covers the entire stateregardless of distribution in the state. The location quotient defines how reflective an area’s economy is of the nation as a whole.A labor quotient of less than 1.0 would indicate a region has less of a specific job than thenational average, while a quotient of greater than 1.0 would indicate the area has more than theaverage. For the state of Tennessee, the labor quotient is 1.2 for machinists. This value indicatesthat Tennessee has a higher-than-average concentration of machinists when compared to theUnited States as a whole. RESEARCH METHODOLOGYResearch Context A survey was created with the goal of determining the employer’s current and future needsfor machinist in the Northeast
appendix 4, andmainly reflects their expectation. Based on the responses to questions 10 to 12 in figure 4, one ofthe key concerns such as diverse views and inclusiveness during the virtual teaching environmentwere addressed and handled well by our teaching approach. In figure 4, over ninety percent of thesurvey participants satisfied by the way we handle the situations. Since this is pioneering work ofthis type of teaching, no prior data is available to compare the improvement in students'interactions and experiential learning; this aspect needs to be assessed in the future. The approachcertainly helped them to understand the importance of interactive and experiential learning. Thisstudy is only a sample; additional studies are needed to reach
-axisCNC machine through a grant awarded by DoD, and in the future we will continue enhancing ourlaboratorial tools and environment on multi-axis machining for aerospace parts such as blisks andturbine blades, and then integrate and evaluate these tools in the Manufacturing Engineeringcurriculum.AcknowledgementThe authors would like to acknowledge support from NASA (award number: 80NSSC20M0015).The blisks machining tasks was also partially supported by DoD (award number:W911NF1910464). Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of NASA and DoD.Reference1 . 2020 Facts and Figures U.S. Aerospace and Defense https://www.aia-aerospace.org/wp
. Helen L. Chen, Stanford University Helen L. Chen is a research scientist in the Designing Education Lab in the Department of Mechanical Engineering at Stanford University. She has been involved in several major engineering education initia- tives including the NSF-funded Center for the Advancement of Engineering Education, National Center for Engineering Pathways to Innovation (Epicenter), as well as the Consortium to Promote Reflection in Engineering Education. Helen holds an undergraduate degree in communication from UCLA and a PhD in communication with a minor in psychology from Stanford University. Her current research and scholarship focus on engineering and entrepreneurship education; the pedagogy of portfolios
. • Writing Assignments: Writing assignments (WAs) were chosen as an assessment method to demonstrate students’ improvements in technical writing. Individual writing assignments included topics ranging from “Explain how something works” to “Reflect on your speaking skills”. • Descriptive Statistics Activity: The topics covered in this lecture include mean, standard deviation, linear regression, significant figures, and measurement techniques.MATLAB workshops:The MATLAB workshops were conducted during the regular lecture meeting times and taughtby the instructor and a teaching assistant. The TAs were male or female, sophomore or juniorlevel engineering students, who took the course before. The lecture consisted of a
doing engineering with engineers [1] - [7]. As part of this culture change, thedepartment implemented several major curricular changes beginning Fall 2019 [1] - [4]. Thesechanges were designed to give students hands-on engineering experiences and engage them withpracticing engineers. The department introduced a new required integrated design sequence forthe first, second, and third-year students [3], [4]. The new design sequence complements theexisting year-long, industry-sponsored senior design experience. The circuits andinstrumentation courses were replaced with a lab-focused, two-course sequence combiningcircuits and instrumentation curriculum [7]. Senior design was retooled to better reflect theexperiences of working engineers [3], [4]. In
suggests that the groups are able to prepare for oradjust to the last two and a half weeks of remote instruction department-wide.With the expected performance defined, the Fall 2020 data can be analyzed. The data is shown inTable 4 and reflect significantly better results across all performance indicators. Performance Indicator Poor Adequate Good Excellent 5.a) Leadership 2% 7% 37% 55% 5.b) Collaborative Environment 1% 5% 31% 63% 5.c) Inclusive Environment 3% 3% 26% 69% 5.d) Establish Team Goals 3% 14% 41% 42% 5.e) Plan Team Tasks 2% 8
taught only book courses, only laboratory courses, and both book andlaboratory courses.The instructors were introduced to the objectives of the study and then were asked to complete thesurvey hosted on Qualtrics. Participation in the interviews was voluntary. Human subjects'approval (PRO18060710) was secured for these various forms of assessment. The survey wascomposed of seven questions (see Table. 2) to identify the meeting mode and the pedagogicalapproaches adopted by each instructor. The motivation and obstacles in the adopted approach werealso collected. Later, we interviewed the surveyed instructors to reflect more on their experienceteaching remote classes, the problem noted in the survey results, and their approaches toovercoming these
be mitigatedthrough scaffolded assignments, regular peer evaluations, and more frequent opportunities forindividual and team-based self-reflection [2], [8], [12].The transition to online instruction due to the COVID-19 pandemic this past year onlycompounded the pre-existing logical and pedagogical challenges associated with engineeringdesign in FYE courses. The most pressing challenge for these courses in an online-onlyenvironment was ensuring students access to essential equipment and materials to design andconstruct a physical prototype. In general, programs responded to this challenge in one of threeways: (1) abandoning physical prototyping for an entirely “paper design” project; (2) requiringstudents to purchase third party construction
workshops. While only two and three states were represented in the first andsecond workshops consecutively, 18 states were represented in the third workshop. Almostsimilar advertising efforts were made for all three workshops, with more outreach efforts madeto regional institutions for the first and second workshops than for the third workshop. Figure 2: On-ground AM-WATCH Studio Workshop Participants with Social Distancing and Use of Mask (Left). An on-ground AM-WATCH Studio Workshop Participant working on his 3D Pen exercise (Right).Despite the increase in diversity by state, the online workshop saw a noticeable decrease inapplicants from high schools compared to higher education institutions. This is reflected in
materialsdevelopment activities that seek to support the success of all students. AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.(DUE-1625378). Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of NSF. References[1] E. Cech, B. Rubineau, S. Silbey, and C. Seron, “Professional role confidence and gendered persistence in engineering,” Am. Sociol. Rev., vol. 76, no. 5, pp. 641–666, Oct. 2011, doi: 10.1177/0003122411420815.[2] K. A. Robinson, T. Perez, J. H. Carmel, and L. Linnenbrink-Garcia, “Science identity
response withintwenty-four hours. Students who choose the asynchronous online learning model may feeldisconnected from the campus environment and thus may particularly appreciate a quickresponse from their instructor.Learning ObjectivesKey learning objectives for the HyFlex version of CSCI 159: Computer Science ProblemSolving were for students to learn how the field of Computer Science applies quantitativereasoning to analyze data, create algorithms, and solve challenging problems.The course was divided into four modules. Students first learned the fundamentals ofinformation systems and network infrastructure with assigned readings and facilitated discussionboard reflections on their use and impact [21]. Learners defined and described
Engineering Education Research: Reflections on an Example Study,” Journal of Engineering Education, vol. 102, no. 4, pp. 626–659, 2013, doi: 10.1002/jee.20029.[10] J. Walther et al., “Qualitative Research Quality: A Collaborative Inquiry Across Multiple Methodological Perspectives,” Journal of Engineering Education, vol. 106, no. 3, pp. 398– 430, 2017, doi: https://doi.org/10.1002/jee.20170.[11] S. Tan, “The Elements of Expertise,” Journal of Physical Education, Recreation & Dance, vol. 68, pp. 30–33, Feb. 1997, doi: 10.1080/07303084.1997.10604892.[12] C. Aaron, E. Miskioglu, K. M. Martin, B. Shannon, and A. Carberry, “Nurses, Managers, and Engineers – Oh My! Disciplinary Perceptions of Intuition and Its Role in
opinions, findings, and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation.References[1] M. K. Orr, M. W. Ohland, R. A. Long, C. E. Brawner, S. M. Lord, and R. A. Layton, “Engineering matriculation paths: Outcomes of Direct Matriculation, First-Year Engineering, and Post-General Education Models,” Proc. Front. Educ. Conf. FIE Proc. - Front. Educ. Conf. FIE, 2012.[2] K. Reid, D. Reeping, and E. Spingola, “A Taxonomy for Introduction to Engineering Courses *,” Int. J. Eng. Educ., vol. 34, no. 1, pp. 2–19, 2018.[3] H. Matusovich, R. Streveler, and R. Miller, “Why Do Students Choose Engineering? A
-related design processes and factors.Keywords: Engineering Education, Civil Engineering Design, Human-Centred Designing,Priming, Empathy, Social Consciousness, Personal Values, Engineering ValuesIntroductionMany have discussed the technocentric engineering curricula [1] – [5], that tend tomarginalise [3] and devalue [6],[7], the less technical and more ‘socially-involved’ aspects ofengineering, and have thus stood with Cech’s [2] call for the integration of public welfareconcern and social consciousness in engineering curricula.An aligning call/prompt for the integration of empathic [8] – [10], compassionate [11],‘socially-just’ [12],[13], and/or human-centred designing [14] – [18] in engineering curriculahave also risen. This is reflected in
details during problem solving[23, 24]. PROCESS was tailored to incorporate relevant steps needed to solve material and energybalance problems [22]. Each of the 6 items in the revised PROCESS consists of four scaling levelsranging from 0 to 3 with zero being the minimum attainable score. PROCESS score is an aggregateof scores earned in all 6 items of PROCESS rescaled from 0 to 100.Prior to scoring with the modified PROCESS, anonymity of students was maintained by replacingparticipants’ names with a project-assigned ID number. In addition, assessment with PROCESSrubric was conducted after the semester does not reflect or have an effect on students’ coursegrades. To eliminate rater bias during assessment, an interrater reliability was conducted
responsibilities as anengineer, what role you have occurring there,” [6, p. 177]. This seems very reflective of the moralitiesderived from professional roles discussed in Smith et al. [7], and helps further indicate a necessity forincluding role ethics and CSR as part of engineering ethics curriculum. Teaching CSR to engineering students acknowledges that professional engineers practice ethicswithin a larger societal and corporate framework with distinct roles that can affect ethical action thatengineers can pursue [7]. CSR itself has many weaknesses, and has been accused of having little influenceon daily corporate practices [22], [23], has not been fully internalized by many corporations [24], and is notclearly linked to engineering [15]. In
. Researchers have used a rangeof approaches to categorize students’ questions, varying in complexity depending on the contextin which student questions were being solicited (e.g., [2], [3]). Marbach-Ad and Solokove [4]used a large sample of questions generated by biology students to develop a six-level, "semi-hierarchical” taxonomy based on question sophistication. Encouragingly, their work also showsthat students are able to pose more high-quality questions after being instructed in the taxonomyfor classifying the quality of their questions [5]. This approach has also been adapted forclassifying questions asked by physics students as part of a written reflection on their learning[6].Along with explanatory question taxonomies, question-asking can be
indifferent zones of the 15-acre field for a preliminary trial. The field which uses corn, soybean, andwheat rotation, was growing soybean during the preliminary trial reported here. A six-bandmultispectral camera that simultaneously images in the visible, near infra-red, and thermal bandshave also been flown on a DJI Inspire II drone to collect aerial imagery.1.0 IntroductionAccording to United Nations Educational, Scientific, and Cultural Organization (UNESCO), only20% of the cultivated land is irrigated and provides 40% of the global food basket, the rest of the80% of farmland is rain-fed and accounts for only 60% global food basket [1]. Growth in worldfood demand will reflect the population growth, which is anticipated to be around 10 billion
, job candidates find them “subjective, arbitrary,unnecessarily stressful, non-inclusive –and at times– demeaning to their sense of self-worth andself-efficacy” [25]. Furthermore, candidates expressed concerns about the amount of timepreparation required, and the inherent bias that may give those with more free time an advantage.Others commented that the types of questions asked, and knowledge of data structures expectedto be known extemporaneously is not reflective of the tasks actually encountered in a computingposition.While these findings indeed revealed major concerns, the research did not consider the nuancesthat may arise from individual differences [11, 25]. On HackerRank, 95% of users were male, andthere was no information about the
wealth,” Race Ethn. Educ., vol. 8, no. 1, pp. 69–91, 2005.[18] C. G. Vélez-Ibáñez and J. B. Greenberg, “Formation and transformation of funds of knowledge among U.S.-Mexican Households,” Anthropol. Educ. Q., vol. 23, no. 4, pp. 313–335, 1992.[19] A. L. Pawley and C. M. L. Phillips, “From the mouths of students: Two illustrations of narrative analysis to understand engineering education’s ruling relations as gendered and raced,” presented at the ASEE Annual Conference, Indianapolis, IN, 2014.[20] J. Walther, N. W. Sochacka, and N. N. Kellam, “Quality in interpretive engineering education research: reflections on an example study: Quality in interpretive engineering education research,” J. Eng. Educ., vol. 102, no. 4, pp
71% 68% Engineering Career Success 77% 66% Expectations Overall Emotional States 68% 64% Programmed students are encouraged by their scholar programs to pursue engineering-based research instead of industry. Potential misunderstanding and expectations of theengineering research, low research self-efficacy, may contribute to the uncertainty leading themto feel they are unable to be successful in the field. However, the virtual environment causedboth groups to have lower than expected engineering emotional states. Reflecting on the SocialPersuasion Vicarious Experiences results, the motivation to pursue
mechanics courses are likely to proliferate in the coming years as the abrupt shifts toonline learning amidst the COVID-19 pandemic has prompted many students, faculty,departments, and institutions to revisit beliefs and assumptions about online courses. The authorsbelieve in the potential of hands-on models to support student learning in mechanics and hopethis paper will provide an opportunity to learn from our experiences and adapt other hands-onapproaches for online implementation.AcknowledgementThis material is based upon work supported by the National Science Foundation under grantnumbers DUE #1834425 and DUE #1834417. Any opinions, findings, and conclusions orrecommendations expressed are those of the authors and do not necessarily reflect
in actual course design/redesign. The lead instructor forthe course has additionally participated in this project via assisting with qualitative dataassessment. To ensure safe spacing, students had designated days when they could attend class inperson, though students could opt to attend online at any time rather than in person.4.2. Data CollectionData included institutional demographic data for students, student survey responses, studentfocus groups, and course observations. Data were collected in the last few weeks of the course sothat students’ responses reflected a full-semester experience. For the written survey, the responserate was 54% (282/522). Missing data analysis pertaining to the four different demographicidentities under study
open-book format.) Studentfeedback from the interactive discussions and the anonymous surveys also reflected thatdiscrepancy, with many students stating that they felt that the course over-emphasized themodeling at the expense of the physiological concepts. A coded analysis of the 34 total freeresponses to open-ended feedback on the course (across both iterations) yielded 35% ofrespondents specifically critiquing the imbalance and/or disconnect between the modeling andthe physiology, which was the most common unprompted critique provided. A typical commentincluded, “While I think I’ve gained computational skills I feel much less confident in myphysiological understanding of the models.” Other students commented that the pre-classreadings, even
assignments with due dates reflective of the workcompleted during that time. The students are still required to meet the rigor of the project bycompleting all the tasks; e.g. brainstorming, engineering drawing, Gantt chart, bill ofmaterials, proposal, prototype build and test, and final report and presentation. Within thiswork, a student with ASD may tend towards the details of the design, or the scheduling anddocumentation. The instructor must help the team with coordinating tasks and keepingeveryone involved. Some other academic accommodations the instructor can make are clearand direct classroom expectations, asking precise questions, hands on learning, performingvisual demonstrations, giving more time on essay type tests, using task analysis with
time to rest, affecting their mental health.Future work will focus on assessing other type of support interventions that were implementedduring the outbreak of COVID-19. Considering the perceived need for a balance academic load,we also plan to explore ways to improve curriculum planning and assessment patterns inengineering education. During the second semester of 2020, we collected students’ self-reports oftime-on-task to identify peaks of academic workload in specific weeks and subjects. Furtherstudies will be conducted to understand how these self-reported data could help teaching staff andstudents reflect about course planning and time management, respectively.AcknowledgementsThis work was supported by CORFO under grant no. 14EN12-26862
online classes.Participating instructors also discussed various strategies to overcome these barriers during thefocus group setting. Our research team is currently working to also identify these strategies andtheir effectiveness in overcoming barriers to using active learning in online teaching.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant NoDUE-1821488. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.References[1] M. Dancy, C. Henderson, &, C. Turpen, (2016). How instructors learn about and implementresearch-based instructional strategies: The
illustrated with a cloud shape in order toconvey the vaguely defined nature of considering context. The contextual process is not a linearsequence of events, but rather a web of interconnected efforts. Contextual approaches will focuson the stakeholders within the client community through every step of the process, not onlythrough conversations, but by placing decision-making in their hands. There is also the importantcomponent of self-reflection, which involves the assessment of one's motivations and objectivesfor the project, so that they can be distinguished from the client community's goals. Figure 2. Example model for community organizing and contextual engineeringHumanitarian engineering, however, cannot be represented by the model
evaluate departmental need for a targeted approached toward certain groups toimprove overall student wellness.AcknowledgmentsA grant from the National Science Foundation Number #1738186 supported this study. Anyopinions, findings, and conclusions or recommendation expressed in this material are those ofthe authors and do not necessarily reflect the views of the National Science Foundation. Theauthors thank Jeanne Sanders for providing feedback on the paper. The authors thank thestudents for participating in the survey.References[1] E. Godfrey and L. Parker, "Mapping the cultural landscape in engineering education," Journal of Engineering Education, vol. 99, pp. 5-22, 2010.[2] R. Stevens, D. Amos, A. Jocuns, and L. Garrison, "Engineering