engineering experience. ©American Society for Engineering Education, 2025 WIP: Mapping Faculty Opinions of Student Skills Development in a Large- scale First-Year Design ProgramIntroductionThis work-in-progress examines the differing perspectives of faculty teaching in the first-yeardesign program at a mid-sized private R1 university. Findings here provide the groundwork of alarger study aiming to address a critical gap identified by the National Science Foundation'sDivision of Engineering Education and Centers [1]: that while significant strides have been madein both first-year engineering education and senior capstone experiences, many essentialprofessional competencies introduced in first-year courses
actually trying toaccomplish with and for these students? This paper describes the 6-step process that was used toinvestigate this issue (1. Team-building, 2. Goal Exploration, 3. Curricular Definition, 4.Interventions and Innovations, 5. Outcomes, and 6. Conclusions). Starting with assembling ateam of invested faculty, we focused on determining the critical outcomes for the first-year usingthe collective wisdom of the group leavened with key findings from the relevant literature. Withkey outcomes at the forefront, it became apparent that defining all the various steps and activitiesand estimating the impact of each on the proposed outcomes across a sprawling enterprise thatcommunally served six distinct program was a must. Key interventions and
co-author of four textbooks, including Digital Design and Computer Architecture: RISC-V Edition (2021, Morgan Kaufmann). Her research interests include computer architecture and applications of embedded systems and machine learning to biomedical engineering and robotics. ©American Society for Engineering Education, 2025 RISC-V System-on-Chip Design Textbook and Course Rose Thompson1, David Harris2, James E. Stine1, Sarah L. Harris3 1 Oklahoma State University, 2Harvey Mudd College, 3University of Nevada, Las VegasAbstractWe wrote a textbook, RISC-V System-on-Chip Design, to bridge the gap between learning aboutthe theory of processor, computer architecture, and system
consumerproduct applications focused on wellness, which may overlap with similar, but highly regulated medicaldevices. Offered since 2017, the course has impacted approximately 120 students. The course takes placeduring a 3-hour studio block which consists of lectures, individual skill-building activities, and groupproject work. Assessment of learning outcomes for accreditation, student feedback, and instructors'reflections are presented. The course's iterative history, curricular benefits, and best practices forimplementation are discussed. Commentary on syllabi, outcomes, logistics, COVID-19 adaptations, andonline resources provides insights for programs interested in replicating this innovative approach tobiomedical engineering education. 1
contributions received and published. Second, in the range of knowledgecontained in those contributions. Third, are the conflicts that such diversity creates. Ofparticular importance is the criticism that engineering students are technologically illiterate, aview that has not permeated ASEE more generally, which might be put down to thefractionalization of ASEE.While it is suggested that the Division should focus on education for technologicalcitizenship it is not suggested that any of its other activities should be discouraged.*cited from Bruce SeelyIntroductionRecent studies in the UK [1] and US [2] show that some problems of engineering educationre-appear as if they have never been considered before. Although the circumstances may havechanged, they
example of an aircraft tire change procedure.The specific questions guiding the study are: 1. How did each team member’s expertise shape the development of the 360-degree VR module? 2. What reflections emerged from the collaborative autoethnographic process? 3. What practical steps (how-to) are needed for effective 360-degree video production and implementation in training contexts?2. Background2.1 Aviation Training ChallengesAviation maintenance training faces well-documented constraints: high operational costs, tightsafety regulations, and limited aircraft availability for hands-on practice [4-6]. Traditionalmethods often rely on face-to-face instruction and supervised practice in hangars, but these canstrain resources when
vernaculardesign and endogenous practices that for centuries had nourished, for better or worse, the lives ofmillions throughout the centuries” (Escobar, 2012, p. 6). Moreover, Escobar observes that thestructures of unsustainability maintaining the “dominant ontology of devastation” must beconfronted in a world transformed by changing climate and the need for economic transitions.Escobar tracks activists, designers and scholars who are enacting a civilization model thatliberates Mother Earth and is built on relational ways of knowing, being and doing. A keyframework for design in his view is called autonomous design. As a design praxis withcommunities, autonomous design centers five principles, shortened here with emphasis by theauthor: 1. Every
selection, model design, model evaluation, thencommunicating results and proposing action. Given this structured approach to data science, it iscrucial to address how these principles can empower individuals, especially young learners, tonavigate a world increasingly shaped by data.Data science education and data literacy in today’s youth are important not only to create andmaintain a well-educated society, but also to combat the increasing issues of widespreadmisinformation, disinformation, misleading data, and privacy violations [1]. Incorporating datascience into K-12 education can equip students with the skills to critically analyze data, identifydiscrepancies, and avoid falling victim to misinformation and misleading data representations
entrepreneurs receive from individuals within their networks (e.g., emotional support,career advice, networking). Third, it investigates how these relationships and support types arelinked to outcomes, including psychosocial factors (e.g., entrepreneurial self-efficacy) andbusiness performance (e.g., future viability). By employing a quantitative approach and socialnetwork analysis, this research aims to offer a more nuanced understanding of how networkcomposition and support structures are differentially related to entrepreneurial outcomes for bothURM and non-URM entrepreneurs in tech fields.Our research is guided by the following questions: 1. Types of relationships: What types of individuals (e.g., industry experts, family) do URM
from AI – and discovered a bimodal distribution. Thus, weshow that the student body at Mines is polarized with respect to future impacts of GenAI on theengineering workforce and society, despite being increasingly willing to explore GenAI overtime. We discuss implications of these findings for future research and for integrating GenAI inengineering education.IntroductionRecent advancements in Generative Artificial Intelligence (GenAI), esp. large language models(LLMs) like ChatGPT, have significantly impacted both industry and educational sectors [1, 2].These models, equipped with sophisticated algorithms and trained on vast datasets, canunderstand and generate human-like text [3], expanding their use from simple text prediction tocomposing
individual engineering courses, such as Fluid Mechanics and Thermodynamics. c. Identify critical factors contributing to engagement and disengagement in online engineering courses. d. Evaluate the impact of VEs on cognitive, psycho-motor, and affective skills through project-based learning. e. Develop strategies to improve teaching practices and retention rates in ERAU’s engineering programs.2. ApproachThe research follows a three-phase approach:Phase 1- Development and Implementation of VEs: In the first phase, VEs are developed andintegrated into Fluid Mechanics courses to create immersive learning experiences. Preliminaryengagement data were collected to establish baseline metrics and identify patterns in studentinteraction
determine (1) whether studentengagement with UDL tools is self-informative and (2) to assess whether these interactions can beused to detect engagement changes. Two key UDL components are studied: (a) digital forms,which facilitate non-graded participation and formative feedback, and (b) multimedia tools thatprovide accessible, self-paced learning opportunities. Student interactions are analyzed usingauto-regressive models, including ARIMA, SARIMA, and advanced machine learning methodslike GRU and CatBoost. The study also employs Pruned Exact Linear Time (PELT) to detectsignificant engagement shifts. Findings suggest that student interaction data predicts futureengagement, with GRU performing best in minimizing absolute errors and ARIMA excelling
oncuriosity, connections, and creating value. These 3Cs are the tenets of the entrepreneurialmindset (EM), a mindset, or mental habits, necessary for engineers to excel at problemidentification, innovation, and value creation [1]. While motivation and autonomy might not bedirect facets of EML, they are linked with the 3Cs, and provide students the opportunity to takeownership of their learning. Furthermore, an entrepreneurial mindset (EM) instills in studentssuch attributes as uncertainty tolerance, opportunity recognition, and healthy competition [2].Research has shown that a student’s motivation in a given educational assessment directlyinfluences their creativity, as well as critical thinking skills [3]. Related to motivation, inclusiveclassroom
. His research goal is to promote engineering as a way to advance social justice causes. ©American Society for Engineering Education, 2025 Engineering Students’ Perceptions of the Dynamics between Students and Instructors: A Humanizing PerspectiveIntroduction Dynamics or interactions between students and instructors shape the learning experiencein engineering classrooms [1], [2]. Research has shown that such dynamics can lead to eitherpositive or unpleasant experiences, depending on how the interactions transpire in class. Inhigher education, such dynamics have shown to be shaped by many factors, which include thebanking and transactional nature of education [3], the chilly
Paper ID #48183GIFTS: Integrating Generative AI into First-Year Engineering Education:From Knowledge Acquisition and Arduino Projects to Defining AccessibilityProblems and SolutionsAnna Leyf Peirce Starling, University of Virginia Anna Leyf Peirce Starling (Leyf Starling) is a founding faculty member and current Director of the First Year Engineering Center at the University of Virginia. She is currently developing curriculum and teaching the Foundations of Engineering 1 and 2 courses as well as advising 1st year engineering students. Starling earned a BS in Mechanical Engineering (UVA ’03); enhanced that with a MAT in
nuanced understanding of how self-efficacy, outcome expectations, career interest, and career intention interact as central constructs[1], [2]. Brown and Lent’s framework [3] also emphasizes the role of personal factors, includingpersonality traits, prior experiences, gender, and race/ethnicity, alongside contextual factors,such as socioeconomic status and prior education, in shaping career and educational outcomes.These elements interact to shape self-efficacy beliefs, outcome expectations, and career interests,highlighting the complex influences that guide career development.In particular, Brown and Lent [3] highlight the critical role of gender in career outcomes, oftenmediated through social learning experiences. Gender-specific opportunities
in helpingstudents recognize their responsibility to create inclusive engineering solutions while developingspecific strategies for preventing, detecting, and mitigating bias in their future engineeringpractice.MotivationEngineering education plays a crucial role in shaping future professionals who will design anddevelop technologies that impact society. Students entering engineering programs often view thefield primarily through a technical lens, focusing on problem-solving and innovation withoutfully considering the social implications of design decisions [1]. However, research shows thatunconscious bias in design processes can lead to products that exclude or potentially harmcertain populations [2] [3]. For example, early automobile crash
solve a real, ill-structured engineeringproblem of reasonable complexity with a humanitarian aspect that required innovation andcreativity.IntroductionTraining students to become effective engineers is a very complex problem that continues toevolve and improve. One of the most important aspects of that training is teaching students howto design processes and equipment to meet client specifications. These projects incorporate manyaspects of actual engineering practice such as design, teamwork, verbal and writtencommunication, and project management. Gutiérrez-Berraondo et al. (2024) [1] wrote, “STEMhigher education faces the challenge of educating its students in top level skills such asabstraction, generalization and transfer required to solve
DesignIntroductionIt has been well documented that hands-on, project-based learning can benefit engineeringcourses [1, 2]. At the University of Denver, the first-year engineering courses have includeddesign projects for several years. These projects have varied from catapults to STEM basedpreschool toys to dog toys. The hands-on learning opportunities are ideal for first year studentsbecause often they are still in introductory math and science courses which can feel like they lackcontext for the greater engineering world. It is also a great opportunity to introduce students toadditional skills such as teamwork, communication, computer aided design, and the overallengineering design process.Recently our department has looked at adding components of human
connected to other contexts and disciplines.ObjectivesThis activity provides first-year engineering students with hands-on, scaffolded experiences thatintroduce them to the potential of generative AI without simply providing “the answers.” It offersthem a space to practice interacting with AI, including priming and prompt engineering forgenerative AI systems, in a low-stakes environment that supports productive client basedengineering design and prepares them for more advanced, in-depth applications of generative AIthroughout their later studies.The design activity is structured around the Stanford d-school’s Design Thinking Process [1],particularly focusing on empathy and iterative design (Figure 1). Figure 1. Stanford d.school
engineering courses, focusing on student success, retention, and fostering a welcoming community for incoming students. ©American Society for Engineering Education, 2025 GIFTS: Introduction of the Engineering Design Process in a First Year Multidisciplinary Course though use of Wind PowerIntroductionFirst-year engineering students seek hands-on learning experiences to introduce them to thefundamental tools they will use in their future careers. Previous research has also shown thatfirst-year design experiences can help support engineering identity formation and retention [1].At Kansas State University, the KidWind competition, a popular design challenge for teachingdesign and critical thinking
these escalating threats, straining the resources and capabilitiesof their existing cybersecurity teams and further underscoring the need for a skilled workforce.Yet, the cybersecurity industry is currently facing a significant skills gap; in 2024 there wasfound to be an estimated global shortage of 4.8 million cybersecurity professionals. Workforcegrowth has plateaued at around 5.5 million globally, while the skill gap widened by 19%compared to the previous year [1]. In the United States alone, the supply of cybersecurityprofessionals met only 83% of employer demand, leaving over 225,000 positions unfilled as ofJune 2024 [2, 3, 4].According to recent industry reports, professionals with the following technical and professionalskills are needed
belonging. The findings aim to uncover HBCUs' unique role in fosteringinclusive academic and social environments, especially for IGES. This research offers insights forimproving broadly the international student engagement, integration, and faculty support. Inaddition, the findings will contribute to broader discussions on diversity, inclusion, andintercultural relations in higher education.Key words: Inclusion, Higher Education, Engineering Graduate Students, Sense of Belonging.IntroductionA sense of belonging is widely recognized as a critical factor influencing graduate students’ overallsuccess, mental well-being, and academic resilience [1]-[3]. This sense of belonging can beparticularly significant for IGES, as they often navigate unique
has emerged as afundamental skill requisite for success in diverse academic and professional domains. However,the journey to mastering programming languages is often fraught with challenges, particularlyfor students encountering feelings of fear and intimidation. This paper endeavors to delve intothe complexities of addressing and overcoming these obstacles, thereby empowering students intheir pursuit of programming proficiency. The significance of programming proficiencytranscends disciplinary boundaries, encompassing fields ranging from computer science andengineering to data analysis and beyond. Rushkoff [1] contends that lacking an understanding ofdigital technology puts us at risk of being controlled by it. He asserts that programming
sub-branch of artificial intelligence that uses machinelearning. It allows machines to understand, analyze, and generate responses that are easy forhumans to understand. NLP already facilitates the interactions between our students and all sortsof artificial intelligence like chatbots (ChatGPT), smart assistants (Siri), and more. Calls formore integration of artificial intelligence into education grow louder by the day. For instance, aspecial committee was established in the US to make recommendations, including around AI ineducation [1]. Outside of academia, regular interaction with AI tools is becoming commonplacein industry. Scholars have already outlined a plethora of opportunities and concerns aroundapplying this technology in the
diverse set of identities and characteristics across the entire deck. Studentsdraw cards randomly and then complete the project or classroom activity with the person ontheir card as the intended user for their design. Initial student feedback suggests that using thiscard deck to complete their project increased students’ experience designing for persons unlikethemselves — a key element of the engineering profession.MotivationMany incoming first-year engineering students cite a desire to help people as one of the reasonsthey chose to major in an engineering discipline [1]. Additionally, first-year engineering coursesoften aim to introduce students to the idea of human-centered design. Teaching human-centereddesign in the first year takes on
questions: (1) When students talk about (local/global) energy systems, what do they concern themselves with? (2) What are students’ overarching narratives found orienting them to energy transitions?We situated this study in a crossdisciplinary undergraduate course on sustainable energies, co-taught bytwo faculty members, one in political science and one in mechanical engineering.BackgroundEnergy Education and Energy LiteracyEnergy is a key element of any engineering curriculum as well as a key element of society. Yet manystudents learn about the science of energy in largely technical, fragmented, and decontextualized waysthrough courses like introductory physics, thermodynamics, circuits, heat transfer, and so forth (Hoople
. ©American Society for Engineering Education, 2025 Change | Makers: What can come next in engineering design?IntroductionThere have been growing calls for engineers and engineering educators to take more completeresponsibility for their role in society as technological developers and technically literatemembers of society, the exclusivity of their practice, and the impact their work has on the worldboth socially and environmentally. These calls appear in various forms including SustainableDevelopment Goals (SDGs) [1], calls to action [2], and academic literature [3-5]. However,change in engineering often comes slowly. While some change has been seen, for example, insome engineering codes of ethics and graduate attributes, others have been
non-disclosureagreement. The purpose of this practice paper is to examine the impact of silencing with non-disclosure agreements, the current legal landscape, and the movements to end their misuse in USHigher Education, including NDAFreeCampus.Attorney Neil Mullin, who represented Gretchen Carlson in her lawsuit against Fox News, said“If you want to eradicate discrimination, harassment and sexual misconduct, you should let thelight of day shine” [1]. The quote is from an article in the Michigan Daily concerning the use ofNDAs by the University of Michigan [1]. The use of NDAs to silence survivors andwhistleblowers stops that light from shining and keeps stakeholders and communities in the dark.NDAs are properly used when they protect privacy
well as biology [1,2], students can feelunderqualified in the depth and breadth of topics, or ‘othered’ compared to their peers. This isoften observed especially in first-year students or those transferring from other fields [3,4].Introduction to Bioengineering (BIOE 120, Table 1) is a 1 credit hour course offered to non-bioengineering majors at the University of Illinois Urbana-Champaign. Students in this coursewish to learn more about the field yet come from a variety of backgrounds, resulting in differinglevels of knowledge and academic experience. As survey-style courses take a broad approachand often offer fewer credit hours, it can be difficult to teach technical concepts, especially tostudents who lack prerequisite courses [5,6