by the National Science Foundation under Grant No.2406798. Any opinions, findings, and conclusions or recommendations expressed in this materialare those of the authors and do not necessarily reflect the views of the National ScienceFoundation.References[1] A. Godwin and A. Kirn, “Identity-based motivation: Connections between first-year students’ engineering role identities and future-time perspectives,” Journal of Engineering Education, vol. 109, no. 3, pp. 362–383, 2020, doi: 10.1002/jee.20324.[2] S. Liu, S. Xu, Q. Li, H. Xiao, and S. Zhou, “Development and validation of an instrument to assess students’ science, technology, engineering, and mathematics identity,” Phys. Rev. Phys. Educ. Res., vol. 19, no. 1, p
research will track how unmet needs evolve into capstoneprojects, intellectual property, and startups or non-profits, demonstrating the platform’s long-term impact on healthcare innovation and engineering education.References:[1] A. Singh and D. Ferry, "Identifying Unmet Needs in Biomedical Engineering ThroughBridging the Gap Between Classroom and Clinic," in 2015 Spring ASEE Middle Atlantic SectionConference, 2015.[2] J. S. Stephens, S. I. Rooney, E. S. Arch, and J. Higginson, "Bridging courses: unmet clinicalneeds to capstone design (work in progress)," in 2016 ASEE Annual Conference & ExpositionProceedings, New Orleans, LA, USA, June 26, 2016. Available: ASEE PEER https:// doi. org/10. 18260/p. 26393.[3] J. Kadlowec, T. Merrill, S. Sood, J
at Case Western Reserve University. She received her M. S. in physics and B. S. in electrical engineering and applied physics from CWRU and her Ph. D. in physics, specializing in physics education research, from The Ohio State University.Dr. Heidi B. Martin, Case Western Reserve UniversityMichael William Butler, Case Western Reserve University I have extensive experience working within research or R&D organizations. Most recently, I have supported undergraduate teaching and research laboratories at Case Western Reserve University, ultimately as the director of the Roger E. Susi First Year Engineering Experience Laboratory. FYEE 2025 Conference: University of Maryland - College Park, Maryland Jul 27
(KPIn ) we used in this effort are listed below and we developedfunctions to drive our algorithms in our custom database dashboard. 1. 100% 1st article 2. Inventory each kit 3. On-Time Delivery 4. Percentage of revenueIn equation 1, KPI1 is defined as how much time (T ) it takes to get a final working product that istested. For example, we can compute the time between dates such as physical work start (P W S)date, material procurement dates, 1st article test (1AT ) dates, and final article test dates. KP I1 = TP W S − T1AT . (1)In equation 2, KPI2 is defined as how long it takes to inventory each kit. For example, we candetermine the function by comparing timestamps
Assessment Program, 2003.[2] C. R. Pace and G. G. Stern, “An approach to the measurement of psychological characteristics of college environments,” Journal of Educational Psychology, vol. 49, no. 5, pp. 269–277, Oct. 1958, doi: http://dx.doi.org/10.1037/h0047828.[3] P. T. Terenzini and E. T. Pascarella, “Twenty Years of Research on College Students: Lessons for Future Research,” Research in Higher Education, vol. 32, no. 1, pp. 83–92, 1991.[4] C. Kandiko Howson and F. Matos, “Student Surveys: Measuring the Relationship between Satisfaction and Engagement,” Education Sciences, vol. 11, no. 6, Art. no. 6, Jun. 2021, doi: 10.3390/educsci11060297.[5] P. C. Wankat and F. S. Oreovicz, Teaching Engineering
Paper ID #38434Work In Progress: Initiating a graduate teaching fellowprogram to support undergraduates transferring intoengineering and computing programsMarian S. Kennedy (Associate Professor) Marian Kennedy is an Associate Professor within the Department of Materials Science & Engineering at Clemson University. Her research group focused on the mechanical and tribological characterization of thin films. She also contributes to the engineering education community through studying the process/impacts of undergraduate research and navigational capital into graduate school.William Ferriell W. Davis Ferriell is a
careers: Leaky pipeline or gender filter?” Gender and Education, 17(4), pp. 369–386, 2005.[2] R. Suresh, “The relationship between barrier courses and persistence in engineering.” Journal of College Student Retention, 8(2), pp. 215–39, 2006/2007.[3] T. Armstrong, Neurodiversity: A Concept Whose Time Has Come. Da Capo Press. 2010. p. 3.[4] T. Armstrong “The Myth of the Normal Brain: Embracing Neurodiversity.” AMA J Ethics.17(4): pp. 348-352, 2015. doi:10.1001/journalofethics.2015.17.4.msoc1-1504.[5] C. L. Taylor, A. Esmaili Zaghi, J. C. Kaufman, S. M. Reis, and J. S. Renzulli, “Divergent thinking and academic performance of students with attention deficit hyperactivity disorder characteristics in engineering
form of steam by findingthe percentage of initial mass 1234 Kg/s distributed along the system by calculating the values ofmass flow rates, y1 to y8. Additionally, the students were asked to find the Carnot efficiency,thermal efficiency, and representing all the states on P-v, T-v and T-s plots.Course grading scale and assessment HW's and presentations 40% Attendance 10% Midterms 20% Final Project 15% Final Exam 15%Innovative techniquesTowards the middle of this course, local engineers from the industry were invited to give a guesttalk related to energy
Paper ID #38459Work in Progress: Engineering Identity Development after Two Years ofUndergraduate EducationJanet Aderemi Omitoyin, Janet Omitoyin is a PHD student in the Department of Curriculum and Instructions, University of Illinois at Chicago (UIC). An astute scholar, Janetˆa C™s quest for a solution to the problems of mathematics learning based on her experience as a student andDr. Renata A. Revelo, The University of Illinois, Chicago Renata Revelo is a first-generation college student, migrated from Ecuador to the United States as a teenager with her parents and sister. She is the first in her family to obtain a
they encounter. Once these processesare articulated, engineers must proceed to engaging in creating the potential solutions for of theproblems that they encounter. Through this, engineers generate potential solutions to theproblem, select an optimal solution, and design and engage in a step-by step-plan(s) andassociated analysis using engineering disciplinary skills. They verify results, evaluate, and adjustthe solutions they work on accordingly, until they reach an optimal solution for their identifiedproblems.10 This is an important process for practicing engineers, however, rarely are first yearengineering students exposed to and able to practice this process. Our program addresses thisimportant practice during the first semester that
also some mismatch between the declared knowledge of atool and its described practice or the instructors’ conceptions. The future inclusion of empiricaldata from observations and artifacts will give us a broader perspective to approach these questionsand arrive at conclusions on the long-term impact of our faculty development program.References[1] R. M. Felder, “Teaching engineering in the 21st century with a 12th century teachingmodel: How bright is this?,” Chemical Engineering Education, vol. 40, no. 2, pp. 110–113, 2006.[2] R. Graham, Achieving excellence in engineering education: the ingredients of successfulchange. London: The Royal Academy of Engineering & Massachusetts Institute of Technology,2012.[3] S. Freeman et al
., examining the nuance in January and Srihari’s disability identities whenconsidering engineering and US cultural stigma regarding mental health disabilities). Bydeveloping a greater understanding of the ways student narratives intersect with their culturalformation as engineers, we can contribute to an engineering education culture that not onlyaccepts, but invites students to freely and simultaneously construct their personal andprofessional identities.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under AwardNumbers 2114241 and 2114242. 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
analysis to analyze the interviews and video transcripts since it allows for asystematic way of seeing and processing qualitative data [38]. We followed Braun and Clarke[38]’s six-phase method for thematic analysis, which encompassed familiarizing yourself withdata, generating initial codes, searching for themes, reviewing, defining, and naming the themes,and creating the report. First, statements in the interview were coded with descriptive labelsthrough emergent coding, and these codes were categorized into themes. Constant comparison,first within each interview and then within each group (i.e., children as a group and parents as agroup), was used to continually sort the data until a robust set of themes explaining the data wasdeveloped for each
past chair of the Research in Engineering Education Network (REEN) and a deputy editor for the Journal of Engineering Education (JEE). Prior to joining ASU he was a graduate research assistant at the Tufts’ Center for Engineering Education and Outreach. ©American Society for Engineering Education, 2023 Examining the Unique Experiences of Transgender and Gender Nonconforming Students in a Pre-College Engineering CourseIntroduction Very little research on transgender and gender nonconforming (TGNC) students inengineering has been undertaken to better understand the experiences of this underrepresentedand largely ignored population. Pawley et al. 's [1] review of published articles in
subscribed users to the rulewould be notified that the door was closed. Likewise, an upper bound of 50 m/s2 was applied tothe acceleration in the Y-axis to determine when the door was opened. Table 1: XDK Acceleration Data for the Door Open State Coded Variable Min Axis 2 Max Value (m/s ) Value X acc_x 18 38 Y acc_y 15 45 Z acc_z 982 1011 Table 2: XDK Acceleration Data for the Door
-calculatealternative (effect sizes) that counteracts this bias.• I will demonstrate how visualizations focusing on differences between demographicgroups can lead stakeholders to underestimate variation within groups. I will present astatistical technique (cluster analysis) that naturally describes within-group diversity. Inaddition, I will provide a simple data visualization technique, outcome-based categorization,that can also be helpful.• I will illustrate how demographic categories commonly used by higher educationinstitutions can fail to represent the rich, multifaceted nature of individual identity(s). I willdiscuss examples of how to integrate standard demographic categories with meaningfulinformation from other datasets, such as hometown information
Co-PI on the NSF S-STEM grant. Her research area is number theory and mathematics education. Her work on Self-Regulated Learning and Mathematics Self-Efficacy won the CUNY Chancellor’s Award for Excellence in Undergraduate Mathematics Instructions in 2013. She participated in the CUNY-Harvard Consortium Leadership program and initiated the CUNY Celebrates Women in Computing Conference.Nadia Kennedy Nadia Stoyanova Kennedy is Associate Professor in Mathematics Education in the Department of Mathematics and Program Director of Mathematics Education. Her research focuses on inquiry approaches to mathematics teaching and learning; mathematics identity; philosophy of mathematics education, and mathematics teacher education. She
proposed VR clinical immersioncourse will provide access to hospital procedures to all BME and medical students at a largescale while increasing the pedagogical effectiveness of the educational materials by developingmore robust remote learning content.Acknowledgements:Research reported in this publication was supported by the VentureWell Faculty Grant Program(Award No. 19823-19) and the National Institute Of Biomedical Imaging and Bioengineering ofthe National Institutes of Health (Award No. R25EB031116). References[1] J. Stephens, S. Rooney, E. Arch, and J. Higginson, “Bridging Courses: Unmet Clinical Needsto Capstone Design (Work in Progress),” in 2016 ASEE Annual Conference & ExpositionProceedings
for places of agreement anddisagreement between coders before moving on to reading the next transcript. Codes wererepeated across multiple days of implementation.Table 4: Codes Codes Description Context Integration When teachers situate students learning in real-world scenarios [CXI] through engineering design challenge Content Integration When a teacher connects content from two or more STEM [CNI] disciplines (S, E, and/or M) Explicit [Ex] When the teacher makes a direct connection between two or more STEM
sociotechnical engineering courses, and aconcentration of their choosing [35]. The majority of students who participated in this study werepursuing the sustainability concentration, however students can also choose a concentration inbiomedical engineering, embedded software, law, or an individual plan of study.MethodsIn Spring 2021, we interviewed five students (out of the nine enrolled in the class) at the end ofthe course using a semi-structured protocol that probed their motivation(s) for choosing anengineering major, as well as their perceptions about engineering and engineers. We asked thestudents: • Q1: Why did you choose to major in engineering? • Q2: How do you define engineering? • Q3: Please describe an engineer. • Q4: What
to capture the participant’s reasoning and thought process during implementation. Thebeing dimension captures the broader values that drive an engineer to be empathetic. We usedthis dimension to explore the intrinsic values that drive the participant to be empathetic. Figure 1: Model of Empathy Framework [1]Action Research: AR was first coined by Kurt Lewin [16] and “is a process of concurrentlyinquiring about problems and taking action to solve them” [17, p. 30]. Based on the applicationand the field, there are multiple variations of AR with their own seminal references. For thisstudy, we chose the AR methodology explored by Pine [17] specific to teacher and classroomresearch. The participant(s) in AR can be a co
of robot and a camera that provides a top view of entire robot work space. The user can then decide what to do next, restarting the process from Step A. Figure 1. Remote Cozmo Robot System Architecture.Remote Control of Cozmo RobotCozmo robot provides a user-friendly SDK app with examples. Users can modify the examplesto fit new applications. The Cozmo Python program also includes a user friendly graphical userinterface that allow users to easily manipulate the robot with a single key stoke. For example, theW, A, S, and D keys can be used to drive Forward/Left/Back/Right; T is Move Head Up; G isMove Head Down; R is Move Lift Up; and F is Move Lift Down. In Figure 2, the image on theleft is the top view of the
. Results showed that there was an increase inthe utilization of DfAM in design concepts. The work will contribute to the field of DfAMintegration in engineering education curriculum and will improve student self-efficacy in DfAM.AcknowledgementWe acknowledge the first-year faculty members, Dr. ChangHoon Lee, Dr. Charles Roche, Dr. J.Benner, Dr. R. Gettens, Dr. A. Kwaczala, Dr. A. Santamaria, Noah Pare, and Roberto DuranBrea for their help in the execution of the experiments.References[1] ISO/ASTM, “ISO/ASTM 52900: Additive manufacturing - General principles - Terminology,” Int. Stand., vol. 5, 2015.[2] B. Motyl and S. Filippi, “Trends in engineering education for additive manufacturing in the industry 4.0 era: a systematic
and graded for completion only, not for correctness. The day that the homework was due, students were given solutions. The following class period, students completed an in-class quiz similar to the quizzes given for assessment Q.Table 1: Assessment modality, instructors, and number of students for each course offering.Offering 2014 2015 2016 2017 2018 2019 2020 2021Assessment H H H H Q Q QH QH# Students 39 54 41 50 39 41 70 63Instructor(s) A A&B A&B A&C A&C A A AFor all three
concepts or materialalready present in the cybersecurity curriculum. Lastly, our methodology plans to evaluate theeffectiveness of the proposed methodology from both student and instructor perspectives.In this paper, we focus on the first component of our proposed methodology, namely Analysis ofLiterature. We build a semi-automated analysis pipeline that helps us to systematically analyzethe cybersecurity literature for the prevalence and distribution of AI and ML in cybersecurityresearch. Our analysis pipeline aims to achive this through the analysis of over 5000 researchpapers collected from the last five years of the top cybersecurity conferences (i.e., IEEE S&P,ACM CCS, Usenix Security, NDSS, ACSAC, ESORICS). Our analysis of over
National Science Foundation (NSF IUSE #2120156). Anyopinions, findings, conclusions, and recommendations are the authors’ and do not necessarilyreflect the views of the National Science Foundation.References 1. J. Kellar, S. Howard, M. West, D. Medlin, and S. Kellogg “The Samurai Sword Design Project and Opportunities for Metallurgical Programs.” MS&T Proceedings 2009: Status of Metals Engineering Education in the United States. 2. M. West, D. Medlin, J. Kellar, D. Mitchell, S. Kellogg, and J. Rattling Leaf, “Back in Black: Innovative Curricular, Outreach, and Recruiting Activities at the South Dakota School of Mines and Technology.” MS&T Proceedings: Status of Metals Engineering Education in the United States
. However, when possible, questions were kept as theoriginal or only slightly modified. The nanotechnology and STEM attitudes survey was a modified version of theStudent Attitude Toward Science, Technology, Engineering, and Mathematics (S-STEM) instrument developed bythe Friday Institute at North Carolina State [16]. The S-STEM includes scales on attitudes towards mathematics,science, engineering, and technology, 21st century learning skills, and STEM career awareness. For the purposes ofthis project, the mathematics scale was removed and replaced by a nanotechnology focused scale developed duringprevious one-week camps provided for high school students. The nanotechnology scale contains nine questionswhich were modified over its early development
Paper ID #37220Assessing Head- Hand- and Heart-Related Competenciesthrough Augmented-RealityLogan Andrew Perry (Assistant Professor of Engineering Education) Logan Perry is an Assistant Professor of Engineering Education at the University of Nebraska-Lincoln. His research interests lie at the intersection of civil engineering and engineering education and include 1) the transfer of learning, 2) diversity for engineering, and 3) cyberlearning technology.Jeremi S London (Assistant Professor) Associate Professor of Engineering Education at Virginia Tech Chair of ASEE's CDEI during the Year of Impact on Racial
Paper ID #38078Community-focused Senior Design Practicum ProjectsVenkat Allada (Vice Provost for Graduate Studies) Dr. Venkat Allada is a Professor of Engineering Management & Systems Engineering at Missouri University if Science and Technology (Missouri S&T), Rolla, USA. He served as Missouri S&T’s inaugural vice provost of graduate studies from 2007-2017. He served as the 2016-17 chair of the Mid-west Association of Graduate Schools (MAGS). Dr. Allada earned his doctoral degree in Industrial Engineering from University of Cincinnati in 1994. His teaching and research interests are in areas of lean
to connect with researchers who have previously exploredsimilar issues or may experience them in their current work. Student Pathways in Engineeringand Computing for Transfer Students (SPECTRA) is an NSF S-STEM program that providesfinancial assistance to students transferring from the South Carolina Technical College Systeminto Engineering or Computing majors at Clemson University [1]. SPECTRA also assistsstudents by connecting them with peers at the technical colleges who move together through thetransfer process to Clemson and are supported by the SPECTRA program until graduation. Inaddition to exploring the experiences of current SPECTRA participants, we investigate how theproject can be scaled to include more students and sustained