the Engineering career pursuit. Therefore,concepts like mechanics, electromagnetism, thermodynamics, and wave theory form a majorfoundation of engineering design, analysis, and innovation. In pre-engineering programs,especially those at Historically Black Colleges and Universities (HBCUs), effective physicspreparedness is vital for professional readiness, academic success, and the highly technical fieldof engineering. Research by [1], described some hindrances in students’ ability to apply theoreticalknowledge to complex real-world engineering problems due to inadequate hands-on physicspreparation. This issue can be observed mainly among second-year engineering students, where abridge in fundamental physics education can hinder academic
Catolica de ChileAmanda Allendes, Pontificia Universidad Catolica de ChileAndr´es Ignacio Guevara, Pontificia Universidad Catolica de Chile ©American Society for Engineering Education, 2025Empirical WIP: The status of creativity among engineering graduatesIntroductionSTEM is the acronym for Science, Technology, Engineering, and Mathematics [1]. Although theacronym is widely used, some authors refer to each discipline independently, while othersconsider it a whole [2]. Since the disciplines are connected in real life [1], the integratedapproach has been considered essential to address real-world problems [3]. Solving real-worldproblems is essential for STEM students. For example, engineers must identify problems, ideate
wished they could see the original comments. These findings suggest an opportunity to use generative AI as a stepping stone for developing students’ feedback literacy. Furthermore, we believe that by understanding students' perceptions of AI in this context, we will gain valuable insights to further refine the integration of AI in the classroom and equip educators with the necessary tools to utilize AI effectively within the current educational landscape.Introduction and Background ngineering students' ability to develop teamwork skills is a key goal of engineering education,Eas outlined in the ABET criteria[1]. Engineeringgraduates have considered teamwork one of the most important
resistance as a function of temperature. A typical way to interface a thermistor with a microcontroller consists in the use of a voltage divider, where one resistor (𝑅𝑅1 ) is fixed and connected in series with the thermistor (𝑅𝑅𝑡𝑡ℎ ).Figure 1 Thermistor interface circuit. The output of the divider, labeled Vout in the circuit of Fig. 1 is then connected to an analog input Channel of the Microcontroller. In this experiment we employ channel zero (A0). The ADC of microcontroller being used (ATmega 2560) has 10-bit resolution. When designing the voltage divider, it is common practice to select the fixed resistor with a
Aviation embodies crucial progress in human advancement. It is a cardinal component ofglobal mobility that facilitates the movement of people, essential commodities, and services andstrengthens socioeconomic links. Consequently, it has emerged as an integral part ofcontemporary society, contributing significantly to cultural interchange and the global economy. However, in recent decades, aviation has undergone steady growth. In 2025, theInternational Air Transport Association (IATA) projects that passenger demand (RevenuePassenger Kilometers) will increase by 8.0 percent [1]. Additionally, IATA predicts that for thefirst time in the history of commercial aviation service, the number of passengers is anticipatedto surpass an unprecedented
theirunderstanding of tolerancing and design for manufacturing.Introduction:Freshmen level Mechanical Engineering students at Washington State University, Pullman lackmachining experience when they take their first engineering class, Engineering Computer AidedDesign and Visualization (ME 116). Without machining experience or exposure to the designprocess, it is difficult for students to construct knowledge [1] about the concept of dimensioningfor manufacturing and the importance of tolerances when multiple parts are designed to connect.The puzzle project allows us to address the knowledge gap by introducing hands-on real-lifelearning. To complement the background of tolerancing and dimensioning learned by the puzzleproject, another project was introduced
, 2025 Work in Progress: A secondary data analysis of qualitative data to create survey items to measure undergraduate student researcher identityThis Work-in-Progress empirical research paper documents the initial steps in the developmentof survey items to measure student researcher identity. Specifically, we focus on the secondarydata analysis of qualitative data to develop items to measure the construct of interest, as it relatesto researcher identity. Undergraduate research experiences (UREs) provide students with theopportunity to engage in authentic complex problem solving [1]. These experiences areconsidered high impact practices because they have been shown to increase student retention,engagement, and degree completion
as panning the graph, be added to the tutorial. The paper willdescribe the various features of the application as well as results from user studies.Keywords: Bond graph generation; State equations; system modeling1. IntroductionMechatronic systems are a class of systems that combine mechanics and electronics [1]. In fact,the four major components of such systems are the mechanism, sensors, control unit and actuators.Mechanisms relate to mechanical translation, mechanical rotation, or thermo-fluids. Sensors areimportant to collect data, which will be instrumental in determining the control strategies as partof the control unit. The appropriate decisions from the control unit are then transferred to theactuator, which will then power the
leaving engineering majors. Previous researchhas presented many reasons why students may leave STEM and engineering; “chilly climates”for students from marginalized backgrounds, difficulty transitioning to college, poor teaching,curriculum design, and cost (financial and time) are among the most cited reasons for switchingout of STEM majors [1]. In contrast, engineering education research indicates that engineeringidentity is pivotal in engineering persistence [2-5]. The process of leaving engineering is morecomplex than a moment in time or a list of reasons. Using engineering role identity as aconceptual framework, this study investigates the nuances of the (in)decision-making process ofleaving engineering. We explore this research question: how
as women engineering students during their college years. Thirty-three first-year women engineering students are included in the sample, with 17 women of Color– 5 Black, 5 Asian, 2 Latinx, 4 Bi/Multiracial and 1 Middle Eastern woman (who indicated basedon how the census at the time classified her as white but shared that she experienced herself as awoman of Color and is included in our analysis thusly) – and 16 white students. The data for this study comes from one semi-structured interview, which occurred duringthe fourth week of the participants’ first semester of college. This interview aimed to understandhow participants came to choose to major in engineering, their inside and outside of theclassroom experiences during their
?” Negotiating the Borderlands of Queer and Engineering EpistemologiesIntroductionPrior research about the experiences of LGBTQ+ engineering students has focused on thecultural aspects of the discipline that negatively affect their educational opportunities, withparticular focus on heteronormativity, masculinity, and prioritization of technical skills at theexpense of social knowledge. The field of engineering values empirical knowledge, which can beat odds with many other epistemologies and ontologies, especially queer ways of knowing [1]. Inthis research brief, we use Riley’s work and Anzaldua’s conceptions of identity borderlands toanalyze one interview with Amelia, as she sits in the tensions between queer and engineeringways of
environments in which majority populations accumulate power that harms students underrepresented in certain contexts. ©American Society for Engineering Education, 2025 “You need to be able to isolate them:” Men allies leveraging mitigation as a strategy towards gender equity in STEM (Work in Progress)Research demonstrates that majority populations have the agency and power to create culturalchange, wielding a particular type of influence among those with whom they share identities.However, literature that explores allyship does not define the term clearly, with allyship 1 oftenreferenced as an identity as opposed to a set of practices [1-3]. This ambiguous understanding ofhow allies may
& Energy Balances, is a foundational course for chemicalengineering students, and serves as the entry point into the major at most institutions [1]. Thecourse builds on fundamental concepts learned in introductory chemistry, physics, and mathcourses and generally serves as a prerequisite for subsequent undergraduate courses in thechemical engineering discipline. The course introduces key concepts in conservation of mass(mass balances) and conservation of energy (energy balances) both with and without chemicalreactions, as well as an introduction to concepts in thermodynamics including equations of state,multi-phase systems, and liquid/vapor equilibrium. These concepts are foundational to laterchemical engineering courses including
, assignments, and lecture slides, convey messages aboutdisciplinary values, assumptions, and beliefs [1]. They help students recognize and learn theways of knowing and doing typical of their disciplines, promoting students’ domainidentification and knowledge construction processes. Textbooks have been used to examine thenature of knowledge presented across various fields, revealing the narratives, questions, andcontent they prioritize and value [2]. For example, Robinson’s [3] analysis of introductoryelectrical engineering textbooks spanning roughly 80 years suggests that more recent versionsprioritize fact-based content through rote procedure application than earlier, more theoreticalversions. These findings align with other disciplinary perspectives
andGirls: A Study of Algorithm Design and Debugging (Work-In-Progress)IntroductionComputational thinking (CT) is widely recognized as a core skill for 21st-century learners,essential for success in STEM fields. Despite efforts to promote STEM education, genderdisparities persist, with women underrepresented in these fields. Scholars recommend earlyexposure to CT concepts in K-12 education to foster equity and inclusion [1]-[4]. Factorsinfluencing the gender gap include cultural stereotypes, limited computing experience, andunequal treatment, leading to negative self-efficacy [5]-[8]. Positive engagement in STEMduring early childhood can significantly influence long-term interest and participation. Whileseveral studies have examined girls
model for student success units across the country.Dr. Marko Lubarda, University of California, San Diego Marko Lubarda is an Assistant Teaching Professor in the Department of Mechanical and Aerospace Engineering at the University of California, San Diego. He teaches mechanics, materials science, computational analysis, and engineering mathematics courses, an ©American Society for Engineering Education, 2025 1 NSF IUSE 2315777: Training engineering students to be better learners: a course-integrated approachProject motivation and backgroundLearning is a lifelong
Paper ID #47763Engineering Student Early Dropout Prediction in Regional Universities UsingMultimodal AIDr. Bin Chen, Purdue Univeristy Fort WayneIrah Modry-Caron, Purdue University Fort Wayne ©American Society for Engineering Education, 2025 Student Retention Forecast in Regional UniversitiesIntroductionThe overall dropout rate of engineering students in the United States is approximately 50%.However, the dropout rate varies significantly across universities [1]. Prestigious nationalengineering schools often have retention rates over 90%. Regional universities and campuseshave much higher student attrition rates. As a
education.IntroductionThe integration of Artificial Intelligence (AI) and Machine Learning (ML) into modernengineering practices has created an urgent need for engineers with AI/ML skills to tacklechallenges in automation, robotics, preventive maintenance, defect detection, system optimization,and beyond. This integration underscores the transformative potential of AI/ML in engineeringeducation, necessitating curriculum advancements to prepare students for the evolvingtechnological landscape [1]. This need is driven not only by industry demands but also by students,who increasingly see AI/ML expertise as vital for their future careers and expect opportunities toapply these skills in real-world engineering projects. Numerous national reports, including thoseby the
instruction and curricula.Search MethodsWe conducted a systematic review of the literature to find relevant articles. We individuallysearched three scholarly engineering databases and excluded grey literature. We used advancedsearch criteria and Boolean logic search parameters for each database. We used four categoriesof search terms as part of our search strategy to retrieve relevant literature. These were (1)microelectronics, (2) microcontrollers, (3) first-year or sophomore, (4) engineering education, aswell as Arduino. We analyzed only literature published in the past 10 years (2015-2024) forinclusion in the study. Seventy-three records were identified in Scopus, and 196 were identifiedin Compendex and Inspec. The search strings used to search
suggests that student performance (as a proxy for studentlearning) remained largely unaffected despite the changes in teaching modalities over the four-year span.Keywords: COVID-19 pandemic, Hyflex teaching, hybrid teaching, teaching modalities,pandemic teaching interventions1. Introduction1.1. COVID-19 Pandemic Impacts on Teaching ModalitiesIn March 2020, the World Health Organization (WHO) declared a pandemic in response torapidly increasing cases of the novel coronavirus SARS-CoV2 (or COVID-19) [1]. Declarationof the pandemic prompted rapid closures of in-person learning venues and incited a nearimmediate transition to remote teaching and learning. This abrupt shift to online learningoccurred at a time when a majority of faculty members in
fewyears ahead on the IDP, and establishing a scaffolded and iterative process to create, adapt andpersonalize the IDP.We performed qualitative analysis of student responses to open ended questions about thecourse. Using Bandura's agency framework [1], we find the new approach has been successful ineliminating the barriers that graduate students previously faced in the initial creation of the IDP.After changes to the course activities, students were more likely to exhibit self-reflection aspectsof agency and discuss their goals, rather than merely evaluating course activities as isolatedtasks. Our data shows students adopting the IDP as a career planning tool with indications thatsome students have transitioned from thinking of IDP as a product to
. Overall,the findings show it is feasible to radically redesign introductory MSE around computationalmodeling while maintaining positive student experiences.1. IntroductionThis paper reports on student perceptions of an introductory materials science and engineering(MSE) course redesigned to center around computational models and taught with a novelinteractive textbook with the computational models embedded. This redesign is in response totwo trends. First, computation is transforming MSE, and the curriculum should reflect that fact.Second, computation and computational representations can be harnessed to create powerfultools for learning. This paper is a continuation of the work presented in [1] which described theredesigned course without
, the research ofdesign cognition offers observational studies and develops models to describe human-centereddesign processes. Common topics of design cognition include design fixation [1-7], problem-solution co-evolution [8-11], and design metacognition [12].As a capstone course instructor, the results of design cognition are interesting because they canexplain why students think or behave in certain ways in capstone projects. For example, thephenomenon of problem-solution co-evolution tells us that it is common for designers to usetentative design solutions to improve their understanding of design problems. With this idea, wemay not insist on having a “perfect” problem statement from a design team before they can startproposing design
providestudents with the interdisciplinary knowledge, practical skills, and entrepreneurial mindset required toexcel in today’s workforce. To bridge this gap, the Vertically Integrated Projects (VIP) model has emergedas a transformative approach, fostering collaboration among undergraduate and graduate students andfaculty to solve complex, long-term, and large-scale challenges. The VIP model was initially introduced byPurdue University [1] and later expanded by Georgia Tech Institute of Technology to address gaps ininterdisciplinary education and research [2]. Since then, the model has been adopted globally, withvariations tailored to institutional goals and cultural contexts. VIP programs emphasize collaborativelearning, long-term project engagement
(averaged daily) for the dormitory’slocation. As part of the design exercise, students are asked to model the HVAC system for eachday for the 5-years, including heating, humidification, cooling, dehumidification processes, andreturn air mixing that complies with ASHRAE standards. The energy analysis of the system canbe completed using component models for the heating and cooling coils, allowing students toevaluate the electrical power/energy required to operate the HVAC system on a per day, andannual basis. Using the local cost of electricity, the energy requirements of the system can beconverted to an annual operating cost. As part of the design, students are asked to consider twocases 1) normal operations where air recirculation is allowed, and 2
student's eventual success. Samson et al. [1] found that college GPAand test scores had almost no predictive ability for a student's future success. These academicmetrics explained only 2.4% of the variance in occupational performance criteria, such as wagesand job satisfaction, with engineering being notably lower than the average. Students are overlyconcerned with collecting points to pad a GPA that doesn't really mean anything.The logical conclusion is to change the way we assess students. When considering any gradingsystem, including the currently dominant points-based system, we can and should demandcertain characteristics of the system. It should always uphold rigorous academic standards andclearly connect with student learning objectives. A
design process, from conceptualization to testing and evaluation.Results from the ISE measurement instrument show significant increases in six of eight ISEfactors exclusively in the research group. Reflective responses support these results and highlightthat active and experiential learning with integrated design elements can be augmented byleveraging technology, leading to a challenging and yet fulfilling and meaningful learningexperience.IntroductionEngineering education is undergoing a critical shift to integrate experiential and design-basedlearning into traditionally analytical curricula [1–3]. Although first-year engineering courses andsenior capstone projects often emphasize creativity and innovation, second- and third-yearcourses
projects on a single wafer has been usedto reduce development costs of Very-large-scale Integration (VLSI) design [1]. The mask is by farthe most costly part of the VLSI fabrication process. Creating an entire mask for a single projectis cost-prohibitive for a low-quantity development run. By packing multiple low-quantity projectsinto a single mask, the mask cost can be shared across all the combined developers, so eachdeveloper gets their design fabricated at a fraction of the cost of fabricating the ASIC on theirown. This is the model this co-curricular uses, as we only intend to use a small number of ASICsper fabrication run for bring-up testing and future development.Efabless OpenMPW ShuttlesEfabless defines itself as “the first creator
and Waymo haveseen success in implementing partial or full autonomous driving in vehicles on live roads; and“Apple Intelligence” was the flagship feature for the launch of Apple’s new smartphone in fall2024. Yet what legal or policy response this technological growth will precipitate is less certain[1, 2]. Nevertheless, it should be expected that the development and enactment of regulatoryframeworks for AI will demand AI engineers with a command not only of the technicalintricacies of AI models, but also of the policy and regulatory landscape for AI development andcompliance [3]. This is made clear by the 2023 U.S. Executive Order on Safe, Secure, andTrustworthy Development and Use of Artificial Intelligence [4], which called for an “AI
their24/7 availability, and enhancing engagement through conversation. However, they should beexamined as a reliable education tool in manufacturing, especially in adapting to different users.Here we present and evaluate an LLM-powered chatbot—the Manufacturing Adviser—inanswering various types of manufacturing questions to 4 user levels from children to experts.ITS are known for personalized learning, enabling students to progress at their own pace whilereceiving feedback. VanLehn [1] presented a meta-analytic review comparing the effectivenessof human tutoring, intelligent tutoring systems (ITS), other computer-based tutoring systems, andno tutoring in facilitating student learning. He found that the effect size of human tutoring wascomparable