retirement plan or layoff, new faculty.The tenured faculty elected to cut the pension plan, which was latter restored in full.ManagementType of management reflects directly on the Dean, and Chair, but could also be a reflection ofProvost and President’s policies. Micromanagement and too many rules can hinder anycreativity or progress. An incompetent manager, who says no just to show his/her command,loses the respect of the faculty, and their enthusiasm. The Chair/Dean must be supportive, bysupportive does not mean give sweet vague words, with no real support. Support needs to be byaction, and fighting for his/her faculty, where it counts, not by the manager’s account. Most ofthe tenure of the author at that University, there was a visionary
skill for learning design thinking and influencing the designoutcome.Design thinking in engineering education and challenges:Design thinking reflects the complex processes of inquiry and learning that designersperform in a systems context, making decisions as they proceed, often workingcollaboratively on teams in a social process, and “speaking” several languages with eachother (and to themselves)(10). In cornerstone design courses, design thinking skills thatsupport an iterative loop of divergent (creative) and convergent (critical) thinking throughindividual and team project-based learning environments are needed in addition toinstruction of graphics and visualization tools. Critical thinking skills have a moreestablished history in academia
variousstrategies to recruit more minority participants that included sending out multiple, individualizedemails to minority student veterans, hiring an African-American graduate student to reach out forthe small number of minority student veterans, and requesting referrals from a minority facultymember in the college. However, the final sample remained largely homogenous due to theoverall demographics of the student population in the college and lack of positive response fromminority student veterans. All student veterans who volunteered to participate were interviewed.Five of the twenty participants were interviewed twice—individually then later in a group.Individual interviews provided students with an opportunity to take time and reflect on
terms “studentskills” and “learning objectives” were each used by a single individual.Using fewer terms to identify and describe course outcomes and using them moreconsistently, suggests participants had greater familiarity with the design and development ofcourses as opposed to programs. Participants were also more comfortable talking about theircourses and course outcomes rather than the program, again suggesting greater familiaritywith the concept of courses having outcomes than they were with programs having outcomes.Familiarity with courses rather than program curricula was noted by Stark et al. 36. Theliterature that discusses curriculum in higher education also reflects this focus on courses 1,with an emphasis on course rather than
matter, pedagogical approaches, political and personal preferences, orother criteria as dictated by a dynamic group of stakeholders. Many changes originate from aclearly defined need or mandate, while others may sneak in without a full analysis of the course.Repeated and often subtle changes compound to have a significant impact on the course, creatinga narrative reflecting the intents of the faculty and the concerns of the institution as course goalsand methods are updated in each subsequent semester.This paper describes a process to employ engineering education research methods to describe thenature, development, implications, and motivation behind of course changes. We define a sixstep process focused on the use of artifact analysis to
startup businesses. The paper discusses theevolution of the student group from the engineering economy course and the work of theentrepreneurship consulting group that is receiving much attention from program advisory boardmembers, startup businesses, and university leadership.DisclaimerThe views expressed in this paper are those of the authors and do not necessarily reflect theofficial policy or position of the U.S. Air Force, the U.S. Department of Defense, or the U.S.Government.Introduction and MotivationCompany executives from Alcoa, ADT, and Armstrong among representatives from othercompanies that serve on the Industrial & Professional Advisory Council (IPAC) and a ServiceEnterprise Engineering Advisory Board (SEE) in Industrial
reading or video assignments that prompted students to thinkabout concepts and strategies for success in what they read or watched, reflect on newknowledge they gained, and how these strategies applied to their own journey throughengineering education.The second hour of the lecture meeting was generally used to explore engineering careersand conceptual background and applications for the lab activities and design projects.Topics included measurements and error analysis, computational methods and analysiswith MATLAB, mechanical properties of materials, trusses and structures, fundamentalelectronics, sensors and signal conditioning, Arduino programming, and robotics andsimple control scenarios.All of the lab activities and design projects listed in
multinational projects in an introductoryengineering design course. This paper reports the preliminary findings from a survey based onthe Intrinsic Motivation Inventory (IMI) given to students before starting their participation inthe multinational projects. The data collected provides information in five constructs which are:interest/enjoyment, perceived competence, pressure/tension, perceived choice, andvalue/usefulness. These constructs provide a perception about students’ interests, belief, andfeelings about the international project that reflect their level of motivation and confidence tocarry on the tasks. The data is evaluated and considered in the development of the learningmodule to be incorporated before the project in the same course in the
and Jerusalem.3.2 Ira A. Fulton College of Engineering and TechnologyThe Ira A. Fulton College of Engineering and Technology at BYU currently has an enrollment of4000 students in five engineering and five technology programs. The college awardsapproximately 600 B.S., 100 M.S. and 20 Ph.D. degrees in a year. These degree totals reflect thedirection of the Board of Trustees that BYU remain predominantly an undergraduate institution.About half of the graduates go on to graduate school.The current college administration began to serve in May of 2005. It was natural that we tooksome time to identify strategic directions we felt would help prepare our students for success inthe 21st century and increase the visibility of the college.Concurrent with
’ qualitativeunderstanding of basic concepts and principles. CI’s typically consist of multiple choicequestions with one correct answer and several “distractors” that reflect common misconceptions.The misconceptions are usually identified through formal research processes, such as using focusgroups in which students answer questions and explain their reasoning in an expository manner. A CI can be used to assess both individual student learning gains and effectiveness ofpedagogical strategies, particularly by measuring differences in performance via pre-test (beforeinstruction) and post-test (after instruction). If the CI is not appropriate as a pre-test, then itsability to measure learning gains might be established via other correlations, such as with
”indicates the validation team’s certainty about their judgements using a three level scale: 1 = notvery sure, 2 = pretty sure, and 3 = very sure, and “Relevance” reflected how well they thought anitem measured what was intended to be measured, using the following scale: 1 = low/norelevance, 2 = somewhat relevant, 3 = highly relevant. Netemeyer, et. al.9 also recommendedretaining items with sureness and relevance levels higher than the means. The items included inthe questionnaire have Sureness > 2.17, which means the judges were quite sure about theirjudgments, and Relevance > 66%, which means more than 66% of the judges rated this item asrelevant to what was intended to be measured. After the content validation process, all 37 itemswere
Criterion 3 modifies and restructures the previous 11 outcomes (a)–(k) intoseven new student outcomes (numbered as 1–7).15Notably, the seven new outcomes omit the phrase “life-long learning.” This motion represents asignificant time of reflection in engineering education: a time when reform to accreditationrequirements could dramatically change the way engineering is taught. Despite the potentialremoval of the phrase “life-long learning” from the prescribed outcomes, professional engineerswill still need to possess the characteristics of a life-long learner to be effective. To this end, ourfindings demonstrate several components of life-long learning that are currently being capturedby different engineering programs. Of these current components
. Parametric solid modelling and surface modelling are the basic CAD technologies. Solid modelling creates a model that has the filled volume. It closely represents a physical object by having data of physical properties such as mass, density, along with the geometric information. Surface modelling, on the other hand, generates the outlook of the objects as a surface model but doesn’t reflect the physical properties as in a solid model. Solid modelling is preferred over surface modelling in AM as it has simpler representation of geometric information and provides information that is useful to generate tool path. 4. 3D scanning technology enables reverse engineering of a physical part
variables such as gender, race, ethnicity, family’seducational background, and socioeconomic status. English et al. (2013) reported findings from a STEM-based lesson in whichstudents explored engineering concepts and principles pertaining to simple machines.The students clearly indicated how the machines were simulated by the materials. Thestudents were also able to reflect on different aspects of their design, especially onmaterial properties and how they affected stability. Allowing students to suggest ways toimprove their designs provided opportunities for further reflection in subsequent designprocesses. In general, students did not make explicit references to underlyingengineering and science principles, but they were able to link
used with cautionand only adjust the model if they are consistent with theory. In this case, the wording ofQ8Eng_k and Q8Eng_l are very similar and these measurement items capture similarinformation about students’ competence beliefs; therefore, this modification was made and theresulting model better reflects the data implied matrix.Figure 2. Confirmatory factor analysis of the latent constructs of identity: interest (Int),recognition (Rec), and performance/competence (PC) beliefs for 2790 students in first-yearengineering at four U.S. institutions during the fall semester of 2015. All paths are significant atthe p < 0.001 level. Image generated using the semPlot package in R74,75.The confirmatory factor analysis indicates that the data do
spaces; is it the same or different?Our studyThis research project is investigating three very different universities with engineering programsthat have embraced the maker culture: University B, University A, and University C. Each ofthe spaces are different, reflecting the differences in the institutions. University B is first andforemost a technological institute with the majority of undergraduates majoring in engineering.Its maker space, housed within the Department of Mechanical Engineering, is operated by a 70person team comprising of 65 undergraduate volunteers and 5 non-student members. The makerspace comprises five rooms totaling 2,500 square feet that includes a rapid prototyping suitewith six 3D printers having various material
Concept Inventory20. Additionally, the moderate correlation coefficientsbetween the inventory scores and exam scores fall in the range of values found in previouspublications comparing concept scores to problem-solving skills16. This fits with the observationthat much of the final grade and the exam scores reflect assessments of problem-solving ratherthan conceptual understanding. Overall, the expert selection of questions for the 11-questionsubset and the significant correlations between the aDCI scores and other assessment metricsprovide evidence that the aDCI is sufficiently valid for use in this study. Table 2. Spearman correlation coefficient (ρ) for aDCI scores and other performance metrics. aDCI Pre-Test
, demonstrate thecapability of mobile platform specially the Android platform which bear the testimony thatmobile platform can be made efficient in controlling robot.Preliminariesi. UMLUnified Modeling Language28 widely known as UML is a software engineering tool used formodeling software systems. Fundamentally it is used as a tool for analyzing, designing andimplementing software intensive systems. UML provides a visual representation of the systemwhich reflects the standard and interactive organization or system’s elements. From thebeginning till now, there are several versions of UML have been evolved and UML 2.0 is usedfor the modeling of our system. UML offers two types of system modeling, one is structural orstatic modeling which require the
paper’s style and structure also meld twodistinctive document types—technical report and narrative essay—in order to reflect upon asmall-scale, field-test type experiment and to identify initial positive or negative trends withinthe experience.Instructional ConceptThe development of a specialized grammar course for engineering and other STEM students waspredicated upon four assumptions. The first was that possessing a complete functionalunderstanding of how sentences work can help students to produce technical documents that areclear, concise, and correct; and second, that adequate grammatical skills are too often missing inengineering and STEM majors.Another assumption was that engineering and other STEM students already have mastery in
; this may suggest that students andfaculty have different ideas of what constitutes a critical thinking skill.(3) Faculty perceived students learning how to work independently as a much lower rate thanthe students themselves identified, likely reflecting what each group would consider to be“independent” work. Faculty also saw significantly less success by the students in learning howto conduct a research project (#6), which may indicate that the students are not fully sharing theirmentor’s vision of what is involved in conducting research at a high level. This is mirrored bybenefit #8, regarding a student’s research skills, which a much larger percentage of studentsthought was a benefit received in comparison to the percentage of faculty who
other outcomes that resulted from offenses. Finally, the findings wereorganized into themes. Several steps were taken to ensure the quality of the findings. First, after thetranscriptions were produced, the audio recordings were checked against the transcripts to verifythe accuracy of the data. Second, transcripts were sent to participants to verify the accuracy ofcontent and meaning; no participants responded with changes to their transcriptions. Finally,because five researchers analyzed the data, and the five had various backgrounds andperspectives, the researchers reflected on their positionalities and subjectivities. This process ofbeing reflective helped them acknowledge who they were – and the biases they held – relative tothe
in the description of both cases—regardless of cultural and institutional differences—there are several common places between the two approaches to change presented in this paper(see Table 2 for a summary). There is a common motivation and goal, which is achievingexcellence in engineering education within the region and worldwide. Although each institutiondescribes its guideline principles in a different way, they all respond to the logic model suggestedby CORFO. Of note is that both leading engineering schools were aware of previous experiencesrelated to cultural change in engineering education and to the creation of university-basedentrepreneurship ecosystems. This awareness can be reflected in three major change strategiesconsidered from
Semantics Belief Statements). In order to “clean up” the databefore analysis, the values of the survey were made consistent. In order to encourage participantsto reflect on each pair in the STEM Semantics Survey, some values are switched. For example, a7 might be a very positive reflection of science in one question (ex. “Fascinating”), but a verynegative one (ex. “Unappealing”) in the next item. Therefore, all of the values were firstconverted so that very positive = 1, and negative = 7. For each statement, a lower score wouldtherefore correspond to a higher level of interest in the subject area. The survey wasadministered immediately at the beginning of the engineering activity and was the last actionitem in the program. This testing sequence
are opposed, there istension in the evaluative process.For the purposes of this study, we have chosen to observe students’ relationship to engineeringethics by looking at how they engage in ethical reflection as a team, in the situation of their Page 26.728.3actual project work. This is an alternative to the more common approach of focusing onindividual students and attempting to measure their understanding with an artificial instrument(such as a survey). We suggest that the dual-process account discussed above works as well forteams as for individuals. This study is thus firmly situated in the approach of “team cognition”(Salas & Fiore
may be more appealing and more readily accepted and adopted by some individualsthan others – as anecdotal evidence collected from design classrooms and design thinkingworkshops seems to indicate. The aim of this study is to determine whether student receptivity todesign thinking might be linked to individual cognitive characteristics that reflect innatestructural preferences. This research could help educators determine the most appropriate designmethodology based on the cognitive preferences of their students, as well as the need to teachcoping strategies when students are required to engage in design activities that do not align withtheir natural cognitive preferences.Our work presents the results of data gathered during a design thinking
problem solving process.IntroductionComputational Science and Engineering (CSE) has emerged as an important tool to solvecomplex engineering problems1. Engineers need an ability to use computational tools, integratedwith strong problem-solving skills, to tackle complex problems 6, 15, 16. For example, in MaterialsScience and Engineering, a sub discipline called Computational Materials Science3 has beenestablished. This trend is reflected in educational settings too --- there has been a call to integratecomputational tools and methods into different disciplinary engineering curricula sooner andoften2. Aligned with this idea, the department of Materials Science and Engineering at JohnsHopkins University started a novel computational course for its
activities when necessary Reflection Procedural Quality efficiency Page 26.747.5 GeneralityResearch MethodThe design-based-research (DBR) method was applied, which intertwined the three goals ofresearch, design, and pedagogical practice in
, through case-study analysis, we present potentialpathways towards including affect and identity in how we model engineering students’ moraland ethical reasoning about socio-scientific issues.Specifically, we present two case-study accounts of how future engineers think about anengineer’s responsibility towards the social and global impact of their work. The case studiesdraw from video-taped semi-structured interviews of two undergraduate students whom we'll callTom and Matt. In the interviews, Tom and Matt reflected on the use and impact of weaponizeddrones in the US war in Afghanistan. Through investigating how they think about the socialimpact of drone warfare and how they think about the responsibility of engineers involved in thedesign of
reflecting in her own experiences as an undergraduate and her preference for activelearning techniques. She also notes that she would like to do more but has not had any formaltraining: Page 26.890.7“Ultimately, I do the best I can but feel that I don’t have a lot of formal training. I’d like to get it,but haven’t found the time, or taken the time, to do it … I have taken a lot of what I observed as astudent and focused on things that I liked and didn’t like. I have aspirations of using moreresearch to help develop my teaching in the future.”The faculty member who scored the highest on the RTOP also had the most formal training ineffectively
Group C 3.47 1.19 Group D 3.24 1.33Team online discussion makes me reflect on the course content Group A 2.88 1.24in a deeper level. Group B 2.72 1.06 Group C 2.75 1.32 Group D 2.91 1.42I frequently respond to the post from my group members through Group A 3.53 1.45online discussion. Group B 3.28 1.11