not organize regularclass survey to get feedbacks from the students. Only one official on-line survey is organized bythe university at the end of the semester, and it turns out the students feel that the author’steaching method has many problems, which the author has not realized until after the end of theclass when he sees the survey results. The evaluation of the students on the author’s teachingquality is shown in Fig. 1, and the average score (with 5 being the full grade) is only 3.54, muchlower than the department’s average, which is 4.25. Page 14.1207.3Previous studies 1-4 show that class evaluation/survey by students provides an
designers, but rather as constructors that willtake design plans and specifications to effectively complete a project. In the educational arena,we train our students to be problem solvers that will use their ingenuity (1) to come to a solution.We encourage students to be able to work independently, to be creative, cooperate with teammembers, and to manage their time to meet a deadline. (2)Course DesignSince simulating an actual working project is difficult in the classroom situation, we concentrateon developing the capstone experience from the bid stage through the preconstruction phase.Our goal is to simulate the needed development that a contractor does from the time ofidentifying a project to the point of the preconstruction meeting. We have
findings are reported in Hsieh (2005)1.One key finding was that system integration engineers generally work in teams and can beclassified into one of three job types: application engineer, control engineer, and mechanicalengineer. Application engineers tend to be the most experienced. Their job is to come up with aconceptual design for an automated system based on a customer's requirements. It is typicallytheir responsibility to develop proposals and to communicate design information to customersand other members of their team.In the second round of interviews, we continued to ask experts about their jobs and the systemintegration industry, with a particular focus on application engineers with 15 or more years ofsystem integration experience. In
by the commonvalues of academic success, civic engagement, intellectual curiosity, and the pursuit of lifelonglearning.Students from all cultural and socioeconomic backgrounds interested in a RC community mustcomplete an additional step in their application to University Housing. Application to participatein a RC community does not guarantee admission. Students must submit (1) a resume outliningpast work experience, volunteer and extra-curricular activities, and (2) an essay explaining theirinterest and commitment to the RC program. Academic record is not a consideration in theresident selections process, so there is no bias toward students with the highest academicpotential. The ERC admission process also does not specifically focus on at
industries that have a presence in Maine or who are graduates of theUMaine CMT program or who hire UMaine graduates. Several small and major companies inboth the public and private sector are represented. The boards for each program meet twice ayear to learn about the progress of the programs and how they may serve the respectiveprograms. (1)The CMT program has a board comprised of 14 members representing construction companiesfrom both in Maine and out of state. Current companies consist of Kiewit Construction, CPM,Cianbro Corporation, Sargent Corportation, Gilbane, Nickeson & O’Day, James W. SewallCompany, Bancroft Construction, the Maine Department of Transportation, SW Coleengineering, and the Lane Construction Company. Additionally, the
cited byDee 1, 2 show little to no relationship between course workload and faculty performance rating oroverall course quality. However, a relationship or lack thereof does not imply causation. In herstudies she assumes that student evaluations represent their opinions reliably and validly. That isstill a long way from a true representation of the actual quality of the course. Perceptions about afact, especially when expressed by people who are not yet qualified to make sound judgments,has a limited validity or none at all. That brings an issue of which questions from an evaluationof faculty and a course the students are really prepared to answer?Ponton et al. wrote that “theories of cognitive motivation assert that to provide maximum self
“expand and diversifythe pool of incoming students who are well prepared and eager to enter as engineering majors” 1. Page 14.705.3This expanded diverse pool of engineering students should help meet the governor’s request for20,000 more engineers, as a baseline requirement for California’s economy2, into the state’sworkforce in the next 10 years.Three main outreach models had shown success at different CSU campuses: 1) The Math,Engineering and Science Achievement (MESA) program3, 2) Project Lead the Way (PLTW) 4,and 3) the Accelerated College Entrance (ACE) program at CSU Sacramento. The ACE modelwas chosen for Humboldt State University as it
students. The students within each group did the experiments and datacollection together. However, each group member was required to submit a separate lab reportindividually. The lab report was graded based on its content, format, and language.Because lasers were involved in the experiments, the students were given laser safety trainingbefore the experiments. Also, during the experiments, the operation of the laser itself was mostlycarried out by the teaching assistant, who is also a graduate student of the author.The schematic of the system used in two experiments is shown in Fig.1 (a) and a real photo ofthe system is given in Fig. 1 (b). The laser module can be operated either in continuous mode orpulsed mode, and the laser beam is expanded by
interest group (see Table 1 below). The second step was to apply a fitnessfunction to the system and ascertain the fitness of each plan. The students decided on the general form of the fitness function which was:(1) limit the rate of increase in energy consumption, (2) limit the rate of decrease in energy production, (3) minimize the unit energy cost, (4) maximize the renewable sources, (5) maximize the domestic sources, (6) minimize the air/water/soil pollution including CO2, and (7) maximize the efficiency. These fitnesses were incorporated into matrices and each normalized as a percentage of 1. The third step was to apply the genetic operations. The students implemented the evolutionarychange functions as:Crossover9,10
the new classroom pedagogyand its benefits is presented.IntroductionThe traditional lecture method of dispensing education is gradually becoming outmoded due toits inherent passivity and abstraction. Especially for certain technical courses, a straight runlecture would not guarantee adequate or high conceptual gains for the students, leaving too muchto the imagination. According to the Dale retention cone 1, students tend to retain only 5% ofwhat they hear, 10% of what they read, 20% of what they see, 50% of what they discuss 75% ofwhat they practice and 90% of what they teach. Kolb’s 2 experiential learning model alsoreinforces the idea that cooperative, hands-on, active and problem based learning greatly enhanceconceptual understanding and
evidence iscollecting it in a way that does not alter it. Computer forensics involves the preservation,identification, extraction, and documentation of digital evidence in the form of magnetically,optically, or electronically stored media (J.P. Craiger 2005). Therefore, law enforcement agentsnowadays face a new challenge; they must be familiar with the proper procedures of seizing andsecuring digital evidence.1. Computer ForensicsComputer forensics may be defined as the retrieval and analysis of data from a seized computeror any other electronic media performed in such a manner that the results are reproducible byanother examiner who, by following the same steps, reaches the same conclusions. Computerforensics has also been described as an
the information contained in documents through aninteractive and intuitive interface.Topic SegmentationThe previous work on automatic topic segmentation can be broadly classified into two types: (1)lexical cohesion models, and (2) content-oriented models. In lexical cohesion models the textsegmentation is guided primarily by distribution of terms used in it. So the lexical co-occurrenceof thematically-related or synonymous terms indicates continuity in topic and the introduction ofnew vocabulary refers to a new topic, implying a boundary between the two. In content-orientedmodels, the re-occurrence of topic patterns over multiple thematically similar discourses areevaluated. We plan to use lexical-cohesion based approach known as TextTiling
. Page 14.887.2© American Society for Engineering Education, 2009 More to Say: Analyzing Open-Ended Student Responses to the Academic Pathways of People Learning Engineering SurveyKeywords – Open-ended Survey Responses, Student Academic ExperiencesAbstractA final, optional open-ended question in the Academic Pathways of People LearningEngineering Survey (APPLES) that asked “Is there anything else you want to tell us that wedidn’t already cover?” elicited free form responses from 37 percent of the 4,266 surveyparticipants. This paper explores their responses. After data cleaning, 880 responses wereanonymized by individual and institution. The responses were rated on a numeric value (1-5)ranging from negative (criticizing) to
.”1 Engineering technology faculty are a part of a field that must embracechange. However, this “built-in resistance” often stems from the reinvestment of the timerequired to master new technologies and maintain quality-learning environments in theclassroom. Therefore, increased value is placed upon the instructor’s technical expertise anddissemination as students become more technologically sophisticated.Is the mastery of contemporary technological theory and application alone enough to adequately Page 14.529.2prepare students for the workplace? Is there a fundamental disassociation between what is beingtaught and learned in the classroom
the world. He recently edited two volumes: Technology & Society: Building Our Sociotechnical Future (MIT Press) and the Yearbook of Nanotechnology in Society, Volume 1: Presenting Futures (Springer).Heather Canary, Arizona State University Polytechnic Heather E. Canary (PhD, Arizona State University, 2007) is assistant professor of communication at Arizona State University. Her primary research areas include organizational communication and family communication in contexts of disability and public policy. She teaches courses across the communication discipline, particularly in organizational and family communication. In her courses, Dr. Canary emphasizes ethical implications of
far, several common interest areas have been explored, includingactivities such as university-spanning workshops and collaborative projects.Introduction – The Product Innovation Engineering Program, PIEpThe Product Innovation Engineering program (PIEp) is a Swedish national research anddevelopment program with the purpose of enhancing product innovation capability withinSwedish universities and companies. PIEp was launched in late 2006, with governmentalfunding,1 as the Product Innovation Engineering Program, PIEp.2 The program is organized as anetwork of researchers, educators and students in innovation engineering with the ambition ofcreating a system change toward innovation and entrepreneurship in institutes of highereducation and
Page 14.1346.2using Hardware Description Language (HDL) utilizing the on-chip FPGA memory, interfacewith on-board memory and clock, and testing the system at a high frequency rate.Hardware platformThe Altera DE2 FPGA educational board13 shown in Fig. 1 has Cyclone II FPGA, 512 KB ofSRAM, 8MB SDRAM, and 4MB of Flash and full range of I/O interfaces. The large Cyclone IIFPGA has 33,216 Logical Elements and on-chip memory of 105 4K RAM blocks. These areused for internal storage and configuration. The EDA tool that comes with this chip is Quartus II6.1 software. It is provided with the DE2 board kit. The board is designed for senior/graduateand small research projects.Description of design projectThe design problem is described in the first part
the instructor are scheduled, or when first and last class sessions meet inperson in a classroom setting. For further details on online teaching techniques the reader canrefer to e.g. Bender[1].Scope:This paper tries to answer the following three research questions: 1. what are the students’preferences for different online delivery techniques?; 2. what is the perception of engineeringand engineering management students towards online courses compared to the traditional on-campus courses?; 3. Do factors such as previous exposure to online programs, differentengineering programs or different demographics affect the outcomes of the survey. In order toaddress these research questions, a survey has been conducted in the engineering college at
education delivery techniques.IntroductionMotivationBased on Gibbons[5], the number of Master’s-degree students enrolled in engineeringmanagement programs doubled between 2003 and 2006, despite an overall decrease of9% in engineering Master’s enrollment. In 1999, the total engineering managementMaster’s enrollment was about 1,767 students. In 2003, this number went further up to2,229 and it was up to 4,625 in 2006. This trend is also supported by the continuedincrease in the number of programs over the past 30 years. As reported in Alvear et al. [1],about 30 engineering and technology management programs existed in 1970s, andcurrently this number is over 160.According to the Bureau of Labor Statistics[4], the need for Engineering and NaturalScience
position new hires for success in the corporate environment. Theinterview candidates were additionally asked follow-up questions about specific personnelexperiences to provide supporting examples for their responses as appropriate.ResultsThe input collected from the interviewed managers was summarized as the behavioral taxonomypresented in Table 1. The responses were categorized as ‘skills’ or ‘experiences’ andsubsequently separated into 7 behavior types. The term ‘skills’ refers to the development of ademonstrable aptitude in a specific domain. The term ‘experiences’ refers to an evolvingmastery of specific knowledge over time. An analysis of the skill and experience data producedthe following behavior categories: adaptation, collaboration
B.S in Engineering from Stanford's Product Design program and has a M.A. in Education from the Stanford School of Education program in Learning, Design and Technology.Larry Leifer, Stanford University Larry Leifer is Professor of Mechanical Engineering Design and founding Director of the Center for Design Research (CDR) at Stanford University. A member of the Stanford faculty since 1976, he teaches an industry sponsored master's course ME310, "Engineering Design Entrepreneurship;" a thesis seminar, "Design Theory and Methodology Forum;" and a freshman seminar "Designing the Human Experience." Research themes include: 1) creating collaborative engineering environments for distributed
indispensable and complimentary component of engineeringeducation. This paper will outline a pilot study based upon one learning outcomeselected through student assessment. A concept will be presented to utilize the pilot studyresults to design a process for integration of co-op learning with classroom learning toincrease student success.BackgroundEach discipline has a skill set that one must acquire in order to become an expert in thatfield. The Accreditation Board for Engineering and Technology (ABET)1, for example,has tried to institute learning outcomes for accreditation which will instill the set of skillsfor successful engineers. Many of these skills are not technical but are considered “soft”or “interpersonal” skills. Unfortunately, though
AC 2009-1155: CHANGING THE MARKS BASED CULTURE OF LEARNINGTHROUGH PEER ASSISTED TUTORIALSEsat Alpay, Imperial College LondonPeter Cutler, Imperial College LondonSusan Eisenbach, Imperial College LondonAnthony Field, Imperial College London Page 14.316.1© American Society for Engineering Education, 2009 Changing the Marks Based Culture of Learning through Peer Assisted Tutorials E. Alpay1, P.S. Cutler2, S. Eisenbach2 and A.J. Field2 1 Faculty of Engineering (EnVision) 2 Department of Computing Imperial College London, South Kensington Campus
studies and the obstacles students face in pursuing advanced degrees.Agreement is measured on a five-point scale where 1 indicates strong disagreement and 5indicates strong agreement. Most statements are phrased positively such that agreement isdesirable, but some statements are phrased negatively and disagreement is desirable. Forexample, one item states “The research requirements necessary to complete a graduate degreeare undesirable.” To score the entire survey, responses to the negatively phrased items are Page 14.872.3reversed so that higher average scores reflect more positive attitudes toward graduate studies.A pilot study was conducted to
with a well developed plan in order to ensure a successfulproduct. Our instructional design process can be summarized as a 6-step iterative process (Figure1); the unfilled arrow represents the iteration point in the process. Some of the productsdeveloped from this process will be discussed to further clarify the design process. Page 14.1198.2Figure 1- Curriculum Design ProcessLaboratory Development ProcessStep 1- Determination of Design ConstraintsAll design problems have a set of constraints and requirements that are important to clearlyidentify at the beginning of the development process. The main requirements for our newlaboratories are
6 Computer Science 5 Electrical and Computer Engineering 4 Other 3 Total 72 Table 1: Students enrolled in RBE 2001 Unified Robotics I according to their majors in the fall of 2008.In the fall of 2008, 72 students were enrolled in RBE 2001, a 300% increase from the firstoffering in the spring of 2008. A breakdown of the students according to their majors ispresented in Table 1. While it is clear that the course attracted students from diverse backgroundsthe vast majority of the students considered themselves RBE
clear that a number of them are not technical andthey are sometimes referred to as “soft skills.” Among these soft skills are ethics (outcome “i”),teamwork (“e”), global perspectives (“j”), diversity (“j”), communications (“g”), and life-long Page 14.720.2learning ( “h”). The focus of this paper is the ethics requirement. “Proceedings of the 2009 American Society for Engineering Education Annual Conference & Exposition Copyright 2009, American Society for Engineering Education” Table 1: TAC TC2K Required Outcomes (Criterion 3) a. an appropriate mastery of the knowledge, techniques, skills and modern
the real world.According to the Accreditation Board for Engineering and Technology (ABET), theaccreditation criteria on industrial engineering or similarly named engineering program’s Page 14.470.2curriculum, “The program must demonstrate that graduates have the ability to design, develop,implement, and improve integrated systems that include people, materials, information,equipment and energy. The program must include in-depth instruction to accomplish theintegration of systems using appropriate analytical, computational, and experimentalpractices.”1 Historically, the ISE curriculum has been developed and modified according toindustry trends
, http://www.us-standards-strategy.org.(ASME) http://asme.org/Codes/.(ASTM) http://astm.org.ConclusionsStandards are a critical but often overlooked aspect of an engineering education, many timesdiscovered only after graduation. Regardless, no practitioner can afford to ignore standards,domestically or internationally. It has been said that standards are the bridge between marketsand technology and that whomever controls the bridge controls the future. As the late PeterDrucker, business theorist, noted,” The best way to predict the future is to have a hand in shapingit.” We as engineering and technology educators hold the future (our students) in our hands. Wemust strive to teach them how to become the experts of our public policy on standards.1
which are too complexto calculate or too expensive to be reproduced in a laboratory, or are simply notaccessible to the senses. The successful use of computer algebra systems does not implythat mathematical skills are no longer at a premium: such skills are important as ever.However, computer algebra systems may remove the need for those poorly understoodmathematical techniques which are practiced and taught simply because they serve asuseful tools. The usefulness of this approach will be evaluated by direct observation andformative assessment, and feedback from other educators will be highly appreciated.1. IntroductionEngineering electromagnetics is considered one of the most difficult courses and mostabstract and conceptually difficult areas