aconcentration. (For this chart and those following, the “influenced enrollment” number includesresponses of “definitely yes” and “somewhat.”) Seniors’ interests may have changed, anotherelective outside the concentration may have been more compelling than completing theconcentration, or some schedule-related obstacle may have prevented its completion. Theconcentrations may not have lost their appeal for seniors, but they may require a greater sacrificethan the students are willing to make. Figure 3. Concentrations by yearFigure 4 displays the concentration participation rate and influence on enrollment for thedifferent majors offered in the college. The rightmost bars in the figure reflect the averagenumbers for all
the goal of building teacher confidence. Finally, the SEP2 appears to be a powerful tool forunderstanding the experience and perceptions of participants in research experiences. AcknowledgementThis material is based upon work supported by the National Science Foundation under Grant No.(EEC-1711543). 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] J. C. Brown, J. R. Bokor, K. J. Crippen, and M. J. Koroly, “Translating Current Science into Materials for High School via a Scientist–Teacher Partnership,” J
problems in engineering mechanics.This paper outlines key findings from this Work-In-Progress study and makes recommendationsfor future work in this area.AcknowledgementThis material is based upon work supported by the National Science Foundation in the U. S.under grants number DRL-1535307 (PI: Perez) and DRL-1818758 (PI: Sorby). Any opinions,findings, and conclusions or recommendations expressed in this material are those of the authorsand do not necessarily reflect the views of the National Science Foundation.References[1] D. H. Uttal, and C. A. Cohen, “Spatial thinking and STEM education: When, why, and how?,” Psychology of Learning and Motivation, 57, 147–181, 2012. https://doi.org/10.1016/B978-0-12-394293-7.00004-2[2] L. G
collected data from multiple sources, including student work,faculty reflection logs, pre-/post-surveys, and student focus groups. Our project did not originallyintend to explore connections to engineering identity formation in students or professionalpractice. However, while analyzing the student focus group data, we observed that engineeringidentity was impacting students’ responses in unexpected ways. Thus, this paper aims to answerthe following research question: How are students’ conceptions of engineering identity linked to their perceptions of sociotechnical thinking?BackgroundSociotechnical integration in engineering educationMultiple studies of engineering practice have underlined the necessity of integrating social
publics • Engineering incorporates many domains beyond technical • Engineers impact the worldColes [12] described his view of professional practice with 10 lessons for practice: • professionals engage on society’s behalf, with people who present them with complex, indeterminate problems • professionals work with high levels of uncertainty • professional practice fundamentally involves making judgement • professional judgement is based on ‘practical wisdom • professional judgement is acquired through experience and conversations with respected peers • the learning process that underpins this is the critical reconstruction of practice • this involves ‘deliberation,’ which is more than ‘reflection • deliberation
expert problem-solving [16]. The proposed approach allows modeling physics to beintegrated into a typical introductory college mechanics course. A third study developed modelsof problem-solving to study children’s problem-solving process [17]. According to the study, theconception of modeling the problem-solving process could provide a unifying framework forthinking about problem-solving in children.In this research, we integrate eye-tracking and VR to collect data from participants during theproblem-solving process. The collected data is used to develop models that allow for quantifyingand understanding the behavior of problem solvers and how their performance is compared toexperts. Performance measures are then developed to reflect the problem
comparative reflection approach. The Internet and Higher Education, 41, 1-10. https://doi.org/10.1016/j.iheduc.2018.11.001Gillies, D. (2008). Student perspectives on videoconferencing in teacher education at a distance. Distance Education, 29(1), 107-118. doi:10.1080/01587910802004878Francescucci, A., and Rohani, L. (2019). Exclusively Synchronous Online (VIRI) Learning: The Impact on Student Performance and Engagement Outcomes. Journal of Marketing Education 41(1), 60–69. DOI: 10.1177/0273475318818864Han, H. (2013). Do nonverbal emotional cues matter? Effects of video casting in synchronous virtual classrooms. American Journal of Distance Education, 27(4), 253-264.Hastie, M., Hung., I., Chen, N., Kinshuk. (2010). A
ofmodern data collection and modeling. The mix of participants was interesting and reflected theinterdisciplinary training needed to implement Smart Manufacturing techniques. Out of 15 thatresponded, 2 were in business operations, 4 were in engineering, 3 were in technicalmanagement, 2 were technicians, and 4 were in Information Technology. Their prior exposure toSmart Manufacturing also varied, as shown in Figure 4a.The training focused initially on Smart Manufacturing definitions and then used the FrEDexample to walk the participants through advanced data collection, data analysis, and simulationsto improve the process. All of the materials were simply demonstrated given the time constraints.The overviews and demonstrations appeared to resonate
universityafter more than 20 years in industry or other nonacademic positions. These faculty benefited from a moretargeted set of discussions focused on learning with understanding. Important here was attention to whatstudents bring to the learning environment (prior knowledge), organization of facts and ideas around aconceptual framework to facilitate its use in various contexts (connections within and across courses), andhelping students reflect on what they do or do not understand (metacognitive strategies) [6].Faculty and student data were collected over the five years of the project. Three sources of faculty datainclude interviews (subset each year beginning Spring 2016), reports/presentations (subset each yearbeginning Fall 2016) and teaching
students had assignments to assess their knowledge and mastery. Again, the lower objectiverating was based on the course score in this area, and the low number reflects the number ofstudents who did not meet the requirement to include a quantitative graphic.The instructor and student assessments will rarely be perfect matches, so some margin ofdifference should be expected. The method analyzes data from the instructor and studentsseparately. This objective assessment of course outcomes with objective data from embeddedindicators and student assessment of their accomplishment can produce a better evaluation of thecourse and areas for course improvement. Over time, historical data can track the effects ofchanges in a course. A review of each course
learning environment- The development of technology and the internet have promoteda rapid change in instructional and pedagogical approaches used to engage digital learners (Chyret al., 2017). Online instruction (i.e., fully online or hybrid) has been used to effectively servedifferent types of learning needs (e.g., distance learning, active learning, collaborative learning,etc.), and to promote students’ continuous development, reflection, discovery, and innovation(Chyr et al., 2017; Tsa et al., 2015; Wei et al., 2015;). Nonetheless, while there is a lot ofevidence of the advantages to online pedagogy, it has been reported that online learning canpromote student segregation, which could negatively affect the learning experience and
sicknesses, as we saw in the US and Italy COVID-19 data from the previousproblem set. When does the growth fall off the exponential curve? What is the reason for thisbehavior?5 Professional writing is an area of emphasis in our department, and all students receive extensive instruction in avariety of writing formats before taking this course.6 Students were allowed to self-select teams. Of the three “large” teams (four students or more) that formed for thisassignment, all three featured diversity in gender and two featured diversity in race, roughly reflecting the level ofgender and race diversity in our department.NB: To set a sense of expectations, you should plan to spend ~4-6 hours of earnest effort perperson on formulating questions
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