and develop actionable solutions. Although this studycenters on freshmen, the findings suggest broader applicability, underscoring the potential ofLean and engineering practices to support students in building resilience and problem-solvingskills across various academic disciplines. Integrating these methods in academia not onlyempowers students but also fosters a culture of continuous improvement within educationalsettings, bridging the gap between industry practices and academic success.Literature ReviewLean tools like value stream mapping and the A3 process aid continuous improvement ineducation by visually structuring communication and problem-solving approaches [1]. Adaptingthese practices from manufacturing is challenging, but Lean’s
-up is a “full-sized structural model built to scale chiefly for study, testing, or display”[1]. They can come in various forms, and the definition does not express the complexity andutility of different construction mockups, which can be varied based on role and purpose.Mockups in construction serve three primary purposes: the first is an aesthetic review, the secondis a constructability review, and the third is for assembly review and testing [2]. As a componentof this, a mockup in construction broadly serves as a means of communication.Understanding the composition of a wall can be challenging in construction with the advent ofnovel systems and components that come together in various ways. A wall mockup on aconstruction site can ensure
primary function is to evaluate how well it can do standard project management tasks,including resource leveling, Gantt charts, critical path analysis, and baseline development. Thesecomponents are essential in construction because project budgets and schedules are frequently limited.The steps in the example follow in building a network model: 1. Defining construction activities; 2.Ordering those activities used in the Project; 3. Establishing the relationships between activities to createa network diagram; 4. Identifying the activities' quantity and assigning duration; 5. Costs and resourcesto assign to each activity; 5. Calculate each activity's early Start, early finish, late Start, and late finish;6. Computation of float values to identify
arepresent in colleges today. These topics include gender gaps in current major fields of study,reasons why female or male students choose their majors, reasons why they do not incline tosome majors, and employability between males and females. From the beginning of highereducation in the United States, there was a significant difference in enrollment between men andwomen. This difference was due to many things like societal norms and the belief that collegewas not for women. In the last fifty years, a difference in this trend has been seen. In the early1980’s, women started to surpass men for college enrollment number. Currently, data shows thatwomen make up around 57% college students in the United States [1]. While this number isencouraging for
practices.Keywords: AI-Driven Academic Evaluation, Artificial Intelligence in Education, GradingBias, Theoretical Knowledge Assessment, ChatGPT, Automated Grading.1. IntroductionAdopting artificial intelligence (AI) in education is revolutionizing traditional teaching andlearning processes, with applications ranging from personalized learning platforms toautomated grading systems. AI-assisted grading has garnered significant attention for itspotential to streamline assessment processes, particularly for large-scale courses wheremanual grading is resource-intensive. Automated systems have been successfullyimplemented for objective tasks like multiple-choice quizzes, but their applicability to morecomplex assessments, such as theoretical understanding and
transference learning, detailing its components and illustratingits integration of adaptive feedback with real-world experiences. Next, we discuss the outcomes ofa pilot study evaluating the model’s effectiveness, focusing on metrics such as latency, accuracy,and learner engagement. Finally, we summarize the findings and propose directions for futureresearch, emphasizing scalability, expanded modalities, and ethical considerations in AI-driveneducational solutions.Literature ReviewArtificial intelligence has been increasingly applied in educational settings to develop IntelligentTutoring Systems (ITS) and Adaptive Learning Systems (ALS). Early work by [1] and [2] demon-strated that personalized instruction could improve user achievement by tailoring
,introducing them to foundational topics in calculus, physics, and programming. Within thephysics portion of the program, students explored quantum mechanics and worked specifically onunderstanding the BB84 quantum key distribution (QKD) protocol. This manuscript focuses onour experience teaching the BB84 QKD protocol, describing what worked well, the challenges wefaced, and the lessons we learned. We share successes, obstacles, and strategies for futureiterations to improve educational outcomes related to this critical aspect of quantumscience.IntroductionThe demand for scientists and engineers equipped with quantum knowledge is rising as QISEbecomes increasingly critical to advancing technology and securing information systems [1].Experts, including
sampling.Due to its interdisciplinary nature, high interest from commercial, scientific, and militaryinvestors, and expanding application areas, UAV research, development, and manufacturingattract scientists and engineers from almost all disciplines. Furthermore, as artificial intelligence(AI) revolutionizes various engineering areas such as aviation [1], robotics and automation [2],[3]and healthcare [4],[5] UAV research will also be revolutionized and will attract even more futureengineers.Considering that preparing future engineers for the jobs of tomorrow is one of the most importantresponsibilities of engineering educators [6],[7] including UAV modeling simulation and controldesign study in mechanical engineering curricula is important.In this
0.024 W·m−1·K−1 [1], andthus prevents an efficient heat transfer from the processor to the heat sink. Imperfect surfacecontact between the processor and the heat sink is a major limiting factor for creating newelectronics. Thermal Interface Materials (TIMs) are thermally conductive materials used toimprove surface contact with a thermally conductive material, displacing the air and increasinginterfacial heat transfer between the heat sink and processor and this prevents overheating of thesystem.The objective was to produce repeatable and reliable results using a setup, which costssignificantly less than commercial testers. This would make TIM testing more accessible to highschool laboratories and developing nations. The goal of this project was
review panel. The resulting matrices are analyzed by faculty to assess theintervention’s impact on requirements development in terms of quantity and type.This intervention may also serve to provide a list of technical requirements at a more consistentlevel of abstraction to enable the effective implementation of the following phases of QualityFunction Deployment considering correlations between technical requirements and productcharacteristics, and characteristics and manufacturing processes.KeywordsRequirements, Requirements Management, Quality Function Deployment, House of Quality,Collaborative DesignMotivationRequirements are a foundational component of the design process and generation begins in thefirst stages of design [1], [2
through pre-class interaction with course materials whileuncovering hidden thought processes to guide the design of skill-focused in-class activities.Implementation of pre-class pedagogical approaches such as pre-class quizzes and exercises,flipped classrooms, and just-in-time teaching (JiTT) demonstrate positive impacts on studentperformance, student engagement, conceptual understanding, and long-term retention [1]-[4].Grounded in cognitive load and constructivist learning theories, these approaches break downcomplex topics into smaller, manageable ‘chunks’ while providing a contextualized foundationfor learning [5]-[6]. Reduced cognitive load minimizes stress on students and generates apositive environment for student participation on topics
structural engineering. Rebar iscrucial in reinforced concrete structures, providing the necessary tensile strength to counteractconcrete’s inherent weakness in tension [1]. Mastery of rebar layouts is essential not only fordesigning safe and efficient structures but also for ensuring compliance with industry standardsand regulatory codes. Traditional educational approaches heavily rely on two-dimensional (2D)drawings and schematics to depict rebar arrangements. While these representations arestandardized within the industry, they often present significant challenges for learners in terms ofspatial visualization [2]. The limitations of 2D imagery can impede students’ ability to fullygrasp three-dimensional (3D) spatial relationships, potentially
onnon-traditional students in foundational engineering courses that have potential to leave theengineering pathway without additional social and academic support early in their academicplan. The project offers peer support through small group activities in online foundationalengineering courses that incorporate structured active learning sessions to enhance theengineering content [1] [2] [3]. These types of active learning scenarios have potential tostrengthen STEM competencies to increase students’ academic persistence [4] [5]. Persistence inengineering pathways is contributed to students’ acclimation and mindset to accomplish theireducational goals [6] and enter the engineering workforce [7]. This paper specifically examinesthe qualitative
to grasp and apply. The authors share samples ofengineering undergraduate students’ work “before” and “after” this teaching approach wasimplemented starting in fall 2024. This paper also points to open-access and/or free onlineresources that serve as easy-to-comprehend primers for students and educators alike who areinterested in learning introductory design principles.The goals of this paper are twofold: 1) to allow engineering educators to incorporate beginner-friendly design principles into their own classrooms quickly and 2) to help engineering studentsbecome better scientific communicators as a core skill for working in industry, strengthening thebroader impacts of their work.BackgroundContent, delivery, and design are often termed the
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
Engineering and Technology (ABET) describes the engineering designprocess as follows [1]. “Engineering design is a process of devising a system, component, or process to meet desired needs and specifications within constraints. It is an iterative, creative, decision- making process in which the basic sciences, mathematics, and engineering sciences are applied to convert resources into solutions. Engineering design involves identifying opportunities, developing requirements, performing analysis and synthesis, generating multiple solutions, evaluating solutions against requirements, considering risks, and making trade-offs, for the purpose of obtaining a high-quality solution under the given circumstances
technologies, ultimately leading to a richer learningexperience for students.Introduction & Literature‘Internship’ is a word typically reserved for undergraduate and graduate students, but seldom is itused in the context of faculty. Student internships generally serve as a link between theclassroom and the profession, but they also engage industry and faculty [1], just not in the directsense. The symbiotic relationship between theoretical knowledge and practical application hasalways been its cornerstone in higher education. Similarly, the relationship between constructionprograms and industry is a foundation for this symbiotic relationship, with industry learning fromacademia and vice versa.Construction programs have traditionally valued industry
Institute (VMI) since thelate 1970s to highlight how Capstone has changed to meet new accreditation standards and to ad-dress new faculty hires. In addition to the historical information, current Capstone instructors andalumni were surveyed, and their experience is summarized herein. To round out the paper, Cap-stone experiences at ABET-accredited civil engineering (CE) programs in Virginia are also com-pared to address the current state of Capstones in the region.The 2024-2025 ABET [1] Criterion 5: Curriculum requires “a culminating major engineering de-sign experience.” This culminating experience is often achieved through a Capstone course; al-ternatively, it may be embedded within a required course. The Capstone experience may be anopportunity
byproduct and its potential use in a concrete mixture, reducing the quantity of cementin the construction of different parts of a building structure. The authors utilizedcomprehensive national and international literature to assist in the condition assessment ofany possible solar or Biomass plant byproducts related to the study.1. IntroductionTo better assess the viability of solar and Biomass byproducts, this study aimed to reviewnational and international literature to identify various solar or biomass power plants andfind their associated byproducts within Gainesville. The plant byproducts determine whattype of plants generate revenue and, in return, reduce expenses related to generatingelectricity and consumer energy costs. Another part of the
computer vision andmachine learning, are revolutionizing multiple industries [1]. Many companies have integratedAI and machine learning platforms into their customer user face and employee workflowsoftware. This trend calls for preparing the next generation of learners for this transformation,which requires innovation in the education sector, especially within the K-12 system [2].ImageSTEAM, an NSF-funded initiative, was created in 2019 to bridge this gap by empoweringmiddle school educators to integrate visual computing and AI technologies into their classrooms.The program provides professional development workshops for teachers and co-creates learningmodules with researchers, fostering a learning environment where students engage with AIconcepts
unprecedented challenges, especially in maintaining or replicating thehands-on, interactive nature of STEM learning experiences [1].Online versions of STEM camps emerged as the obvious, pragmatic solution to these challenges,attempting to provide students with opportunities to engage in coding, robotics, and othertechnical subjects remotely. Developed and distributed through a partnership between TexasTech University and the University of Memphis, and funded by the National Science Foundation(NSF) under Grant No. 2105766, our coding camp aimed to empower high school studentsthrough an immersive, online coding experience. While the camp was initially designed to teachfundamental programming skills, the overall approach was structured to boost
, particularlyin compact systems where natural convection alone is insufficient to manage the heat generatedby high-performance components. [1] To mitigate the risk of exceeding the maximum operatingtemperature of sensitive electronics, heat sinks are widely employed to enhance heat transfer.Positioned on top of central processing units (CPUs) or other heat-intensive components, heatsinks facilitate the conduction of thermal energy away from the device, followed by dissipationthrough natural or forced convection [2]. Understanding the role of heat sinks in improvingoverall thermal management is essential for designing reliable electronic systems.Problem DefinitionA new laboratory module centered on heat sink performance was proposed after recognizing
band. The focus of this competition was on mechanics, more specifically onforces and motion.Competition Ruleset 1. Competition Weight Limit: 250 grams of PLA 2. Locomotion Device: Standard-issued Rubber Band (Provided) 3. Adhesive: Super Glue (Provided) 4. Bearings: 2 Standard-issued Double Sealed Bearings (Provided) 5. Total PLA Allocated for Prototyping: 500 grams 6. Total PLA Allocated for Competition and Prototyping: 750 grams 7. Hull must fit in one print. 8. Design must be solely powered by the rubber band. (No Slingshots) 9. If there are any ties, the shortest time will be utilized to determine a winner.Beyond providing the supplies, ESG hosted a demo of the 3D Printing Lab to the teams
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
Intelligence for Aspiring Project EngineersAbstractEmotional intelligence (EI) can play an influential role in enhancing the future careers ofaspiring project engineers [1], [2], [3]. Using EI, project management competencies, andTuckman’s Five Stages of Team Development, also known as the Tuckman’s Ladder, mayprepare future project engineers to efficiently navigate various project environments [4], [5].Engineers with EI may become more inclined to exert project management competencies such aseffective communication, empathy, and conflict resolution skills [2].The appropriate application of EI can improve self-awareness, emotional regulation, teammotivation, job satisfaction, and the mental well-being among engineering professionals [1], [2].The lack
advanced tools, such as theHusky A2000 UGV, stereo cameras, LIDAR, GPS, IMUs, and manipulators, to provide real-timeupdates and precise predictions. Furthermore, it employs machine vision techniques and digitalsensors such as the Reyke Soil Moisture Tester for continuous monitoring and responsive action.This project demonstrates how AI can transform agriculture to meet global food needs and fos-ter innovative thinking for engineers. By combining theory and practice to empower engineers toaddress critical agricultural challenges through innovative solutions.Keywords: Artificial Intelligence (AI), Agriculture, Computation Intelligence, ML.1. IntroductionAgriculture is among those things that form the backbone of the economic development of anation
on homework gradingand exam cheat sheet preparation.Literature ReviewIn engineering courses, long- answer homework and comprehensive course projects are commonpractice for the course assessment process. The evaluation of student work is crucial aspect ofthe learning process. Traditionally, instructors have borne the sole responsibility for gradingthese assessments. However, in recent years, there has been a growing interest in exploringalternative grading methods, including self-grading and peer review. To validate the effectivenessof self-grading homework, numerous studies have been conducted to explore the benefits of thisevaluation method. Kevin Chang and Lucas de Lemos Coutinho [1] presented their findings andrecommendations for
outlines the proposedframework, a future "Phase-2" study will report on the program’s implementation and outcomes.By integrating mentorship, hands-on learning, and industry engagement, this initiative aims toset a new standard for student development and retention.IntroductionRetention of engineering students is a crucial concern for higher education institutions. Manystudents, particularly in rigorous fields like computer engineering, face challenges such asacademic pressures, lack of belonging, and unclear professional pathways [1], [2]. At theauthor’s university, the CPE department seeks innovative approaches to address these issues,aiming to improve retention and graduation rates [3], [4].This paper introduces a comprehensive onboarding
Paper ID #45562Empowering Undergraduates with NLP: Integrative Methods for DeepeningUnderstanding through Visualization and Case StudiesNilanjana Raychawdhary, Auburn UniversityChaohui Ren, Auburn University [1] Mohamed, Abdallah. ”Designing a CS1 programming course for a mixed-ability class.” Proceedings of the western Canadian conference on computing education. 2019. [2] Shettleworth, Sara J. Cognition, evolution, and behavior. Oxford university press, 2009.Dr. Cheryl Seals, Auburn University Dr. Cheryl Denise Seals is a professor in Auburn University’s Department of Computer Science and Software Engineering. She
hands-on tools, compared to traditional lecture-based classes where they oftenappear bored and sleepy.Introduction and motivationMost undergraduate engineering programs follow a traditional structure centered around lecturesand laboratory sessions. This format provides students with a theoretical foundation throughlectures, where concepts and principles are explained by instructors. Laboratory sessions, on theother hand, offer hands-on experience, allowing students to apply their theoretical knowledge topractical problems and develop essential engineering skills [1][2]. However, the increasingprevalence of digital distractions and the rapid pace of modern life have significantly impactedstudents' attention spans. Engineering education