andprinting orientations, and drawing conclusions based on the results. This laboratory serves as abridge between theoretical concepts and real-world applications, equipping future engineers withthe skills and knowledge required to meet the challenges of advanced and rapid manufacturing. Itnot only deepens their understanding of material behavior and structural analysis but also fostersproblem-solving skills essential for engineering careers.References: 1. Dey, A.; Yodo, N. A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics. J. Manuf. Mater. Process. 2019, 3, 64. 2. Ahn, S.-H.; Montero, M.; Odell, D.; Roundy, S.; Wright, P.K. Anisotropic material properties of fused deposition
). Crosshairs indicate the average standard error on the mean.Matz, R. L., Koester, B. P., Fiorini, S., Grom, G., Shepard, L., Stangor, C. G., Weiner, B., & McKay, T. A. (2017). Patterns of genderedperformance differences in large introductory courses at five research universities. AERA Open, 3(4), 2332858417743754. 6One of the first courses that may negatively shape experiences is in programming. 7 We define the difference in academicoutcomes as an equity gap because
across different demographics[37]. This was some of the groundwork for Main et al.’s conceptual model used in this study. Cruz & Kellam found that enjoyment of tinkering, a desire to be creative, and a need for multiple career options were predictive of engineering major choice along with math and science interest[11]. he decision to study engineering and succeed in the major is rarely separated from an interest inTmathematics and math class placement. Due to the impact COVID-19 has had on students' academic readiness, there is a need to understand more about pre-math-ready students pursuing engineering. Pre-math-ready engineering students have different math competence compared to their peers, and their
/5597e10c27ddb4430a61deb20101a1ec4b2b5421• Issapour, M. and K. Shepard, “Evolution of American Engineering Education,” CIEC Conference 2015.• Pines, D.J., “Democratizing Engineering for Every High School Student,” Issues in Science and Technology, March 16, 2022.• Margulies, S., Pearson. Y., and Barabino, G., Presentations at NAE Workshop on Public Understanding of Engineering, April 2022.References• Arnaud, C., “Weeding out inequity in undergraduate chemistry classes,” Chemical & Engineering News, 98 (34), September 2020.• Issapour, M. and K. Shepard, “Evolution of American Engineering Education,” CIEC Conference 2015.• Greenstein, D., “Greenstein: ‘Time is Not our Friend’ in Solving the Enrollment Puzzle,” February 2022, https://www.wccsradio.com/2022/02/22/greenstein
Project 6 Lab, Design ProjectProceedings of the 2024 ASEE North Central Section Conference 5Copyright © 2024, American Society for Engineering Education 7 Workshop. Lab, Design ProjectDiscussionThe curriculum design is currently under development, with select laboratory and projectconcepts set to be incorporated into the Fluid Mechanics course during the Spring semester of2024. Further research findings and student feedback will be gathered and shared in subsequentupdates to refine and enhance the program.References[1]S. A. H. S. Hassan, K. M. Yusof, S. Mohammad, M. S. Abu, and Z. Tasir, “Methods to Study Enhancement
is generated in the QAorepeating sequence block in the model, which provides flow as a predetermined function of time.This function takes a user-defined heart rate and stroke volume to calculate a time waveform ofblood flow out of the heart. This blood from the heart flows into an “Add” block that subtractsoff the flow of blood leaving the artery into the systemic circulation. The output of the “Add”block is the net flow of blood into the systemic artery. This is then fed into an integration block,indicated by “1/s.” Integration of the flow gives the volume of blood in the artery, indicated byV. This volume of blood results in stretching the walls of the artery to create pressure. Theconversion of volume to pressure is done through the
influences (EQ) and understanding the rules underlying asystem (SQ), as it relates to this curriculum experience. Systemizing is defined as the drive andability to analyze the rules underlying a system, in order to predict its behavior and appears to becentral to the understanding of engineering. Empathizing is defined as both the interest andability to identify another's mental states and to respond to these with one of a range ofappropriate emotions.10The SQ-EQ model places these cognitive styles in tension and compares the relative strength ofthese styles within individuals as a predictor of their cognitive behavior. For example, S>E is anindividual that favors systemizing thinking over empathizing thinking, while E>S is anindividual that
-sourceweb-based tool that will guide individual or collaborating STEM educators, step-by-step,through an outcome-based education process as they define learning objectives, select content tobe covered, develop an instruction and assessment plan, and define the learning environment andcontext for their course(s). It will also contain a repository of current best pedagogical andassessment practices, and based on selections the user makes when defining the learningobjectives of the course, the IMODTM system will present options for assessment and instructionthat aligns with the type/level of student learning desired. While one of the key deliverables ofthe project is the software tool, the primary focus of this initiative is to advance the
learning system continuously tabulates and communicates student and classprogress in a variety of ways, including progress (a list of objectives indicating what a studentcan do and what s/he is ready to learn), percent mastery since the last assessment, and a pie chartshowing the state of overall course mastery. Each pie slice represents a topic module, and themastery of each module is represented by the “filling up” of the slice. Complete module masteryis indicated by a completely full slice of pie, as demonstrated with the case study examples inFigures 6 and 7 that show ALEKS initial and final assessment pie charts. All students completedthe ALEKS math placement assessment before the fall term; the initial and final ALEKSassessments were
in-person outreach to that college’s community. That said, the opening ofHunt Library changes the dynamic of library service delivery on Centennial Campus. HuntLibrary’s effect on the continuing viability of the Ask Us Here service will need to be examined.Works Cited 1. The James B. Hunt Jr. Library. (2013). Retrieved 1/7/2013, from http://www.lib.ncsu.edu/huntlibrary 2. Duke, J., Hartman, S., & Locknar, A. (2006). Reaching the Engineering and Science Communities: New Technologies and Approaches at MIT. Issues in Science and Technology Librarianship 1(45). Retrieved from http://www.istl.org/06-winter 3. Axford, M., Bedner, R., Carpenter, C., Critz, L. J., Madden, M. L., Mathews, B. S., et al. (2006). Creating a
who might not have had other chances to learn aboutengineering. One female counselor noticed that girl campers were less confident speaking if boycampers were present and worked with another female counselor to “all show each other girlscan do it”. Two counselors were interested in applying for the job as a means of challenginginjustice by providing the camp opportunity to “students like them”. Participants spoke about nothaving such camps available when they were in middle and high school, and how they wouldhave benefited from such programs. One shared that she chose to be a counselor to be a “spark ofinspiration” for “underrepresented kids” because she “really like[s] the message”. Anothershared what it meant to him to be able to be a
ASEE Annual Conference & Exposition Proceedings, Atlanta, Georgia: ASEE Conferences, Jun. 2013, p. 23.120.1-23.120.13. doi: 10.18260/1-2--19134.[2] K. J. Reid, D. Reeping, T. Hertenstein, G. Fennel, and E. Spingola, “Development of a Classification Scheme for ‘Introduction to Engineering’Courses,” in 2013 IEEE Frontiers in Education Conference (FIE), Oklahoma City, OK, USA: IEEE, Oct. 2013, pp. 1564– 1570. doi: 10.1109/FIE.2013.6685101.[3] 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,” J. Eng. Educ., vol. 99, no. 4, pp. 319–336, Oct. 2010, doi: 10.1002/j
(Engeström), Daiute [48],[49] recognizes the social, dynamic nature of narratives to inform data collection and analysismethods. According to these theories, it is important to consider the interdependence inherent inthe broader context of experience and narration. This perspective aligns with this research as oursurvey reinforced the complexity of individual experiences of lifelong learning.In narrative research, the researcher needs to make plausible interpretations within the bounds ofthe narrative(s) because they capture complex experiences that are not aligned with hypothesistesting paradigms [50]. To bring forward meaningful evidence in interview approaches involvinghomogenous groups, 12 participants are typically sufficient for thematic
individual(s) involved in thematic analysis toengage in a phase of reflexivity [26] after the data have been classified. Reflexivity involvesquestioning the assumptions made during coding of the data to identify potential biases in thecoding and ultimately in the conclusions drawn from the data. Potential reflexivity bias isexplored in the Limitations section of this manuscript.In this study, thematic analysis was initially applied to identify broad themes in the data. Afterinitial patterns in the data were identified, one or more of these broad (primary) themes wereassigned to each student response. Once the dataset was broken down into this primary set ofthemes, responses within each theme were re-examined to determine whether secondary themeswere
new, marketable job skills,including IoT hardware, cloud technologies, cryptography, planning, budgeting, intellectualproperty rights, and networking. However, more importantly, the students delivered a productwith their newfound skills to help protect people's privacy. Team SIHDD (from left to right): Garrett Orwig, Nadaa Elbarbary, Krizia Ragotero, Hayden JonesReferences[1] S. Sami, B. Sun, S. Tan, and J. Han, "LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors," in SenSys '21, Coimbra, Portugal. November 15- 17, 2021. Available: https://dl.acm.org/doi/pdf/10.1145/3485730.3485941[2] Z. Yu, Z. Li, Y. Chang, S. Fong, J. Liu, and N. Zhang, "HeatDeCam: Detecting
autonomous agent that provides automated feedback on students' negotiation skills,” in Proc. of the 16th Conf. on Autonomous Agents and Multiagent Syst., pp. 410–418, May 2017.[4] M. Wheeler, “Introduction to special issue: Artificial Intelligence, technology, and negotiation,” Negotiation J., vol. 37, no. 1, pp. 5–12, 2021.[5] Reuters, “ChatGPT sets record for fastest-growing user base - analyst note,” 2023. [Online]. Available: www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base- analyst-note-2023-02-01/[6] D. Kolb, Experiential Learning: Experience as the Source of Learning and Development, Englewood Cliffs, NJ: Prentice Hall, 1984.[7] S. Brookfield, Understanding and Facilitating
Paper ID #44407Lighting a Pathway to Energy Transitions: Collecting, Interpreting and SharingEngineering Designs and Research Data Across a School-based AgrivoltaicsCitizen Science Network (Pre-College Resource/Curriculum Exchange)Dr. Michelle Jordan, Arizona State University Michelle Jordan is as associate professor in the Mary Lou Fulton Teachers College at Arizona State University. She also serves as the Education Director for the QESST Engineering Research Center. Michelleˆa C™s program of research focuses on social interactMs. Katie Spreitzer, Arizona State UniversitySarah Bendok ©American Society for
in Spring2024 and informal in-class feedback from students indicated that the activity improved theirtheoretical knowledge and problem-solving skills. Mechanisms to assess the effectiveness of thishands-on activity in improving student outcomes will be implemented in future semesters.References[1] J. R. Grohs, T. Kinoshita, B. J. Novoselich, and D. B. Knight, "Exploring learner engagementand achievement in large undergraduate engineering mechanics courses," in 2015 ASEE AnnualConference & Exposition, 2015.[2] J. W. Giancaspro, D. Arboleda, N. J. Kim, S. J. Chin, J. C. Britton, and W. G. Secada, "Anactive learning approach to teach distributed forces using augmented reality with guidedinquiry," Comput. Appl. Eng. Educ., vol. 32, no. 2
classroom problem based learning and design thinking, he also co-founded the STEPS program (funded through NSF S-STEM) to support low-income, high-achieving engineering students. Budischak holds a Doctorate in Electrical Engineering and enjoys outdoor activities with his family.Dr. Haritha Malladi, University of Delaware Haritha Malladi is an Assistant Professor of Civil and Environmental Engineering and the Director of First-Year Engineering at the University of Delaware. She received her Bachelor of Technology degree in Civil Engineering from National Institute of Technology, Warangal, India, and her MS and PhD in Civil Engineering from North Carolina State University. She is a teacher-scholar working in the
] Case study: Ayesha and the Trade Show [14] – addressing invisibility and “old-boy 6 network” in workplace 7 Continue the case study from the previous week 8 Panel: Women in Engineering 9 No lecture. Students attend Women+ in Biomedical Engineering Lunches 10 Wrap-upReferences[1] M. J. Johnson and S. D. Sheppard, "Relationships between engineering student and faculty demographics and stakeholders working to affect change," Journal of Engineering Education, vol. 93, no. 2, pp. 139-151, 2004.[2] G. Lichtenstein, H. L. Chen, K. A. Smith, and T. A. Maldonado, "Retention and persistence of women and minorities along the engineering pathway in the United States
3 higher; EGR 2600. Co-requisite(s): EGR 2710 EM 2900 - Advanced Machining (*) 3 MET 2800 EM 3100 - Additive Manufacturing Processes (*) 3 EM 2900 EM 3200 - Advanced Additive Manufacturing (*) 3 EM 3100 Total Credits Required 18 (*) New coursesThe courses EGR 1710, EGR 2710 and MET 2800 are currently part of the MechanicalEngineering Technology program and they will lay the foundation needed for students toprogress towards more complex subjects. The courses EM 2900, EM 3100 and EM 3200 are newand will be focused on advanced manufacturing
for the Course Design Institute and faculty development workshops on Equity in Collaborative Learning, Universal Design for Learning (UDL), and Specifications Grading. As former Director of Undergraduate Research for the UVA School of Engineering and Applied Science, Brian created Starting an Undergraduate Research Experience (SURE), a student-led program to lower barriers to entry in research experiences for 1st-year engineering students. Brian has received the Harold S. Morton Teaching Prize for excellence in 1st- and 2nd-year teaching in engineering, and he is a BMES Fellow. Brian is co-founder and Deputy Editor in Chief of the journal Biomedical Engineering Education. Brian’s science and engineering research
preliminary findings point towards a positive experience. Future researchwill include interview responses and response analysis, which will draw the study conclusionsand recommendations for enhancing practical, authentic learning experiences within engineeringcurricula.VII. References[1] AE Brooks, DL Ewert, "Discovery-Based Learning: A Bridge Between Research andTeaching." ISA Biomed. Sci. Instrum. Symp.. Vol. 53. 2017.[2] D. Gürdür Broo, O. Kaynak, and S. M. Sait, “Rethinking engineering education at the age ofindustry 5.0,” Journal of Industrial Information Integration, vol. 25, p. 100311, Jan. 2022, doi:10.1016/j.jii.2021.100311.[3] [S. R. Brunhaver, R. F. Korte, S. R. Barley, and S. D. Sheppard, “Bridging the Gaps betweenEngineering Education
solving sessions to engage students in a highly theoretical Random SignalAnalysis course.Research has shown that when students are in constructive and interactive modes of engagement,they gain deeper understanding of knowledge. To help students learn better, six interactive andactive problem solving sessions are incorporated in the Random Signal Analysis course. In eachproblem solving session, students are presented with one or multiple non-trivial problem(s).They work in teams of two while interacting with a table of eight students. While students areengaging with problem solving, the instructor and teaching assistants walk around the classroomanswering questions and giving feedback.At the end of each problem solving session, students complete a
. Questions for Discussion We hope that sharing our in-progress unit at the ASEE conference will createopportunities for us to share our unit design with others seeking to embed engineering designexperiences into required high school science courses. Acknowledgement This material is based upon work supported by the National Science Foundation underGrant No. 2149782. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. References Banilower, E. R., Smith, P. S., Malzahn, K. A., Plumley, C. L., Gordon, E. M., & Hayes, M
may serve as an indicator that a student is struggling academically. An outreach program to connect with the most underperforming students and having them participate in events might help them. Literature Review [1] M. W. Ohland, S. D. Sheppard, G. Lichtenstein, O. Eris, D. Chachra, and R. A. Layton, “Persistence, Engagement, and Migration in Engineering Programs,” J of Engineering Edu, vol. 97, no. 3, pp. 259–278, Jul. 2008, doi: 10.1002/j.2168-9830.2008.tb00978.x. [2] D. R. Simmons, Y. Ye, M. W. Ohland, and K. Garahan, “Understanding Students’ Incentives for and Barriers to Out-of-Class Participation: Profile of Civil Engineering Student Engagement,” J. Prof. Issues Eng. Educ. Pract., vol. 144, no. 2, p
Institutional Culture Change. Journal of Learning Analytics, 6(2), 86-94. Retrieved from https://eric.ed.gov/?id=EJ1224131[2] Chan Hilton, A., Blunt, S., and Mitchell, Z. (2022). Capacity-Building to Transform STEM Education Through Faculty Communities in Learning Analytics and Inquiry. ASEE 2022 Annual Conference and Exhibition, June 2022, Minneapolis, MN. Retrieved from https://peer.asee.org/42085[3] Barron, K. & Hulleman, C. (2014). Expectancy-Value-Cost Model of Motivation. In: International Encyclopedia of Social & Behavioral Sciences (Second Edition) (ed. J. D. Wright), 503-509. DOI: 10.1016/B978-0-08-097086-8.26099-6[4] Wigfield, A. & Eccles, J.S. (2000). Expectancy-value theory of achievement motivation
develop aresource efficient prediction model for any quantifiable data set. By continuing this research andtesting a wider variety of data sets, we can get a better understanding to the strengths andweaknesses of this system. While it is a relatively new method within the computationalsciences, LS-SVM is a very promising and exciting avenue for computer scientists interested inregression, function estimation, classification, and prediction.Acknowledgments:The authors would like to express thanks to the University of the District of Columbia STEMCenter (NSF/HBCU-UP/HRD-0928444) grant and DC Water Resources Research Institute(WRRI) Grant.References: 1. Potomac Conservancy, State of the Nation‟s River, Potomac Watershed. 2007. Available:http
thefilter in the discrete domain, is converted to the analog domain, and then the transfer function ofthe filter is calculated in the analog domain. Once, this is achieved the transfer function in theanalog domain is converted to the digital domain by performing a one to one mapping of thefunction in the s-domain to the function in the z-domain. Usually in order to obtain the stabilitycondition, it is necessary to make sure that all of the components in the left half side of the s-plane is mapped to the values inside the unit circle of the complex z-plane. II Design Methods FIR filters can be designed in a number of ways. The most common methods ofdesigning FIR filters are design by windowing, frequency sampling method and the