majors. In the beginning, the program’s seminar-basedworkshop curriculum primarily enlisted the help of faculty from the College ofBusiness, and students received a $1,000 stipend upon completion of thesemester. Student participants attended lectures, discussed leadership topics withmentors from a variety of industries, and composed either a reflection essay orposter to conclude their participation.Chevron Leadership Academy redesignedSettling on specific goals and methods for creating a new leadership program isoften an iterative process, with a good deal of trial-and-error in the beginning [5].Voice-of-the-customer techniques determined in fall of 2017 that the program wasnot meeting corporate sponsor goals, nor was it effective in assessing
at the end of the semester after theworkshops (spring 2017). This study was framed by the following research question: To what extent are faculty beliefs about student-centered strategies reflected in instructional practices in the undergraduate engineering classroom?Literature ReviewStudent-Centered Teaching in EngineeringStudent-centered teaching, or active learning practices, engage key course concepts and materialin an adaptive and interactive manner. Scholars have conducted many empirical studies whichdemonstrate the effectiveness of student-centered teaching practices in higher education. Thesestudies have shown that student-centered instruction promotes greater learning andunderstanding than traditional content
positively because learners who fall into this group tend to be motivatedby learning new things, are persistent in completing difficult or ambiguous academic tasks, andtend to use cognitive strategies to support learning such as metacognition and reflection [20, 21].Task oriented students tend to view mastery as dependent on effort, and perceptions of ability areself-referenced [22]. Task oriented students focus their attention on the task, not on extrinsicrewards; learning, understanding, developing new skills, and problem-solving are motivators [17,23]. Task orientation, like mastery orientation, is the most adaptive orientation for self-regulatedlearning [24, 7]. Task oriented students set self-improvement and learning as their goals; as aresult
satisfactorilycomplete the lab objectives (Figure 4). Here, we present the results of both the student self-reportand instructor evaluation because they assess different things. Instructor assessment mayunderestimate learning if the work they turn in does not reflect their understanding, for exampleif a student rushed to complete lab notebooks, and their entries do not actually reflect theirunderstanding or actual performance. Whereas, student self-reports of learning, may likelyreflect a measure of confidence of the material or some other bias. For instance, self-enhancement and self-diminishment bias may be at play. It has been previously shown that somelow-achieving students tend to over-estimate their abilities and high-achieving students tend tounder
means toreach these objectives. Relying on the positive effects of active learning, we designed a novelcourse format in which the class-time was divided around three different types of activities thatsucceeded each other using the pattern presented in Figure 1. The topic of each unit reflected a keyarea that we aimed to investigate in the course, and we focused on topics that are both morefavorable to generate longer class discussions, and who have been the researched in the past.Examples of such topics include: “Nanobiotechnology and its applications”, “Lifetime ofnanobiodevices” or “Reproducing macroscale bonds at the nanoscale”.Figure 1. Basic unit of the course. Each “cycle” focuses on one key topic related tonanobiotechnology. It consists
otherdecisions about the agent’s role(s). There many ways in which intelligence can be defined ormeasured17, however one way in which it can be defined is as the capacities or the tasks somesystem (whether biological or computational) can complete or learn to complete to address somegoal18, 59-60. In this context, this can be translated to a systems capable of performing some set ofdesign practices or parts of the design process toward some goal. The design practices aninstructional design agent perform are a direct reflection of its responsibilities within its role andthe larger design challenge or project. Note also that this is a definition of domain-specificartificial intelligence or weak AI59, where the domain is the area of design education.Past
absolute frequencybecause some students repeatedly discussed a single topic, which may or may notproportionately reflect that topic’s relative importance. Analysis of the distribution of codesacross all interviews was complemented by exemplary quotes for each category, with in-depthattention given to the categories with the highest number of students commenting.Results and DiscussionExplicit references to the importance of TA confidence and TSE emerged from almost half of thestudents interviewed. These excerpts were used to answer our first research question andunderstand why students found TSE important in their TAs. The second phase of coding focusedon identifying behaviors cited by students that are associated with high TSE and understandinghow
] indicates is one way to ensure robust qualitative research.The research team also wrote analytic memos after each interview and openly discussed broadthemes that emerged from discussions with transfers. Miles, Huberman, and Saldaña [36]assert that analytic memos allow scholars to “reflect on and write about how [they] personally[relate] to the participants” (pp. 34-35). All transcripts were systematically and inductivelycoded individually by at least two members of the team; after coding transcripts individually, theteam then convened as a group of two or more to openly discuss and categorize themes thatcaptured crucial elements of participants’ experiences.In consideration of prior studies involving underrepresented racial and ethnic minorities
statistically significant interactions at the scale level, although several occur at theitem level. As expected from research regarding female engineers and technologists, they haveabove median measures of traits representative of both masculine and feminine genderorientations. They exhibit below median levels of explicit sexism as measured by the SATWscale, but above median levels of implicit sexism as measured by the implicit associations tests(IATs).Higher levels of implicit sexism are also reflected in the SATW items that drew the greatestdisagreement as measured by the Net Support Percentage (NSP), i.e., the percent of responsesthat were not 4s or 5s. Selecting “3 – Neither Agree nor Disagree” on particularly embeddedideas is a typical approach of
skills can include: Understanding of and ability to use relevantmaterials, equipment, tools, processes, or products, awareness of quality issues and theirapplication to continuous improvement. Graduates must have developed transferable skills,additional to those set out in the other learning outcomes, that will be of value in a wide range ofsituations, and plan self-learning and improve performance, as the foundation for lifelong learning[12]. “Service-learning is a form of experiential education where learning occurs through a cycle ofaction and reflection as students work with others through a process of applying what they arelearning to community problems, and at the same time, reflecting upon their experience as theyseek to achieve real
shown below. 4 Table 1 Current ABET Minimum Standards Review year Semester hours of Semester hours of engineering science science and mathematics 2018-2019 [23] 48 32 2019-2020 [24] 45 30These changes reflects a change in approach. The minimum number of hours was once definedin terms of numbers of years (1.5 years of engineering science and 1.0 years of math/science). Ithas now been changed to just require a certain number of semester hours
additional projectdata in combination with the survey data, ensuring that students understand that their instructorsare not performing the detailed survey analysis will help to mitigate concerns that students mayanswer in the manner that they believe they are expected. The influence of different instructorswithin a specific class is outside of the scope of this paper.The survey alone is not well-suited to assess which specific pedagogical elements were moreinfluential in promoting sociotechnical thinking or shaping engineering habits of mind. Instead,the other data sources generated within the overall project – namely, focus groups, assignmentdata, and faculty reflection logs – are being analyzed to better answer this question. Analyzingthese data
include a prescriptive number of credit hours. Mentored Experience (ME): Early career experience under the mentorship of a civil engineer practicing at the professional level, which progresses in both complexity and level of responsibility. Prior editions of the CEBOK referred to this as “E” for experience. The CEBOK3TC wanted to emphasize early career mentoring as part of the experience and adopted this new terminology to reflect and promote the importance of mentoring. Self-Developed (SD): individual self-development through formal or informal activities and personal observation and reflection. This is a new component of a typical pathway that was introduced in the CEBOK3. The CEBOK3TC
fewer formulas a world violates, the more probable it is. Each formula has an associated weight that reflects how strong a constraint it is: the higher the weight, the greater the difference in log probability between a world that satisfies the formula and one that does not, other things being equal.”Given a set of statements (F ) and a set of weights associated with them (w) representing theimportance of each constraint, Markov Logic Network could evaluate multiple design alternatives,or test multiple values for each priority. Such an approach, potentially allows engineering designersto systematically adopt a more inclusive and reflective attitude by being conscious of the normative,and subjective aspects of
high school science classroom, each student 1)located and drilled holes in metal and plastic, 2) tapped a threaded hole in metal, and 3)assembled a completed working pencil-top fidget device.Cycling a classroom of ~25 students through a safety talk and all fabrication process steps todevice competition took four 45-minute class periods, and these activities were repeated acrossmultiple periods each day. To assess indirectly the activity’s impact, students (N = 79) filled outan exit survey with questions posed against a Likert-like response scale. 35.44% and 65.82% ofrespondents respectively reported never using a drill press or threading a hole prior to thisproject. Reflecting on the project, 89.87% agreed or strongly agreed it demonstrated
: 1) collaboration; 2) data practice; 3) published information; and 4)scholarly communication. Given the semi-structured nature of the interviews, it is not surprisingthat the themes reflect the sections of the interview instrument itself. The first theme“collaboration” describes the natural of research practice among the researchers in the privateinstitution; the remaining three themes show the activities related to their research practices. Table1 summarized the main themes and sub-themes found in this study. The details of these themesare described below in this session. Table1: Summary of main themes and subthemes in this study Main Theme Subthemes Collaboration Collaborating for
what theresults mean and how they compared with engineering students. The results showed that thefaculty participants tended to prefer a more reflective than active learning style, a more intuitivethan sensory learning style, a more visual than verbal learning style, and were essentially neutralwith regard to preferring a sequential or global learning style. Comparison data fromengineering students provided contradictory learning styles preferences. Students tend to prefermore active than reflective learning styles, more sensory than intuitive learning styles, and amore sequential than global learning style. The only category where faculty participants learningstyles preferences aligned with engineering students’ learning styles preferences
student language reflect or challenge entrenched ideologies in the engineeringcurriculum? Do student’s perceptions of Con/Decon problems help us gain insight into how theyprescribe a proper engineering education? What do students believe to be a complete education?In Cech’s [19] phrasing, what is supplemental and what is fundamental?Our primary study questions are as follows:R1: Given that students are conditioned to work with decontextualized problems, what is theirattitude towards contextualized ones?R2: What strategies are students using to create context?4Research Design and MethodologyIn fall 2018, we adapted the Problem Rewrite Assignment (in an engineering ethics course,ENEE200) in order to better understand how students perceive
regarding theinstructional process. One girl appreciated the neat and detailed power point lecture notes, whileanother girl wished that hand outs had been given out. This would have been beneficial as the girlscould write notes for further reflection. It would have been a great addition to the lecture notes thatthe girls already had online access through the HBCU’s Blackboard Education Suite.Mixing of Cement Pastes: Four themes emerged from data analysis.Doing: Sixty-two percent (62%) of the girls made ‘doing’ statements to include calculating,measuring, timing, mixing, and ramming. One girl noted that ‘…mixing and ramming the cementpaste was really fun, and exciting but also pretty messy at the same time…’Comprehending: Fifty-two percent (52%) of
i. Adequacy of reference material 10. Support for you as an individual learner i. Individualized instruction 11. Course Summary i. Course reflection in open formatResults from these assessment tools for the overall class are presented after the technical portionis first discussed.Course Setup: Defining the ProblemThis 15-week ‘Engineering Experimentation’ course was divided into three modules. ModuleOne spanned the first three weeks and consisted of experimentation on foundational knowledgeexperiments, where students learned basic concepts about the measurement chain, uncertainty,technical writing, and presenting. These experiments are briefly described as follows: 1. Sump
7 Understanding imposter syndrome 7 Visualizing data 8 Writing Abstracts 9 Making Posters 10 Closing SymposiumWorkshop presenters were experts in the workshop topic areas, and presented interactive, one-hour sessions. All workshops were presented by faculty or staff on campus with the exception ofone workshop for which a post-doctoral researcher was brought to campus. The openingsymposium welcomed students and allowed students to get to know their fellow summerresearchers. Specifically, students were asked to reflect on M.A. Schwartz’s essay, “TheImportance of Stupidity in Scientific Research”. As a result of this activity, most studentscommitted to documenting a non-academic, “novice” experience during the summer
programs through theanalysis of undergraduate curriculum offerings. The focus of this research is to identify trends inthe supply chain, technology, engineering technology, science, management, and other typical“core” course mixes in technology-related supply chain programs at different universities in theUnited States. During this investigation of different programs, it was found that changes occurringin the industry and market needs have been reflected in differing programs’ curricula. This researchis also intended to develop a better understanding of how technology-related supply chain contentis being taught in institutions of higher education and to compare the development ofundergraduate programs over time. An interesting outcome of this
evaluating their model--whether they were considering their model tobe good or bad based on the conditions in the real world or the requirements of the course.Table III: Evaluation of Open-ended Modeling Problem OneEvaluation Frameof Model Course Real WorldGood Broderick: The model used all Broderick: His model reflects his personal experience with the of the course content that he behavior of people and weather (his representative elements) in had learned up to the point at Michigan during the winter. which OEMP1 was given
sucheducational opportunities13. Students participating in “science communication activities inauthentic settings, creating written, oral and visual science messages suitable for various non-technical audiences, and engaging in fruitful dialogues with those audiences”13 (p. 288).Reviewing articles that report on public science communication learning, Baram-Tsabari andLewenstein13 found that academic programs attend to goals ranging from “affective issues,content knowledge, methods, reflection, participation, and identity” (p. 285). Ideally, a scienceprogram gives students an opportunity to speak, think, and do as scientists and engineers withreal audiences if they are to make inroads to attain these goals 12. This means students andaudiences negotiate
obtain employment outside of academia. In termsof the effect on career outcomes, previous studies found evidence that postdoc training enhancesresearch productivity and increases research output [14], [15]. However, postdoc experiencedoes not significantly influence STEM PhDs’ earnings up to 15 years after PhD graduation [15],[19], [21]. The importance of analyzing the effect of postdoc experiences that vary by field of studyhas been stressed by Horta [14] and Kahn and Ginther [19], for example, in part because thedifferences across fields of study reflect their distinct traditions and identities, especially atadvanced levels of academic training [22]. Since the differences in postdoc experience acrossfields of study exist even within
asmall subset of the resources provided (typically 2-4 resources) while overlooking the others,rather than consistently using all nine resources at their disposal. Four resources stood out asbeing most popular: peer collaboration, the lecturebook, online videos, and the course blog,which reflected the findings of Wirtz et al.34 at the departmental level within the context of theFreeform environment.Examining relationships between resource usage and academic performance Using the cluster analysis from their previous paper, Stites et al.18 examined how the nineresource-usage correlated with the students’ academic outcomes in the course (i.e., a higher finalgrades and better exam performance). Combining survey data and academic
. Weconclude that the FLDoE framework may be used as a foundation, but not the sole source, forimportant AM knowledge areas, leaving opportunity for the development of an AM body ofknowledge that reflects employer expectations and geographic variations.1.0 IntroductionManufacturing has evolved from the time that Henry Ford operated the first assembly line in1913. The ability to make products in volume, allowed the US to dominate the world inmanufacturing output, and increase its gross domestic product. In 1951, units of operation inproduct assembly began to be infused with technological innovations, evolving into what is nowknown as advanced manufacturing. Advanced technologies, systems, and processes have notonly transformed the assembly line, but
describing that theclassroom did not provide the same exposure, Byron stated, “In the lab, you have to do a lot of 13outside research to find out what you need to do. No one is telling you what to do step by step.It's a very creative [space]. Inside the classroom, the same creativity isn't necessarily used.”Shortly after, he reflected on the effect that the project process had on his ability to design andmake, stating, “Basically, it taught me how to break things down into parts in my head, and thatmakes it so much simpler.” Due to his ability to apply the concepts learned in class to the maker-projects he was simultaneously working on, Bryan considered
populations in other studies theproject evaluators have been involved with. To examine the reliability of the items for each keyconcept, Cronbach’s alpha coefficient was computed. Cronbach’s alpha is a common metric forgauging reliability. Its calculation reflects the internal consistency, that is, how related a set ofitems are as a group. Higher values reflect a set of items that are more closely related (valuesrange from 0 to 1.0). For all concepts the calculated Cronbach alpha coefficient was acceptable(above .70), and a composite score was generated using the average response value acrossassociated items. The instrument continued to be refined year to year in keeping with thechanging nature of the project, and new constructs with new questions