technologies; 5. to reflect now on the responsibilities ofprofessional engineers; 6. to work as members of multidisciplinary teams; 7. to communicatethe results of their work to involved stakeholders; 8. to be motivated to take USE aspects intoaccount when developing technologies.Through the three courses of every USE trajectory, students are gradually exposed to thetheory and practice of a given topic. The first course (exploration) is based mostly on lecturesand students have to conduct only small cases studies. At the end of the course there is a finalexam. In the second course (specialization), there is a mix between theory and practice.Students attend lectures on a weekly basis but they also have to conduct one or two smallprojects with a group
as children, and fourstudents did not cite either one of these experiences. The different experiences of first-generationcompared to continuing-generation college students were further captured by interview questionsthat asked students to think back to experiences/activities they engaged in as children oradolescence and determine if they now see them as engineering related experiences. By askingstudents to reflect on the pre-college activities that fostered their interest in engineering, we wereable to understand the cultural and historical practices that brought them to seek an engineeringdegree. With this theme, we sought not to capture every micro experience students have had intheir life, rather obtain a general understanding of the
contexts [1], [2]. This study is part of a broadcurricular reform project in 11 core studio courses using assignments that support students’learning of engineering practice [3], [4]. The reform is motivated by research that relates thedevelopment of higher-level capabilities such as systems thinking, communication skills, ethicalstandards, and critical thinking to students’ success in the workforce [5]. It also addresses callsfor greater emphasis on complex, open-ended design problems reflecting work done byprofessional engineers [6].Such tasks contrast with more typical school worksheets that require an algorithmic applicationof course concepts, with an emphasis on reaching a single correct solution through an instructor-determined solution path
Dictionary Wordscould not simply select the most common feedback (e.g. “good” or “great work”) because it didnot add meaningful information. Instead, we cut through the noise by selecting unique words andphrases that provided rich meaning but were used frequently enough to be matched. Table 2 showssome sample dictionary key words. Questions and answers were created from the selected wordsand phrases and grouped based on the category under which they best fit (Section 4.3). Every ques-tion has three answers with the exception of overall score, which has eight. Answers were chosento provide the maximum possible semantic distance between choices. For the third iteration of thereview algorithm, answers were chosen to reflect the question weight of 0
Manufacturingindustrial segments. For each industrial segment, two engineers were invited to engage in face-to-face qualitative interviews. Interview is one of the most important sources of evidences incase studies and is commonly found in this research design (Yin, 2017). At the time when theinterviews occurred, all participant engineers were working in senior leadership positions,ranging from managers to directors, and had between 15 and 34 years of professional experience.Purposeful and convenience sampling (Creswell, 2013) were utilized in the process of selectingthese engineers, since participants were identified from the alumni pool of Utah State University.In this study, interviews with practicing engineers were expected to reflect their
this material are those of the author(s), and do not necessarily reflect the views ofthe National Science Foundation.References[1] A. Hunter, S. L. Laursen, and E. Seymour, “Becoming a scientist: The role of undergraduate research in students’ cognitive, personal, and professional development,” Sci. Educ., vol. 91, no. 1, pp. 36–74, 2007.[2] E. Seymour, A. Hunter, S. L. Laursen, and T. DeAntoni, “Establishing the benefits of research experiences for undergraduates in the sciences: First findings from a three‐year study,” Sci. Educ., vol. 88, no. 4, pp. 493–534, 2004.[3] A. D. Patrick and M. Borrego, “A review of the literature relevant to engineering identity,” in American Society for Engineering Education (ASEE
, 1524601, and 1524607. Any 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.References1. K. Schneider, A. Bickel, and A Morrison-Shetlar, “Planning and implementing a comprehensive student-centered research program for first-year STEM undergraduates,” Journal of College Science Teaching, vol. 44, no. 3, pp. 37-43, 2015.2. K. Schneider and A. Bickel, “Undergraduate research apprenticeship model: graduate students matched with STEM first-year mentees,” Council on Undergraduate Research Quarterly, vol. 36, no. 1, pp. 25-31, 2015.3. J. Frechtling. “The 2002 user-friendly handbook for project evaluation,” National
National Science Foundation for their support through a Graduate ResearchFellowship (DGE-1333468). Any opinions, findings, and conclusions or recommendationsexpressed in this material are those of the authors and do not necessarily reflect the views of theNational Science Foundation.References[1] C. E. Foor, S. E. Walden, and D. A. Trytten, ““I wish that I belonged more in this whole engineering group:" Achieving individual diversity,” J. Eng. Educ., vol. 96, no. 2, pp. 103–115, 2007.[2] J. M. Smith and J. C. Lucena, “‘How do I show them I’m more than a person who can lift heavy things?’ the funds of knowledge of low income, first generation engineering students,” J. Women Minor. Sci. Eng., vol. 22, no. 3, pp. 199–221, 2016.[3
students to work cooperatively in interactive learning groups. Participants were then asked to complete an online Figure 1. Venturi survey administered over Qualtrics© at the end of the semester. flow meter The survey prompted participants to reflect on their LC- DLM instruction and report how well they believed being taught concepts with LC-DLM influenced their learning experience Figure 1. Venturi flow meter compared with other course concepts they learned with regular lectures in the same class. Participation in theexperiment was
traumatic events are perceived and handled within engineering environments by allmembers of the engineering education community. Specifically, the messaging around emotionalexpression should be examined to determine what explicit and implicit barriers are constructed inengineering. Through advanced understanding in this area we can begin to create models thatsupport students through challenges that manifest in and out of the engineering classroom.AcknowledgmentsThis work was funded by grants from the National Science Foundation (EEC-1531586/1531174,DGE-1333468). Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National ScienceFoundation. The
as density, transitivity, and reciprocity in the network [2]. This approach is amethodological and pedagogical innovation because it has the potential to inform and providefeedback about the participants’ work, promote reflection on their collaborative practices andcontribute to cohesion, dialogue and the flow of knowledge within the team to continuouslyimprove the internalization of the new educational model.Keywords: Educational Change, Teacher Collaboration, Social Network Analysis, EducationalInnovationResearch Background and MotivationThis work-in-progress research is being carried out at a large multi-campus private university inMexico and focuses specifically on the area of engineering and sciences. The institution ischaracterized by
Nativeand Native Hawaiian or other Pacific Islander students was too low to draw meaningfulconclusions about racial differences in scholarship receipt. The source of these racial/ethnicdifferences is unknown. For instance, they might reflect different levels of opportunity forscholarships or differential application processes for scholarships, as we did not measure whetherstudents had applied for, but not received, a scholarship. The phi value indicates that this overalleffect size for the distribution of scholarships across race/ethnicity was small.Research Question 2With respect to research question 2, there were statistically significant differences in motivationbetween scholarship recipients and non-recipients. More specifically, independent
note that time survey data is inputmanually and anonymously at the beginning of every lesson. The value is input in units ofminutes, and generally reflects the preparation time for the lesson that the student is about toparticipate in.Instructors collected time survey feedback from four mechanical engineering courses thattransitioned to the new 30 lesson format over the fall (two courses) and spring (two courses)semesters of the 2019 academic year. Because the spring semester is currently on-going, datapresented from these courses only includes that pertaining to the first half, or 15 lessons. Similartime survey data for the previous ten years under the 40 lesson format was obtained. To maintaina fair comparison, only the data from the first
development,” Personality and Individual Differences, vol. 49, no. 4, pp. 344–351, 2010.9. C. Mclaughlin, “Emotional well-being and its relationship to schools and classrooms: a critical reflection,” British Journal of Guidance & Counselling, vol. 36, no. 4, pp. 353– 366, 2008.10. L. Murphy and L. Thomas, “Dangers of a fixed mindset,” Proceedings of the 13th annual conference on Innovation and technology in computer science education - ITiCSE 08, 2008.11. S. A. Sorby, “Educational Research in Developing 3‐D Spatial Skills for Engineering Students,” International Journal of Science Education, vol. 31, no. 3, pp. 459–480, 2009.12. S. A. Sorby, Developing spatial thinking. Houghton, MI.: Higher Education Services, 2016.13. H. Wauck
because culture influences what constitutesintelligence and intelligent acts [9, 10]. Indeed, conceptions of “smartness” in school often caterto analytical abilities, while ignoring other types of intelligence, such as creative or practicalabilities [11]. This emphasis on analytical abilities is magnified even further in engineeringschool, where math and engineering science dominate the curriculum. This cultural norm ofvaluing analytical intelligence above all else reflects white, middle class constructions ofintelligence. This reality contributes to the exclusionary narratives about who belongs inengineering as the qualities that are revered in academia (e.g., brilliance, rigor, seriousness,rationality, objectivity, etc.) are all traditionally
Webb’s instrument [22]. This measureexplored participants positive or negative attitudes toward communicating about engineeringrelated topics in various contexts. Participants completed 18 items, each with a 7-point bipolarscale reflecting the participants’ attitudes about engaging in communication regardingengineering.Motivation: Student motivation was measure using Christophel’s [23] student motivation scale.This measure explored the participants degree of motivation to put forth effort in the currentclass. Participants completed 16 items, each employing a 7-point bipolar scale reflectingparticipants’ feelings toward their current class.Intended behavior: Intended behavior was measured were measured using an adapted version ofPoliakoff and
each team as well as expert involvement.Table 1Additional details on the composition of each team, as well as relevant information on the SIL experts. Data Collection and Analysis During the IDC, the first author assumed the role of a non-participant observer and collected all the data used in this study. Following an ethnographic approach, he did not engage in any of the activities in which the students participated throughout the IDC and interacted with them only when observation alone did not provide data on instances he believed to be relevant to answering the overarching research question (e.g., when participants worked quietly, independently, or engaged in self-reflection). Main sources of data consisted of extensive field notes, videos
asking the participants about their “story” (for example, “How did you get intoengineering?”), followed by reflecting on their engineering identity, sense of belongingness inengineering for themselves and for other students, and their present and future activities and plans in CE.Interviews were conducted by two members of the research team and were approximately one hour long.Qualitative Data AnalysisInterviews were professionally transcribed, and transcripts were reviewed by the interviewers to correcterrors. Initial qualitative analysis was conducted using descriptive coding (Miles and Huberman, 1994);responses to questions about belongingness were coded with the intention of capturing how participantsdescribed their sense of belongingness in
GeneralizedObservation and Reflection Platform (GORP), hosted by UC Davis(https://cee.ucdavis.edu/GORP). While there are limitations to the GORP tool, the advantage ofbeing free, intuitive, and able to be run on a touch screen laptop far outweigh limitations. The dataare captured in real time and outputs as a spreadsheet file, which reads the categories as a functionof time points. The resulting data file can be manipulated in MATLAB or other programs. Table 2: Codebook and Numerical Values Assigned for Data Processing Numerical Level Definition of Level
International Conference on Web and Open Access to Learning (ICWOAL), 2014.[3] E. D. Lindsay and J. R. Morgan, “Passing our students while we fail upwards: Reflections on the inaugural year of CSU Engineering,” in 45th Annual SEFI Conference, Terceira, Portugal, 2017.[4] J. R. Morgan, E. D. Lindsay and K. Sevilla, “A "MetroGnome" as a tool for supporting self- directed learning,” in 2017 Australasian Association for Engineering Education Conference, Sydney, Australia, 2017.[5] M. van den Bogaard, C. Howlin, E. Lindsay and J. Morgan, “Patterns Of Student's Curriculum Engagement In An On-demand Online Curriculum,” in 46th SEFI Conference, Copenhagen, 2018.
in a post-typographic world (pp. 283-301).[15] Maxwell, Joseph A. (2013). Qualitative research design: An interpretative approach (3rd ed.). Los Angeles: SAGE Publications, Inc.[16] Emerson, Robert M, Fretz, Rachel I, & Shaw, Linda L. (2011). Writing ethnographic fieldnotes: University of Chicago Press.[17] Walther, Joachim, Sochacka, Nicola W, & Kellam, Nadia N. (2013). Quality in interpretive engineering education research: Reflections on an example study. Journal of Engineering Education, 102(4), 626-659.[18] Stevens, Reed, O'connor, Kevin, Garrison, Lari, Jocuns, Andrew, & Amos, Daniel M. (2008). Becoming an engineer: Toward a three dimensional view of engineering learning. Journal of
(which changes semesterto-semester). Notably, both projects have a final event that is framed as a competition. Winningthat competition is worth a few extra credit points, as well as bragging rights. These elementsmight work to induce a performance orientation.MethodsWe will report on a subset of the results from a larger study investigating individual differenceson teams, collected in Spring 2017 (n=60), Fall 2017 (n=50), and Spring 2018 (n=60). Before theproject started, students completed a trait goal orientation instrument modified from [15] tomeasure their state achievement orientation. As part of a reflection on each project and their teamexperience, students completed a wrap-up survey with items addressing their individual
the author(s) and do not necessarily reflect the views of the NationalScience Foundation. The authors wish to thank the STRIDE team and survey participants fortheir engagement with this study.References [1] M. Credé and N. R. Kuncel, “Study habits, skills, and attitudes: The third pillar supporting collegiate academic performance,” Perspectives on Psychological Science, vol. 3, no. 6, pp. 425-453, 2008. [2] A. Godwin, “Unpacking Latent Diversity,” in American Society for Engineering Education (ASEE) Annual Conference and Exposition, Columbus, OH, 2017. [3] J. J. Lin, P. K. Imbrie, K. J. Reid, and J. Wang, “Work in progress—Modeling academic success of female and minority engineering students using the student attitudinal
information independently[66]. This mechanism reduces the cognitive load of storing information and allows for greaterinformation processing capacity.When engaging in problem solving, experts have been shown to participate in systematic real-time “reasonability” checks, contrasted with novices who proceed to the end without taking timeto reflect [67]. This behavior of expert problem-solvers perfectly aligns with our definition ofengineering intuition as the ability to assess solutions. In real-world engineering, ill-definedproblems are of particular interest. Studies have shown that ill-defined problems are often notsolved systematically, but rather through reactionary, intuitive processes to navigate thedecisions of problem-solving [68].Motivation
, Effective Learning.”, Palo Alto, CA: Davies-blackPublishing, 1995.11. Dale, E., “Audiovisual Methods in Teaching,” (3rd ed.), New York: Dryden Press, 1996.12. Wankat, P.H., “Reflective Analysis of Student Learning in a Sophomore Engineering Course,” Journal ofEngineering Education, Vol.88, no.2, 1999, pp.195 -203.13. Finelli, C., Klinger, A., & Budny, D.D. (2001), “Strategies for Improving the Classroom Environment,” Journalof Engineering Education, Vol 90, no.4, 2001, pp. 491-497.14. Smith, K.A., Sheppard, A.D., Johnson, D.W. & Johnson, R.T. (2005), “Pedagogies of Engagement: Classroom-Based Practices,” Journal of Engineering Education, Vol. 94, no.1, 2005, pp. 87-101..
the experiences theygain through their funding. Additional attention should focus on the role of postdoctoralpositions both in industry and academia on engineering doctoral career advancement. Educationwas categorized for all positions within academia and K-12 employment. Future work shouldinvolve looking at what types of positions graduates obtain within Education, such as tenure-track faculty positions or lecturer or other part-time positions.AcknowledgementsThis research was funded by the National Science Foundation through grants #1535462 and#1535226. Any opinions, findings, and conclusions in this article are the authors’ and do notnecessarily reflect the views of the National Science Foundation.ReferencesAustin, A.E. (2002). Preparing
capstonedesign curriculum, the current two year research project was designed to implement and assessthe efficacy of the activities as an integral part of the course. IC activities have been incorporatedin the USAFA capstone design course previously, but their effects were not directly studied.Nevertheless, faculty observations and customer feedback suggested that creativity and productinnovation improvements occurred. Thus, sufficient anecdotal evidence existed to motivatefurther formal examination of the impact of IC activities on the USAFA engineering designprocess and capstone design course.Since the underlying conceptual process of the capstone design course and the DI activityexperience reflects the divergent thinking processes it is appropriate
. Kimball, and R. D. Reason, “Understanding Interdisciplinarity: Curricular and Organizational Features of Undergraduate Interdisciplinary Programs,” Innov. High. Educ., vol. 38, no. 2, pp. 143–158, 2013.[8] B. A. Masi, A. E. Hosoi, and S. A. Go, “Re-Engineering Engineering Education: A Comparison of Student Motivation, Ability Development and Career Paths in Traditional and Cross-Disciplinary Engineering Degree Programs,” Am. Soc. Eng. Educ., 2011.[9] J. Berglund, “The Real World: BME graduates reflect on whether universities are providing adequate preparation for a career in industry.,” IEEE Pulse, vol. 6, no. March- April, 2., pp. 46–49, 2015.[10] R. H. Harrison, J.-P. St-Pierre, and M. M. Stevens, “Tissue
evaluate theseresults in the context of a larger and a more longitudinal study. Nevertheless, the resultspresented here offer strong support for including more engineering challenges that embracesocial responsibility in the undergraduate engineering curriculum.AcknowledgmentsThe authors would like to gratefully acknowledge the National Science Foundation for theirsupport of this work under the TUES program (grant number DUE-1245464). Any opinions,findings, and conclusions or recommendations expressed in this material are those of the author(s)and do not necessarily reflect the views of the National Science Foundation.References[1] National Academy of Engineering, “Grand Challenges - 14 Grand Challenges for Engineering,” 03-Feb-2019. [Online
]need to implement a rigorous system of evaluation of their pedagogical assessments through theuse of a measurement model that makes such demands on the data. To that end, theimplementation of Rasch measurement models will provide robust validation for the measures ofstudent learning outcomes, which in turn can improve course curricula by accurately targetingdomains and transferable skillsets critical to the development of this generation’s chemicalengineers.AcknowledgementsThis material is based upon work supported by the National Science Foundation under Grant No.DUE 1712186. 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