for design project 2 like design project 1.In summary, this project exposes the student to the highly iterative nature of the design process,using the combination of distinct machine elements integrated into a gearbox assembly.Computer tools are shown to reduce the iteration time and to reduce the chance of mistakesduring the iteration process. Although specific tools were mentioned in this paper, there are awide variety of acceptable tools that can accomplish the same task. These tools are also evolvingto allow for system level optimization of the components.The Student-Selected Design ProjectAs mentioned previously, with the student-selected design project, the student(s) decide whatmachine or device they want to design. Students are
through the University’sLearning Management System (Canvas), students who are not assigned to a particular classroomsession are required to view that lesson’s content online asynchronously. Each classroomsession starts with a 1015 minute overview in which the instructor highlights the key conceptscovered in the online lesson and provides an additional example(s). In doing so, he makes aconcerted effort to relate the current lesson content to previous and upcoming lessons, the course,and real-life applications. The instructor intentionally limits the lesson overview to 15 minutesbased on the finding from Swartz, Butler, and Laman whose literature review identified thattypical student attention spans range from 5-15 minutes9 . The remainder of the
and converts energy fromthe sun to electrical energy. This energy is used to power the chicken farm and also usedto charge a battery for future use. This project would benefit both small and large scalechicken farms by reducing cost of operation, manual labor, and increasing productivity. IntroductionBy the 1900’s, an average chicken farm was an extension of the family kitchen. Most ofthe chicken farms were usually owned and operated by families and had no automation.Very few sold poultry products. Chickens were used for the same purpose as they arenow which includes meat, eggs, and money. Most chicken or poultry farms today areowned and operated by companies and machines perform several tasks on the
technical and social content.This need for energy education is the main motivation for the energy awareness efforts at BaylorUniversity. According to the National Energy Policy2, the U. S. must have between 1,300 and1,900 new electricity generation plants in place to meet the projected 45% increase in electricaldemand by the year 2020. There is little chance that this need in new electricity generation plantswill be satisfied. Economic and political policies often reflect the unspoken assumption that theUnited States will be able to continually increase its reliance on natural resources and moreimportantly, energy resources. Goals for “energy independence” have continually slipped sincethe term first appeared in 1980. For instance, with plentiful
Summary (if known) Author Becca First-year engineering Fall 2018 Female, probable first-year, K. Johnson projects course, other data not collected University B Dorothy First-year engineering Fall 2019 Female, first-year, other data J. Blacklock projects course, not collected University B Cleopatra Second-year Spring Female, self-described S. Claussen introduction to 2019 sophomore, probable ME mechanical major, other data not
transition to amore stable, efficient, and reliable solution to hardware access. Accordingly, a remote labbecame an appealing approach.Remote laboratories evolved since the early 90’s and they have continued to gain attention ineducation research since that time [4]. There have been numerous definitions of remote labenvironments in the literature where the terms “remote lab” and “virtual lab” are often usedsynonymously [5, 6]. However, it is important to establish a clear distinction between the twoterms. Virtual laboratories are simulated, non-physical environments that model a real-life labwith a computer-based application. Conversely, remote laboratories give the user the ability toaccess and control physical equipment from distant locations
might not account for structural nuances intransfer student pathways.Figure 1. Example calculation of course cruciality using the blocking factor and delay factorTo provide grounding for what kind of values to expect from structural complexity, Table 1presents a series of examples that increase in interconnectedness. Empirical values of curricularcomplexity for four-year programs from 63 schools ranged between ~50 and ~500 with anaverage of 273.6 in Heileman et al.’s program quality study [3]. Within institution variation isalso notable; the range was 191-618 in a study by Grote et al. at Virginia Tech [6]. Note that themetric depends on the number of courses in the plan of study, so comparisons using the rawmeasure between plans of study with
opportunity to integrate evidence-based education practices into the lab portion of the coursethat aimed to aid in students’ learning of technical writing practices. Table 1 compares Autumn2019’s lab schedule and associated technical writing post-lab assignments with Autumn 2020’slab schedule and associated technical writing post-lab assignments.Table 1: Autumn 2019’s lab & assignment schedule compared to Autumn 2020. Post-labs with technical writingfocus that are part of the complete quantitative analysis for this paper are denoted with blue text. Post-lab Full LabReports used for comparisons through t-tests are denoted with red **. Week Autumn 2019 Autumn 2020 Lab
M.C. Richey, “The wisdom of winter is madness in May,” Journal of Engineering Education, vol. 108, no. 2, pp. 156-160, 2019.[3] R.A. Cheville, “Board # 22 : Ecosystems as Analogies for Engineering Education,” in ASEE Annual Conference & Exposition, 2017.[4] W. Lee, “Pipelines, pathways, and ecosystems: An argument for participation paradigms,” Journal of Engineering Education, vol. 108, no. 1, pp. 8-12, 2019.[5] S. Lord, M. Ohland, R.A. Layton, and M. Camacho, “Beyond pipeline and pathways: Ecosystem metrics,” Journal of Engineering Education, vol. 108, no. 1, pp. 32-56, 2019.[6] L. Vanasupa and L. Schlemer, “Transcending Industrial Era Paradigms: Exploring Together the Meaning of Academic Leadership for Diversity
defined as whether a tool was used correctly, for example, whetherthe correct power and speed settings were used on a laser cutter to pierce 4mm plywood.Technique was defined as whether the student achieved the intended outcome with thetool/technique, such as applying a plasma cutter correctly to execute a clean and detaileddesign. A heavy focus was placed on whether the student/s recovered based on an error andproduced a final part that met the expectations for the homework assignment. Additionally,students’ willingness to modify their design to make better use of the machine weighed heavilyon the technical proficiency score. This scoring system was developed by two independentscorers based on an iterative revision of the rubric after
in IEEE Transactions in Professional Communication, the Nell Ann Pickett Award for best article in Technical Communication Quarterly, and the NCTE Best Article in Theories of Technical Communication (in both 2015 and 2018). She is also the co-founder of Women in Technical Communication, a mentoring organization that received the 2015 Diana Award from ACM Special Interest Group in the Design of Communication.Dr. Nathan R. Johnson, University of South FloridaDr. Fernando S´anchez, University of St. ThomasRev. Walter R. Hargrove American c Society for Engineering Education, 2021The Politics of Citation Practices in Engineering Education: A CitationNetwork Analysis
look into the student writing samples. A B C D E F G H I J K L M N O P Q R S A Centrality of Military & Corporate 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 2 0 0 B Commitment to Problem Solving 0 6 3 9 0 0 3 0 1 0 5 1 2 20 0 6 0 2 C Narrow Technical Focus 0 6 0 0 1 0 1 0 1 0 2 0 0 4 1 1 0 1 D Persistence 0 3 0 1 0 0 0 0 1 0 0 0 0 3 1 1 0 0 E
belief that professional skills are necessary for engineeringstudents’ career preparation. Along the same lines, Dr. Natalie also believed that students shouldacquire both technical and professional skills for career preparation. She particularly emphasizedobtaining management skills for competitive employability: I mean, management is what? Management is basically being able to [be] enabling, achieving common goals, right? I mean that's what brings business. That's what keeps you in business…. So I think it's very true, except that I think in the business that I observe, is you hire for both technical and people skill[s].These responses indicated to us that faculty members understood and suggested the importanceof
“Provideoptions for Perception” item in the “Provide multiple means of Representation” category becauseit offers a new way to “customize the display of information” by enabling students to search andgather information they need. UDL Guideline UDL Guideline item(s) ClassTranscribe Feature Provide multiple means Minimize threats and - Distraction/stress-free learning of Engagement distractions interface Provide options for - Student personal usage analytic Sustaining Effort & reports based on interaction with Persistence the platform Provide multiple means
supports only some KPIs in aparticular SO, the program did not adopt these assignments. Instead, the program favoredassignments that could be used to score all KPIs of a SO to help make the process morestraightforward.Implementation of Assessment PlanOnce the courses and individual assignments supporting each SO were identified using the SOMap Results provided by the faculty, the assessment plan could officially begin. The following listroughly outlines how faculty were asked to deliver the assessment to the students in their coursesand then subsequently score the results: • Instructor for course must decide which assignment(s) will be used to measure the required SO o Instructors were allowed to change assignments to best meet
mid-nineties. In contrast, AE enrollment grew till 1988 and then sharply declined. The reason forthis disparity is that while engineering went through downsizing and mergers, 1980’s was adynamic time for aerospace engineering [8]. Truly the eighties were an exciting time for AEindustry where the U.S. increased its funding in the sector to almost excessive [9]. Followingthis period of excessive spending, the sector faced huge downsizing due to the slash infunding and recession. To put it in perspective, the AE sector slashed half a million jobs from1989 to 1995 [9]. Undergraduate enrollment in AE followed similar trends to that of itsindustry. Fletcher (1998) warned that the sharp decline in enrollment in AE will have seriousconsequences on
of how to avoid hardwiring societal bias into our computing machines. As AshleyShadowen, a student at CUNY sums up in her Masters’ thesis, “ Machine ethics is a complicatedand multifaceted problem. But if we get it right, we will unleash the full benefit of machinelearning for humankind.” [28]References [1] Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14(3), 330–347. https://doi.org/10.1145/230538.230561 [2] S. Noble, Algorithms of oppression: How search engines reinforce racism. New York: NYU Press, 2018. [3] C. O’Neil, Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Crown 2016. [4
literature alongwith research study outcomes, and address the need to use an intersectional lens when exploringthe experiences of racially minoritized populations. 5 MethodsThis review uses a combination of pre-established methods. We used a method adapted fromFerrari [23] which focuses on conducting narrative style reviews. Also, we used Borrego’s [7],[8] methodology for conducting a systematic literature review in engineering education in sixsteps: (1) deciding to conduct a systematic literature review, (2) identifying the scope andresearch question(s), (3) defining inclusion criteria, (4) finding and
that need to be cast through, the texture coordinate, and the depth of the entry point. Theray direction is given by the vector from the entry and exit points in the texture space. Each sample’s position alongthe ray direction is computed via linear interpolation. In terms of how many samples we should take, we set the stepsize as half of a voxel. Users can adjust the Sampling Rate parameter to change the number of samples taken alongeach ray. Note that the assigned opacity also depends on the sampling rate. For example, when using fewer slices, theopacity has to be scaled up, so that the overall intensity of the rendering results remains the same. We use Equation 1to correct the opacity whenever users change the sampling rate s from the
Accessibility Initiative, “Making Audio and Video Media Accessible,” Accessed November 2020.Available at [11] Bureau of Internet Accessibility, “Checklist for Creating Accessible Videos,” Accessed November 2020.Available at [12] G. Morin, J. Rubin, and R. Leisinger, “508 Accessible Videos – Why (and How) to Make Them,” Available at[13] Directory of Coursera University Partners. Accessed November 2020. Available at[14] Directory of edX University Partners. Accessed November 2020. Available at[15] Cal Poly Pomona Mechanical Engineering Department YouTube account. Available at[16] Cal Poly Pomona Mechanical Engineering Department video content website, ME Online. Available at[17] S. Tosun, The Effects of Blended Learning on EFL Students’ Vocabulary
; Overcoming Resistance to Cooperative Learning,” Coll. Teach., vol. 58, no. 2, pp. 52–57, Mar. 2010, doi: 10.1080/87567550903418594.[2] J. M. Langer-Osuna, “How Brianna Became Bossy and Kofi Came Out Smart: Understanding the Trajectories of Identity and Engagement for Two Group Leaders in a Project-Based Mathematics Classroom,” Can. J. Sci. Math. Technol. Educ., vol. 11, no. 3, pp. 207–225, Jul. 2011, doi: 10.1080/14926156.2011.595881.[3] N. Dasgupta, M. M. Scircle, and M. Hunsinger, “Female peers in small work groups enhance women’s motivation, verbal participation, and career aspirations in engineering,” Proc. Natl. Acad. Sci., vol. 112, no. 16, pp. 4988–4993, Apr. 2015, doi: 10.1073/pnas.1422822112.[4] C. L. Colbeck, S. E
, Indiana, Jun. 2014, p. 24.360.1-24.360.13. doi: 10.18260/1-2--20251.[8] A. García-Aracil, S. Monteiro, and L. S. Almeida, “Students’ perceptions of theirpreparedness for transition to work after graduation,” Active Learning in Higher Education, vol.22, no. 1, pp. 49–62, Mar. 2021, doi: 10.1177/1469787418791026.[9] A. E. Coso and A. R. Pritchett, “Role of Design Teams in the Integration of StakeholderConsiderations,” Journal of Aircraft, vol. 52, no. 4, pp. 1136–1145, Jul. 2015, doi:10.2514/1.C032796.[10] M. Lande, “Methods for Assessing Epistemic Identities