CCSI was not created to assess the outcomes of interest forthis study (sense of belonging, STEM pathways, gender differences). If it were, the independentvariables could have had more predictive statistical explanatory power on each of the dependentvariables. Finally, in merging institutional data with the results of the survey, the research teamwas limited to the information that the institution gathered and the IR office at Blue LakeCollege was willing to share with the researchers. Results There were three primary dependent variables of interest that guided the study. They aredeveloping a sense of belonging, examining STEM pathways, and highlighting disparateperceptions based on gender
gatheringsurvey data to assess how the program influences participants' perceptions of STEM careers andtheir feelings about being women in STEM fields. We aim to analyze this data in detail and publishthe results in a subsequent paper. This will allow us to better understand the impact of the programand further refine our approach to engaging young girls in engineering.8- AcknowledgmentsWe would like to acknowledge the Gina Cody School of Engineering and Computer Science,Concordia University, for their support in terms of finances, lab access, and access to resources.We would also like to extend our gratitude toward the faculty, technical and administrative staff,students, and industry professionals who selflessly contributed their time and expertise to
thinking skills before engaging with the software interface. Furthermore, theplanning board scaffolds students' executive functioning, particularly around planning andworking memory, in a way that teachers can lean on or remove depending on their assessment ofstudent needs, serving as a way to transition from supported to independent programming. Figure 1. Early iteration of laminated paper planning board, which scaffolds coding sequences with descriptions of actions to be completed by a robot.Possibly most helpfully the planning board establishes a routine for engaging in computationalthinking practices. It reinforces two important habits of going through a coding sequence step-by-step, and of decomposing coding tasks from task to
) assessing the reliability of the survey byanalyzing the consistency of responses across similar questions; (5) distributing the final surveyto the targeted audience; and (6) analyzing the collected data. The survey questionnaires weredistributed to professionals in construction-related industry from the authors’ professionalnetwork. The survey was conducted from October 2023 through March 2024. The population forthe study consisted of current construction industry professionals with a wide range of ages fromunder 25 to over 65. The survey also represented a variety of genders, ethnicities, constructionexperiences, work sectors, sizes and types of firms. This approach ensured a diverse and relevantcross-section of industry representatives. It was
had multipleresponses over the nine surveys, the response with the least blanks was kept or one wasrandomly selected.Descriptive statistics from the nine surveys were calculated for both individual statements andcomposite factors. The combined results were disaggregated according to student type(scholarship participant vs. other respondents), binary gender identity and ethnicity (white vs.underserved). In addition, a series of independent one-tail t-tests with a 95% confidence levelwere conducted to assess whether any of the differences in means between scholars and otherrespondents were statistically significant. Additional one-tail t-tests were conducted to compareresponses based on gender identity and ethnicity. A one-way Analysis of
: Potential of the concept, stateof the evidence," Review of Educational Research, vol. 74, no. 1, pp. 59–109, 2019. [Online].Available: https://doi.org/10.3102/00346543074001059[10] J. Lönngren and M. Svanström, "Assessing emotional aspects of learning in engineeringeducation: A new perspective," European Journal of Engineering Education, vol. 47, no. 3, pp.383–400, 2022. [Online]. Available: https://doi.org/10.1080/03043797.2021.1889463[11] K. Murphy, Engineering Education and Emotional Resilience in Elementary Students.Chicago, IL: Education Research Press, 2024.[12] B. Fredrickson, Positive Emotions and Their Impact on Creativity and Learning. New York,NY: Basic Books, 2013.[13] Y. Lin, M. A. A. Kadir, and D. Kaur, "Preschool educators
Journal of Psychological Assessment. 2019.[18] M. Pett, N. Lackey, and J. Sullivan, Making Sense of Factor Analysis. Thousand Oaks,California: SAGE Publications, Inc., 2003. doi: 10.4135/9781412984898.[19] S. Wilson, I. Blaber, J. Hancock, G. Pitcher, and J. Hammer, “Delivery Of Mental HealthTraining Across A College Of Engineering”.[20] K. Beddoes and A. Danowitz, “Engineering Students Coping With COVID-19: Yoga,Meditation, and Mental Health,” in 2021 ASEE Virtual Annual Conference Content Access,Virtual Conference: ASEE Conferences, 2021.[21] N. Ban, L. O. Mensah, M. Whitwer, L. E. Hargis, C. J. Wright, J. H. Hammer, and S. A.Wilson, “‘It’s very important to my professors… at least most of them’: How messages fromengineering faculty and staff
interested in self-test learning because they learn tomaster a specific subject and tackle problems in the manner of which they were taught [6]. Highschool students are motivated to learn to earn a good letter grade relating to the subject matter.College students on the other hand are more interested in vocational oriented learning, meaningthey seek to apply problem-solving skills related to the concepts learned to their future careers.College students learn to gain a deeper level of understanding than the surface levelunderstanding high school students aim for [6]. In the same study, the study assessed thecognitive processing strategies of both groups. College students were found to use significantlymore critical processing and analyzing strategies
–984, 2011, doi: 10.1002/tea.20439.[10] D. W. McMillan and D. M. Chavis, “Sense of community: A definition and theory,” J. Community Psychol., vol. 14, no. 1, pp. 6–23, Jan. 1986, doi: 10.1002/1520- 6629(198601)14:1<6::aid-jcop2290140103>3.0.co;2-i.[11] G. Crisp and I. Cruz, “Mentoring College Students: A Critical Review of the Literature Between 1990 and 2007,” Res. High. Educ., vol. 50, no. 6, pp. 525–545, Sep. 2009, doi: 10.1007/s11162-009-9130-2.[12] R. M. Felder and R. Brent, “The National Effective Teaching Institute: Assessment of Impact and Implications for Faculty Development,” J. Eng. Educ., vol. 99, no. 2, pp. 121–134, 2010, doi: 10.1002/j.2168-9830.2010.tb01049.x.[13] W. B. Johnson
establish a new vision of engineering education. Who defines what scholarlymodes of knowledge generation are? And why can’t we?References[1] L. M. Vaughn and F. Jacquez, “Participatory Research Methods – Choice Points in theResearch Process,” Journal of Participatory Research Methods, vol. 1, no. 1, Jul. 2020,Available: https://jprm.scholasticahq.com/article/13244-participatory-research-methods-choice-points-in-the-research-process[2] C. Wang and C. A. Pies, “Family, Maternal, and Child Health Through Photovoice,”Maternal and Child Health Journal, vol. 8, no. 2, pp. 95–102, Jun. 2004, doi:https://doi.org/10.1023/b:maci.0000025732.32293.4f.[3] C. Wang and M. A. Burris, “Photovoice: Concept, methodology, and use for participatoryneeds assessment
Participation Research: Lessons Learned and Future Possibilities,” presented at the 2022 ASEE Annual Conference & Exposition, Aug. 2022. Accessed: Apr. 27, 2023. [Online]. Available: https://peer.asee.org/audio-dissemination-for-qualitative-and- broadening-participation-research-lessons-learned-and-future-possibilities[4] J. Sweller, J. J. G. van Merriënboer, and F. G. W. C. Paas, “Cognitive Architecture and Instructional Design: 20 Years Later,” Educ Psychol Rev, vol. 31, no. 2, pp. 261–292, Jun. 2019, doi: 10.1007/s10648-019-09465-5.[5] T. J. Impelluso, “Assessing Cognitive Load Theory to Improve Student Learning for Mechanical Engineers,” American Journal of Distance Education, vol. 23, no. 4, pp. 179– 193, Dec. 2009
Civil Engineering in 1854 after itsmentor James Thomason’ [4].As of 2020, India ranks second, following China (3.57 million), with 2.55 million STEMgraduates, making it one of the top 11 countries with the number of STEM graduates [5].Despite this fact, Indian education institutions do not find top places in the world ranking [2].This could be because of the emphasis on imparting theoretical knowledge with littlepractical application and extensive practice of memory-based assessments [2]. Some keyissues in the teaching and learning process include a shortage of qualified faculty, outdatedinstructional methods, and inflexible curricula and pedagogies. Theoretical knowledge aloneis not sufficient ‘the learning experience of the students should be
careers inengineering.Discussion In this section, we discuss the major observations from the literature review and their implicationson future directions for engineering education researchers and engineering design educators. We alsoposit the use of digital twins in the capstone and cornerstone projects in engineering design. Forengineering education researchers, future work should focus on evaluating the effectiveness of digitaltwins in improving learning outcomes, particularly in enhancing students' problem-solving abilities,design thinking, and technical skills. From the literature review, we identified that empirical studies areneeded to assess how digital twins influence student engagement, retention, and knowledge transfer toreal
at the faculty level, cultural and insitutional challenges for engineering teaching, and the improvement of faculty development programs.Hong Tran, Purdue Engineering EducationDr. Edward J. Berger, Purdue University at West Lafayette (PWL) (COE) Edward Berger is a Professor of Engineering Education and Mechanical Engineering at Purdue University, joining Purdue in August 2014. He has been teaching engineering mechanics for over 25 years, and has worked extensively on the integration, adoption, adaptation and assessment of instructional systems for mechanics education. His work integrates anthropological lenses that explore how culture affects teaching and learning from both instructor and student perspectives. He
process fosters student agency, pushing them to critically assess thebenefits and drawbacks of AI rather than using it passively.However, there is also value in having students directly engage with AI’s limitations and biases.Many students in the class were surprised and even unsettled by the biases they encountered inAI-generated personas. Their discomfort sparked meaningful discussions about the sources ofthese biases and the broader ethical implications of AI in research and education. Educators canbuild on these moments by designing activities that intentionally expose AI’s flaws, much likeJoy Buolamwini’s facial recognition workshop using drag to trick biased facial recognitionsoftware [19]. By guiding students to uncover and critique AI’s
compliance with security and privacy standards. The VW uses age-appropriate visuals animations and layouts to match developmental stages. The VW incorporates scenarios that allow players to reflect on the consequences of their decisions. The VW fosters empathy through activities that encourage understanding and includes age-appropriate ethical dilemmas to support moral development in young children The VW provides real-time feedback and visual progress indicators to engage players and track achievements. It rewards positive behaviors and discourages harmful actions through constructive feedback. The VW provides session reports and saves artifacts for assessment and reflection. The VW hosts virtual events that bring players parents and educators
theliterature on different types of clinicians (Figure 2).The discussion led by the facilitator was targeted to help students articulate a change in their understanding ofhow ECG signals relate to cardiac behavior (i.e., Kember’s topmost critical reflection stage). This discussion isguided progressively - reflect on individual team’s diagnoses and knowledge of cardiac physiology, reflect oncourse level performance to assess mastery, and then reflect on reasons their performance might be higher thancertain groups of clinicians but lower than Cardiologists. We found that students were surprised that theiraccuracy in diagnosing AF2 was more accurate than most groups of clinicians. As statistics is not a prerequisitefor this class, the discussion here also
Table 2: List of Participants In these semi-structured interviews, participants were first asked about their rationale for signing up for thecourse and their initial assessments, based on syllabi and first day’s instructor presentation overviewing the class.They were then asked to detail the contributions (or lack thereof) of each of the class components – DiscussionLeading and in-class discussions, readings, Reading Responses, Final Project, and Final Paper – on their learning ofthe topics. They were then asked to reflect on their overall experience with the class, in terms of achieving their ownlearning goals and those set out in the syllabus, as well as pointing out specific things they liked and disliked aboutthe course. Finally
of Engineering Leadership,”Daley and Baruah [18] identify six knowledge bases or skills required for engineering leadership,including management skills. Bariraktarova et al. [19] explored leadership training in BulgarianSTEM education, noting the importance of partnerships between businesses and STEM schoolsto offer management training. Zhu et al.’s [20] qualitative study of Chinese engineers'perceptions of engineering leadership found that, under the Four Capabilities Model (4-Cap), theaspect of inventing includes discussions about project management skills as important forleadership. Management skills have also been critical in leadership assessments, particularly theManagerial Behavior Instrument, which aims to measure students
Education V. 1, 2024, pp. 680–686.[10] G. Press, Internet traffic from mobile devices stats (2024), https://whatsthebigdata.com/mobile-internet-traffic/, 2024.[11] L. Su, “Web accessibility in mobile applications of education sector: The accessibility evaluation of mobile apps of higher education sector in portugal,” M.S. thesis, Universidade de Tr´as-os-Montes e Alto Douro, 2021.[12] G. Agrawal, D. Kumar, and M. Singh, “Assessing the usability, accessibility, and mobile readiness of e-government websites: A case study in india,” Universal Access in the Information Society, pp. 1–12, 2022.[13] L. C. Serra, L. P. Carvalho, L. P. Ferreira, J. B. S. Vaz, and A. P. Freire, “Accessibility evaluation of e-government mobile
presented at 2018 ASEE Annual Conference & Exposition, Salt Lake City, Utah. 10.18260/1-2—30888 (2018)[2] Raghu Echempati, New Course Development and Assessment Tools in Automotive Lightweighting Technologies, Paper presented at 2018 ASEE Annual Conference & Exposition, Salt Lake City, Utah. 10.18260/1-2—30837 (2018)[3] Nuno Manuel Mendes Maia and Júlio Martins Montalvão e Silva, eds., Theoretical and Experimental Modal Analysis, Research Studies Press Ltd., Baldock (1997)[4] Jacob P. Den Hartog, Strength of Materials, Dover Publications, New York (1961)[5] Daniel J. Inman, Engineering Vibration, Pearson, 4th edition (2013)[6] Gloria G. Ma, Siben Dasgupta, and Anthony W. Duva, Cantilever Beam
) capabilities,ANSYS is extensively utilized in the design and evaluation of wind turbine blades, offering asuite of tools that enable engineers to address aerodynamic, structural, and thermal challenges. Inwind turbine blade design, ANSYS provides advanced tools for analyzing aerodynamicperformance using CFD modules such as ANSYS Fluent and ANSYS CFX. These tools simulateairflow around the blade, assess lift and drag forces, and calculate power coefficients undervarious wind conditions. Engineers can use these insights to optimize blade geometry, twist, andchord distribution for maximum energy capture. Blades are subjected to complex and varyingloads, including aerodynamic forces, gravitational effects, and centrifugal forces. Using tools likeANSYS
responses from the Fall 2024 cohort to score their quantitativeanalysis (application of engineering anthropometry) and to determine if the design seeds(presented in Table 4) embedded in the qualitative interview transcripts by the research teamwere picked and implemented in the workstation design. This is done based on a three-levelscoring system: level 0 if the seed is absent or not considered at all, level 1 if it is minimallyconsidered but not fully developed or justified, and level 2 if it is adequately considered with areasonable justification. Also, there is an extra point when students used design seeds planted byChatGPT in the qualitative transcripts in the workstation design. This scoring system allows usto assess how effectively students
Community Norms by Agarwalla et al. (2024). Other practical strategies includedesigning feedback and assessment opportunities on the MIRN’s culture, and providingresources not only for academic research purposes, but also for professional and personaldevelopment related to researcher well-being. Leaders and administrators of multi-institutionalresearch networks are encouraged to adopt these recommendations and seek to exploreframeworks for building successful research networks during the initial setting up phases.Future research should explore the long-term impact of psychological safety on researcherproductivity and well-being across researcher roles, as well as strategies for scaling thesepractices to broader institutional contexts. Comparative
: Python simulation at T2 (frame #11) Once the Python simulation was completed, the student transitioned to implementing theYOLO framework. YOLO is an object detection system known for its real-time detection. Byutilizing this framework, the collision detection system could swiftly assess the threat before itgets too close. As mentioned earlier, this system used YOLO3D, a specialized version of YOLO wherethe bounding boxes around the vehicles are 3D, allowing us to detect the angles it creates. Thecode environment was set up on Google Colab, a Jupyter Notebook service that would providefree access to GPUs, accelerating inference. However, the student encountered a significantchallenge: YOLO3D, being a somewhat dated open-source
. Curretnly at The Citadel teaching full time.Dr. Alyson Grace Eggleston, Pennsylvania State University Alyson Eggleston is an Associate Professor in the Penn State Hershey College of Medicine and Director of Evaluation for the Penn State Clinical and Translational Science Institute. Her research and teaching background focus on program assessment, STEM technical communication, industry-informed curricula, and educational outcomes veteran and active duty students.Dr. Robert J. Rabb P.E., The Pennsylvania State University Robert Rabb is the associate dean for education in the College of Engineering at Penn State. He previously served as a professor and the Mechanical Engineering Department Chair at The Citadel. He
participant mentioned learning to adapt theirfacilitation approach “on the fly” after realizing that a fixed planned approach may not resonatewith a particular group of students, requiring instructional flexibility, while others discussed howover time they gained more comprehensive and nuanced understandings of student needs andused those understandings to adapt their plans and facilitation strategies. One participant shared: “I learned to identify key learning objectives that were practically achievable within a set amount of time. I learned how to implement active learning strategies, and how to adapt to the flow of these activities during class time. I learned how to assess student learning within the context of
statistical testwas not explicitly conducted for this distribution, the proportions highlight a notable differencein participants' expertise levels between the two domains.Learning with simulations and gamesParticipants' agreement with statements related to learning from online simulations and gameswas analyzed using a five-point Likert scale. The responses were treated as rank-ordered data,and the distributions were assessed using descriptive statistics and Wilcoxon signed-rank tests tocompare responses against a neutral midpoint of three. Table 1 shows the proportion ofresponses for the different equations. Table 1. Proportion of responses Strongly
(PMP). Paul is currently completing a M.Sc. in Engineering Education at the University of Saskatchewan, studying the career pathways of engineering graduates.Dr. Jason Grove P.E., University of Waterloo Jason Grove is the Graduate Attributes Lecturer in the Department of Chemical Engineering at the University of Waterloo. He is responsible for leading the continuous program assessment improvement process for the chemical and nanotechnology engineering prDr. Carolyn G. MacGregor P.Eng., University of Waterloo Carolyn MacGregor is the Associate Dean, Teaching for the Faculty of Engineering and an Associate Professor in the Department of Systems Design Engineering at the University of Waterloo. Prof. MacGregor has been
through the ASCE ExCEEd teaching workshop.” J. of Civil Engineering Education 150, no. 1 (August). https://doi.org/10.1061/JCEECD.EIENG-1954.[4] Estes, A. C. 2019. “Diversity, Inclusion and the ExCEEd Teaching Workshop.” ASEE Annual Conference & Exposition, (June). 10.18260/1-2--32671.[5] Niewiesk, S., and E. G. Garrity-Rokous. 2021. “The academic leadership framework: A guide for systematic assessment and improvement of academic administrative work.” Applied Research 40 (4): 50-63. https://doi.org/10.1002/joe.22083.[6] Estes, A., and R. Welch. 2007. “Orientation For New Department Heads.” ASEE Annual Conference & Exposition. https://doi.org/10.18260/1-2--1967.[7] Kruse, S. D. 2022. “Department chair leadership