-Fitzpatrick and G. D. Hoople, “Cultivating an Entrepreneurial Mindset: An Interdisciplinary Approach Using Drones,” Advances in Engineering Education, vol. 7, no. 3, 2019. www.advances.asee.org/wp-content/uploads/vol07/issue03/Papers/AEE-25- Hoople.pdf15 G. D. Hoople, A. Choi-Fitzpatrick, and E. Reddy, “Drones for Good: Interdisciplinary Project Based Learning Between Engineering and Peace Studies,” International Journal of Engineering Education, vol. 35, no. 5, pp. 1378-1391, 2019. https://www.ijee.ie/latestissues/Vol35-5/12_ijee3801.pdf16 E. Reddy, G. D. Hoople, and A. Choi-Fitzpatrick, “Interdisciplinarity in Practice: Reflections on Drones as a Classroom Boundary Object,” Journal of Engineering Studies, vol. 11
testing week.There is a spike in student motivation. Many students share that they enjoy finally being able tobegin building their project. During the Week 7’s construction week, many report struggles,setbacks and trouble with coding, resulting in a decrease in motivation. Week 8 is the lastconstruction and testing week. Some teams report their design starts functioning properly whileothers still struggle to get it to work. Week 9 is the presentation and demo day. Many reflect theyenjoy growing together as a team, have fun building the project and learn a lot. Some complainabout uncooperative team members and challenges of the project. 7 6 Self-Determination Index (SDI
” may be a more effective strategy forultimately attaining a distribution of gender within engineering that reflects the largercommunity.References1. Roy J, ASEE. Engineering by the Numbers [Internet]. 2018. Available from: https://ira.asee.org/wp-content/uploads/2019/07/2018-Engineering-by-Numbers- Engineering-Statistics-UPDATED-15-July-2019.pdf2. Bystydzienski JM, Brown A. “I Just Want to Help People”: Young Women’s Gendered Engagement with Engineering. Fem Form. 2012;24(3):1–21.3. Diekman AB, Clark EK, Johnston AM, Brown ER, Steinberg M. Malleability in communal goals and beliefs influences attraction to stem careers: evidence for a goal congruity perspective. J Pers Soc Psychol. United States; 2011;101(5
EGEE-420 EGEE-445Figure 2. The achievement gap between URM and non-URM students in all ECS disciplines. The performancedemonstrates a perpetuation of the achievement gap in lower-division math and science courses that continued intheir senior years. The net result of this achievement gap is a delayed graduation rate. Figure 3 shows the 4-,5- and 6-year graduation rates for URM and non-URM ECS students at CSUF. It appears thatthere is an upward trend over the years in the graduation rate of both URM and non-URMstudents. However, the graduation rate of URM students still lags the graduation rates of generalstudent peers.Figure 3. The perpetuation of achievement gap reflected in the overall graduation rate of
. By the end ofthe semester, 75% of the students reported being satisfied with their assigned teams while 7%were not satisfied (there was decrease in the number of students with neutral opinions).Overall, these results are very encouraging, since it reveals an overall positive perception and itdoes not reflect a drastic change of opinion as the semester progresses. Indeed, based on anecdotalfeedback, instructors have noticed a decrease in the number of interpersonal conflict within thestudent teams compared with previous versions of this course. Unfortunately, the instructors didnot collect any data prior to the use of junto for a more rigorous comparison.Figure 2: Survey results illustrating student’s perception about the team selection and
media presence. 3. Develop technological currency in the student body.The first priority was identified as the most important with the other two priorities to be carriedout with an eye toward the first. A couple challenges affect the primary goal. First, unlike mostU.S. research institutions with a seperate college of engineering, CSE grants degrees in thephysical sciences, math, computer science, and engineering. Students in science and math areless encouraged by their course curriculum to seek out the use of design and prototypingresources so those students need additional programming and attention if the Anderson Labs is tomore closely reflect the diversity of the college as a whole. Second, the primary space is locatedin the Mechanical
objective is to make the hook from stainless steel so it does not rust or stain the tools. AVernier Calipers were made available to students to measure tools and hooks dimensions. Theinstructors explained how to read a Caliper.The students should make 3-4 hooks (figure 5) for a separate tool. Each student has to make hisown design, reflecting his creativity and his own imagination. 3D-printing of these hooks is agood exercise to test them on a real pegboard. A more in-depth analysis is performed, such asheat transfer through the hook, and stress analysis (figure 6) to test the strength of the hooks tohold the objects they were designed to support and search the weaknesses of such an object. Inaddition, as part of this work, each student needs to
person holds the idea that they are incapable of achieving success in a math class, they have a “fixed” mindset about math. These students place themselves at risk of failure because they do not work towards growing in their understanding of mathematics [9]. A person’s dislike for math often occur during the beginning stages of school [9]. Moreover, one’s dislike and uncomfortableness towards math may remain with a person for many years and can lead to a lack in self-confidence by hurting one’s rationale and thoughts [4]. Possessing a fixed mindset can hinder one’s potential for math success as this mindset lacks self-reflection and embracing new ways of learning [3]. A person with a fixed
pre-and post-test surveys as well as program activity attendance, course enrollment, and mentoringteam constellation will be used.EQ3, “To what extent are student and faculty competencies and interdisciplinary andtransdisciplinary skills changing over the course of the training program? Additionally, to whatextent are these changes reflected in longer-term outcomes?,” is an outcome evaluation questionfocusing on competency, technical, and professional skills change over the programimplementation. Data from or about NRT trainees, students attending open activities, andstudents receiving no program exposure (retrospective cohort) data will be compared usinginstitutional research sources, faculty-administered student competency assessments, and
, consistency of contracts and recognizes valuable contributions • Employ an open loop evaluation system that allows ongoing tracking, [12] analysis, communication and synthesis and communication of findings for continuous improvement of the faculty and the institution • Require more equitable scrutiny and evaluation among various faculty [13] groups to communicate the need for quality irrespective of faculty status •Allow time for active learning for adjunct faculty including reflection, [12] writing and self-improvement audits College Communication • Integrate the use of two way communication platforms and powerful [4] technological tools into processes to help build rapport
also required in 2019 for the first time. These required the students to reflect on what theylearned and did the previous week and set goals for the following week. These were added to encourageself-paced learning, effective use of videos, and goal setting. The percentage of course grade comingfrom homework and a semester-long, group project was decreased to allow for the addition of videoquizzes and journal entries to the grading scheme.Population AnalysisThe dataset includes 156 students who completed the course across three years – 2017 and 2018, whichwere taught with a traditional instructional model, and 2019, which was taught using a flipped coursemodel. Student grade in the course, final exam score, cumulative GPA entering the semester
made strong statements such as “AI projects human needs or intentthrough computational reduction to serve human needs” and that AI is, “an automated method tospeed and improve decisions and outcomes to advance benefits to society.” These positivestatements were surprising since the second day of the workshop was dedicated to AI ethics,security and privacy. One possible explanation could be the optimism shared by workshopparticipants pertaining to AI and its potential to have positive impact in STEM and society.Participants’ AI definitions did reflect that although they didn’t have a common definition of AI,they recognized the role of computers and machines in expanding human knowledge andcapabilities. None of the participants parsed AI into
more severe. Onecurrent type of violation is contract cheating, first coined by Lancaster and Clarke in 2006, whichinvolves paying a third-party to complete an assignment instead of the student enrolled in theclass [4]. Some researchers have even discovered “ghost students,” in which a fee is paid foranother person or company to enroll in an online course for an entire semester on behalf ofsomeone else [5]. Even though contract cheating and ghost-students are extremely severeviolations because of the awareness of the deviousness of the act, the underlying motivations forthese types of violations often reflect the same causes as other forms of academic integrityviolations [4].Students have cited a variety of motivations for engaging in academic
tasks I do out of work vs play, not some and don’t boredom, reflect work and play vs over-anticipate on fulfillment sleep. others Resources Resources Resources Resources Keep physical Talking it through Like task 1, keep There’s several track of To-do’s, with people who physical sleep related buy a notepad know me well and documentation, apps that couldthat I can always who know what write it out to help carry with me to stresses me out keep myself keep me
1, 33 < 0.001 0.319D. Learning Environments and Course EvaluationAs shown in Figure 8, students were all positive in describing the learning environment createdduring the course, such as collegial, motivating, productive, innovative, and positivelychallenging. While the course was neither harsh nor exhausting, it might be somewhat stressful,considering the rate of 4.28 over the neutral point.Figure 8. The Site learning environmentStudents all positively reflected the delivery of the Site program as shown in Figure 9.Figure 9. Overall delivery of the Site programThe effects of the Site program were all positive in their future plans, as presented in Figure 10.Figure 10. Impact of the Site program on future plansIV. DiscussionAfter
connect withone another and reflect on the information they have been exposed to throughout the day.As shown in fig. 6, the majority of teachers have already used, or are intending to use the activitykit provided. Some have even mentioned using activities from the website that were not part of thetrack they attended at the workshop but fit their classroom curriculum. There was a wide breadthin the ways teachers implemented their classroom kits. Some teachers mentioned they use theactivity as an introduction to a new concept, while others used them as hands on reinforcement ofa concept they had already taught in a traditional fashion. Teachers who used the kits mentionedthat they encouraged them to try new teaching strategies in their classrooms
-Technologist [30], [31] and sequencing exercises such as explaining how to put on a coat verbally to another girl. 7. Expand data collection to include reflection, usually in the form of exit slips at the end of class. 8. Design tools specifically targeted at the CT skills and practices relevant in the lesson of the day. 9. Add real-time in-class data collection hardware, such as Swivl video systems to capture conversations at multiple locations in the classroom.Data Analysis. Quantitative data, such as the engagement surveys, will continue to be analyzedby descriptive analysis. The small participant size excludes the application of popular methodsfor affective construct analysis. All qualitative data will continue
have been skewed by thepresentation of the topics in classes and the assignments. However, the data was collected across6-7 sections of the course taught by different instructors in each of the two years during whichdata was collected. Therefore, the data should reflect some averaging. For example, if oneinstructor taught sustainability well and with enthusiasm in one class, while another taught thetopic poorly, then the responses of students from one class should offset the responses from theother class when the data is pooled.Summary and Conclusions A survey-based study of first-year engineering students was conducted at the University ofNew Haven to determine their personality types and interest in topics such as visualization
modernized bachelor-level program at BSU’s MBE Department was developedbased upon a range of stakeholder inspirations, one of the most critical being student feedback.Through course evaluations and direct reflection of learning, undergraduates had requested moreflexibility with class selection, more hands-on engineering, and more themed learning tracks.The faculty recognized these inquiries to be of similar premise to those presented by numerousmechanical engineering education reform initiatives and publications. In these documents, thediscussion of the disassociation between industry needs and what mechanical engineers new totheir careers are prepared to provide is relentless. With the understanding that the presentcurriculum had not been
hands-on immersion experience at FERL (describedpreviously) and co-enrollment in numerous course offerings. This knowledge helps createsbalanced teams which is critical to ensuring healthy competition in the fourth phase. During the competition, teams assume the identity of a design-build firm, dividing upindividual roles by sub-discipline as outlined previously. Team leaders are charged withproviding overall guidance and ensuring the final products reflect a fully coordinated design andconstruction plan between sub-disciplines. Teams compete to “win” the job by having the mosttechnically sound approach and best integrated design. Examining Table 1 highlights a few important similarities between The Crucible and theASC design-build
course ends (e.g., as peer tutors or project mentors), • help improve the course (e.g., by creating new active-learning exercises over the material, or scoping out new technological developments that could be incorporated into the course), and • keep you in contact with current industrial practice (e.g., by serving as a scrum master or training others in the practice).This way of looking at a course reflects a subtle, but important, difference in devising courseprojects. The question is not, How can I specify projects that will familarize students with thecourse content? but rather, How can I design projects that will help students find their role inpromoting their own learning and that of their classmates? This
differentiatedhigher education market. The prevalence of these rankings in the public mind have promptedmany universities to strengthen their enrollment management strategies in order to expandenrollments, maintain better balance across enrollment swings, and to manage their discountrates (amounts offered through financial aid) to keep their institutions solvent—a pressure that islikely only to intensify due to the fiscal impacts of the current COVID-19 pandemic. Manyinstitutions report that the downside of national rankings is that they do not accurately reflect thequality of education offered by their institution, and therefore do not make for an efficientmarket. Indeed, our data point to specific gaming behaviors, often tuned to the algorithmsemployed by
or recommendations expressed inthis material are those of the authors and do not necessarily reflect the views of NSF.References[1] L. Farrell, “Science DMZ: The fast path for science data,” Sci. Node, May 2016. [Online]. Available: https://sciencenode.org/feature/sciencedmz-a-data-highway-system.php[2] E. Dart, L. Rotman, B. Tierney, M. Hester, J. Zurawski, “The science dmz: a network design pattern for data-intensive science,” in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, Nov. 2013.[3] “NSF 2017 PI Workshop CI Engineer Breakout Survey.” [Online]. Available: http://www.thequilt.net/wp-content/uploads/NSF-2017-PI-Workshop-CI-Engineer- Survey_v4.pdf[4
about. In contrast,amongst women born overseas, more explanation was needed, both on their part and mine. Thiswas reflected in their opening narratives as well.Here is Kalpana, a young engineer who was educated in India and came to the United States forgraduate school: Me: How did you become interested in engineering as a career? Kalpana: So I think the main reason goes back to my family, and what my parents, even my grandfather, what they did, how they thought about things. That’s what got me into physics or math or engineering in general. My grandfather was a schoolteacher and eventually the principal of the school. He never got to study more than a bachelor’s level. In spite of that, the amount of
storage spaces, build spaces, andworkbenches. High-resolution tool-use data collection is set to begin spring of 2020 at TexasA&M, including details that will remove some of these limitations.Because of these limitations a hypothetical dataset was created to reflect student-toolinteractions. This hypothetical dataset is guided by current data and engineering curriculum forTexas A&M, so the results are reasonable. These results present a picture of the design advicemodularity analyses will be able to provide once additional data is available.Hypothetical student-tool network creation © American Society for Engineering Education, 2020 2020 ASEE ConferenceA hypothetical-realistic
demonstration; come to a consensus on their interpretations of the concepts; orcomplete a quick example of each concept. A key component of the interactive lecture is that no“solution” slides are provided. The teams must work through the calculations or reflections togain a complete set of slides. This forces all students to engage in the lecture. Answers are sharedout in the larger group and the instructor guides the discussion of the answers so as to ensure acommon understanding of the concepts.Our initial assessment shows a marked improvement in student understanding of the relevanthydrodynamics concepts necessary to designing an underwater vehicle. Students are able toconverse more knowledgeably on hydrodynamics, and the ROV designs are more
and the breakdown of each assignment in the project (i.e. market analysis, business model…) • It was cool • The project was interesting and realistic data collection process was good to experience • Let’s you reflect on what I’ve learned • I liked the real world applications and going over data analysis techniques • It was well rounded, showed another side to research • I liked how we went through the whole process in regards to creating and running a study • Benefited senior project exploration, technical writing and formatting skills • Working on real world problems • Project based class • I liked how we had freedom to decide what we wanted to do for our human performance project, it
completion of the group project proves untrue. Though this is the case, it is worth noticing that team dynamics in Section 1 deteriorates in a statistically significant manner (see Table 7 where t = 1.38, nu = 29, and p<0.05) whereas there is no statistically significant difference when comparing answers to Question 2 (Q2) for pre- and post-project survey results in Section 2—see Table 12 where t = 0.83, nu = 41, and p>0.05. This is also reflected in the respective median score as that of Section 1 decreases from 9 (pre-project) to 8 (post-project), while that in Section 2 stays constant at 8. Put differently, the answers to
community colleges that can supportand facilitate their transition into community college faculty positons.9 AcknowledgmentsThis material is based upon work supported by the National Science Foundation under Grants No.1723209 and 1723245. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.10 References[1] R. W. Fairlie, F. Hoffmann and P. Oreopoulos, "A Community College Instructor Like Me: Race and Ethnicity Interactions in the Classroom," The American Economic Review, vol. 104, no. 8, pp. 2567-2591, August 2014.[2] A. Perrakis and L. S. Hagedorn, "Latino/a Student Success in Community Colleges and
DiscussionBy fall 2019 semester, about 260 students had participated in the online survey, 68.92% male,and 30.28% female and 0.8% identified as other. The online survey addressed pre-college,family background, campus life and faculty interaction, peer interaction, extra-curricularactivities, internship experience, and social life. Charts reflecting this data can be seen inAppendix C. Of those surveyed, 41.67% were Mechanical Engineering students, 13.33% wereBiomedical Engineering, 18.33% Civil Engineering, 16.67% Chemical Engineering, 8.33%Electrical Engineering, and 1.67% Engineering Entrepreneurship.When surveyed about family background, almost 29.49% of the participants responded that theyhad an immediate family member in the engineering field. As