-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
investigatesubpopulation differences in MH distress and MH related help-seeking perceptions.Help-seeking behavior in college studentsIn the broader college student population, it has been hypothesized that the most effective way toincrease MH help seeking behaviors in college students is to change their self-perceptions andattitude toward professional MH services [8]-[10]. Research has also examined help seekingbehaviors of students in self-identified high-stress academic programs (e.g., law [11], medicine[12], [13], nursing [14], dentistry [15], [16]). In these studies, the most significant factors for notseeking help for MH concerns pertained to perceived stigma(s), fear of disclosure, and perceiveddetriment to academic and/or career success; students in these
virtuallyand the instructor session conducted in person. Ideally, the sessions with students would includemultiple members from their project team. In future applications of the method, it may also beinteresting to conduct sessions with students and instructor (and/or TA) in the same session. 3) Decide on focal factor(s): To start the mapping process, participants were asked to identify the focal factor(s), which should be a variable within the system that is central to the problem, per the original protocol. To assist participants in focal factor identification, we used the following prompts: What were the key inputs in your project? What were the key outputs? What were the key elements of the project that you controlled? What
/10.1061/9780784415221[4] A. Johri and B. Olds, “Situated engineering learning: Bridging engineering educationresearch and the learning sciences,” Journal of Engineering Education, 100(1), 151–185, 2011.http://dx.doi.org/10.1002/j.2168-9830.2011.tb00007.x[5] S. R. Brunhaver, R. F. Korte, S. R. Barley, and S. D. Sheppard, “Bridging the gaps betweenengineering education and practice,” In R. Freeman & H. Salzman (Eds.), Engineering in aglobal economy. University of Chicago Press, 2018.[6] A. R. Bielefeldt, K. Paterson, and C. Swan, “Measuring the value added from servicelearning in project-based engineering education,” International Journal of EngineeringEducation, 26(3), 535-546, 2010.[7] K. Litchfield, A. Javernick-Will, and A. Maul, “Technical
is ongoing. The results will inform future implementationand program communication and seek to understand if the student experience is consistent withthe literature previously mentioned. Additionally, this will serve as the beginning of alongitudinal study to understand student career development over their entire college career. It iscritical to understand the longevity of this structure on a student’s pathway into an engineeringcareer and inform continue intervention of these skills at the first-year level.[1] B. D. Jones, M. C. Paretti, S. F. Hein, and T. W. Knott, “An Analysis of Motivation Constructs with First-Year Engineering Students: Relationships Among Expectancies, Values, Achievement, and Career Plans,” Journal of
investigation which includes investment in infrastructure such as internetaccess, capacity, and equipment, as well as in teacher training. Constant communication andversatility in using remote delivery tools can help as well. Innovative methods relying on newtechnology such as AI and VR are desperately needed to revolutionize education on the long run,but for now, access seems to be a pressing issue in both the technical and social sides.References[1] S. Nagarajan and T. Overton, "Promoting Systems Thinking Using Project- and Problem-BasedLearning", Journal of Chemical Education, vol. 96, no. 12, pp. 2901-2909, 2019. Available:10.1021/acs.jchemed.9b00358.[2] J. Krajcik and N. Shin, "Project-Based Learning", in The Cambridge Handbook of TheLearning
Protector with other learning contexts and audiences.References[1] J. M. Hoekstra, T. M. Boucher, T. H. Ricketts, and C. Roberts, “Confronting a biome crisis: global disparities of habitat loss and protection,” Ecology letters, vol. 8, no. 1, pp. 23–29, 2005.[2] T. J. Lark, S. A. Spawn, M. Bougie, and H. K. Gibbs, “Cropland expansion in the United States produces marginal yields at high costs to wildlife,” Nature communications, vol. 11, no. 1, pp. 1–11, 2020.[3] D. M. Engle, B. R. Coppedge, and S. D. Fuhlendorf, “From the dust bowl to the green glacier: human activity and environmental change in Great Plains grasslands,” in Western North American Juniperus Communities, Springer, 2008, pp. 253–271.[4] V. J. Horncastle, E. C
creating supports that aid in identity development. Creating spaces for exploring identity development over the course of the engineering and computer degrees, particularly working with student-led Latinx organizations. Redesigning engineering and computer science spaces to be more culturally relevant and inclusive, rather than exclusionary and white.Acknowledgments: This work was supported by NSF grant numbers 1647181 and 1647104 atThe University of Texas at Arlington with Principal Investigator Panos S. Shiakolas and TexasA&M University - Commerce with Principal Investigator Sarah L. Rodriguez. Any opinions,findings, and conclusions or recommendations expressed in this work are those of the authorsand do not necessarily
resources.References[1] P. D. Crompton, J. Moebius, S. Portugal, M. Waisberg, G. Hart, L. S. Garver, L. H. Miller, C. Barillas-Mury,and S. K. Pierce, "Malaria immunity in man and mosquito: insights into unsolved mysteries of a deadly infectiousdisease," Annual review of immunology, vol. 32, pp. 157-187, 2014.[2] A. S. Fauci, and D. M. Morens, "Zika virus in the Americas—yet another arbovirus threat," New Englandjournal of medicine, vol. 374, no. 7, pp. 601-604, 2016.[3] V. Vijayakumar, D. Malathi, V. Subramaniyaswamy, P. Saravanan, and R. Logesh, "Fog computing-basedintelligent healthcare system for the detection and prevention of mosquito-borne diseases," Computers in HumanBehavior, vol. 100, pp. 275-285, 2019.[4] S. Sareen, S. K. Sood, and S. K. Gupta
topics related to the structure of the course; assignments; enthusiasm, or lack of it; andpersonal concerns and tragedies that students share [6]. Indeed, research shows that teachersapply empathy in their interactions and relationships with students [6].Researchers have conceptualized empathy in multiple ways. Empathy is a complex concept thathas been generally defined as an individual’s ability to understand and respond to anotherperson’s perspective and feelings [7]. Cuff et al.'s [8] review of empathy research identifiedforty-three distinct definitions of the concept. In another review, Batson describes eight distinctyet related concepts of empathy [9]. In the context of nursing education, Kunyk and Olson [3]categorized types of empathy into
Paper ID #36785An Analysis of STEM Students’ Integral and Area Under theCurve KnowledgeEmre Tokgoz (Associate Professor)Samantha Scarpinella Pennsylvania State University Industrial Engineering PhD Student © American Society for Engineering Education, 2022 Powered by www.slayte.comAn Analysis of STEM Students’ Integral and Area Under the CurveKnowledge1 Emre Tokgöz, 3Samantha Scarpinella, 3Michael Giannone, 1Elif. N. Tekalp, 1Berrak S. Tekalp, 2Hasan A.Tekalp1 Emre.Tokgoz@qu.edu, 1Elif.Tekalp@qu.edu, 1Berrak.Tekalp@qu.edu, 2Hasan.Tekalp@qu.edu3 ses6506@psu.edu, 1Michael.Giannone
success variables, college grades a (i.e., first year GPA) and creativity.Preliminary findings suggest that specific college experiences have a greater influence on first-year GPA and that students with ADHD are more likely to self-report high levels of creativity.We also plan to conduct the analysis for resilience, a less-common measure of collegiateacademic success that may be relevant for students who have ADHD.Table 2. Model components, constructs, and survey items from the HERI instrument [32], [33]. Components and constructs of our model Item(s) from the HERI instruments Precollege characteristics & experiences Gender Gender of respondent; Survey choices: Female, Male Sociodemographic
had been highly rated at the time of original review. Inpart because of this and in part because it is an important part of proposal review, our reviewerswere asked to closely read the current program description and calls for proposals and evaluatethe proposals with respect to how well they matched the current call. This allowed for apotentially greater range of quality evaluations, with the understanding that there would be amismatch between the current call and the call the original proposals responded to. The callsused in this training were the Preparing Future Engineers: Research Initiation in EngineeringFormation (PRF: RIEF), Scholarships in Science, Technology, Engineering & Math (S-STEM),and the Faculty Early Career Development
/translating-theory-on-color-blind-racism-to-an-engineering-educatio n-context-illustrations-from-the-field-of-engineering-education.[10] S. Johnston, A. Lee, and H. McGregor, “Engineering as Captive Discourse,” Society for Philosophy and Technology Quarterly Electronic Journal, vol. 1, no. 3/4, pp. 128–136, Oct. 1996, Accessed: Jul. 06, 2021. [Online].[11] M. G. Eastman, M. L. Miles, and R. Yerrick, “Exploring the White and male culture: Investigating individual perspectives of equity and privilege in engineering education,” J. Eng. Educ., vol. 108, no. 4, pp. 459–480, Oct. 2019.[12] E. Rap and M. T. Oré, “Engineering Masculinities: How Higher Education Genders the Water Profession in Peru,” Eng. Stud., vol
chemical engineer before, and mentorvideos and interactions helped them meeting with professional chemical engineers and seeingtheir future in them.Future WorkWe had collected both qualitative and quantitative data during three semesters ofimplementation. All data was cleaned, organized, coded individually and as a group. This data iscurrently being analyzed.AcknowledgmentsThis work was supported through the National Science Foundation’s funding under a PFE: RIEFGrant No. (2024960). Any opinions, findings, conclusions, or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the National ScienceFoundation’s views. We wish to thank survey and interview participants for their participation inthe
; less than 28% of the total IT workforceand only 12% of engineers are female [2]. By the time students reach college, 1 in 5 young menplan on majoring in engineering or computing while only 1 in 17 young women declare the same[3]. Since 1990, the percentage of female computing professionals dropped from 35% to about24% today, and if that trend continues, the share of women in the nation’s computing workforcewill decline to 22% by 2025 according to Girls Who Code [4]. These statistics provide themotivation for a program called Project-based Work Studio (PWS) developed at a mid-sizedAppalachian primarily undergraduate university supported by an NSF S-STEM grant to build amore proportionate female workforce in computer science, engineering, and