many workplace plans and initiatives togrind to a screeching halt. This curriculum renewal initiative of the mechanical engineeringprogram at Ohio State University was no exception. Over the course of 2020-2022, slowprogress was made on writing specific program goals to match each of the six guiding areasdeveloped during the 2019 retreat. Next, progress was made on developing the student learningoutcomes that would comprise each program goal. Starting in 2022, the curriculum committeewas finally able to move the project off the back burner and work with more focus and purposeto build out the student proficiencies, which are the fine-grained skills that make up studentlearning objectives.By the beginning of the 2023-2024 academic year, the
details with peers, supervisors, reports, and clients[9], [21]. Engineers often rely on inscriptions: domain-specific sketches, figures, diagrams, and charts to think through and communicate their ideas,[22]and interpret noisy data in a way that allows them to productively progress in the project[9]. he oral assessments were designed to replicate authentic engineering practice. In the first oralTassessment, students had to draw a possible mold for the yogurt cup and use it to communicate design attributes. They also had to weigh the tradeoffs of different design decisions and make suggestions based on their understanding of process parameters, design attributes, and their own engineering
largedatasets generated by the built environment, including air quality, structural health, and energyconsumption data. The field has been further disrupted by the emergence of powerful generativeAI (GenAI) tools in the last few years, such as OpenAI’s GPT-4 which powers the popularChatGPT chatbot. These large language models can perform sophisticated tasks like interpretinglarge datasets, writing code for wrangling and analyzing data, brainstorming ideas, and explainingcomplex statistical and mathematical concepts in ways that closely mimic natural humanlanguage. In CEE education, GenAI holds the potential to make data analysis and programmingmore accessible to students who may lack a strong background or interest in these areas. However,this raises
applications. To evaluate the impact of the redesigned CS 101 course, a CS1assessment was developed to measure students’ understanding of programming fundamentals,pseudocode interpretation, and Python-specific skills. Future work will focus on incorporatinggroup activities into lab sessions, expanding mini-project offerings, and refining the assessmenttools to further align with the needs of engineering students.1 IntroductionIntroductory computer science (CS) courses, commonly known as CS1 [1], serve a critical role inequipping students with important computational skills, including error handling strategies [2, 3],code-writing proficiency and syntactic accuracy [4, 5], and the development of viable mentalmodels for problem-solving [6, 7, 8]. While
predefined outputs [42]. Unsupervisedlearning in education is used to group students by factors such as engagement and learningbehavior [43, 44, 45], academic performance and outcomes [46, 47], student reflections [48], andbehavioral states [49]. While not predicting success directly, these methods guide personalizedteaching strategies and targeted interventions.Generative AI - Focus of ApplicationStudent-Focused ApplicationsDespite concerns about the impact of ChatGPT on student learning, generative AI offers valuableopportunities in academia, including personalized learning paths [50, 51], peer collaboration [52],and additional tutoring support beyond classroom hours [53]. Leveraging these capabilities cancreate more dynamic and engaging
programs are a critical mechanism for enhancing teaching effectiveness(e.g., [9], [10], [11]). These programs aim to equip educators with the necessary skills andknowledge to improve their teaching methods, such as integrating technology, employinginnovative assessment strategies, and centering student learning [12]. Research indicates thatcomprehensive faculty development programs that include workshops, peer and studentfeedback, and communities of practice can significantly enhance faculty teaching abilities andstudent learning experiences [13]. In STEM, these programs offer faculty members theopportunity to engage with contemporary pedagogical theories and practices, participate in amulti-disciplinary learning community, practice active
formidable communicative, embodied resources forgrounding principles in STEM. Discussing torsion, a student may enact angular deformation bygesturally communicating their emerging understanding to peers (see Figure 1). Gestures canindicate a students’ reasoning processes as sensorimotor activity is engaged in problem solvingand analysis [7; see Figure 1]. In engineering, students and instructors often produce gestures whilereasoning about physical and mathematical phenomena [9] and carry nonverbal information thatcomplements verbal reasonings [10]. Grondin and colleagues [10] catalogued the gestures engineering students produced in anengineering lab as they mechanically reasoned about the concept of torsion. These gestures oftendepicted the
tryexperimenting with other AI-powered techniques that are likely to become more common inengineering education and higher education at large.IntroductionThe rise of ChatGPT, and other generative AI tools, has led to a number of debates in educationas to what this means for teaching and learning. From early on in its release, multiple newsarticles point out the many ways students are using it in classes and how instructors have had toadapt—from changing how and where students write drafts or shifting to oral exams [1], tofocusing on thinking processes or return to pen and paper [2]—and the debate around its use inhigher education has intensified with continued uncertainty. The Digital Education Council(DEC) Global AI Student Survey, which ran in 2024
and identifying as people of color. This paper attempts to shedlight in this area.Conceptual FrameworksOur conceptual framework is underpinned by the hidden curriculum and funds of knowledgetheories with intersectionality to elevate systems-driven implications.Hidden curriculum. Villanueva et al. write “Within educational and professional environmentsand settings, individuals don’t just learn ‘what is formally being presented . . . but alsoaccumulate other hidden lessons in the process’ [27, p. 1550]. The hidden curriculum inengineering is likely a significant factor in enculturating and socializing people into themeritocratic, hegemonic, and masculine norms of engineering [27]. Hidden curriculum duringgraduate education is receiving
(mean = 37.6) and post-measure (mean = 83.2) on a 100-point scale, a significant increase.Despite the large increase in self-efficacy, increases in self-reported identity as a “maker” or“engineer” did not achieve significance, whereas a small but significant increase in sense ofbelonging was observed. Students’ ability to successfully build a circuit with no assistance basedon its schematic in a lab practical exercise did not correlate with student-reported self-efficacy,suggesting that students may factor in social support from peers as part of their ability toapproach future electronics projects. This work provides insight into an understudied group inengineering education: non-majors in an elective course. This sort of outreach course is
meeting room, with moveable chairs and tables, a projector andFigure 1. The Bioengineering, Society & Policy lab at ASU screen, a large white board, and – importantly – a coffee machine and snacks. This space servesmany purposes: project meetings with colleagues and student researchers, a classroom (when classsizes are small), a venue for hosting faculty writing groups, occasionally a space for doing yoga.Having spent 10 years “alongside” BME colleagues [18], Author 2 has had many informal andlong-running conversations about the ups and downs of running a lab. Over the years, somecommon features across PIs and career stages seem
analysis [25, 26]. Specifically, we engaged insix phases of thematic analysis, including (1) data familiarization, (2) generating codes, (3)constructing themes, (4) reviewing themes, (5) defining themes, and (6) writing up the results toguide data analysis. We executed our analysis by reading through each semi-structured interviewtranscript and open-ended survey response and then rereading to identify quotes of interest. Next,we engaged in two rounds of coding using our conceptual framework (e.g., the ECSJ pillars) as apriori codes. We used thematic analysis as a guide rather than a prescriptive method. Initialcodes and transcript quotes were documented using a spreadsheet software program individually.Then, we discussed them through peer
policy for AI, it is relevant to share the boundaries bywhich this course approached using AI on assignments. In line with the university and departmentpolicies, this course allowed AI on homework and laboratory assignments while requiringstudents to document its usage consistent with receiving help from another resource (such as helpfrom a classmate). Each assignment type has unique limitations. Quizzes and tests, for instance,are individual effort so no outside help is authorized. Homework and laboratory write upsauthorize help from other students, but the work must be primarily that of the submitter. Thus werequire students to specifically state what they received help on for a given problem (e.g. helpwith a particular stein a problem, how to
. Additionally, Dr. Buckley has authored and co-authored several peer-reviewed conference and journal papers, contributing to research in pedagogy.Bogdan Carbunar, Florida International University Bogdan Carbunar is an Associate Professor in the Knight Foundation School of Computing and Information Sciences at FIU, and directs the Cyber Security and Privacy Research (CaSPR) Lab, where he develops secure and usable systems. His research interests are at the intersection of security, privacy, and distributed systems, where he derives novel insights through the use of machine learning, applied cryptography, and user studies. He holds a PhD in computer science from Purdue UniversityDr. Juan P Sotomayor, Florida International
tools, machining, circuits/breadboards/soldering,microcontrollers, and instrumentation (i.e., thermocouples, pressure transducers). Andprofessional skills or project experience with: report writing, oral presentations, statisticalanalysis of data, problem identification/problem formulation, creative ideation of designalternatives, project management tools (i.e., Gantt chart, Kanban board), research literaturereview, conflict resolution, time management, website creation. Within the pre/post programevaluation survey students about their interest in science and engineering, what they know aboutengineering careers, and if they see themselves pursuing engineering in school or jobs as seen inTable IV. A subset of survey items were repurposed from
Paper ID #48438Preparing Fab-skilled Engineers for the U.S. Chip Industry Through Hands-onIntegrated Circuit FabricationDr. Sandip Das, Kennesaw State University Sandip Das is an Associate Professor of Electrical and Computer Engineering at Kennesaw State University, GA, USA. Dr. Das received his Ph.D. in Electrical Engineering from University of South Carolina, Columbia, in 2014. He has 15+ years of research experience in semiconductor materials and devices. Dr. Das has authored more than 35 peer-reviewed journal articles and conference proceedings, and has authored two book chapters. He has served as a PI/Co-PI for various
class upbringing as well as a decadeworking with community groups in northern Haiti on ecological sanitation projects. As a white,cisgender, straight-presenting, US trained engineering professor with the associated privilegesafforded and potential biases, she is working to learn from colleagues and students holding otherintersectional identities about their experience of engineering culture in an effort to expand bothits welcome and self-critique. Her motivation for creating the class was to create space fordiscussion, reflection, and peer to peer co-learning around engineering and social justice issues -something that would have helped her thrive as a female engineering student. 3. Course Description Following a faculty learning circle
groups showed increased curiosity in Faith & Ethics and Aesthetics & Creativity.While engineering students maintained higher overall curiosity in Science & Problem Solvingcompared to their peers, and non-engineering students showed higher curiosity in Diversity & TheCommon Good, both groups demonstrated similar growth patterns in humanities-oriented domains.This suggests that while students may enter college thinking they are primarily curious aboutspecific disciplinary interests, their intellectual curiosity can expand into new domains during theirfirst semester. 1.8 Non-EGR Students - Start of Term
racial diversity decreases, and thefact that between high school and graduate school or the profession the racial diversity of theengineering field decreases [4], then we can presume that undergraduate engineering educationcontributes to racial inequity. Although many aspects coincide within undergraduate engineeringeducation, including advising, finances, curriculum, pedagogy, grading, peer groups, etc., we canpresume that classroom practice constitutes a bulk of student lives and therefore is a primaryplace we may expect to find mechanisms of racial inequity.Methodologically, classroom observations through ethnography or video research are theprimary tools for investigating classroom practice and interaction as mechanisms of inequity.While
-economic disparities, inadequate K-12 preparation, and social isolation[8-9]. Studies have shown that these students often experience lower self-efficacy and a weakersense of belonging, which can negatively impact their persistence in engineering programs [10-11]. According to researchers, well-structured first-year seminar courses permit students toexperience a better transition from high school to college, understanding the new expectationsand work demands, developing time-management and study skills, particularly for students atrisk [12]. Besides, small group seminars facilitate the interaction with faculty and peers creatinga community of support leading to a better outcome of persistence and performance [13]. First-year seminars offer also an
-projects were designed to re-inforce active learning, enabling students to engage with the material through practical exercises.For instance, during the week on circuits and sensors, students built a light-dependent circuit. Thisapproach not only deepened students’ understanding of the current week’s IoT concept but also in-troduced skills that would be valuable in their semester projects and future personal IoT endeavors.A further example of a mini-project involved learning to read and write digital and analog valuesin a microcontroller, demonstrating the practical application of IoT concepts.This was a project-based class, but a few knowledge quizzes were given to test certain concepts,such as definitions, formulas, or engineering scenarios
-generation, LGBTQ, and special needs backgrounds [15]. These groups reportfeeling invisible and questioned about their scientific competencies compared to their White andAsian peers [15], [32]. Latine Doctoral Students and Cross-Cultural Mentoring Relationships in STEM FieldsAmong the full-time STEM doctoral program enrollment in the US context, Latine students, whoare citizens or permanent residents, constitute only about 6.9%, despite representing the nation’slargest marginalized group (19.5%) [5], [17], [25]. Parallel to the lack of diversity in the STEMstudent body, the percentage of Latine faculty members in the US higher education system isreported to be around 6% [26]. This number is even more drastically scarce within theengineering and
’ interests and can lead to an increase in student engagementand agency.Recent innovations aim to address these limitations by integrating ML and NLP technologies intoautograding systems. These advancements enable tools to assess nuanced aspects of code, suchas design patterns, code readability, and logical structure [4]. For instance, ML models can ana-lyze code comments and programming styles to provide more personalized and detailed feedback.These systems balance the efficiency of automation with the depth of personalized evaluation,particularly for open-ended and creative assignments [5]. Furthermore, peer grading systems andML-based similarity detection are being explored to handle diverse outputs in open-ended projects.These innovations hold
meaningfully in civicsolving, while low numeracy skills have also become more and everyday life.prevalent among working-age adults [18]. These deficiencieshinder individual achievement and limit workforce F. Addressing the Challenges: the Path Forwardadaptability. Several key factors contribute to these While broad, systemic reforms to strengthen criticalchallenges: thinking education are urgently needed, history shows that • Curriculum Constraints: Widespread reliance on large-scale educational changes—whether in math, reading, rigid curricula, high-stakes tests, and lecture-based writing, or critical thinking—are difficult
same access to STEM as their typically developing peers,specifically, as discussed in this paper, access to computational thinking and robotics. We reporton the co-design of technologies for Opportunities for Robotics, Building, and InnovativeTechnology (ORBIT), an educational robotics program for autistic middle school studentsdesigned to integrate learning computational thinking (CT) practices with executive functioning(EF) skills. The program includes a computer coding component and several student-facingscaffolds. We are developing this program through a research-practice partnership betweenresearchers at a private northeastern university and practitioners at a local public school within asub-separate, special education program designed
equitable teaching practices and encouraged student agency to ensure positive learning outcomes. Their first year of PhD research focused on undergraduate student perceptions of social responsibility in STEMM, with special emphasis on science communication and policy advocacy, as well as the intersection of institutional culture and transformational change towards cultivating more inclusive and equitable access for underrepresented STEMM students. They are currently exploring undergraduate perceptions of STEM mentorship within student organizations and near-peer mentorship between undergraduate student mentors and K-12 student mentees within educational out-of-school time STEM programs. Outside of their research, they
design firsthand, fostering a deeperappreciation for the importance of integrating diverse perspectives and disciplines inproblem-solving.This paper describes the redesign of the Introduction to Engineering Design course for Fall 2024.The structure and rationale of the course design is discussed, including the integration of theEOP Framework and an assessment method based on writing engineering memos. Results of anIRB approved (Protocol 2232887-1) survey taken at the beginning and end of the semester arealso included. The goal of the survey was to assess how the students’ understanding of theinterconnected nature of design decisions, especially in terms of how sustainability encompassesmore than just environmental concerns, evolved throughout
, collaborating/working on a team 6 research Conducting theory, practice, critiquing, writing, presenting 18 Development of content, assessment, pedagogy, EngE teaching research driven teaching 16 other Did not fit into an identified category 7Of the 67 POs, 34 (50%) focused on research or teaching. 21 POs (31%) were in the categoriesof career, DEI, engineering expertise, engineering education issues, and professionaldevelopment.The remaining 13 POs (19%) were not neatly categorized, as we disagreed on the category (6),or the POs did not fit into an identified
included their research experience, fieldof study, or the life events that led them to this program. Student choices of topic were diverse,and some created multiple story spines. Most students wrote about the life events that led them tothis program, with many others writing additional story lines about their love of science, thesummer research experience, or aspects of their research project.The incubators were intended to foster a low-stake space for experimenting and practicing.Students had fun creating narratives about familiar topics, while also practicing and developingskills for effective communication. Additionally, students shared their work with each otherduring the incubator sessions helping them to learn from everyone’s experiences