question.In this paper, several case studies are examined to explore the role of ChatGPT in generatingembedded systems solutions for lab practices. These case studies are based on actual studentproject assignments in a sequence of embedded systems courses, including 1 - Introduction toMicroprocessors, 2 - Embedded Systems, and 3 - Real-Time Operating Systems. Our studies havefound that though ChatGPT is a valuable tool in embedded systems teaching, it cannot replace thefoundational knowledge essential for mastering embedded systems. Practical experience and adeep understanding of embedded systems’ intricacies are still essential for success in this field. Inthe era of ChatGPT, instructors teaching embedded systems design should incorporate pop
– JSIMAbstractUnderstanding the behavior of electrical circuits poses significant challenges for today’sexperiential learners. Traditional teaching methods that rely on static circuit diagrams andmanual calculations often fail to engage students who thrive on hands-on learning, programming,and simulation—an approach increasingly prioritized in modern engineering programs [1]–[3].To address this gap, we introduce JSIM, a real-time circuit simulation tool designed to lowercognitive barriers in circuit analysis while enhancing practical interfacing with hardware throughphysical breadboarding and low-level programming. Developed exclusively in C++ with anoriginal codebase and optimized for embedded systems, JSIM requires less than 100 KB ofstorage and achieves
indicated improved satisfaction with course organization andperceived relevance of the material.By offering a detailed case study, this paper provides practical insights into how structuredcourse design and alignment can enhance the effectiveness of teaching methods and assessmentsin electrical engineering education. The findings suggest that this approach can be successfullyapplied to other courses to improve student outcomes and enrich the learning experience.IntroductionConstructive alignment is a vital framework for designing curriculum and assessments that alignwith course learning outcomes, introduced by Biggs and Tang [1]. This approach emphasizessynchronizing teaching methods, activities, and assessments with course objectives to
Strategies Responses to ECE Exam Success and FailureIntroductionIn engineering as a profession and in engineering education, failure is commonplace[1]–attempteddesigns fail, experiments fail about 90% of the time, and students do not achieve the scores theydesire on homework, quizzes, and exams. Thus, the ability to navigate and respond to failure asan opportunity for growth and learning is a key component of the scientific enterprise. However,engineering education research is sparse on how students respond to failure.Research on response to failure has been extensive in workplace settings[2, 3], in which there is avery wide range of negative and positive responses to failure, including denial, anger, bargaining,depression
challenging endeavor due to the abstractnature of its concepts. Students often struggle to connect the mathematical for-mulations, such as matrix manipulations and transformations, with real-world ap-plications. The disconnect between theory and practical understanding can leavestudents feeling disengaged and overwhelmed [1], particularly when faced with rapidsuccessions of equations that lack context or intuition. One promising way to address this challenge is to draw on familiar experiencesand relatable analogies to make abstract concepts more tangible. Learning is mosteffective when new information builds on prior knowledge and lived experiences, al-lowing students to form meaningful connections. Without such connections, evenwell-structured
achieve the survey’s objectives aswell as to inform the development of an assessment strategy.IntroductionIndustry 5.0 emphasizes a human-centric design approach, in which humans and cobots(collaborative robots) collaborate in shared working environments [1],[2]. Cobots will handlerepetitive and labor-intensive tasks, while humans will focus on customization and critical thinkingactivities to solve complex issues [3]. Industry 5.0 has gained increasing attention in recent yearsbecause it is considered the next major global industrial revolution. As part of the industry 5.0implementation, the demand for a skilled workforce capable of solving problems creatively andadapting to changing situations has been on a steady rise. As a consequence, current
traditional knowledge acquisition to emphasize practical applications and real-world experience. Virtual learning tools, specifically virtual laboratories, play acrucial role in this shift by offering hands-on learning opportunities through realis-tic simulations. These virtual laboratories enable students to test, experiment, andrefine their skills in environments that closely mimic real-world conditions.This paper will focus on four virtual laboratories, where two cover electrical appli-cations and the other two cover robotics one. It should be noted that the focus hereis on virtual laboratories, not remote ones, the distinction being well explained in[1]. The justification behind selecting these four labs out of the 35 that have beendeveloped
Teaching. The project not only succeeded in adapting to theconstraints of remote learning but also demonstrated a forward-thinking approach to embeddingpractical, real-world skills in the curriculum, serving as a model for future education strategies inembedded systems and other hands-on disciplines.1. INTRODUCTIONThe COVID-19 pandemic in 2020 fundamentally disrupted educational systems worldwide,forcing a sudden shift from in-person to remote learning. For engineering disciplines likeembedded systems, which rely heavily on hands-on laboratory work and real-time hardware-software integration, this transition posed unique and significant challenges. The inability to accessphysical tools and collaborative environments risked undermining the
).In addition students reported several unexpected positive outcomes, such as gaining insights intoindustry-standard security measures and securing related internships.1 IntroductionQuantum computing represents a revolutionary paradigm in computational technology, offeringunprecedented capabilities to solve complex problems across various domains. Examples includemachine learning 1 , security 2 , drug discovery 3 , and optimization 4 . The integration of quantumcomputing and cybersecurity presents a paradigm shift that demands a comprehensive reevaluationof our approach to education and workforce preparation. In response, ensuring a secure cyberspacehas been recognized as one of the National Academy of Engineering’s (NAE) Grand
grade.IntroductionIntroductory STEM (science, technology, engineering, and math) courses typically have highattrition rates. For STEM bachelor’s degree students in the United States, 48% leave STEMbefore completing their degree. They either switch to another major, or exit college beforeearning a degree [1]. This is of significant concern, as demand for skilled professionals in STEMis high, and attrition reduces the number of graduates available to fill these roles. STEM fieldsare critical for innovation and economic growth, and a lack of STEM talent impacts a country’sability to compete globally [1][2][3][4]. Research has shown that (among other factors),students’ belief in their own competence, how interesting or enjoyable they find tasks, and howmuch is required of
-grained interpretation of results thatmay be transferable to other institutions.Introduction and BackgroundMany engineering educational researchers have worked with large-scale datasets of students’ aca-demic records to better understand influential factors on students’ performance [1, 2, 3, 4]. Suchdatasets enable robust statistical analyses that uncover generalizable trends across diverse studentpopulations, providing valuable insights into the systemic influences on student outcomes, as wellas to identify students who may need additional support to achieve the academic success of whichthey are capable. These studies have shed light on critical factors such as high school preparation(e.g., [5]) and first-year experiences (e.g., [6]), which
, leadingto poor performance [1], [2]. Misunderstandings of foundational concepts such as Kirchhoff's Laws,Ohm's Law, and voltage often persist despite prior coursework in physics or math [3].The Circuit Teaching with Real-World Analogies (CTRWA) framework was developed to addressthese issues [4]. CTRWA systematically applies analogies to help students relate circuit conceptsto familiar real-world systems, such as comparing voltage to water pressure or using a running trackanalogy for Kirchhoff’s Voltage Law (KVL). This paper evaluates the preliminary effectiveness ofCTRWA in improving understanding, addressing misconceptions, and building student confidence.MethodologyThe inventory of Circuit Teaching with Real-World Analogies (CTRWA) was developed
allows for the centralization of a variety of student resources within the School and at theuniversity, a cohesive strategy to address the wellness of students, and a singular point of contactfor students [1]. The goal of the Office is to both further the welcoming and community focusedenvironment in the School of ECE and establish programs and initiatives to aid student growthand well-being. A lot of the responsibilities and roles of the office are not novel, but traditionallyspread across multiple positions with different primary responsibilities at the school/major level.The centralization into one Office and position is unique and serves both the undergraduate andgraduate student population in the School. The ECE School is large and is
engineering, developing innovative ways of merging engineering fundamentals and research applications. ©American Society for Engineering Education, 2025 WIP: Promoting Undergraduate Student Success through Faculty MentoringIntroductionAs previous studies recognize, the transition from school to college often requires a supportsystem for students[1]. In engineering education, mentoring plays a crucial role in student successby providing personalized guidance and fostering a sense of community[2]. Mentors typicallyassist with academic challenges, decision-making, and personal development. While mostresearch focuses on mentoring for research activities, there is also a need for general
through hands-on learning to research projects, withmany reporting increased interest in pursuing careers or further research in embedded systemsdesign. Furthermore, the study highlights the importance of integrating PCB (Printed CircuitBoard) design, system debugging, and industry collaboration into the embedded systemscurriculum to maximize student learning outcomes. As the demand for embedded systemsengineers continues to grow, equipping students with practical, hands-on experiences throughtools like the MISL-ASE board is crucial for preparing the next generation of engineers. 1. Introduction The field of embedded systems plays a pivotal role in the development of modern electronicand computer technologies. With applications spanning from
, these tools provide personalized feedback, adaptive learning paths, and real-time support. For example, AI platforms can analyze a student's performance, identifyweaknesses in their work, and recommend specific resources to help them improve. In hands-oncourses, AI simplifies tasks like coding, simulation, and debugging to allow students to focus ondeveloping crucial problem-solving skills. Additionally, AI fosters collaboration and inspirescreativity by offering insights into innovative design and optimization methods. As a result, AI ismaking engineering education more accessible, efficient, and relevant to the skills students needfor today's industry [1-3].In modern digital systems design courses, Field Programmable Gate Arrays (FPGAs
: 1) in their sophomore cornerstone course vs. senior capstone course, and 2) in their senior capstone course before and after introduction of the cornerstone course.II. Background on capstone and cornerstone courses A. CapstoneEvery senior in the ECE department at Portland State University must do an industry-basedsenior capstone project [1], as is also required by ABET. The purpose of these projects is to givestudents the opportunity to: (i) apply their knowledge to solving real-world problems, (ii) gainexperience working as part of a multidisciplinary team, and (iii) become actively involved in acompany or other community organization. Students are expected to practice a systematic andthorough design methodology, do detailed and
undertaken a study to determine which aspects of existingdepartment and instructional culture students identify as providing the biggest obstacles to theirsuccess. We also try to identify areas of strengths that can be leveraged as we complete ourtransformation. While this study was originally designed to help improve our department, weshare the results here in the hopes that it can help other engineering departments betterunderstand their students’ needs and experiences.BackgroundSignificant research exists on barriers to graduation for students in engineering and manydifferent explanations have been proposed for the chronically low retention rates seen inengineering programs across the country [1]. Danowitz and Beddoes, for example, haveexamined
3Dmodels are intended to aid in the estimation of the mussel’s configuration once they are underwater. This is to help build adataset that can be used for training AI-based recognition models. The system proposed in this article enables the generationof photorealistic 3D models using an commercially available ESP32 camera connected microcontroller developed by Espressif[11] and a rotating stage. We made use of state-of-the-art 3D reconstruction tools known as Neural Radiance Fields (NeRF)[26] to build the models. Fig. 1 provides an overview of our system components and the preliminary outcomes from thephotorealistic 3D model. We worked on making this platform accessible to STEM enthusiasts and the K-12 community in particular by making
the basis forMastery Learning, developed by Bloom [1]. We first present Carroll’s theory and MasteryLearning. We then discuss examples of Mastery Learning in engineering education, and theguidance Carroll’s theory provides. We conclude with results of the author's application ofMastery Learning in two engineering classes at Oral Roberts University.Mastery learning, developed by Bloom [1], has shown great promise in encouraging students tolearn, enabling a large number of students to perform at a high level, and enabling students totruly learn the fundamentals of a subject. In mastery learning students are given multipleopportunities to demonstrate mastery of course concepts, with feedback and opportunity forimprovement, which enables a large
first year of studies. This perception can lead to a lack of engagement with courses theydeem irrelevant to their chosen fields. The issue is exacerbated when course assignments focusnarrowly on specific concepts, without demonstrating real-world relevance or interdisciplinaryconnections. As a result, students may struggle to appreciate the broader applicability of thesefoundational topics taught in various courses [1]. Perhaps later as students progress in theirstudies or when they transition into professional roles, they often realize how related theconcepts they learned truly are.Research shows that students learn better when they can see clear, real-world connections amongthe topics they study [2]. However, creating strong links between
assessment ineducation, largely due to their efficiency at administering at scale. However, the implementationof these examinations brings several important concerns to light, revealing that they may not bean accurate or effective measure of a student’s knowledge and/or capabilities [1]. High-stakesexams often focus on a narrow range of knowledge and skills, primarily emphasizing rotememorization and recall rather than deeper understanding and application of concepts. Thisapproach can lead to superficial learning, where students prioritize short-term retention ofinformation over long-term comprehension [2]. Furthermore, in electrical engineering, wherepractical application and problem-solving are crucial, such assessments fail to capture a
Transforms, Filters, Fourier Series and Fourier Transforms, and two-portcircuits. The process of solving problems generally involves following a structured sequence ofsteps. Using Laplace Transforms as an example, students usually need to go through thefollowing steps to solve a problem: 1. Examine the circuit diagram and identify the circuit elements, and note the initial conditions. 2. Convert the circuit to the s-domain using Laplace Transforms by replacing all time- domain elements with their Laplace equivalents, and transform time-domain sources into the s-domain. 3. Apply circuit analysis methods correctly, such as Ohm's Law, Kirchhoff’s Voltage and Current Laws, Node Voltage, Mesh Current Analysis, Thevenin/Norton
goals but also effectively prepare students for successfulcareers in their chosen fields.IntroductionABET accreditation [1] is a mark of distinction and quality assurance for programs in appliedscience, computing, engineering, and engineering technology. It signifies that a program meetsthe rigorous standards set by ABET, ensuring that graduates are prepared to enter theirprofessions with the skills and knowledge needed to succeed. ABET accreditation is globallyrecognized as a symbol of quality in technical education.ABET SOs are specific, measurable statements that describe what students are expected to knowand be able to do by the time they graduate from an ABET-accredited program. These outcomescover a broad range of skills, including
involve a type of active learning, whichaims to engage the students in their learning experience rather than being the silent audience inthe classroom. Flipped, problem-based, and collaborative learning are examples of activelearning pedagogy that aim to increase the students’ engagement in their education experiences,knowledge retention, and information applications. These pedagogy approaches also enhance thestudents’ critical thinking, investigation, verbal communication, and teamworking skills (e.g.,[1]—[4]).Whereas these active-learning pedagogical methods and approaches have addressed many of thechallenges in STEM educations, they may not have been able to address additional challengesthat are unique to electrical and computer engineering
assistants, and mentoring of under-represented students in ECE. ©American Society for Engineering Education, 2025 WIP: Gamification as an Engagement Tool in ECE CoursesIntroductionThe current cohort of college students prefers visual and interactive learning environments [1, 2]and enjoys frequent and short educational stimuli. As a result, educators are exploring theintegration of gamification principles into classrooms [3–5]. Particularly in engineering courses,abstract concepts often pose challenges for students’ understanding. Gamification offers anapproach to turning traditional, passive learning styles into more interactive and dynamic learningexperiences [6–8]. Gamification reduces the personal
electrical circuit course forsophomore level mechanical engineering students with the required prerequisites of calculus andphysics [1-2]. The course has a weekly structure of a 2-hour lecture, 2-hour lab, for a 3-creditcourse. Since this is the only electrical circuits class in the mechanical engineering curriculum, awider set of topics are covered, including DC and AC circuits with resistors, capacitors, andinductors, as well as analog and digital electronics, including some digital logic.The course has two learning objectives. Students who pass this course will have demonstrated theability to: • Determine voltage, current and power in DC and AC electrical circuits. • Build electrical systems and test for function using laboratory
-efficient computing. ©American Society for Engineering Education, 2025 Toward a Fair and Unbiased Debugging Evaluation InstrumentIntroductionDebugging skills are critical to the semiconductor industry, as deficiencies can incur significantcosts. The unpredictable nature of debugging tasks has earned it the nickname “The ScheduleKiller” [1] with some electronics engineers spending up to 44% of their time on debugging [2].Despite the critical economic importance of this million-dollar question [3], undergraduate ECEcurricula often omit hardware debugging skills [4], [5]. Instead, it is left to develop indirectlythrough design projects and labs. To help fill this gap, we are developing a circuit debuggingcurriculum
features ofthe domain, and reduce floundering time on task [1]. Following this approach, traditionalengineering courses often guide students through structured instruction before attempting tosolve problems, which can limit opportunities for exploring the underlying complexity ofconcepts. In the recent decade and a half, however, DI was criticized for not scaffolding enoughactive learner engagement. This criticism was based on findings showing superior learningoutcomes for active learning in comparison to DI, especially in STEM fields (Science,Technology, Engineering, and Mathematics; [2]–[4]).One way to implement active learning in the classroom is using Problem-solving beforeInstruction (PS-I). PS-I is a pedagogical approach in which students
criticalto developing broad, long-term technical literacy.IntroductionTechnical literacy is essential for modern careers and informed citizenship in the 21st century[1]. While many undergraduate programs require technical elective courses in science andengineering [1], [2], [3], [4], few studies have examined the long-term development of studentattitudes, such as self-efficacy and identity, regarding engineering in non-major populations [4],[5]. Non-engineering graduates must be technically savvy in today’s workplace. Therefore,students outside of the engineering disciplines should be able to develop technical skills withoutthe traditional barriers of calculus and physics that gatekeep the engineering major at theuniversity level. Our course 18-095