/eliminate thisundesired effect, quasi-random number (QRN) sequences are introduced. The generators of thesesequences are so designed and developed that they produce more uniformly distributed randomnumbers. The study of uniformly distributed RNs was started in 1916 by Weyl [5]. Heintroduced the notion of discrepancy that reflects clustering and that measures the quality ofuniformity of a random point set. Hence the QRNs are also known as low discrepancysequences. An ideal QRN sequence is one where discrepancy/clustering is nonexistent. Forinstance, if we generate K, say 1000, random points over a finite area having A, say 10, unitsquares, then in each of the A=10 unit squares we should have exactly K/A = 100 points for anideal QR sequence. If we
asmeasured with the Likert scale questions of Part 2 of the survey. Limitations of the survey werethat even though the survey was designed to measure conception, the respondent was limited andsomewhat guided by the options listed on the survey. These may not fully reflect therespondent’s conception of engineering design. To help address this, the survey did provide theoption for the respondent to provide additional comments and add design activities. The questions used in the survey addressed content validity in that they presented astudent’s knowledge of design; construct validity in that selection of the most and least importantdesign activities gave some insight into the student’s reasoning; and criterion validity in that wealso gained
BOK2 and Blooms Taxonomy before providing that assessment, an effort that takes time away from their research. The second is that the assessment is hypothetical, it reflects the authors sense of the extent to which BOK2 outcomes are presently being achieved and commentary about what would be required if the department were to make these outcomes a driving force in its curriculum development. Other faculty members, in the same department, might have opinions which are different, and even perhaps more informed, especially those that are directly involved in the day today delivery of the undergraduate program. Where NC State stands relative to BOK2 is presented in assessment
researchers have argued that this model does not provide a complete picture ofengagement. They pointed out that it does not consider the commitment of psychological energyor the relationship between the quality of student effort and student learning.30 Other theorieswere developed to explain the impact of engagement on student development. For example,experiential learning theory took a dynamic view of learning entirely separate from the IEOmodel. Instead, it was based on a learning cycle which in turn was driven by the resolution ofdual dialectics which it defined to be action/reflection and experience/abstraction.24 Regardless of the model, it has been well established that engagement has a positiveeffect on student outcomes and development
found thatpersistence rates and levels of engagement varied significantly from institution to institution.They also reported that engineering students have the same level of engagement as students inother majors and, despite heavier course loads, reported levels of satisfaction with the collegeexperience and involvement with campus organizations and volunteer work at levels similar tostudents in other majors. One notable difference reported by engineering students in that study;however, was that those students rated themselves lowest in terms of personal and socialdevelopment, as well as in regard to reflective and integrative learning, when compared to theirpeers in other majors. The authors of that study speculated that this perceived lower
State University (MSU) recognizedthe need for restructuring its curriculum in part to modernize its undergraduate program andincrease enrollment which had begun to decline rapidly since 1990 following nearly a decade ofsteady growth as indicated in Fig. 1. This decline in enrollment was in most part a reflection ofcareer opportunities available to ASE graduates and was not necessarily unique to MSU. In fact,Mississippi State University is on par with the national average* in the percentage of enteringfreshmen choosing ASE as a major (1.8% compared to national average5 of 1.6%), thepercentage of engineering BS degrees awarded to ASE majors (2.25% compared to the nationalaverage6 of 2.2%), and has exceeded the national average in recent years in
StudyThe first study was conducted as part of a Pilot Program for Internet Based Reflective Learningfor Cooperative Education Students which was funded by a University InstructionalDevelopment Fund Grant and an Asa Knowles Research Grant7. . During the 1997 winter andspring quarters, Canale and Duwart conducted 11 focus groups in which more than 80% of the Page 5.145.14ECE students, sophomores through seniors, participated. Within a written survey, they wereasked to identify the learning that took place in each of the 11 attributes as a result of theirclassroom, laboratory, and co-op learning. Each group of students then discussed
reduction of design fixation26. In the experiment, student groups were given differenttasks across multiple design stages. Some were allowed to build one or more prototypes early onand then reflect upon what they had built, some were allowed to consistently improve theirprototypes, some were only allowed to start working on physical prototypes at the end, and somewere not allowed to build any prototypes. All groups received technical critiques of their designsin between the three design stages. The overall takeaway is that early prototyping efforts that arenon-continuous are likely to quickly settle on one concept and perfect it. Allowing for constantprototyping allows a team a chance to develop entirely new concepts with time to evaluate
has been known to significantly increase success, retention, and graduationrates. We noticed the differences in the level of preparedness and its influence on the student’sperception of their journey. We also explored the influence of soft skills, outlook, scholarlyattributes, and support on the perception of the journey through the program. Although ourparticipants have reported that they did not perceive any overt sexism or racism, we present thefindings correlated with gender and race/ethnicity.Our future work will include fine-tuning the protocol to explore intersectionality and reflect uponthe situations where the students might feel minoritized. Additionally, the students in the futurestudy will be purposefully selected to examine
items passed the .32 criteria, and together, the model explained a totalof 46.26% variance. Therefore, we proceeded with the more parsimonious one-factor solution.The one-factor CFA model fitted poorly to the data. Therefore, we explored the modificationindices. By allowing error covariances of similarly worded items (i.e., between items 16 and 18,19 and 21, 17 and 23, 19 and 22, 19 and 20, and 20 and 21), we reached an acceptable model fitfor the one-factor solution of the CFA sample (χ2 = 137.52, df = 16, p < 0.001, RMSEA = 0.1095% CI [0.085, 0.116], CFI = 0.96, TFI = 0.93). All items loaded above .50 onto the mindsetfactor. These modifications reflected the covariance among items that focused on intelligenceand among items that focused on
specificallyformatted with prompting questions that the student answers at the end of each class period (forthe class discussion notes) or at the completion of each project (for the project summary notes).These completed “worksheets” 1) get the first year students in the habit of documenting thematerial that they have learned, 2) allow the student to look back to previous work for reference(both during the course and in subsequent courses), and 3) allows the instructor to follow theprogress of each student when the binders are checked formally (mid-semester and end ofsemester or whenever the need may arise). In addition to obtaining/creating a binder after thefirst class period, the students write a reflection paper (with prompting questions) about a seriesof
, Page 22.221.6race/ethnicity, parents’ education, class-year, disciplines, and SAT scores) and then on measures of six academic (classroom and curricular) and ten out-of-class student experiences that theliterature indicates are related to learning and skill development18, 19.Variables UsedThe Design Skills scale is the criterion measure for this paper. This scale contained 12 items(alpha = .92) reflecting engineering students’ reports of their self-assessed ability on design skills.Table 1 gives this scale’s item-content and descriptive statistics.Four sets of independent variables are used: sociodemographic (Table 2); classroom experiences(Table 3); curricular experiences (Table 4); and out-of-class experiences (Table 5
Directorat the Center of Engineering Education and Outreach at Tufts University. Hynesreceived his B.S. in Mechanical Engineering in 2001 and his Ph.D. inEngineering Education in 2009 (both degrees at Tufts University). Inhis current positions, Hynes serves as PI and Co-PI on a number offunded research projects investigating engineering education in theK-12 and college settings. He is particularly interested in howstudents and teachers engage in and reflect upon the engi- neering designprocess. His research includes investigating how teachers conceptualizeand teach and how students engage in engineering through in-depth case study analysis
CS.Next, the theme of collaboration was also found to be beneficial for students’ formation of bondsin CS. This result is reflected in prior work whose results suggest that the long-term impacts ofproject-based learning in STEM transcend traditional learning outcomes to also includeprofessional advancement and friendships [60]. Further, authors demonstrate that students’exposure to collaborative assignments are a significant, positive predictor of their persistence inCS [26]. Interestingly, however, the more recent work of Lehman et al. [32] found that students’exposure to collaborative pedagogy in introductory CS courses was a significant, negativepredictor for persistence. In their discussion, they suggest that the surprising result may
betelling of how students approach learning with the affective domain [14]. Also, returning to theidea that the domains are connected is reflected in the fact that many of studies found focus on twodomains at a time instead of only one domain at a time [4-7], [14-19]. Several studies exist thatresearch the domains, but they focus on testing a specific class within engineering or non-engineering majors [4-6], [9], [14-16], [18], [20]. Similarly, the studies that focus on math orchemistry classes may not have tested solely engineering students, which could still distort or skewresults towards conclusions that may not apply to engineering students overall [4-5], [21]. Theproblem with these studies is that their findings cannot be generalized for all
level students as they graduate. To supporttransitions between quarters or semesters, students maintain rigorous documentation of theirefforts, typically in the form of VIP notebooks or institution-approved electronic portfolios. VIPprograms also involve peer evaluations, reflecting the team-based nature of the course. GeorgiaTech has developed a web-based peer evaluation tailored to VIP, which will soon be piloted witha handful of consortium members.Cost EffectiveAlthough VIP projects are not limited by quarters or semesters, the VIP program is curricular,with all students participating for a letter grade. This differentiates VIP from paid researchexperiences, as students do not receive stipends or hourly wages. This makes the program cost
, 63% minored in Math, CS and/or arelated discipline. Some respondents indicated that they had earned two or more minors intargeted programs. 11% of respondents had earned a Masters in Math, CS or a related discipline.Analysis of survey questions yielded the following results:Respondents were given a checkbox question with a list of statements, and asked to check allthat apply:Question: What did you think of your PESP experience? Check all that apply.Responses: The following are the 5 most endorsed statements out of 16 statements. Thestatements that were endorsed by the 102 respondents are reflected in percentages of respondentsbelow. 1. PESP was fun–selected by 84% of respondents 2. PESP gave me insight into the types of problems that
interface or application. Thus, Python, with strong compact ability across this area, is mostrequired. Fortran is second on the list mainly due to its efficiency in math calculation, whichmade it suitable to simulate large physical systems, and the existence of legacy code in theindustry practice. In short, while policy and investment fuse the growth of the electric power industry, theworkforce, however, is facing growing skills shortages [5, 6]. On the other hand, academicshave seen the need to renew the power system engineering curriculum and attempts to integrateup-to-date knowledge into the curriculum are reflected in the literature [30, 31, 34, 39, 40, 42-44].4.3 Soft Skills The resulting ranking of soft skills extracted from the job ads
selecting items from the MCA that aligned with targeted five out of sixcompetencies and created additional items to reflect the content in the online module [17]. Asmentioned earlier, Young and Stormes (2020) discussed a unique mentor program at CSULB asa two-semester operation. In the first semester, faculty mentors attended a 10-week hybrid-training format with one in-person meeting and 8-week online sessions focusing on the learningobjectives of the EM curriculum. In the second semester, the mentors would practice their skillswith students in mentor-related projects. Based on the learning goals of the tailored EM trainingand the critical aspects of the mentor-mentee compacts from the projects, the researchers selectedand modified items in each
round of interviews was scheduled to take place in the spring of 2020. Due to theCOVID-19 pandemic, and the abrupt switch to online learning in March of 2020, data collectionfor the first cohort was delayed until mid-summer. We also needed to update IRB protocols toinclude remote interviews as well as update the interview protocol to ask about any positive andnegative experiences the students had due to the COVID-19 pandemic. Consequently, weinterviewed cohort 2 (AY 2020-2021) in the spring of 2021. We then interviewed students fromcohort 1 (AY 2019-2020) in the fall of 2021. Although these students were entering their thirdyear of college (typically junior level), we used the same protocol and asked them to reflect ontheir first year. Finally
interviews contained seven questions intended to allow instructors to reflect upon andsuggest improvements for anchor deployment: 1. How many anchored lessons did you offer in your course this semester? 2. What did a typical anchor look like in your course? 3. How difficult was it to add anchored lessons into your existing curriculum? 4. What were some challenges you faced when implementing anchored lessons? 5. How did you perceive the students’ opinions of the anchored lessons? (i.e., Did they seem to like the content? Did they ask good questions? Were they attentive?) 6. Do you feel that the anchored lessons added positive value to your class? In what way? 7. Any suggestions on how to improve anchors in future semesters
relative to their peers - reflecting opportunity gaps but notdeficits in capability. To normalize each applicant, students summarize their skills and interestsin an application consisting of demographic information, short answers, and eight 200-500 wordessays. The essays focus on the lived experiences of each student, offering students an opportunityto demonstrate their qualifications for the CIRCUIT program in their (1) potential for leadership 3 Table 1: A summary of the CIRCUIT pillars and benefits to stakeholdersPillar Description Student Benefit Nation BenefitHolistic Student selection Critical enabler for Evidence-driven
authors acknowledge partial support of this research from the National Science Foundation In-novations in Graduate Education in Cyber-Physical Systems Engineering under Grant No. #2105701.Any opinions, findings, conclusions or recommendations expressed are those of the authors and do notnecessarily reflect the views of the National Science Foundation.The authors thank Prof. Susan Tripathy and Prof. Trina Kershaw for providing valuable resources ontechnical communication and teamwork during the IGE workshops. R EFERENCES [1] E. F. Barkley, K. P. Cross, and C. H. Major, Collaborative learning techniques: A handbook for college faculty. John Wiley & Sons, 2014. [2] M. Dollinger, J
School: Youth Reflect on Mentoring Their Younger Peers,” J. Early Adolesc., vol. 41, no. 2, pp. 332–362, Feb. 2021, doi: 10.1177/0272431620912472.[49] T. Ngoma, “It is not whom you know, it is how well you know them: Foreign entrepreneurs building close guanxi relationships,” J. Int. Entrep., vol. 14, no. 2, pp. 239–258, 2016.[50] D. K. Dutta and M. M. Crossan, “The Nature of Entrepreneurial Opportunities: Understanding the Process Using the 4I Organizational Learning Framework,” Entrep. Theory Pract., vol. 29, no. 4, pp. 425–449, Jul. 2005, doi: 10.1111/j.1540- 6520.2005.00092.x.[51] S.-Y. Liu, C.-S. Lin, and C.-C. Tsai, “College students’ scientific epistemological views and thinking patterns in socioscientific