Page 15.51.9design principles to broaden participation in science, technology, engineering, and mathematics.Retrieved 10-25-2008 from http://www.bestworkforce.orgCampbell, J.O., Bourne, J.R., Moserman, P.J., & Brodersen, A.J. (2002). The effectiveness oflearning simulations for electronic laboratories. Journal of Engineering Education, 91, 81-87.Contero, M., Naya, F., Company, P., Saorin, J.L., & Conesa, J. (2005). Improving visualizationDavidovitch, L., Parush, A. and Shtub, A. ( 2006). Simulation-based learning in engineeringeducation: Performance and transfer in learning project management. Journal of EngineeringEducation, 95(40), 289-299.Dede, C. (1995). Artificial realities, virtual communities, and intelligent artifacts
undergraduate years as a liminalspace or time[4,7] during which students can explore possible selves and possible professionalidentities. Ibarra and Petriglieri characterize this kind of activity as identity play, acharacterization we share. They define identity play as “people’s engagement in provisional butactive trial of possible future selves”[6]. We have identified a number of course experiences aspotential sites for this identity play. These include: • the lab courses where students put on lab coats and safety goggles as they become familiar with standard laboratory equipment and protocols and the technical knowledge of chemistry; • a communication course where students visit schools as the subject matter expert to
, China published the national pilotimplementation plan for the integration of industry and education, which requiresthe organic connection of education chain, talent chain, industrial chain, andinnovation chain, and the formation of an innovative mechanism for the integrationof industry and education in higher education.[21] In fact, the integration ofproduction and education is reflected in many mechanisms, such as the "university+ enterprise" double tutor system in the faculty construction mechanism; thepractice platform, practice base and laboratory of university-enterpriseco-construction in the practice training mechanism; the enterprise to provide somemodules such as courses in the resource sharing mechanism. However, theimplementation of
innovations in instruction work because they allow the presentationof material in new ways that students find more accessible to their native learning styles[9-12].Examples of this include the successful integration of laboratory exercises or simulations incourse like chemistry, physics, and engineering[13] to allow students who are more "hands-on"or are sensing students to practice the concepts in the ways they learn best. One quantitativestudy showed that students raised exam scores by an average of 16 percent on a straight scalewhen they were exposed to a simulator of signal processing equipment in electricalengineering[13]. Other examples include using instructional videos or demonstrations onstreaming media that allow visual learners to benefit
AC 2009-315: REASONING ABOUT CATEGORICAL DATA: MULTIWAY PLOTSAS USEFUL RESEARCH TOOLSRichard Layton, Rose-Hulman Institute of Technology Richard A. Layton is the Associate Director of the Center for the Practice and Scholarship of Education and an Associate Professor of Mechanical Engineering at Rose-Hulman Institute of Technology. His areas of scholarship include student team management, assessment, education, and remediation, laboratory reform focused on student learning, visualization of quantitative data, and engineering system dynamics. He is a guitarist and songwriter in the alternative rock band “Whisper Down”.Susan Lord, University of San Diego Susan M. Lord received a B.S
; Benson, Kirn, &Faber, 2013; Felder & Brent, 2016; Vogt, 2008) all contain central features of interaction-dominantcomplex systems. These features include complex, dynamic qualities that produce emergent outcomes(Kaplan, et al., 2012; Mitchell, 2009; Richardsen, et al., 2014). Research conducted within learningenvironments (i.e. classrooms, laboratories, etc.) necessarily involves the interaction of settings, tasks,teachers, and students (Schwab, 1971) and the study of motivation and engagement involves competingintraorganismic and extraorganismic factors (Deci & Ryan, 2002). Because cooperation, competition, andinterference are ever present features of these areas of study, changes in any system variables results inchanges to another
Senior Research Associate (Auditory Protection and Prevention - US Army Aeromedical Research Laboratory, Fort Rucker Alabama), Joint Adjunct Assistant Professor in the Department of Applied Engineering Technology and Built Environment at North Caro- line Agricultural and Technical State University, as a visiting professor at University of Ibadan, Nigeria, Industrial and Production Engineering Department, as a research assistant with Dr. Denise Tucker at University of North Carolina Greensboro in the Department of Communication Sciences & Disorders, School of Health and Human Science, as a Facilities Engineer at Maryland Motor Vehicle Administra- tion Glenn Burnie. Dr. Fasanya holds a B.S. in mechanical engineering
addition to using the concepts and skills of a traditional engineering field [24].Situated Learning in TERM. Learning environments in BME labs have been extensively studiedto identify features (i.e. skills gained, concepts learned, and how) of positive learningexperiences for students and create strategies to improve those lab experiences [24]–[28].Various learning theories have been used to study these lab environments (e.g. distributedcognition [26], cognitive apprenticeships [25], situated learning [24], and agentive learning [28]).One of the studies specifically focused on a TERM research laboratory identified two skillswhich are relevant to the situated student learning environment [28]: the observed need formembers to persevere in the face
systems for internet ser- vices providers and mobile service companies. He has trained engineers and technicians through formal courses, on-the-job training, and supervising on field. His research interest includes self-regulated learn- ing, abstraction in problem solving, and troubleshooting problem solving in laboratory environments. His long-term goals include improving laboratory hands-on activities based on how students improve their metacognitive skills. c American Society for Engineering Education, 2016 Abstraction Thresholds in Undergraduate Electrical Engineering CurriculaAbstractA great deal of work has been done to study the types of problems posed to students in variousdisciplines and
5 of 5 Literacy in Materials Science Undergraduate Students” #11347 11. Manufacturing Materials M735 Teaching the Latest 1 • “Improving Student Lab Report Writing Performances in Materials and & Processes Manufacturing 4 of 4 Manufacturing Laboratory Courses by Implementing a Rhetorical Processes & Materials Approach to Writing” #14083 Concepts 12. Multidisciplinary W241 Multidisciplinary 1 • “Strategies to Integrate Writing in Problem-Solving Courses: Promoting Engineering
course. In essence, all of their prior program baggage went into the classroom every day;they could not hit the “reset” button as students typically do every semester as they encounterdifferent instructors. We became convinced through student testimonials that they needed to feellike, and be “regular” engineering students. Yes, they were admitted through a special programbecause of their potential, but once in the engineering college, students just wanted to be“normal.”To boost both students’ learning and their beliefs that they belong in engineering, in fall 2013 weconverted the traditional preparatory physics course to a hands-on format, implementing weeklyengineering-focused laboratories that focused on data collection, analysis and synthesis
populations.Visscher-Voerman [23] conducted retrospective interviews to identify 16 “principles” used byinstructional designers. Kirschner and colleagues [24] explored how instructional designers (inboth academic and business contexts) used Visscher-Voerman’s 16 principles through a Delphi-type study and a team design task. Perez and colleagues [25] used a laboratory think-aloudprotocol to investigate instructional design practices among both novices and experts.Despite differences in sample populations and data collection methods among the studies byPerez and colleagues [25], Visscher-Voerman [23], and York and Ertmer [6], these studiesreported some similarly themed heuristics/approaches. Each of the studies featured at least one(and usually more) heuristic
people had little interaction with computers at the time [14]. Throughout theeighties and nineties, he continued to explore ways for learners to use computers as “objects tothink with” [20, p. 23] and cofounded the MIT Media Lab, an interdisciplinary research centerwhose members developed and popularized much of the technology that is currently associatedwith Maker Education, from Makey Makey microcontrollers to the kid-friendly, visualprogramming language of Scratch [21].Another off-shoot of the MIT Media Lab was the Center for Bits and Atoms, a group thatemerged out of Neil Gershenfeld’s popular class “How to Make (Almost) Anything” and that ledto the creation of the first Fabrication Laboratories or “Fab Labs”, high-tech workshop spacesthat
80% Apply experimental engineering/scientific tools (e.g., machining, oscilloscopes, 80% instrumentation, laboratory equipment) in engineering/scientific practice Increase perseverance 80% Recognize my strengths and weaknesses 80% Page 13.1372.11According to results from NESLOS, (1) eight participants stated that they spent 1 to 5 hours perweek with their faculty mentor, one stated they spend 6 to 10 hours per week, and one spent 21
’ skillsand knowledge will be directed. From the perspective of faculty, Fromm 3 defines a detailed listof characteristics which future engineering graduates should possess to become leaders of theprofession, including a strong foundation in basic sciences, math and engineering fundamentals,the capacity to apply these fundamentals to a variety of problems, among others.The Millennium Project 4 at the University of Michigan is a research laboratory designed for thestudy of the future of the American universities. The mission of this project is to “provide anenvironment in which creative students and faculty can join with colleagues from beyond thecampus to develop and test new paradigms of the university”. The Millennium Project proposessome key
engineering. International Journal of Engineering Education, 26(5), 1097-1110.7 Boxall, J. & Tait, S. (2008). Inquiry-based learning in civil engineering laboratory classes. Proceedings of the ICE - Civil Engineering, 161(4), 152 –161.8 Burns, R. A., Butterworth, P., Kiely, K. M., Bielak, A. A., Luszcz, M. A., Mitchell, P., Christensen, H., Von Sanden, C., & Anstey, K. J. (2011). Multiple imputation was an efficient method for harmonizing the mini-mental state examination with missing item-level data. Journal of Clinical Epidemiology, 64(7), 787- 793.9 Busch-Vishniac, I., Kibler, T., Campbell, P. B., Patterson, E., Darrell, G., Jarosz, J., Chassapis, C., Emery, A., Ellis, G., Whitworth, H., Metz, S., Brainard
simple inquiries about what they read [13]. This givesthe instructor the ability to adjust where necessary the class content based on student concerns. Inthis strategy, the class session can better maximize what concepts such are focused on and howwell the students engage themselves since the class would have been formatted to reflect theirlevel of understanding.It has also been discussed that while much attention has been paid to the use of active learningapproaches in lecture class, laboratory classes themselves have some measure of passiveengagement that requires the application of active activities [14]. The use of laboratory manualswith step-by-step discussions of how to conduct experiment causes students to learn concepts byrote
found that graduate student mentors who work closely withstudents on their projects served as “coping models” in developing undergraduates’ self-efficacyfor research and graduate school. Specifically, we reported that the REU program served as a“taste” of graduate school, and gave participants access to graduate students and professors whoserved as both role models and sources of information about academic and career options.Several factors contributed to their reported increased in self-efficacy for graduate school andresearch careers: their accomplishments in the laboratory, new knowledge about graduate schooland potential career options, and vicarious learning3 that took place over the summer via theirgraduate student mentors. In particular
(interactions, delivery), in class(interactions, delivery), assessment, laboratory support, and educational technology. Theseresults are summarized in Table 8 for faculty support and in Table 10 for TA support. Somestudents did not have any additional suggestions to provide for faculty or TAs to support theirlearning. These responses were coded as "None." Some responses were off topic in that neitherfaculty or TAs had control over what was being requested. These responses were coded as "OffTopic." Finally, some responses were descriptive and not specific enough to place into anyprimary category of course planning and delivery. These responses were coded as "Intangible."In order to understand whether student expectations shifted from traditional to
technology(ABET), the different engineering program outcomes include applying knowledge of mathematics,science and engineering, designing and conduct experiments, designing a system, components tomeet realistic needs, functioning in a multidisciplinary team, formulating and solving engineeringproblems, communicating effectively, etc. [3]. Various researchers have made attempts toincorporate these requirements in their courses independently. For example, various researchstudies exist on related topics such as problem solving [4-8], course or laboratory projects [9-13],technology in classroom [14-17], teamwork [18-21], experiential learning [22-25], design skills[26-28], etc.BackgroundPublished literature in the past [1-4] presents details about
. Otherplans included graduate study in STEM fields, professional school (e.g. medicine or law), orother jobs not in engineering fields.Respondents were classified as feeling like they belong or feel like an engineer (Q13 of thesurvey) if they selected “Somewhat Agree”, “Agree”, or “Strongly Agree”. Most respondentsreported that they feel like they belong in the school (86%) and their major (84%), and theyfeel like an engineer (80%). Interestingly, 67% of respondents who agreed that they feel likean engineer indicated that the experience that made them feel that way occurred at their ownuniversity (i.e., UVA).Research-Experienced RespondentsExcluding capstones and course-structured laboratory projects, 39% of respondents (n = 303)have participated in
[1].Along with class time schedules packed with lectures, laboratories, and tutorials, there are asignificant number of course assignments that occur outside of class, such as team-basedprojects and experiential learning tasks [1]. Researchers have encouraged the incorporationof these constructivist approaches into engineering education [2], aiming to help studentsdevelop a wide range of abilities (such as complex-problem solving skills andinterdisciplinary thinking [3]). However, this increasing number of assignments stressesstudents [4], [5], negatively affecting their learning results [1], [6].To understand what students define as a demanding course, several researchers haveexplored the concepts of academic workload and course difficulty
and Fire Research Laboratory at NIST as a Post-Doctoral Researcher before joining the faculty of the School of Mechanical and Materials Engineering at Washington State University. His research is in thermodynamics and heat and mass transfer. Over the last five years he has become involved in developing and disseminating research based learning methods. He was a participant in the NSF Virtual Communities of Practice (VCP) program in Spring, 2013, learning research based methods to instruct thermodynamics. More recently he introduced the concept of fabricating very low cost thermal fluid experiments using 3-D printing and vacuum forming at the National Academy of Engineering’s Frontiers of Engineering Education in
Paper ID #19460Work in Progress: Using Conceptual Questions to Assess Class Pre-Work andEnhance Student Engagement in Electromagnetics Learning Studio ModulesProf. Branislav M. Notaros, Colorado State University Branislav M. Notaros is Professor and University Distinguished Teaching Scholar in the Department of Electrical and Computer Engineering at Colorado State University, where he also is Director of Electro- magnetics Laboratory. His research publications in computational and applied electromagnetics include more than 180 journal and conference papers. He is the author of textbooks Electromagnetics (2010) and MATLAB
, and professional skills in diverse inengineering environments. This paper describes the University of Southern California, Viterbi School ofEngineering’s response to this important National Academy of Engineering challenge. Thispaper will describe both curricular research and metrics associated with global preparedness forworking in diverse engineering contexts. In this study, engineering students receivedinterdisciplinary globally focused training via their coursework and laboratory experiences andwere assessed as to their preparedness to work in global workforces and research environments.A global preparedness index was developed and administered to assess the impact of theseeducational and research experience with a summative focus
instructional designers through retrospectiveinterviews. Kirschner and colleagues27 compared university and business instructional designersthrough a Delphi-like study (using Visscher-Voerman’s 16 principles) and a short team designtask. In another study, Perez and colleagues28 compared expert and novice instructional designprocesses using a think-aloud protocol in laboratory setting. Although these studies do not reporton their findings as heuristics, they all rely on data collected from expert practices anddemonstrate several similarities, including an emphasis on learner and context analysis, theapplication of proven techniques, and problem framing. However, these studies also showimportant differences between contexts (e.g., university and business
in an Engineering ClassroomIntroductionThis research paper describes a study that examines a testing effect intervention deployed in anengineering classroom setting. The testing effect is based on the premise that learning isimproved when students engage with newly acquired information by challenging themselves toanswer questions about the content instead of using other means of interacting with the content,such as rereading a text. The testing effect has been established in laboratory research studies[1]. To translate this finding into educational practice, classroom research studies [2]-[6] aim todefine the conditions for which the testing effect remains robust in authentic classroom settings.In the classroom domain, a testing effect
MANUFACTURING TECHNOLOGY.Prof. Branislav M. Notaros, Colorado State University Branislav M. Notaros is Professor in the Department of Electrical and Computer Engineering at Colorado State University, where he also is Director of Electromagnetics Laboratory. He received a Ph.D. in elec- trical engineering from the University of Belgrade, Yugoslavia, in 1995. His research publications in computational and applied electromagnetics include more than 150 journal and conference papers. He is the author of textbooks Electromagnetics (2010) and MATLAB-Based Electromagnetics (2013), both with Pearson Prentice Hall. Prof. Notaros served as General Chair of FEM2012, Colorado, USA, and as Guest Editor of the Special Issue on Finite
virtual worlds for research alliances (e.g. virtual and remote laboratories, intelligent assistants, semantic coding of specialised information). Sabina Jeschke is vice dean of the Faculty of Mechanical Engineering of the RWTH Aachen University, chairwoman of the board of management of the VDI Aachen and member of the supervisory board of the K¨orber AG. She is a member and consultant of numerous committees and commissions, alumni of the German Na- tional Academic Foundation (Studienstiftung des Deutschen Volkes), IEEE Senior Member and Fellow of the RWTH Aachen University. In July 2014, the Gesellschaft f¨ur Informatik (GI) honoured her with their award Deutschlands digitale K¨opfe (Germany’s digital heads). In
use of these policies by faculty members. However, to provide quantifiableresults, two survey instruments were developed to collect relevant data and feedback fromfaculty and students across the campus. RLC members volunteered to promote the surveyamong their faculty, and registration staff helped broadcast the survey among the students. Thesurvey questions used are listed below in Tables 1 and 2. Table 1: Faculty survey questionnaire (89 responses) 1 Which school are you in? 2 For a 1000 level lecture course, do you enforce attendance policy in your class? (excluding laboratory/studio courses) 3 For the above 1000 level course, do you have an attendance policy written in your syllabus? 4 For the above 1000