design and conduct37, 38, 39 to guide our process. We piloted ourinterview protocol with several returning and direct-pathway engineering PhD students or recentgraduates who were not a part of our survey sample. Feedback from participants in our pilotinterviews helped us to test and refine our protocol.Our final interview protocols addressed seven primary topics: 1) an introduction to the interviewand basic background information about a participant’s current position in their PhD program, 2)a characterization of their pre-PhD work and research experiences, 3) their process in deciding topursue a PhD, 4) characterization of academic experiences and the their doctoral research,including the progression of their research agenda, 5) students’ plans
shown in Table 1.Table 1. Participant demographic distribution for total survey response group and free-response group Demographic Percentage of total Percentage of response response population population with write-in (N=1448) response (n=406) Gender Male 63 62 Female 32 36 Prefer not to say 3
that a good understanding of topics covered in prerequisite courses,is essential for students to become successful in engineering courses.1-3 This is especially true inmore advanced courses in thermodynamics. Thermodynamics is perhaps the most highlystructured subjects among all engineering courses. It is based on a number of definitions andbasic concepts, such as the definitions of open or closed systems, and the understanding of thedifferences between intensive and extensive properties. Any new topic in thermodynamicscontinuously builds on knowledge gained in previous topics. Students who lack theunderstanding of the basic concepts will have difficulty grasping new materials as moreadvanced topics are introduced in thermodynamics.When the
withregards to these courses.IntroductionThere are a number of professional skills that are important for engineering graduates topossess.1-10 These skills are particularly important in creating engineers capable of addressingcomplex global challenges. Professional skills are included among the outcomes in the ABETEAC criterion 3 (Table 1).11 As well, the American Society of Civil Engineers (ASCE) definedan expanded list of professional outcomes in its Body of Knowledge Second Edition (BOK2).12Similar professional skill outcomes can also be found in the ABET accreditation criteria forengineering technology (ABET ETAC)13 and computing programs (ABET CAC)14, as well asinternational accreditation standards.15-17 Some faculty in engineering refer to
theuniversity standards. In the Hart Research Associates study (2015), the researchers found twokey points that define the reason for the gap. 1. “Employers are more likely than college students to see room for colleges and universities to improve in ensuring graduates possess the full set of skills and knowledges needed for success.” (p. 9) 2. “Many employers feel that colleges graduates are falling short in their preparedness in several areas, including the ones employees deem most important for workplace success. College students are notably more optimistic about their level of preparedness across learning outcomes, however.” (9. 11)The top four skills that employers look for that cut across majors are oral
cell efficiencies ranging from 16-22%, with higher efficiency panels costing more than lower efficiency panels.While designing, students filled out a design report. The report contained a summary of each oftheir designs and its performance against the criteria, a series of scaffolding questions to guidetheir analysis of each design and a comparative table where students listed the advantages anddisadvantages of each design and made a final recommendation.Energy3D and Data-LoggingEnergy3D is a publically available professional-grade CAD software with extensive solarsimulations and analytical tools. Figure 1 is an example of a home that can be built in Energy3D.When a student opens Energy3D each of the actions they take from the data schema10
students (in the ILS sense) tend to lecture more. Inour data, faculty learning preferences and teaching preferences do not appear to be stronglycorrelated. Results suggest that faculty who are more instructor focused than average tend to useactive and collaborative learning activities, and formative evaluation to a lesser extent.Conversely, faculty who are more student focused than average use lecture as a teaching tool to alesser extent.IntroductionFaculty choices about how they teach in undergraduate engineering courses have importantimpacts on student learning. Past research has found that faculty’s implicit beliefs and thoughtsinfluence their behavior in class [1]–[3]. The strategies and actions faculty adopt to teach in class, ithas been
curricular analytics techniques to these patterns in order to quantify the extent to whichparticular reforms should improve graduation rates. Our work involves breaking curricular com-plexity into two components: (1) the structural complexity, which is determined by the manner inwhich the courses in a curriculum are organized, e.g., prerequisites, number of courses, etc., and(2) the instructional complexity, which is determined by the inherent difficulty of the courses inthe curriculum, the quality of the faculty and academic support, etc. We then demonstrate howthese measures can be used within a simulation environment to estimate the impact that particularcurricular improvements will have on student outcomes. This will reveal that many
explosions killedcrews of seven and the Mars orbiter explosion cost NASA $125 million.1,2 These catastrophicevents have one thing in common – miscommunication between engineers and other projectmembers. In the hotel walkway disaster, a structural engineer submitted preliminary drawingsthat were taken to be final from the steel fabricator.1 Internal flight safety problems werebypassed and miscommunication between engineering and management foolishly launched aspacecraft based on incomplete and misleading information, causing the Space Shuttleexplosions.1 The Mars orbiter disintegrated because of a mismatch in units between groundcontrol and the actual spacecraft.3 When there is miscommunication between engineers and otherindividuals involved in
individualsevaluate both the personal and academic fit of each institution, along with advice for interpretingand comparing offers of financial assistance. While the specific focus of this paper is oncomparing offers to graduate programs (Master’s or PhD) in engineering in the United States, thegeneral principles may be helpful for a wide variety of post-graduate applicants.IntroductionA recent internet search on “making the choice between graduate programs” offered nearly 10million results, with the “most relevant” options being a variety of blog posts and opinionarticles. Such accounts have been published in popular media [1], [2] and by sites that focus onhigher education [3], [4], and their content ranges from identifying the pros and cons of
values and tolerance. Then the students are instructed to build the circuit in Figure 1. This circuit is simply a resistor mounted in series with a dc voltage supply and dc ammeter. The dc voltage measured across the resistor should be the same as the voltage supply. The objective of the assignment is toFigure 1 Circuit for Laboratory Assignment
their world continues to evolve [1].Knowledge is more like a web than a chest of drawers. There are no subjects that are unrelated toothers. It happens the same with engineering training; the program is a web of knowledge,provided by studies, delivered in a time frame, interconnected and necessary to get the pertinentknowledge and development of skills that will enable them to learn by themselves. This is whystudents have to see the big picture from the beginning. It is important to show them, in the firstweek of classes, the whole program, as a big frame and its parts and the details of each part. It isa way to locate them within the program. It is hard but not impossible and it the effort is worth.The knowledge of the entire program has an
(2015-2016) I have the privilege of being a Course Assistant for three classes at Stanford: (1) E14: Introduction to Solid Mechanics; (2) BIOE51: Anatomy for Bioengineers; (3) BIOE80: Introduction to Bioengineering and Engineering Living Matter. I also have pleasure of serving as the Safety and Operations Manager at the Volkswagen Automotive Innovation Laboratory, which includes managing the machine shop and teaching students how to use the machinery. In this role I am able to advise and educate students on design choices for their personal and research projects from ideation phases to functional products, with an emphasis on design and manufacturing techniques. c American Society for
materials.Dr. Usama El Shamy P.E., Southern Methodist University Dr. Usama El Shamy is an associate professor in the Civil and Environmental Engineering Department at Southern Methodist University. He received his Ph.D. in Civil Engineering from Rensselaer Polytechnic Institute in 2004. He is the Principal Investigator and Project Director of the NSF funded TUES-Type 1 project: ”A Multi-Institutional Classroom Learning Environment for Geotechnical Engineering Educa- tion.” c American Society for Engineering Education, 2017 Classroom Implementation of Game-Based Module for Geotechnical Engineering EducationAbstractThis paper highlights an ongoing effort to address the
projects focused on STEM education and mentoring.Dr. Monique S Ross, Florida International University Monique Ross holds a doctoral degree in Engineering Education from Purdue University. She has a Bachelor’s degree in Computer Engineering from Elizabethtown College, a Master’s degree in Computer Science and Software Engineering from Auburn University, eleven years of experience in industry as a software engineer, and three years as a full-time faculty in the departments of computer science and engineering. Her interests focus on broadening participation in engineering through the exploration of: 1) race, gender, and identity in the engineering workplace; 2) discipline-based education research (with a focus on computer
racialgroup. Table 3 showed the percentage agreement on the Maternal Wall questions by gender.Table 4 showed the percentage agreement on the Tug of War questions by gender. Table 3 andTable 4 showed that women suffered more Tug of War and Maternal Wall bias than menregardless of their racial background.Regression analysis: Models 1 and 2 in Table 1A4 show that women, African Americans andAsian Americans reported higher level of Prove-It-Again bias compared to their male or whitecounterparts while controlling for other demographic variables such as, age, education, seniorityas engineers, and if working in the academia. Interestingly the difference on reporting Prove-It-Again and Tightrope bias is not statistically significant between Latino/Latina and
organizing preparation for the next general review. Previously, he has worked in promoting reflection in courses within Stanford University.Dr. Helen L. Chen, Stanford University Helen L. Chen is a research scientist in the Designing Education Lab in the Department of Mechanical Engineering and the Director of ePortfolio Initiatives in the Office of the Registrar at Stanford University. She is also a member of the research team in the National Center for Engineering Pathways to Innovation (Epicenter). Chen earned her undergraduate degree from UCLA and her Ph.D. in Communication with a minor in Psychology from Stanford University in 1998. Her current research interests include: 1) engineering and entrepreneurship education
Intensive CareUnits. Scholars also participated in discussions with doctors, nurses, technicians, hospital staff,secretaries, and patients. Scholars followed a three-step process: 1) observe clinical processes,2) identify problems associated with that process, and 3) formulate a need statement. EachScholar maintained an “innovation notebook” to ensure that observations were accuratelycaptured.8 For a few hours twice each week, engineering and clinical faculty met with theScholars to discuss their observations. Through discussions, debriefing sessions, and writtenassignments, the faculty team facilitated students in identifying problems and defining needs, inpreparation for writing need specification statements and brainstorming potential solutions
capstone design courses, including the longstanding core senior design sequence and the recently launched interdisciplinary medical product development course. She also serves as co-Director of the Freshman Engineering Success Program, and is actively involved in engineering outreach for global health. Miiri received her Ph.D. in Bioengineering and M.S. in Mechanical Engineering from the University of Illinois at Chicago and a B.S. in General Engineering from the University of Illinois at Urbana Champaign. c American Society for Engineering Education, 2017 Clinical Immersion Internship Introduces Students to Needs Assessment 1. AbstractA summer Clinical Immersion
topic in recent years. Many questions arise that point toone theme: what can we do to bridge the gap found among minority students? There are a multitude of programs and organizations designed to increase the success ofminority students at engineering colleges across the US, such as the Society of WomenEngineers, Society of Hispanic Professional Engineers, and National Society of Black Engineers.Summer bridge camps and similar programs that help increase students’ academic preparation inmathematics have also been beneficial for underrepresented students (1, 2). At Louisiana StateUniversity (LSU), we provide a program that offers a potential solution to bridging the gap forminority students–Supplemental Instruction. Supplemental
hosting server for the simulation to be performed at the server. The simulationresults are subsequently presented to the remote user via the GUI. This paper details thetechnical development process and highlights its advantages and shortcomings. A number ofcase studies are also provided to demonstrate the potential of this environment for educationalactivities.1. IntroductionSimulation is a powerful method of studying the behavior and functionality of engineeringsystems. With the advancement of Internet and computing technology cloud simulation isbecoming more popular. Cloud simulation is an arrangement in which the simulationenvironment is hosted on a remote server and users have access to the simulation environmentover the web. A detailed
is amethod developed from a diverse range of fields for empirically identifying groups among data.Despite its wide use, in engineering education research few examples of cluster analysis wereidentified in a content analysis of articles from the Journal of Engineering Education. Wedescribe cluster analysis for the educational researcher in terms of 1) selecting features uponwhich to cluster, 2) computing similarities among cases, 3) clustering methods, and 4) calculatingand verifying cluster results. To further introduce cluster analysis we present an example of usingdecision-making ratings to identify high-performing, moderate-performing, and low-performingdesign teams in our freshman design thinking course. We verify this cluster solution
fluency, design fluency, cognitiveflexibility (the mental ability to think about multiple concepts), planning, response inhibition,handling novel situations, working memory, reasoning, problem solving, and abstract thinking(Alvarez, Emory and Emory 2006; Lezak, Howieson, and Loring, 2004; Monsell, 2003). Normanand Shallice (1980) outline five types of situations where routine activation of behavior wouldnot be sufficient for optimal performance: 1. Those that involve planning or decision making 2. Those that involve error correction or troubleshooting 3. Situations where responses are not well-rehearsed or contain novel sequences of actions 4. Dangerous or technically difficult situations 5. Situations that
and other unnecessary stress concentrations as design flaws when a student’s finiteelement analysis failed to detect them. Students were pressed on how they validated their analyses.Finally, the project revealed areas for improvement for the finite element course itself, particularlyto help students synthesize FEA with concepts and tools from earlier courses.IntroductionFinite Element Analysis (FEA), a numerical method for solving complex problems in engineering,continues to gain traction as a valuable design tool within a variety of industries.1–3 This trendlies in part due to advancements in the development of user friendly FEA software. As a case inpoint, Dassault Systemes, designer of SolidWorks Simulation, promises their software can
systems class to 1) help students gainfamiliarity with key concepts, 2) give the instructor an opportunity to correct misconceptions in-class, 3) expose students to multiple instances of the same concept to promote patternrecognition in the class, and 4) promote peer learning for each student.Students were assigned randomly into groups of 3, and each group was given one of 4 similarproblems. The group worked collaboratively to solve a problem reinforcing concepts discussedin class. The instructor assigned a number to this group’s problem, which was then referred to asthe home problem. After solving the home problem, the group was dispersed. One memberwould move to the next problem up, another would move to the next problem down, and thethird
Paper ID #20134Combining Active Learning Approaches for Improving Computing CourseOutcomes at Minority-Majority InstitutionsDr. Debra Lee Davis, Florida International University, School of Computing and Information Sciences Dr. Debra Davis is an Instructor in the School of Computing and Information Sciences at Florida Interna- tional University. Her research interests emphasize interdisciplinary topics including understanding and improving: (1) Computer Science education, including increasing participation of women; (2) educational applications and techniques for online STEM learning; and (3) complex human-machine interactions
,the investigators of this project also provided each group with the project milestones and finalproject deliverables. The outcome for the first meeting was for each group to brainstorm ideasfor their exhibit and to develop an initial concept idea. The second meeting was to present theoverall project concept. The third meeting was to review the preliminary CAD for the project.The fourth and final meeting was to review all of the components of the project. The authorsconsidered each of these meetings as a milestone towards the execution of the project, and eachmilestone was accompanied by a design review, which will be discussed below. Details abouteach meeting, activities and outcomes are described in Table 1.Table 1Collaborative Meeting
disciplinary, cultural, political,and economic boundaries. Every day, engineers are confronted with complex challenges thatrange from personal to municipal to national needs.1 The ability for future engineers to work inmultidisciplinary, interdisciplinary, and transdisciplinary environments will be an essentialcompetency.2 Furthermore, with greater emphasis being placed on understanding social,economic and environmental impacts of engineered solutions, another essential competency isthe cognitive flexibility to think about the whole system at different levels of fidelity and indifferent time scales.3,4 Undergraduate education must train students to not only solveengineering challenges that transcend disciplinary boundaries, but also communicate
. Historical development and current trends in quality arealso presented. The textbook Fundamentals of Quality Control and Improvement by AmittavaMitra is used. Specific topics covered and book sections used are shown in Figure 1. Figure 1 Course Topics Total Quality System Chapter 1 Quality Advocates Chapter 2 Quality Philosophies Quality Management Practices Chapter 3 Quality Function Deployment Quality Management Standards & Tool Basic Axioms of Probability Chapter 4.1-4.4 Probability
during the final project in the Design Thinking course. For thisstudy, only the team member contribution scores for the final design journal and final teampresentation were collected from the surveys. CATME surveys yield numerical data based on thevarious levels of interaction between team members on a scale of 1 to 5 where high qualityinteractions receive a score of 5, intermediate interactions receive a 3 and poor interactionsreceive a 1. The CATME interface asks students to rate themselves and their peers by selectingone of five behavioral descriptions per metric pre-selected by the instructor. These five standardcategories in brief are: 1.Contributing to the Team’s Work, 2.Interacting with Teammates, 3.Keeping the Team