pilot of this class prior to this grant combined a small,bioinspired robotics course with PSTs in the educational technology course, where the UESs andPSTs could work together on a larger project. Therefore, future collaborations will include eitherthe bioinspired robotics course or electromechanical systems course, both which have smallerclass sizes.Preliminary results [7,8] suggest that the instruments developed for collaborations 1(engineering design process) and 3 (computational thinking) may not be sensitive enough todetect changes in content knowledge. Therefore, additional research is being implemented toimprove those assessments. The reflection assignments provided valuable information on bothwhat students learned and how they viewed
].Aesthetics in engineering, utilizing objects from nature, is being stressed by modern industrial designers,whether in building architecture or in the design of a shopping mall. Industrial design is in the domain ofvisual education applicable in fine arts and performing arts. The pioneering works of Leonardo da Vinciare some the earlier examples in the Hellenistic-Judeo-Christian cultures that convey the importance ofaesthetic aspects in mechanical design [4]. His extensive note books and sketch pads on all mechanicalmodels, ranging from water pumps to helicopters, put aesthetics on a solid foundation in the domain ofdesign, and reflect the union between beauty and technology, harmony and synthesis, art and artisan’swork in a creative endeavor.Over
and persistenceto graduation. Scholarship recipients also participate in focus groups and one-on-one interviewsand that data is being analyzed with the goal of gaining a holistic understanding of studentretention and finding trends in longitudinal change in students’ perceptions of the engineeringprofession as well as in their motivation and persistence.This material is based upon work supported by the National Science Foundation under GrantNo. DUE-1644119. 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.References[1] J. Kruger, and D. Dunning, “Unskilled and Unaware of It: How Difficulties in
underlying factor structures for items across all fourteenmodules through the exploratory factor analysis. A confirmatory factor analysis will thenevaluate the proposed emerging factor structure. The analysis will conclude with a finalizedfactor structure, completing steps four and five in the instrument development process. Futurework past this project will extend to step 6, in which we will work to interview current science,engineering, and mathematics graduate students to ask them to comment on the final surveyinstrument and reflect on what areas regarding to their current mental health experiences aremissing.The ultimate purpose of this work is to create an instrument that measures science, engineeringand mathematics graduate students’ mental
billion pounds of plastics were manufactured in the US. What questions does this statement raise for you?After students list as many questions as possible, they could be instructed to improve them andprioritize them. In my case, the next step was for groups of four students to compare theirquestions and prepare a team list. The list of student questions does not necessarily need to beturned in. But if they are, they can be used to drive lecture material or as an assessment ofstudent curiosity. The questions in fall 2019 reflected a wide variety of interests on the students’part. Some focused attention on the environmental issues, some on the material property issues,some on the industries that use plastics the most. By better understanding
experience with the design cycle by designing a helmet to protect the brain. Students iteratively design the helmet using practical arts and crafts materials and engage in testing to determine the performance of their design. Students also reflect on their designs to influence further iterations. On day 3, students use the engineering design cycle to iteratively design surgical tools. Students evaluate their tools by performing mock surgeries on gelatin models to remove embedded masses. Students evaluate their tool performance and use that to inform further design improvements. On day 4, students revise their tools to enhance performance and prepare for day 5 challenges. The day 5 competition includes
ofobjectives, CATME peer evaluationdata from both years was used toevaluate whether students believetheir team members i) possessedrelated knowledge, skills, andabilities and ii) contributed todeliverables (objective 1). CATMEalso rated how efficiently the Fig. 2: SPOC subteam communication dynamicsubteams communicated relative to 2018-2019 results with the embedded ID team structure.End-of-semester reflections for both years and a survey in the fall of 2019 (Appendix B)provided more data on task allocation and subteam communication.Results and Discussion:Objective 1: CATME peer evaluation data reported that engineers scored higher than IDs (bothyears) and point differentials were slightly but not statistically less (two-sided t-test, α
work throughproblems, and when they should rely on calculations to help adjust their intuition. This exercise hascertainly provided a moment of self-reflection for the authors and a direction towards improvement oftheir courses. Bibliography[1] D. Hestenes, M. Wells, and G. Swackhamer, “Force concept inventory,” Phys. Teach., vol. 30, no. 3, pp. 141–158, 1992.[2] C. Henderson, “Common Concerns About the Force Concept Inventory,” Phys. Teach., vol. 40, no. 9, pp. 542–547, 2002.[3] J. Docktor and K. Heller, “Gender Differences in Both Force Concept Inventory and Introductory Physics Performance Gender Differences in Both Force Concept Inventory and Introductory Physics Performance,” Am. Inst. Phys
students to evaluate their team’s ability tonegotiate. The responses to this question in comparison to the pre-assessment results, reflect asimilar result as the previous question (Figure 5). On the pre-assessment survey only 9% ofstudents indicated a strong reliance on compromise and in the post-assessment survey 58%responded that they had either frequently or always used compromise as a negotiating method. Post-Assessment Q2 - Group Evaluation 50% 40% 30% 20% 10% 0% Never Rarely Sometimes Occassionally Frequently Always Avoidance Aggression Accomodation Compromise CollaborationFigure 5: Comparison of Post-Assessment Question 2 and Pre-Assessment ResultsConclusion/Future
formats. i. Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree 7. CATME Team Assessments were beneficial in giving feedback to my team members. a. Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree 8. CATME Team Assessments were beneficial in receiving feedback from my team members. a. Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree 9. CATME Team Assessments accurately reflected my contributions to the team. a. Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree 10. Viewing the CATME Team Assessments helped develop my self-awareness as a member of a team. a. Strongly Agree, Agree, Neutral, Disagree, Strongly
and home. He left hisemotional side at home and was a commanding force at work. He identified as an “extremeprofessional.” William described that he deliberately did not bring his family to work events,attend happy hours, or befriend coworkers. Because he described this separation as being “basedon race,” we interpreted his experience as inauthentic in comparison to the White participants.William also experienced isolation because of the lack of peers on his level in the workplace.Structural racism was reflected in various forms throughout the interviews. All three participantsdescribed the hiring process as based on merit. This can prove to be disadvantageous tominoritized individuals, given they often don’t have the same opportunities to
instructed to write constructive self-reflection and feedback to other team members based on a validated teamwork behavior modelthat was introduced and assessed via CATME [21].In our formulation, our problem is to determine whether a word in a sentence is the name of aperson. We can perform such analysis from two perspectives. One is a cloze-like task wheregiven the context of the word of interest, we can make predictions on the grammatical andsemantical representations, and the other is to perform classification on words with character-level information. To extract as much information as possible, we need to leverage both word-level and token-level information. We propose to use a machine learning model, an ensemble of5 models trained on different
beenmeasured through the use of student surveys and improved student passing rates [16]. Within theHCRD course various methods to ensure student knowledge gains and perceptions towards theircareer preparedness and progress towards degree completion will be assessed through pre andpost-semester surveys, reflections, and final exam/presentation scores. At the two south valleycampuses, students will be primarily be assessed to identify the length to which FC-E-POGILpedagogy is successful in improving knowledge gains. The impact of the two pedagogies onknowledge gains will be evaluated by conducting a one-way repeated measure analysis ofvariance (ANOVA). The ANOVA analysis will assess the difference in participants’ summativeknowledge gains based on final
, with additional members from the math department.The committee for the mechanics course (ENGR/PHYS 216) was comprised of faculty from thecivil, mechanical, and aerospace engineering departments, as well as members from the physicsdepartment. Likewise, the committee for the electromagnetism course (ENGR/PHYS 217)consisted of faculty from the electrical engineering department and physics department. Thedifferent faculty appointed to these committees took different levels of ownership of the work.These differing levels of involvement meant that the vision of some faculty members was morestrongly reflected in the committees’ final work.The implementation committees were formally independent of each other, save for the constraintthat the later
of their own knowing orunderstanding (Schraw, 1998). Such self-awareness reflects in their awareness ofcontent, task and strategic knowledge that are germane to learning (Fin & Tauber,2015). For example, students should be able to monitor and recognize how well theyunderstand engineering content knowledge, calibrate the difficulty level of thelearning tasks, and recognize what strategic learning skills they would need tosucceed (Dunlosky & Rawson, 2012).Research suggests that the levels of students’ self-awareness and self-confidencecould positively or negatively affect learning (Finn & Tauber, 2015). For example,Dunlosky and Rawson found that inaccurate self-evaluation undermines learning andretention (Dunlosky & Rawson
towards degree completion will be assessed through pre andpost-semester surveys, reflections, and final exam/presentation scores. At the two south valleycampuses, students will be primarily be assessed to identify the length to which FC-E-POGILpedagogy is successful in improving knowledge gains. The impact of the two pedagogies onknowledge gains will be evaluated by conducting a one-way repeated measure analysis ofvariance (ANOVA). The ANOVA analysis will assess the difference in participants’ summativeknowledge gains based on final exams and presentations as the summative assessment method ateach respective campus. Institutional data on student’s majors and progress towards graduationand will indicate if participation in these courses helps meet
that ourapproach can be replicated in other fields and other student populations.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grants1842166 and 1329283. Any opinions, findings, conclusions, or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National ScienceFoundation. We thank the SPHERE research group for their helpful feedback.References[1] S. Kovalchuk, M. Ghali, M. Klassen, D. Reeve, and R. Sacks, “Transitioning from university to employment in engineering: The role of curricular and co-curricular activities,” in 2017 ASEE Annual Conference & Exposition, 2017.[2] R. Korte, S. Brunhaver, and S. Zehr
]–[4]. Engineeringknowledge is not value neutral and—depending on how it is selected, organized, demarcated,delivered, and evaluated—it can have discriminatory effects on different populations (e.g., [5]–[7]. Often students are implicitly asked to leave aspects of themselves at the door before enteringthe classroom in order to learn “objective” engineering knowledge [8]. This history of theengineering profession means that class biases were baked into its educational systems, helping toexplain why students from low-income and working-class backgrounds describe the culture andcontent of undergraduate engineering programs as foreign, if not hostile (e.g., [9]). Critically reflecting on what knowledge “counts” as engineering knowledge is
students to earn academic credit for their work on these projects. Based on the individualprogram, this credit bearing course is typically structured in one of the following ways:independent study, capstone design, or a stand-alone course. While historically, an independentstudy course has been a more common approach for academic credit, more recently stand-alonecourses such as Humanitarian Design Projects and/or integrated programs such as the EPICSprogram at Purdue and other universities are becoming more common.[7-9] This manuscriptpresents the Humanitarian Design Projects course, its structure and major assignments, andprovides evaluation data and reflections on the successes and challenges of implementing thecourse in its current form.2
and their sub-categories, adding the elements ofself-motivation and human interaction. A hierarchical structure is followed in Bloom’staxonomy, whereas Fink’s taxonomy is circular, indicating multidirectional learning. It isclaimed that using Fink’s taxonomy, enhancement of a student’s learning ability in any onearea improves the abilities in the other areas, delivering a better significant learningexperience [12]. For instance, an improvement in caring category will motivate to learnfoundational knowledge, while integration skill will reflect in learning more about themselves(human dimension).Figure-2 Bloom’s taxonomy of cognitive goals (left), and Fink’s taxonomy of significantlearning (right)Foundational knowledge in Fink’s scheme covers
writing habits and on the first day of thermo-fluids lab to describe how the goabout writing lab reports. Later in the term the students were asked to reflect about working onprojects with team members; they were then asked to describe the process by which they write ateam tech memo. The responses were thematically coded. Their responses are summarized inTable 3. Table 3: Tally of student responses to short surveys regarding their writing habits. Describe your process for writing lab reports? Spring 2020 Fall 2019 Carry out analysis 1st 8
, cooling, heating, pumps andcooling towers [7]. Thus, a reduction in the HVAC energy consumption load would reflect asignificant reduction in the total energy consumed. According to Madison Gas and Electric Company, “on average, a U.S. office building spendsnearly 29 percent of its operating expenses on utilities, and the majority of this expenditure goestoward electricity and natural gas. For the average office building, energy costs can exceed$30,000 per year,”[5]. Cooling towers contributes toward 6% of the energy consumption by office buildings [7].Whatever type of refrigerating system is being used in the HVAC system, it is fundamental tominimize the required heat extraction and to keep the difference between condensingtemperature (Tc
below the minimum standard to reflect a form of “partialcredit” for work, but also an effective “zero” standard for when work completely fails to addressa criterion.Once constructed, the algorithm for assessing student work is as follows, for each criterion: (1) Decide if the work matches the description of the highest standard. If so, mark this level; If not, move to Step 2. (2) Decide if the work matches the description of the minimum standard. If so, mark this level; if not, move to Step 3. (3) If the work is between the two descriptions, decide if it is closer to the highest or the minimum standard and mark the appropriate level; otherwise move to Step 4. (4) If the work appears to attempt to meet this criterion
Assistance in Areas of National Need (GAANN) under Award No. P200A180031.Any opinions, findings, and conclusions or recommendations expressed in this material are thoseof the author(s) and do not necessarily reflect the views of the U.S. Department of Energy or theU.S. Department of Education.The authors would like to thank Dr. James Freihaut for his advice during the development of thepresented Excel tool.References[1] V. Jones and J. H. Jo, “Ubiquitous learning environment: An adaptive teaching system using ubiquitous technology,” in Beyond the comfort zone: Proceedings of the 21st ASCILITE Conference, 2004, vol. 468, p. 474, [Online]. Available: https://www.ascilite.org/conferences/perth04/procs/jones.html.[2] T. L
students. Responding to the statement “My military experience has positioned meto move into a leadership role as I transition to my next career,” only 50% of those surveyedstrongly agree with this statement, while 33.33% somewhat agree, and 16.67% remain neutral. Itis not well understood if this result reflects veterans’ modesty and humility, or points to feelingsof ambivalence toward civilian organizations as they contemplate a transition to a corporateenvironment.VAD students also slightly preferred a military style organization (58%) over a civilianorganization (42%). This result was a little closer than anticipated, as informal discussionsamong student veterans and with faculty tend to emphasize a stronger preference for military-style
some additional maker-technologies like the ShopBot, and troubleshoot student projectsin progress. Further, as it often takes multiple academic years for a project to be optimallyeffective, we would like to return to past participants and encourage them to update their Cardsto reflect their project in its final form.AcknowledgementB-Fab workshops from 2017-2019 were offered with support from the Kern EntrepreneurialEngineering Network (KEEN).References1. Prince M (2004) Does active learning work? A review of the research. Journal of Engineering Education 93(3):223–231. Available at: https://doi.org/10.1002/ j.2168-9830.2004.tb00809.x.2. Deslauriers L, McCarty LS, Miller K, Callaghan K, Kestin G (2019) Measuring actual learning
providers of professionaldevelopment opportunities and educators of prospective K–12 teachers ofengineering should align their work with guidance documents that draw on themost up to date understanding of research and best practices in teacher educationand professional development. As new knowledge accumulates about theprofessional learning of K-12 teachers of engineering, adjustments in programsshould reflect new insights gained from rigorous, high quality scholarshipRECOMMENDATION 5: As evidence accumulates about effective approaches topreparing K–12 teachers of engineering, it will be important to establish formalaccreditation guidelines for K–12 engineering educator preparation programs,such as those developed by the Council for the
the Rochester Institute of Technology. Thiscourse is typically taken by students in the 1st year of mechanical engineering and studentspursuing a minor in mechanical engineering. The structure of the course is shown in Figure 1. Thecontent is provided by two Mechanical Engineering faculty, one mechanical engineering staffmember, and a group of undergraduate teaching assistants. The design project for the course is arobotic chime machine. CAD modeling techniques are demonstrated to enable team members tocollaboratively design their chimes early enough to be able to build. Additionally, a full Figure 1- Engineering Design Tools Course Structuredocumentation and drawing package reflects the parts created in the context of the machiningportion of
Foundation under GrantNumber [redacted]. 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. We also wish to thank [redacted] and [redacted] for help with datacollection.References[1] C. Carrico, H. M. Matusovich, and M. C. Paretti, "A qualitative analysis of career choice pathways of college-oriented rural central Appalachian high school students," Journal of Career Development, 2017.[2] C. A. Carrico, “Voices in the mountains: A qualitative study exploring factors influencing Appalachian high school students’ engineering career goals,” Ph.D. dissertation, Engineering Education, Virginia Polytechnic
activity. With a traditional homework problem studentswould go on to manipulate equations to get the correct result, then evaluate the validity of theiranswer. With computational modeling, the only difference is during the manipulation ofequations the student should let the computer do most of the work. Students write out the firstpart of the problem before they start modifying code. Students are encouraged to transition from amathematical description to a code model by rewriting the equations in terms of variable namesas pseudo-code before typing anything into the computer. The questions that make up the activitythen ask students to reflect on their results, which takes the place of the evaluation step whenworking homework problems.Great pains