environmentalists, among others. Therefore, Colombia presents a test case of the scope and limits of globalization and of the efforts of various groups to resist it to bring more livable and dignified social and ecological models.[17]Hence, Colombia’s problematic history with the project of development makes it an ideallocation to reflect on the colonizing and perverse effects of this project, map possibilities forlocally led development, and for develop HE education and practices that could enhance LLD.PositionalityThe author of this paper was born and raised in Colombia until the age of 13 where I learn tovalue, understand, and work with people from the poorest sector of society even at a young age.He then moved to the US to finish high
safe space had to be created for everyone tobe in a position to trust another mentor/mentee again. In respect of the graduate student’s wishes,only the faculty story will be shared: “Mentoring for me had to be in the forefront […] my first Ph.D. mentoring experience ended up not being the best experience. Not necessarily because I was not committed to the mentoring relationship, but because the student was not. That led me to reflect on what mentoring means. It just so happened I was recruiting my second Ph.D. student at the time. They had just come out of a very difficult mentoring relationship with a previous advisor. Both of us were very hurt, scared, and confused. We weren't sure if this new
). This distribution highlightsthe general recognition among students of the importance of evaluating risks when using ordeveloping AI tools.Moreover, Figure 8b demonstrates a striking split in the student population in estimates ofP(doom). Among 664 responses, the mean estimate was 46.7 (SD = 28.0). However, the KDEplot highlights two prominent clusters, confirmed by a kurtosis test as another bimodaldistribution (Kurtosis = −12.9, p < 0.05), with one peak around 21.1, representing studentswho are relatively less concerned, and another at 60.8, reflecting higher concern. Separating thestudents into two subgroups delineated by the inflection point between these two peaks at 40,44% of the population (n = 290) estimated low P(doom) while the
transfer, with knowledge generally flowing unidirectionally from universities toexternal entities (Striukova & Rayna, 2015). This dynamic has created a clear division whereuniversities primarily generate knowledge, and industries focus more on implementinginnovation. However, many businesses, particularly in China, appear to in buildingindependent innovation capacity and in translating cutting-edge technologies fromuniversities directly into market applications. As a result, they tend to prioritize short-terminnovation activities, such as patenting and consulting, over long-term R&D initiatives,reflecting their limited capacity for industrial research (Chen & Liu, 2017).To address the challenges faced by businesses in adopting advanced
cited theories are constructivist in nature; that is, focused on how personal experiencesrelate to new knowledge in order to construct meaning [9, 22]. Robust VR design also incorporatescognitivist ideas including the cognitive theory of multimedia learning and cognitive load theory,which is particularly relevant for developers and discussed in a later subsection. For educators,constructivist theories such as experiential learning theory, situated learning theory, and guideddiscovery learning theory have great potential to be enhanced by VR.Experiential learning theory states that learning occurs through a cyclical process of concrete expe-riences, reflective observation, abstract conceptualization, and active experimentation [23]. Experi
,which revolved around three major themes.Seminar Impact 1: Engaging with CommunitiesAn area of emphasis across multiple seminars was the importance of building relationships withmembers of the community that a project is meant to serve. Those ideas resonated with the Fellows,who expressed that many of the seminars expanded their understanding of what it means to trulywork with communities in meaningful ways. The fellows’ reflections on the seminarsdemonstrated how they are moving beyond the basic view that working with communities isimportant, toward a deeper understanding of the complex dynamics that underlie any partnership: Sometimes, a new technology might sound good to the people who develop it because they have the data and
demonstrate the feasibility of the approach adopted butalso reveal critical areas for reflection:● Architectural decisions: FastAPI was wisely chosen for the backend because of its flexibility and ability to integrate with AI tools such as LangChain. However, reliance on a single language model (Llama3.2) limited customization capabilities compared to GPT-4, although it significantly reduced costs.● Comparison with previous work: In contrast to the use of Petri-Net in earlier research, the use of LLMs allowed a more dynamic and adaptable analysis, especially in educational settings with high variability. However, the unstructured approach also entails a more significant effort to ensure consistency and validation of workflows.● Impact on
first-year course without expectedprerequisite knowledge, this content has been de-emphasized in subsequent offerings of the BSI.This is also reflected in the GPA data, as there was little disparity between BSI and generalstudent performance in the first-year programming course. Student comments on curriculumcontent highlight a need to further refine the continual improvement practices of the BSI contentto ensure the physics fundamentals taught align as content is updated within the first-yearengineering program.Student experiences within the BSI program varied depending on their cohort year. Studentscompleting the program during the COVID-19 pandemic indicated their learning experiencesuffered. Students completing more recent iterations of the
understandable by rising10th to 12th graders.” This comment reflected instructors’ efforts in designing and deliveringlectures appropriate for high school students; maybe the number of lecture slides and the lengthof presentations could be reduced based on student feedback. When asked about any recommended changes for future NSTI programs, a few studentsleft the question blank, indicating they were happy with the program and had no suggestions.Some students just gave positive comments rather than suggestions. For example, one studentwrote, "I will not change a bit about the programs. I think I know this is a good program whenmy sister goes to freshman year I'm going to tell her”, and another student responded, "no, notreally it was very fun and
they put into the program, themore benefits they will reap from it [13].References:[1] C. S. E. Jamison, A. A. Wang, A. Huang-Saad, S. R. Daly, and L. R. Lattuca, “BME Career Exploration: Examining Students’ Connection with the Field,” Biomed. Eng. Educ., vol. 2, no. 1, pp. 17–29, Jan. 2022, doi: 10.1007/s43683-021-00059-8.[2] J. Berglund, “The Real World: BME graduates reflect on whether universities are providing adequate preparation for a career in industry,” IEEE Pulse, vol. 6, no. 2, pp. 46–49, Mar. 2015, doi: 10.1109/MPUL.2014.2386631.[3] R. A. Linsenmeier, “What makes a biomedical engineer?,” IEEE Eng. Med. Biol. Mag., vol. 22, no. 4, pp. 32–38, Jul. 2003, doi: 10.1109/MEMB.2003.1237489.[4] R. M. Desing et al
interviewee shared “new graduatesshould have a natural sense of curiosity and a desire to learn. They should not be afraid to ask forhelp or guidance”. One of the attitudes that graduates must develop is the drive to build a career, whichencompasses behaviors like showing initiative, asking thoughtful questions, and maintainingpassion for their work. This drive indicates to employers that a candidate is committed tocontinuous growth and taking responsibility for their professional development. One employernoted, “The willingness to take on challenges and show enthusiasm for learning is what setsapart candidates who thrive in the industry.” Such behaviors reflect an intrinsic motivation that isessential for navigating the complex and dynamic
in this course if Ifollow the course curriculum and do the assignments as given to me by my instructors” in bothpre-course and current perceptions (mean ± std: 4.51 ± 0.64 and 4.37 ± 0.83 on a 5-point Likertagreement scale, respectively).Table 1: Survey data reflecting student pre-course and current perceptions of the senior designexperience. Differences were considered significant for αadj = 0.0125. a indicates assessment on a7-point Likert agreement scale (1 = entirely a course for graduation, 4 = equally a course forgraduation and a project experience, 7 = entirely a project experience); b indicates assessment ona 5-point Likert agreement scale (1 = strongly disagree, 3 = neither agree nor disagree, 5 =strongly agree); ** indicates
tend to have a stronger sense of engineering identity, demonstrated by their responses on ascale measuring engineering identity developed by Borrego et al [23]. There is one outlier, arespondent who reports a current “complete overlap” between their personal identity and theidentity of an engineer (see Figure 1). The fact that respondents who have relocated also reportmore overlap when reflecting on their identity as a student (Figure 2) implies that there may be arelationship between the strength of engineering identity in an individual and their likelihood torelocate. This relationship will be probed in further research, specifically in interviews during aforthcoming qualitative research phase. 8
the sample reflect the contributions of computer science and engineering to the development of QISE courses and programs. • Minority Serving Institutions (MSIs) play a crucial role in advancing educational opportunities for historically marginalized populations, aligning with the goals of fostering diversity in QISE. Additionally, three legislative initiatives motivating this work—the National Quantum Initiative Act (NQI), the CHIPS Act, and the National Science and Technology Policy (NSP)—all emphasize the need for increased diversity in QISE [1, 3, 2].All of the institutions included in this study account for 98% of engineering bachelors degreesawarded in the most recent year for which Integrated Postsecondary
that this paper serves as apractical guide for using LLMs for the simulation and early optimization of experimental designsacross disciplines.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under MCAGrant No. 2120888. The first and second authors (MF and MV) were supported by an NSFResearch Traineeship (TRANSCEND) under Grant No. 2152202 at the time this research wasconducted. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.References[1] B. Dong, J. Bai, T. Xu and Y. Zhou, "Large Language Models in Education: A Systematic Review," in 2024 6th International
diversity of population andmatches the student outcomes criteria for ABET accreditation [23]. The skills needed to enter theengineering industry in the past decade remain mostly unchanged from previous years andclosely follow the ABET standards, which reflect industry input.AI Effect on Professional Skills Using AI in engineering undergraduate classrooms has been gaining in popularity withChatGPT being the far most used generative AI tool. Chatbots have been used since the turn ofthe century, initially as rule-based systems and algorithms for frequently asked questionresponses and tutoring types of person-computer interactions [13]. In 2023, the use of ChatGPTby students in undergraduate engineering showed only a 4% AI use on a weekly basis
researchers have focused on assisting faculty withimprovement of their teaching practices through self-reflection or on providing faculty with newteaching resources or technologies that are broad and applicable in a variety of contexts. Withinthe research literature, successful strategies have been identified to specifically focus onchanging faculty conceptions and beliefs [2]. These strategies often create meaningful conceptualchange in faculty, which results in changes in practice [3], [4]. This has also been demonstratedwithin other educational levels, with the K-12 literature also demonstrating that significanteducational change necessitates changes in beliefs [5]. Over the past four years, our National Science Foundation (NSF) IUSE
. This allowed for analysis of the responses to this question by the career path type ofthe respondent. Figure 2 below shows the percentage of respondents from each career path typethat chose a certain undergraduate activity as being influential to their career. Figure 2. Figure 2. Influential Activities for each Career PathIn the survey, engineering graduates were also asked to reflect on their undergraduate studentexperience and identify which activities they had participated in. This question was framed as“When you were an undergraduate engineering student, did you participate in any of thefollowing (select all that apply)?”The responses to this question were also matched
understanding of the interactions between these three factors canpositively impact student retention and learning of information.There is an interaction through the different things that people experience for them to believethat they are capable of something. Bandura [10] states that people “construct for themselvestheir own standards through reflective processing of multiple sources of direct and vicariousinfluence”[10, p. 254] Thus, students are subject to be influenced by a variety of perspectives.They will believe that they are capable of achieving based on what their environment supports,what their role models in their life are doing, and their personal beliefs.Self EfficacySelf-efficacy is a key component of Bandura’s social cognitive theory
essential to foster a sense of belonging andreduce dropout rates [5]. Comprehensive DEI initiatives should reflect the needs and experiencesof disabled individuals; however, students with disabilities may have vastly different—or evencontradictory—needs. For example, prior research has found that testing accommodations andsupportive environments are particularly effective in closing the achievement gap for studentswith learning disabilities in higher education [6]. Conversely, other research in the field hasfound that presuming student competence and encouraging students to self-advocate can improvesense of belonging, and therefore retention [7]-[8]. Therefore, it is important to examine disabledstudents not as a monolith, but rather as a diverse
environment like Gazebo based ROS provided. The simulation also did not considerother environmental factors like wind, obstacles, etc. making the simulation less reflective of areal life flight. It was difficult to control the flight path due to the lack of a direct control system,instead relying on a separate console to send simple commands to the flight control system. Thismade it difficult to perform complicated maneuvers. It was also difficult to track the variousparameters of the flight, such as fuel, coordinates, altitude, etc. due to them being displayed oncemore on a separate monitor window. This made running the simulation more arduous andinconvenient.Despite these drawbacks, the convenient setup and ease of running cybersecurity tests
programsin construction to be the course content for industry professionals. As the general trend of theconstruction industry, safety is always seen as a vital component of a project’s success. Any toolthat improves this aspect is valued the most by industry professionals. The same was observed forAI tools for Schedule and resource management. These responses reflect the trends of thepreference for operations.The learning format and preferred duration also indicate a requirement for a properly structuredprogram design while allowing the learners to balance technical education with professionalobligations. The preference was predominantly seen for shorter courses of less than 4 hours andonline self-paced courses which shed light on the importance of
questions accurately reflect students' experiences, challenges, andperspectives related to the Pre-Engineering (PENG) program. This study guaranteesmethodological rigor and enhances its credibility by incorporating the IPR framework.MethodologyThe study employs quantitative and qualitative methods to examine the challenges faced by Pre-Engineering (PENG) students at a public University, providing a comprehensive understandingof the program's effectiveness and areas for improvement. It includes three participant groups:current and former students, faculty members teaching PENG courses (e.g., MATH 090, MATH105, CHEM 134, ENGR 100), and advisers.Research ContextThe Pre-Engineering program (PENG) at the public University in this research study
CI Challenges ChallengesFigure 8: Grand Challenges AI concepts pre-survey and post-survey responses for UnderstandAI: good understanding of AI concepts, Understand CI: a good understanding of computational intelligence concepts, Grand Challenges: a good understanding of AI used to solve Grand Challenges, and Algorithms: a good understanding of AI algorithms.Integrating AI into coursework, particularly in the Grand Challenges course, enabled students toapply AI techniques to address Grand Challenges in health. The growth in understanding how AIcan be used to solve Grand Challenges (from 6.50 to 8.47 in question 4), as shown in Figure 8,reflects the potential of such modules to prepare students for careers requiring both
under varying conditions, and calculating the performancemetrics of electromagnetic systems. These challenges, while achievable, require criticalthinking and problem-solving skills, helping bridge the gap between theoretical knowledgeand real-world applications. By participating in tasks that reflect industry scenarios, learnersdevelop both technical expertise and confidence in solving complex engineering problems.The integration of these active and collaborative learning techniques creates a comprehensiveand immersive educational experience. Through preparatory work, peer collaboration, andreal-world problem-solving, learners not only enhance their technical knowledge but alsodevelop essential soft skills, such as teamwork and effective
broaden students’ perspectives and enhance student learning, and the use of digital portfolios for students to showcase and 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
capture and analysis into an application. AdditionalAI and Machine Learning (ML) content is also planned for the next iteration of exercises.Students will learn about AI models and the difference between accessing an external modelthrough an API (Exercise 7) and the possibility of running a model locally on-device. With acontinued emphasis on affordability lower cost hardware options will be explored for the toolkitsuch as the Raspberry Pi AI Kit [14].Acknowledgement and DisclaimerThis material is based upon work supported by the National Science Foundation through GrantNo. 2044255. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the
of transferstudents in engineering programs.As human participants in the research process, we also recognize the ethical responsibilityinvolved in collecting and analyzing both quantitative and qualitative data from students. Beyondcompliance with IRB approval, we took deliberate steps to protect participant confidentiality,ensure voluntary participation, and accurately represent student experiences. Given thatopen-ended responses often contained personal reflections on academic struggles, transitions,and institutional barriers, we de-identified responses before engaging in the data analysis tomaintain participant anonymity.AcknowledgmentThis work is supported by the NSF (S-STEM) Award #2221671. Additionally, the authors wouldlike to
improvedcollaboration and engagement.IE Education in the GAI eraDespite the limited number of scholarly publications, existing work shows that GAI is making itsway into the workplace. This limitation is likely a reflection of the academic publication processwhich tends to be lengthy. The rapid advancement of the field is making it difficult to keep pacewith the advancement and adoption of GAI tools in industry, and study and document their usein scholarly work. Nevertheless, it is evident that AI, particularly GAI, is changing how we thinkand learn [25], [26]. Therefore, its integration into education is becoming essential - not only tosupport student learning but also to equip students with the knowledge and skills necessary tothrive in today’s tech-driven
] 0.0267 0.0268 0.0265 0.0264 0.0265 0.0265 µ AIR [kg/m-s] 1.89E-05 1.89E-05 1.87E-05 1.87E-05 1.87E-05 1.87E-05 Reynolds Number 8471 7363 6837 5321 4241 3422 Nusselt Number 117.1 121.4 106.2 94.1 82.3 80.12.3 Experiment 3: Free Convection from the DiscFor free convection from the disc, we place it in still air and provide heat electrically. In thissituation, the conduction loss through the Styrofoam is significant and needs to be accounted for.The lab manual illustrates the heat flow paths as illustrated in Figure (9) Figure (9)Treating the Styrofoam cylinder as a plane wall does not reflect the real situation. However