Linux is used for firmware compilation, Windows is used for debugging and visualizing what is happening with the program. OpenCV is an open-source software library that provides tools for computer vision and machine learning. This toolbox enables us to use drones and AI to automatically detect wildfires. With OpenCV that are certain detection methods, one of them being color segmentation. Color Segmentation uses HSV color space to isolate fire like colors, masks are then created for each color (like red, orange, and yellow) and then combined to highlight what represents a wildfire. OpenCV also uses contour analysis, which allows for data to be filtered
his teaching excellence with the Accessibility Champion Award (Fall 2022 and 2023) by the Disability and Access Office and the Disability Cultural Center. Additionally, he was honored as the Professor of the Year (2023-2024) by the Biomedical Engineering Society, UT Austin Chapter. ©American Society for Engineering Education, 2025 1 Session XXXX Integrating Biological Context into Computing Education: Enhancing Interdisciplinary Learning in Biomedical Engineering Ernesto A. B. F. Lima Oden
2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgpeort, CT, USA. Pedagogy of Artificial Intelligence with Machine Learning and Computer Vision in a Community College Setting Sunil Dehipawala, Guozhen An, Arkadiy Portnoy, Tak Cheung Physics Department CUNY Queensborough Community College New York City USA Abstract—A deployment of artificial intelligence-based (AI- environment would expedite the student research progress. Thebased) examples was
previousexperiences).Next, we had to decide how to structure the accelerated version of the course, and what we weregoing to do with all of the extra class sessions? The literature has indicated that one of the bestways to learn computer programming in engineering education is to learn it within disciplinarycontexts [4], [5]. Thus, the extra class periods were used to connect the dots between thecomputer programming they were learning and how it was used over the next three years acrossdifferent engineering disciplines. This led to us calling the more rapid version of the courseApplications of Engineering Computing. Figure 1 below provides a high-level overview of thestructure of the two courses, highlighting their differences and similarities. Figure 1
learn about computer during my 10th-grade year. The journey of starting andprogramming and the impact of artificial intelligence (AI). When expanding the club was both challenging and rewarding,I started the Computer Programming and AI Club at Boston providing valuable lessons in leadership, organization, andCollege High School in 10th grade, we only had three members. engagement [1].By using a mixture of creative marketing, fun activities, andteamwork, I helped grow our club to 16 members the following When I first started the club, participation was low, with onlyyear, peaking at 43 people signing up during the school’s club fair. three members attending meetings. Lack of knowledge
and gain practical experience in an accessible way. In this paper, we detail theprogression of technical expertise, problem-solving abilities, and creative thinking fosteredthrough exploration.The student joined this project with minimal robotics knowledge and only a basic understandingof computer vision. He learned about theoretical mathematical algorithms developed prior to hisinvolvement and was introduced to existing Python and Excel simulations. After learning thetheory, the student assembled a HiWonder JetAuto Pro Jetson Nano robot, created an artificial3D environment, developed a Python program using OpenCV, and implemented and verified thetheories and simulations. He also recorded and processed relevant videos.As part of a team
. ©American Society for Engineering Education, 2025 A narrative study of food insecurestudents in engineering and computing Justin C. Major, Ph.D 2025 CoNECD Conference This material is based upon grants supported by the New Jersey Office of the Secretary of Higher Education (NJOSHE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the reviews of NJOSHE. 1 Hunger is a serious issue among college students. • ~24-30% of college
Paper ID #49469Inclusion of Sustainability into a First-Year Engineering Technology CourseDr. Punya A Basnayaka, Cuyahoga Community College, School of Advanced Manufacturing, Engineeringand Computer Science Punya Basnayaka (she/her/hers) is an Associate professor of Mechanical Engineering Technology at the Cuyahoga Community College, OH, where she teaches Mechanical Engineering Technology courses and Pre-Engineering courses. She has been involved with ABET accreditation process in preparing the self study report and course evaluation,. Additionally she is a member of the inaugural faculty learning community (FLC) for
on blood cell images, the methods are validated for versatility and can be extended to other imaging modalities, such as MRI, CT, and X-ray. This work advances the field by simplifying the adoption of state-of-the-art segmentation models in medical practice, bridging the gap between advanced computer vision techniques and clinical applications. Github: https://github.com/james-gaoxu/ Segmentation-on-medical-Images-colab.git I. I NTRODUCTION The advent of computer vision and deep learning has significantly impacted various fields, with medical imaging standingout as a major beneficiary. Medical image segmentation, a critical technique for diagnosing and planning
thinking and problem-solving capabilities by incorporating this research experience into engineering education. Ourgoal is to enable students to analyze and address engineering challenges effectively in alldisciplines. This study promotes the implementation of theoretical knowledge with real-testedexperiments, highlighting the importance of experiential learning in engineering education. Inthe end, the results were helpful in bridging the gap between theoretical learning and practicallearning and preparing students for successful careers in engineering by preparing them to matchstate-of-the-art technology with practical demands.KeywordsInternet of Things, IoT Applications, Edge Computing, Distributed Stream ProcessingFrameworks, Virtual Machines
Paper ID #45255Engineering and Computer Science Faculty Members’ Personal and ProfessionalPerspectives on Diversity, Equity, and InclusionDr. Yvette E. Pearson P.E., University of Texas at Dallas Dr. Yvette E. Pearson is the former Vice President for Diversity, Equity, and Inclusion at The University of Texas at Dallas. A Fellow of the American Society of Civil Engineers (ASCE) and the American Society for Engineering Education (ASEE), she is recognized globally for nearly 30 years in higher education, particularly for her work to advance sustainability, access, and opportunity in STEM education and practice. Her
Ph.D. in Higher Education from The Pennsylvania State University with aminor in Educational Psychology—Applied Measurement. Her research focuses on Gender and RacialEquity in STEM Education, Learning Experiences and Outcomes for Marginalized Students, and CriticalQuantitative Research and Assessment.Debbie Huffman, North Central Texas CollegeDebbie Huffman, Dean of Instruction for Career & Technical Education (CTE) at North Central TexasCollege, holds a Master of Science in Computer Education & Cognitive Systems and a Bachelor of AppliedArts & Sciences in Applied Technology & Performance Improvement from the University of NorthTexas. She is dedicated to providing students the opportunity to positively change their lives
Paper ID #49651Learning Languages through Interactive GamingMr. Colby Edward Kurtz, Houston Christian University ©American Society for Engineering Education, 2025 1 Learning Languages through Interactive Gaming 1Colby E. Kurtz, 2Matthew Z. Blanchard, 3Marian K. Zaki 1,2 Undergraduate Cyber Engineering Students, 3Assistant Professor of Computer Science College of Science and Engineering Houston Christian University kurtzce, blanchardmz, mzaki @hc.edu
13pioneering effort and novel contribution to engineering education and outreach. Themethodologies and design principles established by this project can serve as a foundation forsimilar projects in the future, broadening its educational impact across institutions.References[1] T. Hamrita, W. Potter and B. Bishop, “Robotics, Microcontroller and Embedded Systems Education Initiatives at the University of Georgia An Interdisciplinary Approach” Computer Science Education, 2005[2] P. Rakshith, S. Shankar, N. Goutham, G. Savyasachi and R. Avinash, “Effective Implementation of Project based Learning in Microcontroller Course,” Journal of Engineering Education Transformations, Volume number 36, January 2023, Special Issue, eISSN 2394-1707[3] Emma
Paper ID #45598Using Modeling Activities to Engage Students in LearningDr. Li Zhang, The Citadel ©American Society for Engineering Education, 2025 Using Modeling Activities to Engage Students in LearningAbstractMathematical models using differential equations are among the most difficult topics for theengineering majors at our institute. Most of them are required to take an introductory differentialequations course during their sophomore years, and some of them take a mathematical modelingcourse as an elective, afterwards. We address how mathematical modeling activities can be usedto motivate and engage students in learning
. Altuger- The ASEE Computers in Education (CoED) IV. CONCLUSION Journal, vol. 4, no. 1, p. 105, 2013. AI interactions can significantly enhance self-directed [16]learning by assessing readiness, learning goals, learning 2023 ASEE North Central Section Conference, 2023. [17]motivation, and outcomes. The adaptability of AI to different in 2023 ASEE Annual
, diverse student groups. Lookingahead, incorporating tailored educational strategies could further optimize learning outcomes byaddressing individual learning needs within such heterogeneous classrooms.KeywordsComputing Education, Visualization, Programming Language Learning, Real-World Hands-OnPractice, Active Learning, Phased Assessment, Data-Driven Results1 IntroductionWith the rapid development of AI and digital technologies, computing education has become acornerstone of university curricula, particularly in engineering disciplines. At Auburn University,the course COMP1200 introduces all undergraduate engineering students to MATLAB program-ming, regardless of their prior experience. This large and diverse cohort primarily consists ofnon
a frequent presenter and publisher on internationalization, strategic planning, globally focused academics, and Collaborative Online International Learning (COIL). Carrie is a 2019 Fulbright recipient and holds an Ed.D. in the Design of Learning Environments from Rutgers University.James Tippey, Excelsior College ©American Society for Engineering Education, 2025 Technology and Society Incorporating ethics, inclusive belonging for excellence, and societal understanding into computer and technology and engineering education curriculum design(2025). CoNECD Conference, February 9-11, 2025, San Antonio, TX Session Outline
student success and the ability of theby adapting and implementing the Affinity Research Group institution to compete for future NSF funding and piloting a(ARG) Model. HSI Pilot Project: Fostering Hispanic Achievementin Computer Science and Engineering with Affinity Research short-term, well-defined goal to enhance the availability ofGroup Model (Project Achieve) aims to enhance the quality of the high-quality undergraduate STEM education at the HSI. Toundergraduate STEM program at UB and to provide a learning realize these project goals, Project Achieve is designed tointervention that improves retention so that all students canrealize future career aspirations in
proficiency, and ability to visualize and interpret fluid behavior. This experiencefosters critical thinking, problem-solving, and a deeper understanding of how changes in physicalparameters affect flow dynamics. Ultimately, this comprehensive learning approach equipsstudents with foundational CFD knowledge and prepares them for more advanced studies orprofessional applications in fluid simulation and modeling.ConclusionThe integration of theoretical knowledge with practical programming skills plays a crucial role inenhancing students' ability to interpret Computational Fluid Dynamics (CFD) results and graspthe underlying principles of fluid dynamics. By combining these two aspects, students deepentheir understanding of numerical methods, sharpen
evaluation. This classroom gave her space to not only learn about AI, bridge the gap between her major and passions. The classroom was able to act and serve as a space that tugs on the interdisciplinary aspect of AI and use it in creative ways that bolster the academic outcome of her career aspirations in the form of labor market outcomes. This is important at the HSCC as computing culture is a “brotopia” and centered in STEM that may not have given a student like Mia the space to understand the content and create the connections between her interests and the computing field that we see in this classroom is helping in developing her future career aspirations in that help in her valuing the class and connecting her
Paper ID #49655Machine learning and Vision Based Embedded Linux System EducationDr. Byul Hur, Texas A&M University Dr. B. Hur received his B.S. degree in Electronics Engineering from Yonsei University, in Seoul, Korea, in 2000, and his M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Florida, Gainesville, FL, USA, in 2007 and 2011, respectively. In 2016, he joined the faculty of Texas A&M University, College Station, TX. USA, where he is currently an Associate Professor. His research interests include Mixed-signal/RF circuit design and testing, measurement automation, environmental
2025 ASEE Northeast Section Conference, March 22, 2025, University of Bridgeport, Bridgpeort, CT, USA. Learning Through Logic: An Educational Digital Guessing Game with LED FeedbackSteven Bercik1, Mehmet Furkan Baylan1, Ansa Brew-Smith1, Don Heiman1, Bala Maheswaran2, Haridas Kumarakuru1 1 Department of Physics, 2Department of Electrical and Computer Engineering Northeastern University, Boston, MA 02115 USA Abstract—This project introduces a digital guessing game, engaging, and fun, fostering an overall deeper understandingwhere player-1, the guesser, attempts to deduce a correct and appreciation of
-time program for third, fourth and fifth graders to introduce them to renewable energy. FYEE 2025 Conference: University of Maryland - College Park, Maryland Jul 27 Workshop: Activity Centric Learning and Teaching with MATLAB Module 1This Workshop Proposal shares the author’s intent to engage participants in the first module of fivefrom the Activity Centric pedagogy developed for a first-year Computer Programming forEngineers class and designed to build Excel, math, and programming skills through purely activelearning [1,2,3]. The author has been working with MathWorks® to create self-paced Live Scriptsfor teaching the MATLAB portion of the curriculum. Prior work has shown that
5epochs to compute their average scores. The results are summarized in Table 1. In this context, ascore of 800 points is considered excellent performance. From the data presented in Fig. 4 and Ta-ble 1, it is clear that incorporating human gameplay significantly enhances both the training speedand the overall performance of the RL-based racing car training solution. RL with Human Experience RL without Human Experience Average Score 859.81 515.21 Table 1: Average scores of the RLHE and RL-alone models, respectively.4 ConclusionsIn this work, we introduced RLHE as a novel approach to overcoming the limitations of tradi-tional reinforcement learning
strategies in a flipped classroom,” J. Comput. Assist. Learn., vol. 36, no. 1, pp. 70–88, 2020, doi: 10.1111/jcal.12392.[15] W.-J. Shyr and C.-H. Chen, “Designing a technology-enhanced flipped learning system tofacilitate students’ self-regulation and performance,” J. Comput. Assist. Learn., vol. 34, no. 1, pp.53–62, 2018, doi: 10.1111/jcal.12213.[16] V. S. G. Silverajah, S. L. Wong, A. Govindaraj, M. N. Md. Khambari, R. W. B. O. K.Rahmat, and A. R. M. Deni, “A Systematic Review of Self-Regulated Learning in FlippedClassrooms: Key Findings, Measurement Methods, and Potential Directions,” IEEE Access, vol.10, pp. 20270–20294, 2022, doi: 10.1109/ACCESS.2022.3143857.[17] L. Tomas, N. (Snowy) Evans, T. Doyle, and K. Skamp, “Are first year students ready
received her PhD in Mechanical Engineering from University of New Hampshire, USA and B.Tech [Hons.] in Ocean Engineering & Naval Architecture from Indian Institute of Technology, Kharagpur, India. Her research interests include Computational Fluid Dynamics (CFD), Numerical Analysis and Applied Mathematics, Heat Transfer Applications, Mechanical Design, Nanotechnology, HP/HT Rheology. She also has strong industrial experience as a Senior Technical Professional at Halliburton [Oil-well Cementing Research & Development].Danaii Anitzel Elizondo, Texas A&M University ©American Society for Engineering Education, 2025 The Impact of Classroom Learning in Smaller Classroom Sizes
Duhem equation using Legendre transforms Thermo: Compute property changes on mixing using65 partial molar properties Thermo: Compute fluid properties from two and three66 parameter corresponding statesProposed Modularization - SafetyImplications and Takeaways• Most LO’s, especially the “important” ones, were sorted as Undergraduate Level Eun B. (2017). The zone of proximal development as an overarching concept: A framework for synthesizing Vygotsky’s theories. Educational Philosophy and Theory, 51(1), 18-30. https://doi.org/10.1080/00131857.2017.1421941• In our BOK, the graduate core curriculum indicated that the mastery of undergraduate learning is essential for CHE
Paper ID #49637Assistive Technologies for Learning Disabilities: A Systematic Review of Trendsand ImpactMr. Aroudra Syamantak Thakur, The University of Texas at Arlington Aroudra Syamantak Thakur is currently an undergraduate student at the University of Texas at Arlington, pursuing a BSc Honors in Computer Science with minors in Mathematics and Business Administration. His research interests include artificial intelligence (AI) and computer vision. Aroudra has experience applying various machine learning algorithms and models for human-computer interaction and assistive technologies, and he is particularly interested in