Market Research from the University of Barcelona, Spain. Industrial Civil Engineer from the Universidad del B´ıo-B´ıo. She has three diplomas in the areas of coaching, digital marketing and equality and empowerment of women. Her professional experience is linked to higher education as a project engineer and university management in the public and private area. Teacher at different universities in matters of entrepreneurship, business plans and marketing. She currently works as a teacher and academic secretary at the Faculty of Engineering of the Andr´es Bello University. The areas of research interest are the impact, relationship and integration of the gender perspective within communications and marketing in the
beyond those covered in the camp to keep students with previous coding experienceor those with natural aptitudes for programming engaged. Once the project has been established, the Python coding lessons held throughout theweek can be planned (see Table 1, Fig. 1). The selection of Python lesson topics might not followthe order in which topics are covered in a formal class setting: lessons should be curated to coveronly the material needed for the students to complete the project. For example, some capstoneprojects might require external modules or advanced data structures that might need to becovered instead of simpler concepts or built-in functions. We visualize this approach using ananalogy to building with bricks (Fig. 2). A formal
partnerships and alliances, and leverage resulting attract support resources; build legitimacy in the eyes of stakeholders Capability to adapt and Proactively anticipate and respond to new challenges; learn self- renew by doing; cope with change; develop resiliency Capability to balance Balance control and consistency with flexibility; integrate and diversity with coherence harmonize plans across a diverse set of actorsThe environment and culture within an organization play a crucial role in shaping how peopleinteract, as supportive environments foster healthy interactions. Research indicates that bothcontextual and individual factors—such as environment, culture, personality traits, andmotivation
the AIoT hands-on hardware learning modules have an impact on first-year engineering students' self-efficacy, outcome expectations, and interest in AIoT?For the next phase of this study, our team is planning to explore potential individualdifferences of the students’ career goals and actions after participating in the module,focusing on genders, ethno-cultural groups, and learning preferences. We believe that thisfuture endeavor will provide crucial information on how to support diverse groups of studentsin the field, aiming to unlock the students’ talent and perspectives not only for moreinnovative ideas in the field of engineering but for ensuring sustainable development of theworld.MethodsStudy Context and ParticipantsSupported by
on Initial Math PlacementAbstractThis work-in-progress (WIP) paper details a study of engineering student success and retentionbased on initial math placement. Many engineering programs' four-year plans of study are basedon first-semester placement in a Calculus math class. This makes math placement a criticaldeterminant for a student's ability to graduate in four years. Post-COVID, the math readiness ofgraduating high school students has been declining, resulting in more first-year engineeringstudents being placed in pre-calculus math classes. Gonzaga University’s School of Engineeringand Applied Science was experiencing a similar rise in pre-calculus math placement. Readilyavailable institutional data showed a decrease in student retention
Excellence in Student Leadership award and the 2025 Outstanding Leadership and Service in Mechanical Engineering award for her contributions to student mentorship and leadership. In addition to her academic work, Taylor is an Engineering and Computer Science Ambassador, where she supports recruitment and outreach efforts. She interned with Lockheed Martin Space from May 2024 to August 2025 in both mechanical and electrical engineering roles. She is a member of the Beta Beta chapter of Pi Tau Sigma, the Mechanical Engineering Honor Society, and is active in Aero at Baylor, where she served as project manager for a 3D Printed Aircraft competition team (2023–2024) and treasurer (2024–2025). Upon graduation, Taylor plans
andeffectively on a team whose execution of team assignments?members together provideleadership, create a 7 5b. Does the student demonstrate effective time management andcollaborative and inclusive project planning skills?environment, establish goals, 5c. Does the student contribute to a collaborative and inclusiveplan tasks, and meet objectives. environment?SO6. An ability to develop and 3, 4 6a. Can the student design and implement user testing to evaluateconduct appropriate system design requirements?experimentation, analyze and 6b. Can the student process and analyze data to evaluate
inclass with pen and paper and no longer linked to a participation grade. This adjustment aimed tostandardize the completion environment and minimize external influences. Since the assignmentwill be completed on paper during class, the instructor can visually ensure that external tools suchas large language models are not used. In future implementations, a statement such as “The use ofAI is not permitted in the completion of this assignment” will be included in the prompt. We alsoplan to develop a rubric to help guide the students in making connections across theircurriculum.We plan to continue incorporating the weekly reflections as an in class activity through the springquarter. Written reflections from winter and spring quarter will be
transposeswell-established concepts from teaching and learning into the context of faculty development. Itis an intriguing intellectual exercise to view the facilitator as a provider of a meaningfulframework in which faculty can share experiences, build a community, and learn collectively.Moreover, there are clear parallels between active learning in faculty development and inclassroom teaching. For instance, organizing an active learning session often requiressignificantly more effort than preparing a traditional slide presentation. When successful,participants are typically unaware of the effort involved in planning the session; however, if theactivity falls short, the organizer often bears the blame. This sentiment may resonate with facultymembers
Postdoctoral Opportunity Summit to bridge gaps in access andawareness. The Summit aimed to connect aspiring postdocs with postdoctoral mentors andopportunities in the STEM education field, fostering equitable access and supporting careerdevelopment in this specialized domain. This paper will examine how the project team designedand implemented the Summit to achieve these goals. We will explore the challenges faced andthe considerations taken during the planning and execution phases of the Summit and offeractionable recommendations for stakeholders seeking to build upon this impactful initiative.PurposeAcademic spaces have often been guided by the belief that “if we build it, they will come.” Inother words, creating opportunities is assumed to be
womenrelaunchers to give up their time and energy to conduct interviews when interviews were alreadyavailable via 3, 2, 1…iRelaunch. We obtained permission from the podcast host to utilize theepisodes in this study. Our IRB approval did not require obtaining permission from podcastparticipants to extract data from the podcast; however, we also plan to reach out to the podcastguests who have LinkedIn profiles to ask if they would like their names to be used and share thepaper for their review. We do not expect to receive responses from all interviewees, andtherefore, we chose to mask the last names of the interviewees. We recognize that thisinformation is available online, but we hope that readers can recognize the interviewees asrepresentative of a large
STEM include thecreation of the “scientific possible selves” instrument, which assessed middle and high schoolstudents’ expectations, fears, hopes, and plans for a career in science [12, 27]. Anotherquestionnaire was developed to measure science career aspirations among elementary schoolstudents [28, 29]. A recent scale [30] extended the questionnaire by DeWitt and colleagues[28] to create an instrument with separate career aspiration scales, each consisting of fouritems, for five disciplines: science, technology, mathematics, engineering, and education.Similarly, for this study, we adapted items from the scale proposed by DeWitt and colleagues[28] to measure GS career aspirations. We selected this scale because it most closely alignedwith our
, all usingthe same small inexpensive cobots. As expected, qualitative student responses show positiveimpact on students’ learning, programming skills development and attitudes towards robotics.Also, this work addressed various students’ robotic learning needs through a discussion of variousmyCobot computer hardware options.In the future, it is planned to extend the robotic tasks to obstacle detection and avoidance as wellas digital twins. Also, the use of cobots in education will be emphasized because of cobots safetyfeatures. Bibliography[1] J. Dewey, Experience and Education, Macmillan, N.Y., 1939.[2] D. A. Kolb, Experiential Learning: Experience as the Source of Learning and Development, Prentice Hall, Englewood Cliffs, N.J., 1984
Paper ID #47851Washington Veterans to Technology (WaV2T): A Pathway for Military Personnelto IT CareersDr. Radana Dvorak, Saint Martin’s University Dr. Dvorak received her Ph.D. in computer science from the University of London, Queen Mary College and Master’s in AI from the University of Sussex. Dr. Dvorak has been working in IT, higher education, academic industry and program development for over 25 years. As a member of Government and University strategic planning committees, task forces, and advisory boards; she has been a key architect of the Microsoft Software and System Academy, a public-private partnership between
specific research clusters. The fact that participants were recruited fromonly two clusters makes it possible that Research Group Experience registers a significantimpact on Sense of Belonging due to some unobserved research cluster characteristics. However,the insignificant standardized regression coefficient associated with membership in a particularcluster (Cluster2 in Model 3) suggests that Research Group Experience is relevant whenassessing the graduate experience.Next steps/Future workFor our next step, we plan another iteration of our survey. We will use modified questions forself-evaluation of research skills as described above and include questions about research groupsuch as size or organizational structure (e.g., hierarchical vs. flat
minoritized students. Lastly, programmatic questions gauged major takeaways fromthe PDS and their future plans of implementation of inclusive practices. Interviews occurred at theend of the first year of the PDS. Interviews were conducted on Zoom and audio recorded.Recordings were transcribed and transcriptions were analyzed.Data AnalysisSurvey analysisSurvey data collection and analysis is ongoing. Cohort 1 (n=12) has completed all 4 surveys.Cohort 2 (n=12) has completed 3 surveys. Cohort 3 (n=12) has completed 1 survey. Once all threecohorts have completed all 4 surveys, survey questions will be analyzed. For items on a Likertscale, Cronbach’s alpha will be calculated to determine internal consistency. For short answerquestions, survey responses
effect on different aspects of team dynamics, including: 1) interpersonalcohesiveness, 2) psychological safety, 3) team satisfaction, and 4) team conflict. The course investigatedwill be an introductory engineering analysis course offered to first-year engineering students in the First-Year Program (FYP) at the school hosting this study. The authors plan mainly to answer the followingresearch question: does the diversity composition of the team affect the overall team harmony and howteam members interact together?In this course, students get divided into teams of three at the beginning of the semester and work on asemester-long project, with the same team, until the end of the semester. The authors used CATME todivide the students into teams
Expression Control via CRISPR (Biology)Manipulating Reaction Rates via Temperature and Pressure (Chemistry)Adjusting Water Temperature in a Shower (Mechanical Engineering)Robot Path Planning for Navigation (Computer Science)Controlling Pollutants in Waste Water (Environmental Science)Managing Traffic Flow with Signal Control Systems (Traffic Management)Producing Salt by Reacting HCl and Na (Chemistry) Table 2: Highlighted Examples of Controllability Across DomainsTitles of Observability ExamplesMonitoring Blood Glucose Levels via Continuous Glucose Monitoring (Medicine)Observing Power System Stability via Phasor Measurement Units (Electrical En-gineering)Monitoring Aircraft Health Using Onboard Sensors (Aeronautics)Monitoring Cell Activity
College. Students electing to apply tothe program must submit a resume, cover letter, high school transcript, and two letters ofrecommendation. Applications are then scored and assessed based on the perception of theirability to successfully manage the rigor of the program schedule and their ability to contributetowards the experiential learning goals of the program overall. Figure 1. Diagram of the STEPUP program structure [12].The estimated cost of participation for the academic year is approximately $6,000 per student,which encompasses summer housing, meal plans, classes, corporate tours, opening and closingceremonies, professional development programming, and program staff. Program costs arefunded through a combination of
]. Metacognitive knowledgesupports the development of metacognitive skills where students can self-regulate by planning,monitoring, and evaluating their learning [3].To succeed academically, engineering students must develop effective study skills and becomeself-regulated learners capable of reflecting on their learning needs and taking action to improvetheir understanding and application of course topics [4]. In engineering, mastering and applyingproblem-solving heuristics (e.g., restating the problem, drawing diagrams, and identifying relevantformulas [5] [6]) is crucial for efficient problem-solving and academic performance [7] [8]. Whileexperts typically know how to organize their knowledge to solve problems quickly, novices canstruggle [9]. To
before segueing into the construction of several classificationmodels for mental health metric prediction. This will be followed by the evaluation of our modelsin Section 4 before we conclude our work in Section 5. We will then finish with our planned futurework to improve our project in Section 6.2 Related WorksMachine learning and its applications within the field of mental health is currently a popular topicin research, with many works revolving around integration into diagnosis frameworks [9]. Crisisintervention is a large part of the field as well, with recent work showing that machine learninghas been valuable in clinical practice for caseload management and ameliorating risk [10]. Currentwork in the field of mental health with applied
key to success is to have a well-defined plan, do in depthresearch on the problem and possible solutions, evaluate the solutions for viability, and then testand prototype those solutions until the result is acceptable. This project contains many differentdesign challenges along with a numerous of solutions. By breaking down the project intocategories and subcategories, the individual solutions can be analyzed for viability and the bestsolution can be selected. The functional block diagram in Figure 1 shows the EM project brokendown into major phases and how those systems interact with each other. The arrows representwhat type of interaction takes place and in which direction the power or information flows. Thisis a surface level
resubmission on student procrastination and academic integrity. Finally, wewill discuss the problems we encountered with the grading system and our plans to improve thesystem and implement it in larger enrollment sections of the same course.IntroductionProgramming assignments create the backbone of most computer science (CS) courses. In theseassignments, students are asked to apply new concepts and develop a program to solve apredefined problem. The instructor or TA reviews the work, uses a rubric to assess its accuracyor correctness, and slaps on a grade. Often, students read the grade, sometimes view thefeedback, and then they move on to their next task. As a result, students focus on the outcome oftheir work (does it solve the task presented or
, and bias: A literature review and synthesis of re- search surrounding student evaluations of courses and teaching. Assessment & Evaluation in Higher Education, 47(1):144–154, 2022. [9] G. D. Hendry, H. Georgiou, H. Lloyd, V. Tzioumis, S. Herkes, and M. D. Sharma. ‘it’s hard to grow when you’re stuck on your own’: Enhancing teaching through a peer observation and review of teaching program. International Journal for Academic Development, 26(1):54– 68, 2021.[10] S. Krishnan, J. Gehrtz, P.P. Lemons, E.L. Dolan, P. Brickman, and T.C. Andrews. Guides to advance teaching evaluation (GATEs): A resource for STEM departments planning ro- bust and equitable evaluation practices. CBE—Life Sciences Education, 21(3):ar42
understanding models (3.98) and contributing to learning (3.76). • The Multiple Linear Regression activity: While rated slightly lower, it was appreciated for its real-world application and contribution to understanding linear regression (3.72).Conclusions and Future Plan This study demonstrates the value of integrating in-class coding games and student-developed R Shiny applications to enhance learning outcomes in a statistical course. These toolshave the potential to improve engagement, problem-solving, and the application of abstractconcepts. Student feedback was largely positive, with comments like, “The tree-modelcompetition helped me see how theory applies in real-world problems” and “Developing Shinyapps was both challenging and
engineering design to allow for the identification of new opportunities?What role will the engineering team play in the reverse logistics chain?Costs. Which costs could be shared or lowered through other users or partners? Could theengineering team shift from an ownership model of underutilized assets to payment for accessand usage? How can cost volatility and dependence on finite resources be reduced? What can bedone to mitigate risk?Revenues. How might opportunities be diversified to increase resilience, growth, andinnovation? How might growth through value creation elsewhere in the system favorably impactthe future success planned? How might the business model help create other types of value? Likehuman, social, or natural capital? How might new
, achievement, and career plans,” J. Eng. Educ., vol. 99, no. 4, pp. 319–336, Oct. 2010.[9] S. Deterding, R. Khaled, L. Nake, and D. Dixon, “Gamification: Toward a definition,” in gamification workshop proceedings, Vancouver, Canadá, May 2011, pp. 1–79.[10]G. Barata, S. Gama, J. Jorge, and D. Gonçalves, “Studying student differentiation in gamified education: A long-term study,” Comput. Human Behav., vol. 71, pp. 550–585, Jun. 2017.[11]L. Brown and M. Tsugawa, “WIP: Case study - Training STEM high school teachers to integrate engineering through gamification,” presented at the National Association for Research in Science Teaching Annual Conference, Utah State University, 2024.[12]L. Brown and M. Tsugawa, “WIP: Using games and
Rhetoric in AI Conference Mission Statements This paper proposes that examining AI conference mission statements is essential forunderstanding the evolving boundaries of the field. Our analysis can support and extendprior research (4; 5; 11) by identifying persistent overemphasis or underemphasis on techni-cal aspects, the marginalization of non-technical or cross-disciplinary engagement, and thelack of coherent or clearly communicated objectives. These issues can lead to misalignedexpectations and limit a conference’s broader societal impact. By surfacing patterns, the larger research can inform more deliberate strategic planning,helping conference organizers design events that promote interdisciplinary dialogue, engagebroader communities
?) b. Can you tell me about your well-being over this time period? Physical, emotional, psychological? c. Tell me about the highs and lows for stress and well-being? Fluctuations vs a consistent build as quals approached? d. Looking more broadly, do you have any throughs on how your well-being and stress compared to your peers? 6. Are there other things you would like to add to what’s been said? a. How did the QEs go? b. We’re planning a study that explores changes in well-being and engineering identity over the quals period. We were thinking of conducting interviews ~3months before, 2–3 weeks before, and then shortly after. Do you think these
professional manner, leveragingtheir mentors’ expertise to achieve educational and career goals. Common discussion topicsincluded workplace culture and dynamics, industry insights, and career planning and strategiesfor success.Mentor circles were developed based on scheduling compatibility. Once the mentor circles wereestablished, the program’s committee handed over communication responsibilities to theassigned mentors for each group. Circles were expected to meet monthly from Septemberthrough December. Meetings were structured around discussions that aligned with theparticipants’ shared interests, but mentors and mentees had the flexibility to adjust topics asneeded, and no specific topics were assigned to any given meeting.To assess the program’s