mechanisms.AcknowledgmentsThis work was funded by the National Science Foundation (NSF) under awards 2302833,2244119, and 220460, and the Engineering Information Foundation (EIF) under award EIF24.03.Any opinions, findings, or conclusions found in this paper are those of the author and do notnecessarily reflect the views of the sponsors.References[1] M. Hovanec, P. Korba, M. Vencel, & S. Al-Rabeei, Simulating a digital factory and improving production efficiency by using virtual reality technology Applied Sciences, 13(8), 5118, 2023.[2] A. A. Malik, T. Masood, & A. Bilberg, Virtual reality in manufacturing: immersive and collaborative artificial-reality in design of human-robot workspace International Journal of Computer Integrated Manufacturing, 33
Assessment – three interdisciplinary programming projects.Rather than relying solely on final grades, we examined project-based assessments as a proxy forcritical thinking development. While direct critical thinking evaluations were not conducted,student reflections and instructor observations indicated improved problem-solving engagement.Future iterations will incorporate validated assessment tools such as pre/post-tests or structuredrubrics for critical thinking evaluation. 3. Sample Course ExercisesSeveral exercises are taught in the class to demonstrate and stress the importance of engineeringproblem solving integrated with object-oriented features of C++. Some of them are
Conference,where the authors introduced the concept of cognitive algorithms as a form of intellectualproperty that belongs to the artist and is not currently protected by copyright law [13]. The panelfurther explained how a systems approach differentiates the traditional value stream (TVS) fromthe algorithmic value stream (AVS) and how current law only protects the former and not thelatter, partly because AVS had not previously been defined. Artists were then invited to completean online questionnaire designed to help them formulate a way to document the processes theyuse to develop their patterns of creativity. The survey is composed of seventeen questions aimedat guiding participants to reflect on their personal histories as artists and articulate
the ability of the technology to accuratelyreplicate human speech was clearly reflected in the student feedback. Utilizing this technologyproved to be an effective way to engage Gen Z students and provides engineering educators anew strategy that can be integrated into their current pedagogy.Faculty Survey ResultsPreliminary survey data was collected from a diverse group of higher education engineeringfaculty (n=12), who provided a range of perspectives. As part of the survey, faculty were askedto rate their comfort level with using AI tools specifically related to their role as an educator on ascale of 1-5, with 5 being the most comfortable. Faculty were also asked to approximate howoften they are using these tools. The results are shown in
synthesis. Finally, throughout the research process, the authors experienced a series of challenges that were reflective of their roles as managers rather than practitioner librarians. All authors struggled to open their Zotero libraries and experienced challenges in sharing the citations among the group of co-authors. While performing searches in literature databases, all the authors learned that the interfaces of the resources had changed since they had last performed any significant searches. They all had to consult with interface help information and revise their searches to meet the requirements of each interface. This served as a reminder to the authors that the tools and skills related to literature
retention. Furthermore,technological advancements and industry trends could also be listed, as they play a pivotal role,particularly for Gen Z international Asian STEM professionals, who seek innovative, impactful,and dynamic career paths in the U.S. market.Conversely, the factors ranked lowest in priority varied between the two cohorts. Cohort 1participants identified continuous learning and job security as the least significant, whereasCohort 2 listed recognition, growth, job location, work environment, and the risks associatedwith starting over. These differences likely reflect the diverse backgrounds and unique lifeexperiences of the participants. Figures 2 illustrate participant responses, as outlined in Tables 5and 6, to the question, “What
reflect on their experiences. Amy has contributed to the development of an interdisciplinary grand challenges focused course and introduction to engineering course in both in-person and online (MOOC) formats at ASU. She is also actively involved in the ASU Kern project and Kern Entrepreneurial Engineering Network (KEEN), focused on students’ development of entrepreneurial mindset. Amy received the national 2019 KEEN Rising Star award from KEEN for her efforts in encouraging students in developing an entrepreneurial mindset. She is also a member of the current interim Executive Committee for the international GCSP Network, and mentors schools to develop GCSPs as part of the GCSP New Programs committee.Sue Ellen
individual question performance, while the averagepre-survey correct response was 35%, indicating that the knowledge questions were not easilyanswered prior to content delivery. However, certain questions, such as Q9 in both UniversityA’s Intro to Geotechnical Engineering class and University B’s Intro to Geotechnical Applicationsclass, showed limited improvements, indicating potential areas for targeted instructionalenhancement or the need to review the phrasing of this question. Students in University A’scourses consistently outperformed students in University B’s course in both pre-survey andpost-survey results, which may reflect differences in instructional methods, course resources, orstudent preparedness, and reminds us of the importance of
of practice should offer professional development programs to help educators stay current with AI trends and pedagogical approaches. CIT-E is well situated to address this need. 6. Engage Industry Partners in Curriculum Design - Collaboration with industry professionals can ensure that academic programs align with evolving workforce needs. Industry input can inform curriculum updates, internships, and capstone projects that reflect current and future AI applications in CEE practice.References[1] P. Lu, S. Chen, and Y. Zheng, “Artificial Intelligence in Civil Engineering,” Math. Probl. Eng., vol. 2012, no. 1, p. 145974, Jan. 2012, doi: 10.1155/2012/145974.[2] J. Duan, S. Yu, H. L. Tan, H. Zhu, and C. Tan, “A
disagree to 4 = strongly agree). Fig. 3 shows thefeedback on various aspects of the course. Overall, students found the course valuable (M=3.5)and well-organized (M=3.67). They appreciated the clarity and timeliness of information provided(M=3.83). Remote instructions were helpful (M=3.67), and students received support when needed(M=3.83). They also appreciate the instructor’s support. Hands-on activities seemed to be helpful(M=3.67) in understanding quantum computing and cybersecurity.3.5 Attitudes towards the disciplineThe student responses reflect a strong positive attitude towards the discipline, as measured on ascale from 1 to 4 (1 = strongly disagree to 4 = strongly agree) (Fig.4). Most students expressed adesire for future careers
going on in cybersecurity, and those can be super helpful. Sometimes, they even line upwith what we’re learning in class, so it all connects and adds to what I’m already studying. So yeah, forme, it’s research first and then educational videos. Those are the two things that really impact my interest.The follow-up qualitative results agreed with the quantitative results with furthermore justificationof the participants’ written survey responses. One major take-away is the reflection of theparticipants’ mixed experiences based on pre-, during, and post-Covid 19 pandemic experienceswith online learning. It was evident that the traditional in-class learning strategies were adopted tomatch with online learning as much as possible. Given that the
their academic journey. These projects foster self-directed learning, encouraging students to develop problem-solving skills, troubleshooting issues,research solutions independently, and interpret complex or poorly documented materials.Feedback from students and their final presentations reflect a strong sense of accomplishment andincreased confidence in their engineering capabilities. Senior capstone design projects continue toserve as a vital component of electrical engineering education and align with the standardsestablished by the Accreditation Board for Engineering and Technology (ABET). Overall,students’ feedback and their final project presentation indicate that they have pride in their projectaccomplishments and have gained confidence
students to leave commentsand no prompt was provided. In Statics & Strength of Materials and in Structural Analysis, thissurvey was embedded in an end-of-course survey. The comment section of that survey promptedstudents to ask questions regarding the final exam review and a self-reflection about how wellthey learned the course material versus their initial expectations. The comments received inthose three Engineering Technology classes did not address the Classicle game.Limitations This data relies on the self-reported measures of the Likert scale survey. At this point a limitedsample size was taken, but a large set of data would be desirable. Like many activities, theinstructor’s role in the activity could influence the students’ overall
OtherFigure 1. Total number of specializations offered (minor, concentration, other) grouped by type.Chemical engineering specializations in a limited number of countries outside the United States(Great Britain, Ireland, Scandinavia, Australia, Canada), have also been surveyed [6]. Similar tothe US, a wide variety of specializations are offered. In general, these specializations are similarto those in the US. Bio-type are again extremely common with environmental and sustainabilityalso quite common. The biggest difference is in Great Britain and Ireland whereBusiness/Management, International Study, and Languages also show up frequently. Obviouslylocal concerns are also reflected in offering: fuel/energy (Denmark, Norway, Canada), pulp andpaper
or physics prerequisites, though students are taking calculus and linear algebra courses inparallel. In order to get to more interesting design and project-based problems we have to firstcover a few circuits basics, such as resistive circuits and voltage and current dividers. Next, webring up time-dependent circuits through R-C charging, which then naturally leads toapplications of NE555. In NE555 timer circuits, charging of R-C networks and use of voltagedividers are essential features.However, the math and physics required for explaining these topics is relatively straightforwardand can be built intuitively. This approach is reflected in the selection of lab topics which arethen followed by applications, such as using a timer IC NE555
INTERNSHIPSREADING FOR COMPREHENSION| ADVISINGALUMNI PANEL – CAREER READINESSGOAL SETTING AND WRAP UP/END OF THE SEMESTER CELEBRATIONCHART 2 - FALL SEMESTER/YEAR 1 – PROGRAM SESSIONSWELCOME BACK |REFLECTIONS |REVISIT GOAL-SETTING| RESUMES & LINKEDINWHAT IS COMMUNITY-CENTERED SERVICE? | CAPSTONE IMPACT PROJECT PREPADVISING| GETTING PREPARED FOR INTERNSHIPS | MOCK INTERVIEWSCAPSTONE IMPACT PROJECT PRESENTATIONS | GUEST SPEAKERALUMNI PANEL TOPIC: CAREER READINESSGOAL SETTING | WRAP-UP/YEAR-END CELEBRATIONMethodologyUpon receiving approval from the Internal Review Board (IRB), participants were invited tocomplete a consent form to join the preliminary study of the intervention program. Due to thetiming of the study the researchers were not able to collect
experience level with Verilog or VHDL? 3 What is your experience level with the Xcelium™ Simulator tool? 4 What is your experience level with the IMC code coverage analysis tool? 5 How familiar are you with floor planning, placement, and routing?The outcomes of these surveys were graphically represented to show a clear comparison of theskill levels before and after the course. Figure 17 illustrates these changes, demonstratingsignificant student advancements in each area. Figure 17: Comparisons of Key Skills Reflected on the Survey Data Before and After the Cadence Modules class.The above pre- and post-survey comparisons indicate significant gains in crucial technical skillsthrough the
, and J. Kadlowec, “Gender across engineering majors,” in ASEE Annual Conference & Exposition, Honolulu, HI, 2007, p. 12.776.1-12.776.14.17.Morocz, R. J., Levy, B., Forest, C., Nagel, R. L., Newstetter, W. C., Talley, K. G., & Linsey, J. S. (2016, June). Relating student participation in university maker spaces to their engineering design self-efficacy. In 2016 ASEE Annual Conference & Exposition.18.Andrews, M. E., Borrego, M., & Boklage, A. (2021). Self-efficacy and belonging: The impact of a university makerspace. International Journal of STEM Education, 8, 1-18.19.Brubaker, E. R., Kohn, M., & Sheppard, S. (2019). Comparing outcomes of introductory makerspace courses: The roles of reflection and multi-age
improve my skills and knowledge" (Increase from 4.72 to 4.87), reflecting a 3% increase in this category. • "I can influence and change my development in general" (Increase from 4.52 to 4.77), showing a 5.5% improvement. • "I can change my skills and knowledge through practice" (Increase from 4.72 to 4.87), demonstrating a 3.78% positive change.One area of concern is the slight decline in responses to "I see learning as my goal," whichwarrants future research should help us further understand the reasons for this decline. Knowingthese reasons may assist in designing interventions that lead to better, more consistent outcomesacross all domains of a growth mindset. The decline may result from students pursuing
]. To counter suchoccurrences, surveys like the LSC-R can be adapted to ensure reflection of context and supportof participants. For example, Humphreys and colleagues [25] employed direct interviews withColombian women whose feedback about the stressful experiences in their lives enabledadaptation of the LSC-R. Other successful validations of the LSC-R adapted to different contextsinclude psychiatric outpatients with anxiety or depressive disorders [23] and assessing prevalenceof stressful life events among individuals with stimulant use disorders [26]. However, none ofthese studies have used the LSC-R in an engineering context.To effectively deploy a modified instrument and achieve the same psychometric goal, there isneed to ensure that the
it an empowering and accessible tool for engineering students.7 Reflections on Impact and Next StepsCalculus for the Modern Engineer seeks to align calculus education with thecomputational and practical needs of contemporary engineering. By rethinking thetraditional structure and emphasizing project-based learning, computation, and real-worldapplications, this course offers a blueprint for modernizing the mathematics curriculum forengineers. The successful pilot has shown that integrating computational tools like Juliawith rigorous mathematical principles not only deepens student understanding but alsoenhances their ability to apply calculus meaningfully in engineering contexts. Studentevaluations indicate strong engagement, with 85
with adiscussion on the implications of the findings for the program and for future research.IntroductionMental health in higher education has gained significant relevance worldwide in recent years[1]. This interest is reflected in the formulation of educational policies as well as in thedevelopment of teaching methodologies and instructional practices [2]. Mental health has agreat influence on the ability to learn [3][4]. In the case of Engineering, where high levels ofstress and anxiety exist [5], it has been reported that mental health issues are usuallynormalized, and that these issues could be exacerbated when professors or teaching assistantsdo not sympathize with the burdens that students face [6].Motivated by the need for supporting
beyond their comfort zone.AI models used in task distribution may unintentionally introduce biases, favoring certain skillsets or work styles while marginalizing others. If the model’s training data reflects existingbiases in engineering education—such as the underrepresentation of certain groups in leadershiproles—it may reinforce rather than challenge these disparities. Additionally, the AI may struggleto fairly distribute tasks among students with varying levels of prior experience, potentiallyleading to unequal learning opportunities. To address these concerns, transparency in AIdecision-making is crucial, and students should have control over final task assignments toensure fair and equitable participation.By introducing AI-driven task
, whereas the steel bridge team was predominantly male, with only 18% femalerepresentation.ConclusionsThis study demonstrates that participation in engineering competition teams, specificallyconcrete canoe and steel bridge, significantly contributes to the development of students'engineering identity. Using a framework centered on recognition, interest, andperformance/competence, survey results showed clear benefits in these areas following teaminvolvement. Specifically:Recognition: Students, specifically juniors, reported increased recognition as engineers frompeers and particularly instructors after joining competition teams. This was reflected in astronger sense of validation and acknowledgment of their engineering capabilities.Interest: While
stakeholders in the decision-making process using MOcan lead to better alignment of project goals, ensuring that the final outcomes reflect the interestsof all parties involved. This collaborative approach not only improves project outcomes but alsofosters a sense of ownership and commitment among stakeholders.Transformative Use of ML In CMThe transformative field within artificial intelligence, known as ML, enables systems to learnfrom data and improve autonomously. The growth of ML is driven by the availability of largedatasets and advancements in algorithms [18]. ML is increasingly being integrated into CM toenhance efficiency, accuracy, and data-driven decision-making processes. This integrationaddresses challenges of efficiency, schedule
thisframework, as it does still closely reflect the holistic experiences of engineering students ingeneral. The dimensions and their learning experiences are shown below in Figure 1. Professional Personal Academic industry internships familial support institutional culture and co-ops peers within research internships program design engineering peers outside co-curricular activities
record.ConclusionLearning to teach and learning to lead a teaching team are ongoing, iterative processes thatrequire practice and reflection, and, most importantly, experience. In the process of developingthat experience, it is still important to make sure that your course is able to function andmembers of your team are able to do their jobs to the best of their current abilities. In this paperwe have provided considerations for instructors as they work with their teaching teams to addresspractical concerns for running their course. These include understanding the background andmotivations of your teaching team, establishing clear expectations, and providing space tonavigate dissent. While not an exhaustive list of concerns, we have focused on these areas
outcomes demonstrates that one major potential pitfall is thatsurvey questions ”may have lacked flexibility and appropriate focus” [21]. AIM feedback allowsfor targeted, specific questions based on the specific course. This may involve probing studentsfor a numerical response on some scale about specific aspects of the semester. This can also allowfor self-reflection by the students, for example, asking if quizzes are helpful for reinforcing theirown knowledge. Structuring the feedback in this fashion can give instructors valuable insights onstudent learning outcomes that would otherwise be unavailable, especially during end-of-semestersurveys. For open-ended style questions, it is beneficial to explore various levels in Bloom’s taxonomy.Rather
who completed the final project,as a summative measure at the end of the course. All students at Mudd and OSU, 87 totalstudents, successfully completed their final projects.Student comments also qualitatively reflected how much they met the student learning outcomes.Many students stated how it was exciting to work on a current processor and to understand thefine details of its design. Some specific student comments were:“It was a challenge to work with so much code, but so rewarding to see Linux boot messagescome across the screen.”“Challenging labs, but the professor was dedicated to helping us learn the how the load/storeunit worked.”“Lots of work for the project but very cool and interesting. Homework is the right amount to helpwith the
termsincremental changes to our original blueprint. Lastly, we share of student reflections and course learning outcomes.results of student surveys on individual topics within thecourses to better understand their experience with the delivery B. CS II Blueprints Description and Reviewof blueprint materials. We plan to write a paper that describes the details of In our analysis of grades, we found that performance be- the CS II blueprint that we have built, which follows atween genders has continued to be equal. Comparing changes similar pedagogical approach to the CS I blueprint. The CSin performance between race/ethnicity groups, we saw that II