improving) in responding to our queries.2.2 Learning Objectives and Designing CoursesA Learning Outcome (LO) is an educational goal for a learner such that they can perform theoutcome once they have learned it. Typically, an LO is described at a level from Bloom’sTaxonomy [2] and applies the process to some content related to a field of study. Bloom’staxonomy provides a hierarchy of cognitive processes from “lower-order thinking skills”, such asrecall and classification, to “higher-order thinking skills” such as creating or planning.“Understanding by Design” [3] provides a design methodology for courses where the coursedesigner starts from LOs, determines how to assess if a student has achieved the LO, and designsclass activities around preparing
accreditation process for baccalaureate-levelprograms requires students to gain “an ability to function effectively on a team whose memberstogether provide leadership, create a collaborative and inclusive environment, establish goals,plan tasks, and meet objectives” [11]. Thus, engineering students need to be exposed to anddevelop leadership skills during their undergraduate studies in engineering.To understand what leadership is in the context of engineering, we must recognize that there isno one universal definition of “leadership.” However, it is nearly impossible to come to thisconclusion as the definition of leadership can be dynamic depending on the context. Scholarsfrom fields such as communication, business, and organizational studies have
America,” Small Business Economics, 31(3), pp. 305–322.[11] Bianchi, P., Kantis, H., Bacic, M. J., Suaznabar, C., Studart, R., Vasconcelos, L. A. T., Koenig, V. M., Federico, J., Martínez, J., Parrilli, M. D., Llisterri, J. J., Angelelli, P., and Baruj, G., 2004, “Desarrollo emprendedor: América Latina y la experiencia internacional,” IDB Publications. https://doi.org/10.18235/0012549.[12] Martínez Guerrero, M. A., and Verjel Rivera, M. A., 2014, “Retención estudiantil en el programa de Administración de Empresas de la Universidad Francisco de Paula Santander Ocaña, análisis de causas y plan de mejoramiento.” [Online]. Available: https://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/1658. [Accessed
facilitate faculty dialogue and inspire action.Later, the focus shifted toward action, culminating in sessions designed to translate theknowledge and reflections from the semester into tangible plans for positive change. Thesesessions were scheduled after a semester of weekly challenges and guided workshops, ensuringthat participants arrived with a shared framework, an understanding of key concepts, andpreliminary ideas for improvement. We used tools such as rubrics to assess current efforts andidentify areas for growth, and held a half-day retreat which included a visioning board exerciseto collaboratively imagine the future of engineering education. This scaffolded approach evolvedover time and provided a deliberate progression from individual
ofthe course work. It is challenging to keep the student engagement rate as high as the start of thesemester, but the mixed clicker model was successful in lowering the drop rate for 80% of thesemester. The lack of sims for the topics that constituted the final 20% of the semester may haveresulted in a higher drop rate. Potentially this can change if more sims are utilized. Similar dropand rise trends for major semester events such as midterms and breaks were also observed, whichpoints out the necessity for planning for successful student engagement. It is also observed that,if there is no sim, the attendance trends are identical, however mixed questions seem to disruptthese patterns, attendance drops are slower and climbs are faster, always a
contains the following features. 1. Interactivity: Students interact in real-time with systems to apply control and setpoints and obtain live plots and results. 2. Real thing: labs should not be perceived as video games and students must carefully plan their experiments, otherwise virtual fuses will blow up and protection will be engaged. 3. Instant Reset & Repeatability: Mistakes can be reset, and tests can be re- peated under identical conditions for consistency. 4. Flexibility: students can change the configuration of the testbench by con- necting/disconnecting components to exhibit a given behavior 5. Self-learning: Students can acquire knowledge at their own pace. Unlike physical labs, which often
further emphasized by the launch of the National Quantum Initiative Act of 2018, whichalso calls for expanded education and workforce development in quantum science andengineering. Similarly, on a global scale, China has its Made in China 2025 and the FourteenthFive-Year Plan, and the European Union has its Quantum Technologies Flagship project. Talentand education play a pivotal role in shaping the future of quantum technology and ensuring acountry’s competitiveness in this rapidly advancing field. As quantum technology continues togain prominence, we have seen a growing demand for skilled professionals who can driveinnovation, conduct groundbreaking research, and develop cutting-edge applications. However,the quantum industry is currently
paper by the authors.The respondents were asked to rate six statements pertaining to equity in the workcommunity on a 5-point Likert scale (1=fully disagree, 5=fully agree, 6=cannot say). Thestatements were the following: “The management of the organization is actively committed tothe promotion of equality and equity”; “Equality is clearly visible in the work community (forexample in official values, in dialogue between the employer and shop stewards)”; “Equityand equality promotion plans have been discussed in the work community (initiated by e.g.shop stewards or the health and safety representative)”; “Equality training sessions have beenarranged for supervisors”; “Equality training sessions have been arranged for the personnel”;“The
., Mosyjowski, E. A., Daly, S. R., & Lattuca, L. R. (2024). Leveraging a comprehensive systems thinking framework to analyze engineer complex problem‐ solving approaches. Journal of Engineering Education, 113(1), 53–74. https://doi.org/10.1002/jee.20565Duivenvoorden, E., Hartmann, T., Brinkhuijsen, M., & Hesselmans, T. (2021). Managing public space – A blind spot of urban planning and design. Cities, 109, 103032. https://doi.org/10.1016/j.cities.2020.103032Fouad, Nadya, Mary Fitzpatrick, and Jane P. Liu. 2011. “Persistence of Women in Engineering Careers: A Qualitative Study of Current and Former Female Engineers.” Journal of Women and Minorities in Science and Engineering, 17 (1): 69–96. https
development life-cycle is less ingrained, we seek to provide abrief description of ”agility” as it is used within the field of software engineering. Agile softwaredevelopment practices emerged in the early 2000s as a response to the limitations of traditional,linear design methodologies that dominated much of the 20th century [17]. Instead of creatinga comprehensive initial design, agile employs sequential iterations that continuously refine bothdesign and implementation. [18]. The principle of observing the state of the process, respondingto current needs, and modifying future plans to address current concerns, embodies the aspects ofagile being employed in this course. For a comprehensive examination of agile practices in soft-ware engineering
]. The plat-form’s Python-based code editor, combined with ROS2 and Gazebo for simulation, en- 2ables students to apply programming concepts directly in a robotics context, bridgingthe gap between abstract coding exercises and real-world applications. One of the key motivations behind the development of the FORE platform is the needfor a flexible and scalable educational tool that can adapt to the varying needs of students.For beginners, the platform provides structured lessons that gradually introduce corerobotics concepts such as motion control, sensor integration, and path planning. For moreadvanced students, the platform offers opportunities to explore more complex roboticalgorithms and systems
Teamwork in Collaborative Learning Environments: Team Learning Beliefs and Behaviors,” Small Group Res., vol. 37, no. 5, pp. 490–521, Oct. 2006, doi: 10.1177/1046496406292938.[3] N. C. Byrom, L. Dinu, A. Kirkman, and G. Hughes, “Predicting stress and mental wellbeing among doctoral researchers,” J Ment Health, vol. 31, no. 6, pp. 783–791, Nov. 2022, doi: 10.1080/09638237.2020.1818196.[4] C. Spooner, L. Lavey, C. Mukuka, and R. Eames-Brown, “Multi-Institution Research Centers: Planning and Management Challenges,” J. Res. Adm., vol. 47, no. 2, pp. 32–48, 2016.[5] R. C. Bindler, B. Richardson, K. Daratha, and D. Wordell, “Interdisciplinary health science research collaboration: strengths, challenges, and case
Thermodynamics 9 Nuclear and radiation 1 Manufacturing 8 Aero structures 1 Materials 8 Energy systems 1*Included a topic on “Vibrations and acoustics”4.3 Techniques for evaluating early-stage doctoral candidatesWe identified multiple assessment techniques, types, and formats of mechanical engineeringpreliminary examinations (Table 4). All 25 programs in our sample required course completionwith varying criteria (e.g., GPA requirements, specific versus flexible course plans) as part oftheir evaluation process. All but one program (24, 96%) conducted a preliminary exam outside ofcoursework
. Theynoted that applying the rubric to one student at a time significantly improved clarity andemphasized the value of repeated viewings to capture subtle dynamics, especially among shy FIGURE 1. DRAFT RUBRIC FOR THE OBSERVABLE BEHAVIOR PRESENTED AT KNC2025.students. This rubric is part of a planned EM Behavior Field Guide currently under developmentthat includes behaviors and performance levels, identifies evidence sources, and tags existingEngineering Unleashed activities that may help develop the behaviors. Once a draft of the FieldGuide is completed, the authors will partner during the 2025-2026 academic year with faculty atNetwork institutions selected through an application process to beta-test the materials ineducational settings
time. While we believe these findings independently add value to theexisting body of literature by summarizing the breadth and depth of existing literature andproviding several areas to emphasize for future research, our later presentation of this work willextend these findings. Continued plans for this study include analyzing the coincidence ofcategorical codes (e.g., identifying and considering the number of papers that were longitudinaland had a sample > 250 participants,) and quantitative clustering of existing papers throughbipartite network projection and subsequent clustering [18], [43], [44]. Finally, we will developrecommendations for efficient strategies to study networks that meet the existing gaps,recognizing the increased
to those who are seeingthe content for the first time, and (2) is there a way to create a “simple” version of this game,targeted at younger audiences (approximately ages 5-10).To answer Question 1 and help educators implement this game, regardless of backgroundknowledge, we introduced a Facilitator’s Guide. This document covers all information needed byan instructor – from game equipment needs to detailed instructions to alternative tips forimplementation. We plan on improving this guide as more user feedback is received.To answer Question 2, we introduced two versions of the game: Simple and Advanced. TheAdvanced version is the same as explained above for this study, where students estimate materialproperties via measurements made using
acquisition system: Arduino Mega at $23, Taiss rotary encoder at $18 each, DCmotor for $15, and jumper wires for $3.Planned implementation. The proposed equipment can be utilized in mechanical vibrations,machine dynamics courses, and vibrations and control theory laboratories. As a low-costsolution, multiple setups can be assembled to conduct experiments for both free and forcedvibration analyses. These can be integrated into laboratory activities so teams of students can Figure 4. The top row displays the CAD models of the device from front, isometric, and left views, while the bottom row shows the physical prototype: (1-4) feature the front view with dual pendulum rods and encoders, both free and coupled together by a compliant spring; (5,6
howthese strategies impact success. In this study, the term “best practices” refers to guidelinesthat have been established for optimizing AI interactions during problem-solving tasks, forexample in (Open AI, 2024; Google, 2024).An IRB-approved plan guided data collection from the competition, where teams of threeundergraduate students were encouraged to use generative AI to solve programmingproblems. Over 100 students participated. After the competition, students voluntarilysubmitted transcripts documenting their interactions with AI tools. These transcripts wereexamined using a directed content analysis (Hsieh and Shannon 2005) to assess how wellstudents followed prompt engineering best practices.The study findings reveal significant variability
), and enhance safety compliance and decision-making capabilities through realistic workscenarios. In addition, an adaptive difficulty mechanism is introduced in the PPE InspectionTraining scenario. Finite automaton improves training efficiency and stability and optimizes theoverall user experience.In future work, will focus on optimizing LLM’s response and developing more diverse teachingmodules as templates to adapt to educational needs flexibly. Also, future studies plan to collectadditional data on user experience and educational research to allow for better statistical analysisby considering more participants and real-world application scenarios and consideringphysiological, socioeconomic, cultural, and other variables. Future work will
Engineering at Morgan State University, where he also serves as a Research Assistant. He holds a bachelor’s degree in Civil Engineering from the Federal University of Technology, Akure (FUTA). His current research focuses on the sustainability and resilience of transportation infrastructure in the face of sea level rise, with a particular emphasis on coastal vulnerability and adaptive planning for future climate scenarios. Tolulope is passionate about engineering education and research, with a strong appreciation for field experiences that bridge theory and practical application.Mr. Pelumi Olaitan Abiodun, Morgan State University Pelumi Abiodun is a current doctoral student and research assistant at the department of Civil
economics of higher education,” J Econ Persp, vol. 13, no. 1, pp. 13–36, Feb. 1999, doi: 10.1257/jep.13.1.13.[31] B.C. Harvey, “Teetering on the demographic cliff, part 1: Prepare now for the challenging times ahead,” Planning for Higher Ed, vol. 49, no. 4, pp. 1–12, 2021.[32] R.J. Ely and D.A. Thomas, “Cultural diversity at work: The effects of diversity perspectives on work group processes and outcomes,” Admin Sci Quart, vol. 46, no. 2, pp. 229–273, Jun. 2001, doi: 10.2307/2667087.[33] J.B. Main, M.M. Camacho, C. Mobley, C.E. Brawner, S.M. Lord, and H. Kesim, “Technically and tactically proficient: How military leadership training and experiences are enacted in engineering education,” Int J Engr Ed, vol. 35, no. 2, pp. 446
can and should be collected. Additionally, our research team plans toconduct analyses on multiple axes of our data. The ones we find most interesting are institutiontype (i.e., in community colleges where everyone is a TFF, institutional constraints are differentthan at R1 institutions where TFF are a small subset), and the immigration status of TFF (theexperiences of international PhD students were significantly different from domestic students).Future papers from our research team cover the topics of culturally relevant mentorship, facultyfit, and the climate of engineering departments for Latine/Hispanic TFF.Limitations We hope to increase servingness for individual students, who as a group of students, areof course not monolithic
practiceimpacts student practices and perceptions, so confirmatory studies of these factors must accountfor the clustering of students based on their instructors. In future work, we plan to usehierarchical linear modeling to account for the nested structures of this data.ConclusionThis study used EFA to analyze 448 survey responses completed by students in statics andmechanics undergraduate courses at twelve diverse institutions to understand their perceptions ofusing the CW in their classes. We found two factors associated with students’ practices and fourfactors that detail student perceptions around their experiences using the tool. While correlationsbetween practice and perception were found for positive learning experiences, the factorrepresenting
information was highlighted in observations that AI eliminates”scrounging on Google to find a good explanation for a question...you get a direct response to yourniche question.” Beyond student applications, participants recognized potential faculty benefits,noting that AI can assist in developing ”assignment description[s] or...lesson plan[s]” and ”helpteachers generate homework and streamline the process of grading,” allowing educators to ”spendmore of their time focused on the students.”3.1.5 Creative SupportAI’s utility as an ideation tool and starting point generator was identified in 42 responses (10.50%total). Participants valued how AI chatbots help students ”get an idea as to how to start a certainproblem...when they have no idea
plan to test the two classifier models on various types of student learningdata, for example, the live student engagement and performance data obtained from a learningmanagement system, as it would assist course instructors to better assess student academic needs.Using our own data instead of an online dataset might also help address severe class imbalanceissues. Additionally, it may be worthwhile to build hybrid models that combine RFC’s featureselection with MOC’s multi-output prediction, as it would allow for more precise predictions anda deeper understanding of how different aspects of student engagement and performance areinterrelated. Furthermore, it may be worthwhile to incorporate some qualitative data, such asteacher assessments
knowledge and skills in fields related to the National Aeronautics and SpaceAdministration’s (NASA’s) mission and the needs of the future workforce.To participate, students must first contact the faculty coordinator at their home institution, whoplays a crucial role in identifying and matching students with appropriate projects. Faculty mentorsfrom participating member institutions submit proposals for projects aligned with NASA’s goalsand relevant STEM disciplines. These proposals include key project components, such as learningoutcomes, timelines, mentoring plans, and expected deliverables. The program places a strongemphasis on inclusivity, actively recruiting women and members of underrepresented minorities,thereby ensuring a diverse pool of
,contributions from individual instructors can continue to be carried out by future instructors withthe coordinator’s help. The coordinator is the bridge between courses and between semesters.Students and new instructors have greatly benefited from standard course policies and consistentexpectations.Reform Project 2: Integration of computational tools in collaboration with computer science andmath prerequisite coursesThe project aimed to modernize essential undergraduate service courses by incorporatingmeaningful computational tools and exercises. While the focus was initially on Statics, the facultyparticipating in this project is the core of a broader Python Working Group initiative, with plans toimplement similar changes in other courses over time
: Tech Presentation 1 and 2 Prototyping, Soldering, Electronics Prototyping, testing plan, build, test. Tech 2 Testing Presentation 3 and 4 Testing, During flex time: power tools, Iterate, Test, Final Refinements. Website and Refining, advanced 3D printing, laser video production. Practice presentation and 3 Presenting cutting, CNC Router, final presentation. programming, solderingThis curriculum closely parallels the Seven Key Characteristics of Integrated STEM proposed byRoehrig, et al [14] which are: 1) focus on real-world problems - using the UN SustainableDevelopment goals as a
identifytrends and the need for additional support for students in each category. Plans for improvedstudent engagement as a result of this study are presented.The Student PopulationIntroduction to Chemical Engineering (Intro) is offered as the first course in ChemicalEngineering at the U of A and covers topics such as chemical engineering as a profession, jobopportunities, ethics, communication skills, unit conversions, limiting reactant calculations andmaterial balances for reacting and non-reacting systems. Prior to 2013, the course was part of atwo-course freshman-level sequence that also included Introduction to Chemical Engineering II(Intro II), which emphasized ideal and real gases, steam table use, humidification and energybalances for reacting