinparticular social and material settings. One cannot separate the individual and the social group;they are co-constitutive. Under this view of learning, ―the mastery of knowledge and skillrequires newcomers to move toward full participation in the sociocultural practices of acommunity16‖.In Industry Fellows, there are two paired practice communities: university academics within aparticular discipline, and professional practitioners of the discipline: lawyers and law professors,engineers and engineering professors, software developers and computer science professors,nurses and nursing professors
0.33 -1.23 -0.48 - -0.94 - -0.16 p-value 0.739 0.110 - - *Factors not computed in 2018As shown in Table 5, the real-world experience and instructional support reported by studentswere not significantly different in traditional vs. remote learning. There are a number ofpossibilities why this may be the case. First, the low survey response rates in the traditionaloffering of the ENGINE capstone (26%) may have generated too small a sample or a biasedsample. Or it is possible that instructors adapted the course expectations to meet the demands ofthe moment in the remote learning offering of the capstone. To some degree, this is
simultaneously learned howto develop research projects and mentored the high school scholars, which allowed them tosimultaneously be researchers, mentors, and emerging experts in computer engineering areas. Wepresent pre- and post- assessment data from the high school scholar's computer science,biomedical, and nutritional courses. We also present longitudinal data from previous year'sparticipant’s successes in applying for colleges, and how the distribution of advisement materialsto high schools from low-income school districts impacted the student's college readiness. Wehave provided college advisement packets to nine school districts in the Mississippi Delta region.We will also demonstrate the potential for success for this approach to STEM outreach
questions:Focus Group Questions 1. Describe your overall experience with the course 2. Describe your overall experience with the industry case studies. 3. Were the case studies motivating? 4. What did you learn from the case studies that was surprising or unexpected to you? 5. In what ways did the hour session with a real-world industry partner contribute to your experience? 6. These were fairly extensive case studies. How did the workload for the case studies influence your learning experience in the class? 7. In order to complete these case studies, you were required to use the computer and computer software. How prepared did you feel to use this software? (Only asked in Summer 2016) 8. What elements of the case studies
cognitive organization of routine elements, and of the relationships betweenthese elements. And with the advancement of technology, this process should be formulated as acollaborative activity between humans and computers where computers help to elicit mentalmodels and analyze results.This is the foundation for the well-established conceptual map learning paradigm, which has seenwide adoption in education and training. Concept maps are at the core of our proposedcompetency mapping model for aviation training, whose underlying mechanism requires bothtrainers and trainees to communicate through visual representation of the task and proposedsolution.Concept Map based TrainingConceptual mapping is a well-recognized approach that uses both content
. However,recently, this has changed: programs in both engineering and engineering technology have begunto spotlight experiential learning. In fact, accreditation bodies such as ABET20,21 haverecommended an experiential component as part of all bachelor’s and even, if appropriate,associate’s degrees in both engineering and engineering technology.The changing curriculum, the corresponding update of ABET criteria, and the redevelopmentand adjustment of pedagogy thus motivate this research, as making experiential learningopportunities more available requires someone to provide the experience. Strong collaborationsin the computing field between industry and academia have resulted in significant strides instudent educational quality, including an