Displaying results 1 - 30 of 73 in total
- Conference Session
- Technical Sessions 5
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Yue Hung, Farmingdale State College; Wenhai Li, Farmingdale State College; John Bussani
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Student Learning Outcomes Introduction to computer Students will be able to explain the basic principles of aided manufacturing computer aided manufacturing. Students will be able to use the wireframe and transform 2D geometry construction commands, such as lines and circles to create 2D geometries in MasterCAM. Students will be able to define stock size for CAD models 2D Toolpaths commands and and apply different types of 2D toolpaths on the models. Toolpath verifications Students will be able to review and verify toolpaths
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Ayush Vasu Gowda, Florida Atlantic University; Daniel Raviv, Florida Atlantic University; Juan David Yepes, Florida Atlantic University
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- Professional Papers
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
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Li Zhang, The Citadel
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- Professional Papers
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
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Chaohui Ren, Auburn University; Cheryl Seals, Auburn University; Nilanjana Raychawdhary, Auburn University
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- Diversity, Professional Papers
, 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
- Conference Session
- Technical Sessions 2
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Gonca Altuger-Genc, State University of New York, College of Technology at Farmingdale; Yue Hung, Farmingdale State College; Wenhai Li, Farmingdale State College
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™ experience in Computer-AidedDr. Wenhai Li, Farmingdale State College Assistant Professor in Department of Mechanical Engineering Technology, Farmingdale State College, Farmingdale, NY 11735 ©American Society for Engineering Education, 2024 Incorporating Applied Learning in a Mechanical Engineering Technology Senior Project CourseAbstractApplied learning is a crucial part of the learning process, and allows the students to gain hands-onexperience by completing projects, courses, or activities that are part of the curriculum. At FarmingdaleState College, all baccalaureate program students fulfill 10 hours of minimum approved Applied LearningGraduation Requirement [1]. This allows students practice the knowledge
- Conference Session
- Technical Sessions 2
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Uma Balaji, Fairfield University
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- Professional Papers
Paper ID #45305Empowering Electrical Engineers: Project-Based Learning for EnvironmentalSustainabilityDr. Uma Balaji, Fairfield University Dr. Uma Balaji received her Ph. D from University of Victoria, B.C., Canada in Electrical Engineering. She was a Canadian Commonwealth Scholar. She is the Chair of Electrical and Biomedical Engineering Department at the School of Engineering and Computing of the Fairfield University. Her research focused in novel modelling techniques to design components for wireless and satellite application. She was awarded the IEEE Region 1 Award as ’Outstaning Teaching in an IEEE Area of Interest (Pre
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Erin Bosarge, University of South Alabama
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- Diversity, Professional Papers
entire MLprocess, fostering computational thinking and problem-solving [18]. Kajiwara et al. employed agamified ML role-playing game, simplifying concepts for high school students [15]. Ethicalconsiderations were integrated through projects like VotestratesML, which explored AI's societalimpacts in democratic contexts [20], and Kong et al.’s collaborative projects addressing fairnessand bias in AI systems [16].3.5 Results for RQ4: Which of the AI4K12 Five Big Ideas frameworks are being included?The AI4K12 Five Big Ideas rubric assessed studies on Perception, Representation & Reasoning,Learning, Natural Interaction, and Societal Impact, scoring from 0 (not addressed) to 4 (thoroughintegration). Results highlighted strengths in Learning
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Shobhit Aggarwal, The Citadel; Gregory J. Mazzaro, The Citadel
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sector corporation in Raleigh. In Fall of 2024, Dr. Aggarwal joined the faculty of the Department of Electrical & Computer Engineering at The Citadel. There, he is the primary instructor for Computer Architecture, Embedded Systems, Data Communication and Networking, and Electrical Lab. His research interests span a wide range of topics, including IoT, Wireless Communication, Embedded Systems, Machine Learning, and Artificial Intelligence.Dr. Gregory J. Mazzaro, The Citadel Dr. Mazzaro earned a Bachelor of Science in Electrical Engineering from Boston University in 2004, a Master of Science from the State University of New York at Binghamton in 2006, and a Ph.D. from North Carolina State University in 2009. From
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Chaohui Ren, Auburn University; Cheryl Seals, Auburn University; Karen Nix, Auburn University
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- Professional Papers
degree in Computer Science with a concentration of Software Development from Columbus State University. She works as a Graduate Teaching Assistant at Auburn University and recently began work for the City of Opelika, AL in the IT department as Assistant CIO. Her research interests include digital learning, UI/UX, web development, cybersecurity, web accessibility. ©American Society for Engineering Education, 2025 Teacher-Guided Project-Based Coding Practice Enhances High-Level Programming Language LearningAbstractJava is a required course for many undergraduate computer science programs and is widely re-garded as a challenging programming language due to its complexity and
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Razvan Cristian Voicu, Robotics and Mechatronics Engineering, Kennesaw State University, Marietta, GA; Muhammad Hassan Tanveer, Kennesaw State University; Ayse Tekes, Kennesaw State University
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- Diversity, Professional Papers
automated tools for user assessment and feedback [19, 20]. Lastly, there is growinginterest in methods to aggregate AI experiences from specialized domains, such as space roboticsor other remote operations, and make these insights immediately accessible in mainstream class-rooms. By harmonizing knowledge across diverse locations and contexts, AI-human transferencelearning can help users appreciate the multifaceted applications of core concepts in engineering,computer science, and beyond.Proposed System OverviewThe proposed AI-human transference framework aims to unify advanced AI-driven experienceswith contextual, personalized learning paths. At a high level, AI aggregates knowledge from var-ious sources, including real-time data streams from
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Kenneth Stafford Sands II, Auburn University; Andrew Hughes, Auburn University
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- Professional Papers
Paper ID #45387A Mobile Wall Mockup (MWM) as a Learning Tool for Construction andEngineering EducationDr. Kenneth Stafford Sands II, Auburn University Kenneth S. Sands II is an Assistant Professor at Auburn University in the McWhorter School of Building Science.Andrew Hughes, Auburn University ©American Society for Engineering Education, 2025 A Mobile Wall Mockup (MWM) as a Learning Tool for Construction and Engineering EducationAbstractMockups play a critical role in construction and engineering, offering tangible ways tounderstand complex system components and improve quality
- Conference Session
- Technical Sessions 5
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Matthew Fried, SUNY Farmingdale; Mohammad Alshibli
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- Diversity, Professional Papers
Paper ID #45369The Impact of AI Assistance on Student Learning: A Cross-DisciplinaryStudy in STEM EducationProf. Matthew Fried, SUNY Farmingdale Matthew Fried is an Assistant Professor with a research focus in machine learning. His work includes the application of advanced mathematical techniques, such as the Choquet integral, to deep neural networks (DNNs). He has presented multiple papers on this topic at international conferences, contributing to the ongoing development of noise reduction and performance optimization in DNNs. ©American Society for Engineering Education, 2024 The Impact
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Oguzhan Oruc, The Citadel; Eva Singleton, The Citadel Military College ; Andrew B. Williams, The Citadel; Kevin Skenes, The Citadel
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evaluation, photoelasticity, manufacturing processes, and engineering education. ©American Society for Engineering Education, 2025 Development Of a New Course: Control Design for Autonomous Vehicles Using a Quadcopter as The Learning PlatformIntroductionModeling, simulation, control system design, navigation and guidance of autonomous vehicles(AVs) have become highly sought research areas in the mechanical engineering community [1]due to the advancement in microelectronics, computational technologies and machine perception.Because of the increasing popularity of self-driving cars, autonomous vehicle refers to self-drivingcars in public perception [2] although it covers a wider research area. AVs can be
- Conference Session
- Technical Sessions 5
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Raul Armendariz, City University of New York, Queensborough Community College; Corey Stalerman; Tak Cheung; Sunil Dehipawala, City University of New York, Queensborough Community College
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- Professional Papers
-101, RAD102, Physics I, Physics, and the two Math courses in the AS Degree requirement.By the same Bureau of Labor Statistics, the attributes of a nuclear technician are listed asCommunication skills, Computer skills. Critical-thinking skills. Math skills. Mechanical skills,and Monitoring skills. All of the above are similar to the learning outcomes in the lab sections ofRAD-101, RAD-102, Physics I and Physics II. The Mechanical skills are fully engaged in thePhysics I Mechanics lab exercises.The extension of Physics II to facilitate the QCC-BNL contract should strengthen Detail orientedamong the attributes in a nuclear engineer. The difference between Algebra Physics andCalculus physics mostly lies in the math tool. The Calculus tool enables
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Kimberly T. Luthi, Embry-Riddle Aeronautical University - Worldwide; David Paul Harvie, Embry-Riddle Aeronautical University - Worldwide; John K Wilson, Embry-Riddle Aeronautical University - Worldwide; Monica Surrency, Embry-Riddle Aeronautical University - Worldwide
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- Diversity, Professional Papers
non-traditional active military and Veteran student groups.References[1] S. E. Lewis, "Retention and Reform: An Evaluation of Peer-Led Team Learning," Journal of Chemical Education, vol. 88, no. 6, pp. 703-070, 2011.[2] L. Gafney and P. Varma-Nelson, Peer-Led Team Learning Evaluation, Dissemination, and Institutionalization of a College Level Initiative, Springer Science & Business Media, 2008.[3] J. Liou-Mark, A. E. Dreyfuss and L. Younge, "Peer Assisted Learning Workshops in Precalculus: An Approach to Increasing Student Success," Mathematics & Computer Education, vol. 44, no. 3, p. 249, 2010.[4] M. Hernandez-de-Menendez, A. V. Guevara, J. C. T. Martinez, D. H. Alcantara and R. Morales-Mendez, "Active learning in
- Conference Session
- Technical Sessions 5
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Mercy Folashade Fash, North Carolina A&T State University; Andrea Nana Ofori-Boadu, North Carolina A&T State University
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- Diversity, Professional Papers
participants attributed their pre-intervention AEC knowledge to both formal and informal learning experiences. Informallearning experiences such as summer camps, personal skills, family members, and role models.Formal learning experiences such as regular school classes. However, RPs’ prior knowledge ofAEC careers was limited and often based on misconceptions. Many participants conflated AEC,particularly engineering, with computer science, robotics, and coding due to previousexperiences in STEM camps and school classes. Their attributed sources of AEC knowledgeincluded summer camps, personal experiences, family members or role models, and schoolclasses. Additionally, some participants associated their understanding of AEC with genderedpeer interactions
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- Technical Sessions 1
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Heather Louise Lai, State University of New York at New Paltz
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- Diversity, Professional Papers
collaboratively developed a number of new and revised courses, including a new System Dynamics Lab. She has also worked with a number of SUNY students to investigate different aspects of 3D printed multi-material structures. ©American Society for Engineering Education, 2024 Introducing AI into an undergraduate Kinematics of Machines courseThe integration of emerging technologies, such as Machine Learning and Artificial Intelligence(ML/AI), into traditional mechanical engineering courses has the potential to transform howfoundational concepts are taught and applied. This paper discusses the initial implementation ofML/AI-related computational techniques in an undergraduate
- Conference Session
- Technical Sessions 1
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Xiaojin Ye, State University of New York, College of Technology at Farmingdale; Nur Dean, Farmingdale State College, State University of New York
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- Professional Papers
interest in Machine Learning classification. Passionate about mentoring undergraduate students, she has guided many in the realms of Game Theory and Machine Learning. Additionally, Dr. Dean has contributed her expertise as a judge at regional events such as the New York State Science & Engineering Fair (NYSSEF) and the WAC Lighting Invitational Science Fair.Dr. Xiaojin Ye, State University of New York, College of Technology at Farmingdale I am an Assistant Professor in the Department of Computer Systems at Farmingdale State College. My primary research interests include theoretical computer science, set operators, mathematical morphology, mathematical education, and computer science education
- Conference Session
- Technical Sessions 3
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Rachmadian Wulandana, State University of New York at New Paltz
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- Diversity, Professional Papers
. Computer programming wasidentified as the most difficult aspect, whereas modeling with commercial software emerged as themost favored task. Despite some difficulties with teamwork, students expressed a strongpreference for project-based learning and group work over individual study and traditional lecture-based approaches. The insights gained from this study provide a framework for structuring FEAcourses to enhance critical skills, preparing engineering students for both academic andprofessional success.IntroductionThe foundation of FEA is the finite element method (FEM) which has its roots in the mid-20thcentury and has its foundational contributions from engineers like Richard Courant and RayClough. Initially developed for aerospace applications
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- Technical Sessions 3
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Arzu Susoglu, SUNY Farmingdale
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- Diversity, Professional Papers
courses in data security, cryptography, computer forensics, and senior project writing. Her research interests include machine learning, artificial intelligence, cryptography, steganography, and security. Arzu focuses on providing hands-on learning experiences and integrating real-world applications into her curriculum, ensuring her students gain the skills needed for successful careers in technology and security fields. ©American Society for Engineering Education, 2024 Integrate the iPad, Apple Pencil, and Goodnotes, to enhance teaching effectiveness.AbstractUsing multimedia such as slides, diagrams, charts, and videos as visual aids during lectures hasproved
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Nilanjana Raychawdhary, Auburn University; Chaohui Ren, Auburn University; Cheryl Seals, Auburn University; Sutanu Bhattacharya
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- Diversity, Professional Papers
computing diversity research.Sutanu Bhattacharya ©American Society for Engineering Education, 2025 Empowering Undergraduates with NLP: Integrative Methods for Deepening Understanding through Visualization and Case StudiesAbstractA thorough approach that successfully balances academic understanding and real-world experience is neededto teach undergraduate students Natural Language Processing (NLP). This research emphasizes the synthe-sis of theory and practical application to overcome the significant obstacles in teaching NLP. We suggestemploying interactive tools and technologies, such as Jupyter notebooks and well-known Python libraries(NLTK and SpaCy), to engage students and improve their learning
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- 2025 ASEE Southeast Conference
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- Frank Andrasik, The University of Memphis; Bashir Morshed, Texas Tech University; Robert W Hewitt Jr., The University of Memphis
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career pursuits in technology-driven fields. Future iterations will explore the balance between collaborative learning, hands-onactivities, and independent problem-solving to further support comprehensive skill developmentin pre-college STEM education.IntroductionThe rapid growth of technology-driven industries has increased the need for robust STEM(Science, Technology, Engineering, and Mathematics) education, particularly at the pre-collegelevel. In recent years, coding and computer literacy have become essential skills for students,laying the groundwork for future success in STEM fields. However, the COVID-19 pandemicdisrupted traditional learning methods, resulting in a swift transition to various online formats.This disruption created
- Conference Session
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- 2025 ASEE Southeast Conference
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- Anna K. T. Howard, North Carolina State University at Raleigh; Sally J. Pardue, Tennessee Technological University
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students for their future careers. Given the pace ofchange in our technical world today, we cannot know what those careers will ultimately look like.In this paper, we set out an argument for what engineering education should include more of andwhat we should probably be cutting out of our classes.Artificial IntelligenceIn 1966 ELIZA was the first computer capable of any natural language processing.1 In 2023,ChatGPT 4.0 was released with “near-human” performance. These large language models work byingesting huge amounts of prose and building a mathematical engine which predicts what the nextword should be. In so doing, coupling this basic machine learning to the ability to search the webhas made computers able to answer many basic
- Conference Session
- Technical Sessions 1
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Wenhai Li, Farmingdale State College; Yue Hung, Farmingdale State College; Reiss Guttman, Farmingdale State College; Sen Zhang, State University of New York, Oneonta; Ning Yu, State University of New York, Brockport
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York College at Oneonta, Oneonta, NY 13820 Yu Ning Department of Computer Science, State University of New York at Brockport, Brockport, NY 14420AbstractThe rapid integration of Artificial Intelligence (AI) across industries necessitates the inclusion of AI-relatededucation within engineering programs. However, AI education remains limited for MechanicalEngineering Technology (MET) students, who often lack strong backgrounds in math and programming.To address this gap, we design a five-week AI-enhanced module integrated within an existing roboticscourse at SUNY Farmingdale. This module introduces foundational AI and Machine Learning (ML
- Conference Session
- Professional Papers
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- 2025 ASEE Southeast Conference
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- Anna K. T. Howard, North Carolina State University at Raleigh; Nicholas Garcia, North Carolina State University at Raleigh
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assess the students in their progress from novice to expert. The assessment isnecessarily individual so at least some of the assessments need to be as well. A good assessmentof student learning needs to be whether they can apply their knowledge to a new, complexproblem without overmuch assistance. The variety of student strengths often limits suchassessments to an untimed assessment.In the past, Statics has been based around a traditional format: derivations followed by a fewexamples in class with homework to follow. Since 2013, Mechanical and Aerospace at NC Statehas taught Statics as a flipped class with concept videos, example videos, and text book readingoutside of class.3 Homework was split between those problems auto graded by a computer
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- Technical Sessions 5
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Arkadiy Portnoy, City University of New York, Queensborough Community College; Sunil Dehipawala, City University of New York, Queensborough Community College; Tak Cheung
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- Professional Papers
pedagogy methods for community college students are sustainable at lowcost using the laser alignment and Michelson interferometer labs, with the free Python codesfrom Ergodic Company. The replacement of the Nd:YAG pulsed laser equipment withNitrogen-dye lasers and advances in camera technology will lower the operating cost for facultymembers with laser experience.ConclusionsWe have concluded that the Rochester Fusion Consortium Internship Program can be used tosustain interests in STEM students to learn the related skills. The instability investigation andPython computation skills are universal such that students switching away from the inertialconfinement fusion career path after learning the skills can still use them in their future
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- Technical Sessions 1
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Zhou Zhang, SUNY Farmingdale State College; Yizhe Chang, California State Polytechnic University, Pomona
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”, Computers in Education Journal, Vol. 11, No. 1, pp. 1-10.[34] Silma, N., Maulida, I., Wulan, A.P., Merawati, J. & Hasan, M.K., 2024, “A comprehensive review of Project-Based Learning (PBL): Unravelling its aims, methodologies, and implica- tions”, Journal of Education, Social & Communication Studies, Vol. 1, No. 1, pp. 10-19.[35] Ly, C.K., 2024, “Teachers’ Roles on English Language Teaching for Promoting Learner- Centered Language Learning: A Theoretical Review”, International Journal of TESOL & Ed- ucation, Vol. 4, No. 2, pp. 78-98.[36] Zhang, Z., Chang, Y., Esche, S.K. and Zhang, A.S, 2022, “Application of internet of things in online robotics class”, ASEE Annual Conference & Exposition, Minneapolis, Minnesota, USA
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- Professional Papers
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- 2025 ASEE Southeast Conference
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- Sean Walker, University of South Alabama; Kari J Lippert, University of South Alabama
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- Professional Papers
impact of flipped classrooms on student achievement in engineering education: A meta-analysis of 10 years of research," Journal of Engineering Education, vol. 108, no. 4, pp. 523-546, 2019.[3] R. Castedo, L. Lopez, M. Chiquito, J. Navarro, J. Cabrera and M. Ortega, "Flipped classroom—comparative case study in engineering higher education," Computer Application in Engineering Education, vol. 27, no. 1, pp. 206-216, 2018.[4] M. Chen, "Synergizing computer‐aided design, commercial software, and cutting‐edge technologies in an innovative nozzle test apparatus for an engineering laboratory course," Computer Applications in Engineering Education, vol. 32, no. 5, p. e22773, 2024.[5] C. Chen, "Flipped classroom with case-based learning
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- 2025 ASEE Southeast Conference
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- M. Hassan Tanveer, Kennesaw State University; Rizwan Patan, Kennesaw State University; Carrington Chun, Kennesaw State University; Razvan Cristian Voicu, Kennesaw State University; Adeel Khalid, Kennesaw State University
- Tagged Topics
- Professional Papers
boundaries of objects within images. p instance, the gradient magnitude G is computed as: G = For (Sx ∗ I)2 + (Sy ∗ I)2 , where Sx and Sy are Sobel opera- tors, and I is the input image and will identify the edges of leaves and fruits.• Color Analysis: The color histogram H(k) of an image is computed to analyze color variations, calculated as: H(k) = PN i=1 δ(I(i) − k), where δ is the Dirac delta function, N is the number of pixels, and k represents color intensity levels. This analysis helps in distinguishing between healthy and diseased crops.• Texture Analysis: Features like entropy, contrast, and correla- Fig. 3 Hybrid learning: red for crops, tion are extracted using the Gray Level Co-occurrence Matrix blue for
Comparison of Undergraduate Engineering Students and High School Students Solving Authentic Problems
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- Technical Sessions 2
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- 2024 Fall ASEE Mid-Atlantic Section Conference
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- Gang Liu, University of Pittsburgh at Bradford
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Paper ID #45310Comparison of Undergraduate Engineering Students and High School StudentsSolving Authentic ProblemsDr. Gang Liu, University of Pittsburgh at Bradford Dr. Gang Liu is an Assistant Professor of Mechanical Engineering Technology in the Division of Physical and Computational Sciences, at the University of Pittsburgh at Bradford. He received his Ph.D. degree in mechanical engineering in 2006 and another Ph.D. degree in engineering education in 2018. He teaches mechanical engineering undergraduate courses, including statics, dynamics, mechanics of materials, etc. His research interests include in-class