currently working towards a PhD in Materials Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC) under Professor Angus Rockett and Geoffrey Herman. Her research is a mixture between understanding defect behavior in solar cells and student learning in Materials Science. Outside of research she helps plan the Girls Learning About Materials (GLAM) summer camp for high school girls at UIUC. c American Society for Engineering Education, 2018 Works in Progress: Integrative Engineering Leadership Initiative for Teaching Excellence (iELITE)AbstractBeginning in the spring of 2017, a team of engineering faculty, in collaboration with professionalsacross
Paper ID #22654Intersecting Identities of Women in EngineeringDr. Ruby Mendenhall, University of Illinois at Urbana-Champaign Ruby Mendenhall is an Associate Professor at the University of Illinois, Urbana-Champaign. She holds joint faculty appointments in Sociology, African American Studies, Urban and Regional Planning, Social Work and Gender and Women’s Studies.. She is currently a faculty member at the Carl R. Woese Institute for Genomic Biology and a faculty affiliate at the Institute for Computing in the Humanities, Arts and Social Sciences, Women and Gender in Global Perspective, and Gender and the Cline Center
student engagement, participation, and perception of competence[5]. However, design-based activities require more resources and planning compared to project-based activities, and, thus, may not be feasible for resource limited institutions. Nedic et al.presented project-based laboratories for first year students studying non-major courses [4]. Theselaboratories included power supply, racing car, and moisture probe and required limited priortechnical knowledge to complete the projects. The project-based laboratories were reported toincrease student satisfaction, reduce attrition rate, and improve student success rate [4].Similarly, electrical engineering laboratory projects developed for non-majors were shown toinduce student interest to apply the
First-Year Mechanical Engineering CourseAbstractAn activity is designed and deployed in a first-year mechanical engineering class to exposestudents to heat transfer. This educational activity is part of an introductory course given duringthe first year to introduce students to mechanical engineering and give them tools to use whilepursuing their Bachelors of Science degree. The activity is scalable and can be easily deployed infirst-year engineering classes at other educational institutes. Its rigor is planned for first-yearstudents who have not yet taken the prerequisites required for heat transfer. It is presented withits goals, goal attainment measures and feedback representing the student perception. Analysis ofthe student’s work and
disabilities who are frontloaded, and a control group, students with learning disabilitieswho are not frontloaded. However, since there is a variable number of students with learningdisabilities enrolled each semester, a statistically meaningful sample size cannot be guaranteed.Instead, all enrolled students were frontloaded.Originally, the authors had planned to gather data via a focus group of students with learningdisabilities (historically 10 to 15% of the enrolled students were registered through the DisabilityServices Office). However, since only one student of the 133 enrolled in Fundamentals ofEngineering was registered with the Disability Services Office, focus groups were not possible touse. Instead, the overall effectiveness of the
Electrical & Electronic Engineering, Masters’ Degrees in Air Traffic Planning and Management, and Aerospace and Aviation Management, Ph.D. degree focused on Aviation Safety Support Systems, Avionics, and Aviation Operations from Purdue University. Chenyu is also a current FAA Certified Advanced Ground Instructor. He has been immersed in a wide range of aviation related research work, and has expertise in solving cutting-edge aviation problems, such as aviation data analytics and modeling, UAS operations, air traffic management automation, flight safety enhancement, aviation emissions assessment, and engineering applications on aviation operations. He has worked closely with related industry leaders and aviation
the facultymember formulate a plan for responding to the feedback. At minimum, we encourage them toacknowledge and appreciate the feedback (i.e., to close the loop with students). Almost withoutexception, there are changes the faculty member can make right away to try to improve studentlearning, as well as changes that can inform future offerings of the course. There are also caseswhere it makes the most sense to ask students to make a change—e.g., prepare a notes page touse in an exam, instead of an open-book exam. In either case, the feedback data is never the onlybasis for making changes, and the instructional consultant’s expertise is obviously critical ininforming teaching decisions. It is by no means a given that SGIDs result in more
learning: Theory and Practice. Ed. James H. Block. New York: Holt, Rinehart and Winston.[4] Keller, F. S., Sherman, J. G., and Bori, C. M. (1974). PSI, the Keller Plan Handbook: Essays on a Personalized System of Instruction. Menlo Park, Calif.: WA Benjamin.[5] Armacost, R.L., and Pet-Armacost, J. (2003). Using Mastery-based Grading to Facilitate Learning. IEEE Frontiers in Education Conference (FIE), Boulder, Colorado.[6] Carver, R. P. (1974). Two Dimensions of Tests: Psychometric and Edumetric. American Psychologist, 29: 512-518.[7] Onipede, O., and Warley, R. (2007). Rethinking Engineering Exams to Motivate Students. 26th Annual Lilly Conference on College Teaching, Miami University, Oxford, OH.[8] Sangelkar, S., Ashour, O.M
Machine, Apress, 2009.[7] T. Spilling, Self-Improving CNC Milling Machine, Master's Thesis, Department of Physics, University of Oslo, 2014.[8] DIY CNC Router Kit, Retrieved, May 12, 2017, https://www.amazon.com/24x17cm-Milling-Machine- Desktop-Engraving/dp/B01NBTLIM8.[9] R. Ginting, S. Hadiyoso, and S. Aulia, Implementation of 3-Axis CNC Router for Small Scale Industry. Int. J. Applied Engineering Research, Vol. 12, No. 17 (2017), pp. 6553-6558.[10] DIY Desktop CNC Machine Plans and Comprehensive Builder's Manual, 2011, Retrieved July 15, 2017, www.MyDIYCNC.com.[11] D. B. Patel and A. R. Kyada, DIY CNC: A Review, Proc. 5th Int. & 26th All India Manufacturing Technology, Design and Research
have contributed high quality work to their team and are likely tosimilarly do well on individual assignments. Additionally, students who submit homeworkassignments before they are due tend to perform better in the course than average, and studentswho submit homework assignments very late tend to preform worse. We plan to provide these results to our students in an attempt to improve theirperformance. Students may be more willing to improve their performance on teams and submithomework assignments earlier if they are given results backed by data which was collected froman engineering course as opposed to anecdotal stories or research collected from differentmajors. However, even though these factors have been shown to be associated with
verticalaxis and the linear position of the transducer is displayed along the horizontal axis. The B-scanhelps to determine the depth of the reflector and its approximate linear dimensions in the scandirection. The C-scan representation is the plan type view of the test piece. From thisrepresentation, it is very convenient to determine the location and the size of reflector as the imageplane is parallel to the scan pattern of the transducer. Figure 3 A-Scan (Left), B-Scan (Middle) and C-Scan (Right)Experimental setup This experiment was conducted as the lab work of Computer-Aided Manufacturing (MFG5359) course offered in fall’2017 in the department of Industrial Manufacturing SystemEngineering, University of Texas at El Paso
programthrough proper planning and execution. The 223 quarter credits, equivalent to about 149semester credits, have been reduced to 120 semester credits, thus enabling full-time students tograduate in 4 years as opposed to 5 years in the quarter system, and also better positioning ourprogram to be competitive with programs of other institutions, especially those in our vicinity.The reduction in the semester credits was possible through reviewing the curriculum, combining,modifying, and eliminating courses, without diluting student learning or deviating from ABETrequirements. Both full-time and adjunct faculty members were involved in the Q2S conversion.The semester system went into effect in fall 2017. There is no quantitative assessment of theimpact
− ℎ1 Fig. 1: a) Schematic and b) 𝑇-𝑠 diagram for an ideal vapor-compression refrigeration cycle.The actual vapor compression cycle is an alteration from the ideal cycle due to irreversibilities andsmart planning by engineers to protect the system components. At the inlet to the compressor, therefrigerant is slightly superheated to ensure that no liquid enters the compressor. The actualcompression is not reversible or adiabatic so it is not isentropic. State 3 is slightly subcooled toensure pure liquid enters the throttling valve. A 𝑇-𝑠 diagram more representative of an actualrefrigeration cycle is given in Fig. 2. Fig. 2: 𝑇-𝑠 diagram for an actual refrigeration cycle. The solid line represents the ideal cycle and the dashed the
logins, etc. Using these factors, we built a model whichcan successfully predict students’ performance based on their navigational behavior. Accordingto our analysis, Support Vector Machine is more accurate and effective than the other algorithms.This model can help the instructors in providing better guidance for the students. The data is notnormally distributed because the output variable contains records with more ones and fewerzeros where we considered 1 for scores above 60% and 0 for scores below 60%. The data hasvery few students with scores below 60% and hence it is not normally distributed. The modelscan perform better if we have a larger dataset.For our future work, we plan to include several other sections that will allow us to
major barrier to adoption of AM. Similarly,these problems can be identified as key obstacles to generate talent in Additive Manufacturing: (1)The Millennial generation’s negative perception of the manufacturing industry; (2) Lack ofinterdisciplinary STEM skills; and (3) Lack of practical hands-on or on-the-job training.Such an acute shortage of human labor calls for a systematic plan to address the workforceshortage. In an effort to address the problem, The National Science Foundation held a workshopin 2015 to discuss the educational needs to equip the industry and academic system for Additivemanufacturing. A unique cohort of individuals from academia, industry, and governmentformulated the way forward to inculcate AM in education at all levels
Paper ID #27057Addressing the Cognitive and Affective Domain of Ethics Across the Civil andEnvironmental Engineering CurriculumDr. Jennifer Mueller PE P.E., Rose-Hulman Institute of TechnologyDr. Matthew D. Lovell, Rose-Hulman Institute of Technology Matthew Lovell is an Associate Professor in the Civil Engineering Department at Rose-Hulman Institute of Technology, and he currently serves as the Interim Senior Director of Institutional Research, Plan- ning, and Assessment office. He is also serving as the director of the Making Academic Change Happen (MACH) program. He received his Ph.D. from Purdue University, and he holds his
actually more comfortable approaching faculty.The differences in our findings offer implications for the cautionary nature of studies of diversityin postsecondary education. More specifically, our findings suggest that the “lens” – the methodsof data collection in a study and the units of analysis – does impact principal study findings, evenfrom the same undergraduate population. Given that the findings of our study were used to drivestrategic planning for diversity and inclusion efforts at our institution, we caution against relyingon a single methodology – however consistent the findings appear to be with existing literature –to set your course of action and/or generalize to larger populations. We most strongly advise amixed methods approach
constraints, a specified budget plan anda timeline first. Students then researched on the difference between mechatronics and robots tofurther develop their insights on the interdisciplinary among mechanical engineering, electricalengineering and computer engineering. They spent 4 weeks to build the prototype. Finally eachteam presented their work and submitted a final report.One team of four students (two in ME, one in EE and one double majored in ME and music)constructed a robot which solved a three by three Rubik’s cube in 24 moves. The robotillustrated in Figure 1 was built with the use of a LEGO Mindstorms construction kit andprogramming environments. The group employed the use of three actuators, two sensors and acontroller to enable their
, we describe future research plans, which includeusing unsupervised machine learning techniques to move beyond basic binary classification.1. IntroductionIn this paper, we explore the process for training two supervised machine learning classificationalgorithms to classify student code comments as sufficient or insufficient using MultinomialNaive Bayes Classifier and a Random Forest Classifier. We are classifying comments fromstudent lab submissions as part of a larger NSF funded writing-to-learn to program project inwhich we are developing a framework for allowing students to self-monitor and self-assess theirown metacognition [1,2]. Students are provided with an Integrated Development Environment(IDE) that allows the students to use
stated goal that PYroMat can enablestudent exploration in the classroom. Generalizability of survey results was limited by a smallsample size, so further research may be warranted to further validate these results. From a facultyperspective, we are hopeful about the potential of this tool to continue to facilitate exploration ofthermodynamic properties by students, and to expand the range of assignment types that canfeasibly be implemented by students.Several key areas of further work are planned to improve the quality of the both the PYroMatbackend and the web interface: • Standardize the property access interface (in the Python backend) across both multiphase substances and ideal gases. • Improve the numerical convergence of the
. The purpose of this activity is to get the students thinking aboutthe relationship between a design on paper and the restriction of what is possible to build in thefield. Furthermore, this planning activity was meant to enhance the students’ reflective learningexperience after they run into trouble when they built their piece in the next activity. [Figure 3] Design Critique Session (Left); Idea Sketch Exercise (Right)3.2 Build Activity DetailsThe build activity was scheduled in two sessions: the first taking up the remainder of the firstday, and the second during the morning of the second day. Students were instructed to finishtheir molds by the first day so that plaster could be poured immediately the following day.Students
could serve as both aflashlight and a bottle opener. In order to successfully complete the project, students had toconstruct a virtual prototype in CAD, present their design ideas to a “customer,” construct aprocess plan using both an NC mill and an NC lathe, select tooling and process parameters,fabricate components, and finally, assemble and debug their prototypes.Table 1: Course content for the pre-2017, 2017, and 2018 versions of ET 349. Week Pre-2017 Fall 2017 Fall 2018 Mill Tooling and Process Survey of mechanical Survey of mechanical 1 Conditions fabrication processes fabrication processes
design; (3) implement basic traffic flow and queuing principles and have the underlying basis for understanding complex queuing systems; (4) conduct highway capacity and quality of service analysis at freeways and multilane highways, (5) design signal timing and phasing plans at signalized intersections and perform quality of service assessment; and (6) analyze traveler trip decisions, with respect to the modes and routes chosen by travelers.Introduction to Transportation Engineering is taught once per year under a variety of formats andby several instructors. The author took over the course in Fall 2014 and has been the onlyinstructor since. In addition to the instructor, one
), as well as department affiliation. With the growth of makerspaces on universitycampuses, some efforts have been made to develop and implement some best practices toestablish new facilities [21] and to create a classification system of academic makerspaces as ameans to compare different makerspaces for planning purposes [27].The tools and equipment available, along with the design and layout of each makerspace, candepend on the community it serves. One of the most common features of any makerspace is theuse of digital tools for the creation of physical artifacts [2], [3], [6], [9], [25]. Along with 3Dprinters, many maker workshops include other rapid prototyping tools that can be used onmaterial like wood, metal and plastics, such as computer
approach basically involves the observation of aphenomenon, the description and measurement of its characteristics in a variety of conditions,the recognition of patterns of data, and the constitution of these patterns in theory (Reynolds,1971). The fasificationist or theory-then-research approach, on the other hand, involves thenotion of a conjecture or hypothesis to be refuted or falsified via the design of a research plan totest it (Chalmers, 1982). In this paper, we would like to emphasize that, whichever approach istaken in the process of building theory, there are elements and procedures in common that needto be identified. Dubin (1978) recognizes the following elements in theory building:Table 1. Elements in Theory Building (source: Chalmers
; (b) requiring groups tocomplete a project planning phase that serves as a roadmap for their experience; (c) encouragingassignment of tasks to individuals rather than condoning ‘group work’; (d) conducting regularcheckups of team progress; and (e) coaching teams or team leaders in dealing with non-performing team members or other team dysfunction. All of these aspects of team guidance bythe instructor are deemed critically important to project success [2, 6].Approach to Capstone Design ExperienceThe project groups or teams for the capstone design in chemical engineering at TAMUK areformed in the fall, at the beginning of the two-semester senior design course sequence (fall-spring sequence, also known as Design II and Design III). The students
their experiences and investigate how students withdifferent attitudes, beliefs, and mindsets may feel more or less like they can become engineersthrough an engineering education pathway. We will also use their narratives to understand howto foster an innovative mindset in the engineering classroom. These findings will inform theeducational plan to develop an inclusive pedagogy to support latently diverse students.Impact of the Proposed WorkThis work impacts the engineering education community by 1) identifying alternativeapproaches to understand how to support latently diverse students, 2) employing a new statisticalmethod used for complex datasets, and 3) highlight narratives of latently diverse students tounderstand how they develop their
network analysis along with studentdemographics (both expressive and latent diversity [24]) to understand how diversity isintegrated into the social structure. Through this line of inquiry, we hope to identify which, ifany, student demographics are predictors of social activity. While this alone would be asignificant contribution to an expansion of the use of social network analysis within engineeringeducation, we plan on combining the social network data with students’ attitudinal profiles toexplore if their attitudes about diversity predict their social activity. This first step will lead to adeeper understanding of what diversity characteristics, either expressive or latent, work to predictsocial activity within an engineering
often resulted in few videos being made. We made concerted efforts in the laterclassrooms to try and collect these data.Figure 2 presents examples of created LEGOscenes.During the revised activity, we noticed thatstudents tended to plan their scene prior tocreating, resulting in the activity being moreidea-driven than LEGO brick-driven. We also Figure 2. Examples of LEGO scenes.observed that students made changes to theirscenes and often noted these on their cards. However, we found that it still was difficult to fullyinterpret the scene and notecard, especially when students did not record a video description. Totry and ensure we understood the scenes when no video data were available, in the last classroomwe modified the note card by
), bothshowed a positive significant gain. Interestingly, participants’ attitudes towards math tasks(Z = -2.4, p=.016) were negatively impacted as a result of participation. Admittedly, there waslittle opportunity for students to conduct mathematical analysis during engineering activities.More detailed analysis of the quantitative data is planned in which comparisons between groupswill be investigated to see if differences between the camps exist.Table 1: Results of Significant Survey Items Pre-test Survey Posttest Survey Survey Item Mean SE SD Mean SE SD I know I can do well 4.21 .118 .806 4.49 .109 .748 in science. I