visualization embedded in the textbook. These visualizationswere integrated in the e-textbook and offered students the chance to see aspects of iterationdemonstrated immediately after the relevent paragraph.The design of the visualization reflected the appearance of the block-based language the studentswere using on their first encounter with iteration. The horizontal green segmented rectangle is thelist which moves from right to left on each iteration so that a single list item becomes the value ofthe iteration variable (”price” in this example). Figure 1: Example of a Textbook VisualizationTo interact with these visualizations, students clicked on the four arrow icons seen at the top ofthe figure. Clicking the ‘¿’ button
multidisciplinary use. We hope that the analysis and reflections on our initial offeringshas improved our understanding of these challenges, and how we may address them whendesigning future data science teaching modules. These are the first steps in a design-basedapproach to developing data science modules that may be offered across multiple courses.1. Introduction As technology advances, familiarity and expertise in data-driven analysis is becoming anecessity for jobs across many disciplines. Data science is an emerging field that encompasses alarge array of topics including data collection, data preprocessing, data quality, data visualization,and data analysis using statistical and machine learning methods. A recent National Academy ofSciences
college consistently ranked at the bottom of student concerns across everyyear and engineering major. We also needed a better understanding of how the studentsexperienced the program structure of our women in engineering program and if it could beimproved to better reflect the needs of this new student cohort. Finally, we wanted to know howprevalent these declining engagement trends were on campus and what, if any, steps could betaken to improve them. This paper focuses on focus groups held with undergraduate women inengineering students, and contextual interviews held with other campus programs, clubs andorganizations. First, we present a summary of what we learned about this new cohort of studentsas well as the key survey findings that informed
approaches related to airport challenges. The design competitionrequires student teams to interact with airport operators and industry experts to get input on theirdesign ideas and solution [2]. This paper explores the number and value of these interactions byevaluating the winning design proposals.Statistics are used to analyze trends in the winning design proposals which may reflect theimportance of number of the experts contacted by student teams and their demographics. Thewinning design proposals contain written sections that discuss the team’s reported benefits oftheir interactions with industry experts. Thematic analysis is used to identify themes for designproposals from first, second, and third place teams. The paper presents a study of
departments.AcknowledgmentsThis material is based upon work supported by the National Science Foundation (NSF) No.EEC-1653140 and 2123016 given to the second author. Any opinions, findings, and conclusionsor recommendations expressed in this material do not necessarily reflect those of the NSF. Wewant to give a special thanks to the institutional liaisons, Dr. Hector Cruzado, Dr. Sindia Rivera-Jimenez, Dr. Heather Shipley, Dr. Kimberly Cook-Chennault, and Dr. Paul Barr who assisted uswith collecting participant data in the first stage of sampling. We also want to thank theparticipants for sharing their experiences with us and the readers of this work.References[1] National Center for Science and Engineering Statistics, “Women, Minorities, and Persons with
for women in science expanded but gendersegregation still existed. In the nineteenth century, women participated in aspects of science butmainly engaged in data-gathering rather than idea-creation [26] and were largely invisible andconcentrated in nurturing career tracks [39]. Prior to the 20th century and beyond, womensupported science but not pioneers in the field; reflective of the patriarchal society they lived in.Commonly known as biological determinism, the physical, psychological, and intellectual natureof women prohibited them from producing great science [38]. The Nineteenth and earlyTwentieth centuries posited if women were incorporated into scientific employment, they weresegregated in it with stereotypes of appropriate sex roles
futureprojects after receiving their feedback from CP 1.The decrease in scores for CP 3 can be attributed to the type of project it is compared to the other projectsin the course. It is the truss analysis program that introduces some linear algebra concepts that studentshave not had much exposure to, and the code theory does not follow the same method as handcalculations for trusses. Also, the route for verification and exploration is more open-ended than the otherprojects (at least that is how students view it) and this is reflected in their overall scores. The value of thisproject, however, can be built upon in future courses for more complex system analysis and often studentsreflect on this project as they get into those upper-division classes.There
rooted at the intersection of my identity as ablack woman. I have had to defend myself at times against tenured professors and illuminatemaltreatment and disrespect. The most frequent abuses I have experienced were at the handsStaff Researchers that direct and maintain campus user facilities (like a cleanroom, or an opticalanalysis laboratory). A Staff Researcher or technician (white males in my instances) either threwaway equipment while I was using it (disposing of my gloves while I was using the scanningelectron microscope) or antagonizing and questioning my “right” to be in the space, in aninstance where I was using an x-ray diffraction tool in a characterization laboratory.Summary: Panelists describe both internal reflection and external
all perspectives.Heuristic for an Accomplice’s Ethic of Care and AccountabilityIn order to establish coalitional accomplice relationships that appreciate and celebrate difference,the authors suggest three heuristic activities that can establish trust and build a sharedunderstanding. This heuristic reflects a Black Feminist epistemology, not only because it is builtin pursuit of an ethic of care but also because it invests in knowledge-making in action. ForBlack Feminist theorists, this means that the experiential knowing that occurs in situ establishesthe basis for relationships. Importantly, we use a heuristic because there is no one-size-fits allapproach to activist work or to establishing ally, advocate or accomplice relationships. Yet
Model [4], [5] andcompleting his learning style inventory survey. The results of the survey provide each studentwith rating on a scale of 1 to 11 regarding their preference for sensory versus intuitive, visualversus verbal, active versus reflective, and sequential versus global learning situations. Usingslides from the ASCE ExCEEd Teaching Workshop [6], the instructor explains what the learningstyle dimensions mean and provides insights as to how students can use this information to assistin their own learning. The survey sheets are collected, the data are assembled and the compositeresults for the entire course are shown at a later date. COVID format: This was one of two activities in the course that were conducted entirelyvirtually
and fears that impactedtheir mental health and reduced learning and performance.3. Adaptation Strategies: Adaptation strategies improved STEM learning(a) Relaxation Strategies: Seventy-seven percent (77%) of RPs tried to reduced stressesthrough relaxation strategies such as working out, taking breaks, meditation, reflection sheets,movies, family support, self-leniency, mental wellness visits, and other mental health strategies.One RP noted that, “Yeah, so, you know, I kind of, I forced myself to, uh, to at least get somephysical activity. Even If I didn't want to or not, I just knew I'd feel a little better, I was able tofocus a little better if I did."(b) Peer Collaboration: Seventy percent (70%) of RPs connected with their peers
last decades of the past half century suggest that while manyfactors are contributing to the actualization of “thinking machines”, paradigms about AI are acritical in translating AI research into effective, reliable and trustworthy real-world applicationsfor learning, health, automation and other domains.References1 Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journalof Artificial Intelligence in Education, 26(2), p.582-599.2 Schön, D. A. (c1983.). The reflective practitioner: How professionals think in action /. Basic Books,.3 Osoba, O. A., & Welser IV, W. (2017). An intelligence in our image: The risks of bias and errors in artificialintelligence. Rand Corporation
the Massachusetts Institute of Technology, and four degrees from Columbia University: an M.S in Anthropology, an M.S. in Computer Science, a B.A. in Mathematics, and a B.S. in Applied Mathematics. Hammond mentored 17 UG theses (and many more non-thesis UG through 351 undergraduate research semesters taught), 29 MS theses, and 9 Ph.D. dissertations. Hammond is the 2020 recipient of the TEES Faculty Fellows Award and the 2011-2012 recipient of the Charles H. Barclay, Jr. ’45 Faculty Fellow Award. Hammond has been featured on the Discovery Channel and other news sources. Hammond is dedicated to diversity and equity, reflected in her publications, research, teaching, service, and mentoring. More at http://srl.tamu.edu
, findings, and conclusions, and recommendations expressed in thisreport are those of the authors and do not necessarily reflect the views of the NSF.References[1] R. Sowell, Doctoral Initiative on Minority Attrition and Completion., Washington, DC: Council of Graduate Schools, 2015.[2] M. Ong, C. Wright, L. L. Espinosa, and G. Orfield, “Inside the double bind: A Synthesis of empirical research on undergraduate and graduate women of color in science, technology, engineering, and mathematics,” Harv. Educ. Rev., vol. 81, no. 2, pp. 172–208, Jun. 2011, doi: 10.17763/haer.81.2.t022245n7x4752v2.[3] M. Cabay, B. L. Bernstein, M. Rivers, and N. Fabert, “Chilly climates, balancing acts, and shifting pathways: What happens to
some engineering disciplines at the larger schools also study rigid body dynamicsat the second-year level. Two instructors (Region 1) were funded by BCcampus, and workedclosely together. The third instructor (Region 2) was funded by the Association of ProfessionalEngineers and Geoscientists of Saskatchewan (APEGS), the provincial professional engineeringregulator. We also focused on different strategies and priorities for problem creation: a largebank of fundamental questions (Site 2) versus fewer, more complex questions (Sites 1A and 1B).This is reflected in our estimates for problem cost: excluding learning objective development andother start-up time, Site 2 estimated $16 CAD/problem in student and faculty time, while Sites1A and 1B
an explanation can be found in the published dissertation. Asis traditionally followed in IRT, item fit statistics were obtained. Cut-off criteria for a reasonablefit were SRMR and RMSEA < 0.08, CFI and TLI > 0.90 or 0.95 [43]. Items with |Yen’s Q3| >0.20 (Q3 fit statistic represents the correlation between the residuals for a pair of items) has localdependence and significant item fit values (p < 0.05) revealed misfit items [44]. Finally, itemand test information functions graphically reflected the reliability (1 - [1 / peak information]) ofthe items and the test as a whole in estimating the construct over the entire scale range [45].FIGURE 3. Hypothesized 2-D measurement model for the APT-STEM instrument [12]ResultsThe results
thinking, data modeling, communication, reproducibility and ethics [11]. In a similar study [13], researchers monitored trends across Europe in order to assess thedemands for particular Data Science skills and expertise. They [13] used automated tools for theextraction of Data Science job posts as well as interviews with Data Science practitioners. Thegoal of the study [13] was to find the best practices for designing Data Science curriculum whichinclude; industry aligned, use of industry standard tools, use of real data, transferable skill set,and concise learning goals. The best practices for delivery of Data Science Curriculum includemultimodality, multi-platform, reusable, cutting-edge quality, reflective and quantified, andhands-on. In
science (statistician,computer scientist, industrial engineering, operations researchers, etc.) are in-demand and requirehighly skilled professionals with knowledge of data science, which has resulted in a highlycompetitive labor market. While the median annual salary for data scientists is quite high, about$122,000, according to the BLS, this reflects the higher educational, experience, and skill levelrequirements needed for such positions, as well as geographical differences related to keyemployer locations.Employers have recognized that data science professionals will be a critical resource to theiroperational excellence, as well as for the future of their innovation ecosystems. This need fordata science professionals has naturally driven an
learn the material and could complete the experiment without instructor intervention.Henke et al [4] used a hybrid approach where students are able to design control algorithms tocontrol electro-mechanical models in the online lab. In this format, the experiment actually takesplace, and the data reflects interactions between physical devices, not virtual entities. However,these remote web-accessible laboratories are in some respect similar to simulations in that thestudent does not have to be co-located with a particular piece of laboratory apparatus. Nedic et al.[5] developed remotely controlled labs called NetLab that allows multiple students to run anexperiment remotely in real time. Amiguid et al. [6] evaluated 100 web-based remote labs
varyconsiderably and we found no evidence of programs sharing the same assessment instruments orprotocols. A few examples are below. They describe evaluation from different viewpoints and we presentthem here to show examples of the diversity of methods employed, and some research outcomes andreflections. • One paper described the use of specific assessment methods including competency rubrics, individual development plans, and ePortfolios for evaluation (Chang, Semma, Fowler, & Arroyave, 2021). The rubrics encompassed professional and technical skills including: 1) interdisciplinary knowledge generation, 2) collaboration, 3) conflict resolution, 4) oral communication, 5) written communication, 6) self-reflection, 7
perspective, we assume the following principles: problematize status quo,look at the use of language as clues to how ways of thinking and behaviour are structured, lookfor existing mechanisms of inequality, and look for creative alternatives for a more just/equitableoutcome.First, in order to describe what mechanisms of exclusion exist and become significant in studentexperiences, we looked for student accounts of their direct experiences (e.g. of barriers to fullparticipation in engineering education). Students also reflected on their observations on thecontrast between exclusion and inclusion. This resulted in the identification of: the location ofrepresentation gap that became influential; socially-mediated mechanisms that actually lead
STEM Education (IUSE) program under Award Numbers DUE-1562773 and DUE-1525112. Any opinions, findings, and conclusions expressed in this material are those of theauthor(s) and do not necessarily reflect the views of the National Science Foundation. The authorswould like to thank the reviewers for their thoughtful and encouraging feedback on improving thepaper.References [1] C. Ebert and S. Counsell, “Toward software technology 2050,” IEEE Software, vol. 34, no. 4, pp. 82–88, 2017. [2] H. Krasner, “The cost of poor quality software in the us: A 2018 report,” Consortium for IT Software Quality (CISQ), September 2018, https://cra.org/data/Generation-CS/ (retrieved August, 2020). [3] R. Florea and V. Stray, “A global view on the hard skills
level contributes to this vision. Despite some gains in recent decades, women faculty inengineering are still underrepresented. Between 2006 and 2016, the proportion of women facultyin engineering grew from 16% to 23% at the assistant level, from 11.9% to 18.3% at theassociate level, and from 3.8% to 10.6% at the full professor level [2], [3]. While the proportionof women faculty at the lower ranks has increased significantly, the limited representation ofwomen at higher faculty ranks limits their potential for reaching leadership roles andcontribution with significant decision-making to influence engineering education [4]. Althoughthe presented gains are of value, and may already reflect the effect of multiple initiativesimplemented to support
Source.Simulation of microwave transmission lines andmicrowave filters (Alabama A&M University)Participants simulate two popular microwavetransmission lines and two microwave filters.First, participants use two simulation software:AppCAD and Sonnet Lite. They learn thestructures of the two transmission lines and theoperations of two software. They use AppCAD tosimulate both transmission lines and to investigatehow the dimension parameters influence thecharacteristic impedances. They use Sonnet Liteto simulate the microstrip on reflection coefficientand insertion loss. Participants also simulate a Figure 6. Coplanar waveguide on AppCADmicrostrip band stop filter and a microstripbandpass filter using AppCAD, and then simulateboth filters using
thinkers, students learn that the process of coming up with something new involves many trials, errors and mistakes and even failure. However, students learn that occasional failure and mistakes are part of the creative and innovative processes rather than a discouragement to an adventurous spirit. They learn to reflect on and to evaluate their experiences and to work with others to improve on those experiences, so as to come up with better or new ways of doing things.” [8]Typically, learning outcomes are used to describe knowledge and skill competencies thatstudents should attain from their learning. In innovation pedagogy learning outcomes are termed“innovation competencies” and are organized into three categories: “1) individual