(% Black) 5.6% 5.5% 4.0% Race (% Latinx) 37.7% 50.9% 32.6% Race (% White) 38.8% 21.8% 44.9% Race (% Other) 6.4% 5.5% 7.1%Survey itemsThis paper will focus on the survey items that explored engineering identity, inclusion, sense ofbelonging to the community and sense of belonging to their major. Additional questions werepresent in the surveys to document on-campus student experiences and reflective open-endedquestions were added each spring.Engineering identity was examined using 11 statements grouped by constructs of Interest,Recognition and
8second place with a relative importance of 0.2, primarily reflecting a negative impact on jobpredictions. Global (G) and team-based (T) experiences have negligible influence, bothcontributing less than 0.1. In the case of GRAD, research (R) is the most influential feature,representing a strong positive predictor of graduate school acceptance, while industrialexperiences (I) act as a significant negative factor, reversing the trend seen in the JOBpredictions. Global (G) and team-based (T) experiences show slightly greater relative importancehere, though this is likely because research does not dominate as overwhelmingly as industrydoes for JOB.The results for SUCCESS offer a more nuanced perspective. Success, which includes both joboffers and
: Innovation, New Product Development, Design Methods, Method AdoptionIntroductionThis work investigates the adoption of design methods in industry through semi-structuredinterviews with individuals from large R&D organizations such as Fortune 500 companies. Theinterviews seek to understand how industrial organizations collectively select and implementmethods, the catalysts and barriers to method adoption, and how organizations evaluate designmethods. The research interviews encourage participants to reflect on their experiences using themethods, asking them to recount what went well, what went poorly, and their thoughts on theirexperience. To gain proper breadth when understanding this adoption problem, interviews areaimed at a spectrum of users
evident that students’perception of their confidence in the ECE fields has improved. More than 50% of the studentsrated their confidence as 4 or higher for both CE and EE in response to Q2. However, 17% ofparticipants indicated that the activity did not significantly enhance their belief in theircapabilities. A notable factor to consider is that only 5% of students in CE and 8% in EE gave a Fig. 9: Survey Results Q3 Fig. 10: Survey Results Q4top rating of 5 for Q2. This may be attributed to a lack of interest in the activity, insufficientengagement, or inadequate guidance and instructions, as reflected in responses to Q9, which willbe discussed in a later section. The primary
wrong units”). This helps students reflect on their mistakes and learn the correct approach. This functionality is currently limited to wrong units when the algorithm detects entered numbers with wrong decimal points.8) Progress Chart Area: One of the most valuable features of the App is the real-time progress tracker, which provides visual feedback on the student’s performance over time. This section is located at the bottom of the main App window. Each grey colored horizontal bar under each chapter and difficulty tier represent a corresponding question. As a student completes a question successfully, the corresponding bar turns green. If a student attempts a question, but got the answer wrong, then the corresponding bar turns
Gender non-conforming (n = 13,5.5%), reflecting a common trend for men’s overrepresentation in engineering disciplines [35],[36]. White students (37.3%) are underrepresented, while Asian students (41.9%) are well-represented. Additionally, there is some representation from Black/African American (4.6%),Middle Eastern or North African (7.8%), and Hispanic, Latino/Latina/Latinx, or Spanish origin(6.5%) students.MeasuresParticipants answered a series of questions on changing lab experiences, changing lab behaviors,and advisor relationships. The first item asked, Have you considered leaving graduate schoolwithin the last month? with eight response options: 1. Yes, I have often seriously consideredleaving my PhD program with no degree, 2. I have
crucialto note that the term "diasporic Indigenous" encompasses a vast range of experiences and people.While I identify as diasporic Indigenous, my experiences and perspectives do not reflect those ofall diasporic Indigenous peoples. Furthermore, I acknowledge the complexities and implicationsof living as a diasporic Indigenous person on another Indigenous people's land. The findingspresented here are guided by these experiences and reflections.Findings and DiscussionOf the 12 articles reviewed, 6 were in a K-12 context, 3 in a higher education context, and 3 wereframeworks and recommendations for educators and researchers. The articles reviewed investigatethe experiences of diasporic Indigenous students in K-12 and higher education settings
-based engineeringdesign course. Across two course sections, four industry mentors participated in five in-classsessions throughout a 15-week semester, including two formal design reviews, culminating intheir role as judges at the engineering college’s design expo. Engaging 63 students, primarilyfrom mechanical, biomedical, and aerospace engineering disciplines, this initiative sought toexpose students to professional design practices to validate what they learn in the classroom asvaluable and applicable to real-world engineering projects. Data collection included instructorobservations and reflections throughout the semester, a focus group discussion with industrymentors, and two student surveys conducted during the middle and end of the
likely butmust be validated by research. Therefore, next steps in this research include looking at 1) validand reliable ways to measure peripheral cognitive load, 2) the effects of techniques that decreasemarginalization on the peripheral cognitive load of students, and 3) if the effects of knowntechniques to reduce cognitive load are more effective for students from marginalizedpopulations.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under AwardNumbers 2114241 and 2114242. Any opinions, findings, conclusions, or recommendationsexpressed in this material are those of the author(s) and do not necessarily reflect the views ofthe National Science Foundation.References[1] G. Van Dyke, C. McCall, M. B
home and inthe workplace.” The authors determined this definition to be the most comprehensive andrelevant for our students. This project will focus on exploration of the use various AI toolsduring the literature search and data analysis portion of their coursework. This definitionemphasizes one’s ability to critically evaluate AI, while using it effectively and reflect on theimplications for current and future use. Table 1. Definitions of AI Literacy [9]AI Literacy Frameworks and CompetenciesTo develop student learning outcomes, a review AI literacy frameworks and competencies isneeded to shape our project. The academic literature on AI literacy is vast and can be a littleoverwhelming to sift through. The following
factor scale showed excellent internalconsistency reliability. Results from this scale have practical implications, indicating specificpolicies, practices, and procedures that shape doctoral student retention and commitment todegree completion.I. IntroductionThe increased participation of diverse historically-excluded groups (including but not limited towomen, Black, Hispanic/Latinx, Indigenous and queer students) in STEM is imperative tomaintain the U.S. standing as a global leader in innovation and has the potential to reduceeducational, social, and economic inequalities [1]. Currently, the engineering doctoral pipelinedoes not reflect the diversity of the U.S. population. For example, in 2023, 2.3% of engineeringdoctoral degrees awarded in
soft robots. Through simulations, they can observe how complianceinfluences motion and overall functionality by comparing these mechanisms to rigid Delta andStewart robots. (2) In what real-world applications would compliance be advantageous? Afterrunning simulations in Modules 2-4, students can observe significant deformations in flexiblemembers and reflect on how compliance provides benefits in various applications. This questionis suitable for students at all levels, from freshman to senior years. (3) What assumptions weremade during the derivation of the equation of motion, and how do they affect accuracy? Thisdiscussion is particularly relevant in Module 4: Soft Robots, where even the Simscape modelrelies on the constant curvature
collaborators in the research [9].Group Level Assessment (GLA) is a process that guides participants through brainstorming theirchallenges, thematically analyzing their responses, and developing action plans to address theissues they have identified [10]. The seven-step process of a GLA is shown below in Table 1.Table 1. Description of Group Level Assessment process steps Step Description Climate Setting GLA process is described to participants Generating Participants respond to prompts placed around room on boards Appreciating Participants make notes on the boards to analyze the initial responses Reflecting Participants individually analyze the data and begin to find shared themes Understanding Participants share
haveshown that students have a positive attitude towards vlogs although most of the researchconducted in higher education has focused on reflective practices [32] [33]. Interestinglyenough, research has shown an improvement in students’ response especially with regards tolanguage subject matters including English as Second Language (ESL) curriculum [34] [35].Lessons – Now, although traditional lecturing is not going to be a top viewer draw, there still is atime and place for engagement through simple graphics and text. PowerPoint slides have shownto be beneficial effective in courses when done appropriately [36]. As such, this presents the casethat not everything has to be a visit to a location or eventful exercise. Content should bereflective of
." The event's capacity to appeal to a broadspectrum of interests while keeping a laser-like focus on current concerns influencing thetechnology landscape is reflected in this balancing. For example, individuals working at thenexus of technology and society found great resonance in conversations about AI ethics and IoTsecurity, while those interested in automation and engineering applications were excited byrobotics demos.Another significant advantage of the symposium was networking, as 63% of participants said theopportunities were "very effective." Participants stressed the importance of interacting withmentors and peers who were as passionate about innovation and discovery as they were. Anumber of participants reported making contacts that
measurable student improvement incomprehension, skill development, or knowledge retention. For instance, research by Constantinouet al. [4] and Zhou and Song [5] provides quantitative data showing significant learning gains whenusing AI-enhanced tools, with improvements in analytical capabilities ranging from 25-40%compared to traditional methods. The selected applications have demonstrated capacity to enhancestudents' ability to visualize complex systems and understand multifaceted sustainability conceptsthat are often challenging to grasp through conventional instructional methods. Industry relevanceserved as another crucial selection criterion, ensuring that the reviewed technologies reflect currentand emerging trends in professional practice
shape their professional identity.The development of engineering professional identity is underpinned by various theoreticalframeworks and grounded research methodologies that offer comprehensive insights into howthis identity forms and evolves. Villanueva et al. [11] propose a working definition of EPI thatintegrates individual, social, and systemic sources of influence. This framework is informed byhistorical perspectives on engineering education, categorizing professional identity into threeroles: Mediator, Designer/Tinkerer, and Social/Servant. These roles reflect the evolving nature ofthe engineering profession, from technical problem-solving to societal service roles, and shapehow students perceive their future roles as engineers.This
from mistakes throughself- correction, thereby enhancing and accelerating the learning process itself. ExperientialLearning Theory [37] defines hands-on learning as a cycle composed of four elements: (1)concrete experience (i.e., doing or having an experience); (2) reflective observation (i.e.,reviewing the experience); (3) abstract conceptualization (i.e., concluding and learning fromexperience); (4) active experimentation (i.e., trying out what has been learned). For concreteexperience, students start with a quantum program that is either functionally incorrect and/orfunctionally incomplete, run the program and record their observations, which forms the initialconcrete experience. For reflective observation, students analyze the outcome of
assess the impact of the pedagogical approach, the teachers conducted pre- and post-surveys with their stu-dents. The survey questions focused on five categories: Interest in science and technology, Available resources forlearning, Preferred STEM activities and subfield for computer science, School environment, Career interest andpreparation, Demographics. They also completed reflection exercises. The parents/guardians had to sign consent forms to give approvalfor their students to be included in the data collection activities. The teacher conducted observations and had aset rubric that identified success criteria. Students evaluated each other using the same rubric in order to provideassistance and feedback. This project was conducted under
theirrespective leadership roles as a way to give back to the communities that supported them. Asthese communities welcomed and supported them at the beginning of their college careers, theywanted to continue supporting and lifting these organizations as upperclassmen. As all studentparticipants were either juniors (third year) or seniors (fourth year), they were able to reflect ontheir experiences since their freshman year (first year) about the impacts of various communitieson their growth and development, either adjacent to or related to their engineering education. Forsome students, friends and family, as well as prior high school experiences, encouraged them tojoin organizations as freshmen: I was encouraged to apply because I had friends and a
engineering coursework at a calculus level and the lack of structured support being offeredto students in this situation.ParticipantsStudents who start the engineering program without being calculus-ready are invited toparticipate in this study. In the first year of the full study that is currently in progress(2024-2025), 10 of 33 first-year engineering students started the program at a pre-calculus level.This work-in-progress paper reflects the pilot trial for this study and follows one student whoentered the engineering program during the 2023-2024 school year not at the calculus level. Thisstudent entered the program enrolled in pre-calculus and had both a strong interest in engineeringand self-reported struggles with math coursework, making him
general programs as having“philosophical”, “flexible,” or “instrumental” purposes.This paper reviews general engineering programs currently accredited by ABET and categorizesthem according to the ABET definition (Engineering, General Engineering, Engineering Physics,or Engineering Science) as well by program characteristics and purposes. It presents a historicaltrajectory of numbers of institutions and programs in the general program category. The paperconcludes with a reflection on the relative success in shifting the balance of breadth and depth inengineering program offerings over the last 20 years.IntroductionThe Multidisciplinary Engineering Division of ASEE (MULTI) was formed in 2003 as a venuefor promoting engineering programs and courses
measuredby developing a survey utilizing the research of Burke et al. (2016), Taylor et al. (2023), Taylor et al.(2017), Aarons et al. (2014), Pearson & Meadan (2021), and Koren et al. (1992), as well as the AdvocacyCapacity Tool (ACT) developed by Bolder Advocacy.VR Development PilotPilot Teacher and Participants During the pilot, the pilot teacher will complete a daily reflective teacher journal and following thepilot, the teacher will engage in an interview. Data collection tools for the pilot participants will explore theeffectiveness of VR as a tool for improving access to engineering education for autistic students. The pilotand data collection tools will also explore whether the VR content supports the development ofengineering
preparation, and (v) high impactpractices. The main research question for this project is; do these interventions improvegraduation and retention rates for students in mechanical and civil engineering? Survey resultsof the current students will be presented, along with reflections from the investigators andplanned improvements for the following years’ cohorts.BackgroundUtah Valley University enrolls 47,000 students and has a dual mission—that of a comprehensiveuniversity, offering 91 bachelor’s and 11 master’s degrees, and that of an open-admissionscommunity college, offering 65 associate degrees and 44 certificates and diplomas. Studentdemographics are similar to those of a community college. There is no University housing, so allstudents are
factors.Part 4: Wrapping up the interventionWe concluded the intervention by reflecting as a class on major takeaways. For example, beingmore “aware of our unawareness” as engineers makes us better engineers. In this part, we alsoconnected back to textbook topics on mitigating errors, biases, and assumptions in problemdefinition; promoting ethical behavior in engineering; and the engineering design process as awhole. With the few remaining minutes, we opened the class to questions for Practitioner 2 aboutthe unconscious, psychoanalysis, and his professional experiences. Many students took us up onthis offer and we ended the intervention with an engaging discussion that extended these topicseven further
support reflective learning andcommunication in computing courses [1].The goal of this work is twofold: 1. Provide a retrospective analysis of a novel instructional model, offering sufficient detail for other educators to adopt, adapt, or extend the approach. 2. Demonstrate the effectiveness of this modified instructional approach in addressing stagna- tion in content delivery, preparing students for the rapidly evolving field of computer science.In a field as rapidly changing as computer science, modifications to the methods of instruction mayhelp intrinsically prepare students for this rapidly changing ecosystem.Theoretical FrameworkConstructivism as an educational theoretical framework has often been applied to the sub-field
, theseframeworks support the iterative updating of curricula to reflect the latest technologicaladvancements, such as artificial intelligence, robotics, and augmented reality [1, 2]. Furthermore,their integration into Continuing Engineering Education (CEE) programs ensures thatprofessional students remain competitive and well prepared for emerging industrychallenges [11, 16].Through a structured approach, dynamic taxonomy frameworks bridge the gap betweentheoretical education and practical industry requirements. They offer a scalable and adaptablesolution to align curriculum development with the future skills landscape, enabling professionalsin the engineering and INFOCOMM sectors to thrive in an era of constant technologicalinnovation and disruption [4
myths with survey items, summarizing both veteran and civiliansemantic polarities. Survey items 8 and 9 capturing veteran combat experience and employmentexpectations do not have a civilian corollary, as indicated in Table 1. These myths or stereotypesare sourced from known veteran stereotypes of veterans [2] and do not reflect the authors’perspectives. Some civilian-focused survey items take an opposite ‘polarity to the veteran-focused statements to allow for counter-validation of the survey [3].Table 1: Veteran and civilian-coded survey items Veteran Veteran-coded Civilian Civilian-coded Item Item 1 Veterans are more likely to suffer 13 Civilians are
mechanical engineering suggests a movement in the field towardsmore a more customizable view of what constitutes fundamental knowledge for doctoralstudents. This shift towards greater customization and personalization also reflects the potentialfor more equitable assessment in preliminary examination formats, as movement away from aone-size-fits-all standard exam model to instead flexible and unique exam strategies suggests thataccommodations for student differences can be recognized rather than ignored or hidden. As thejourney to complete a doctoral degree is undoubtedly personal and one-of-a-kind, incorporatingpersonalization in each step of doctoral examination is consistent with the desired eventualspecialization in individual research