Engineering Education.3. Steif, P.S. and M. Hansen, Comparisons between performances in a statics concept inventory and course examinations. International Journal of Engineering Education, 2006. 22(5): p. 1070.4. James Giancaspro, P., Just a Moment–Classroom Demonstrations for Statics and Solid Mechanics, in 2019 ASEE Annual Conference & Exposition. 2019: Tampa, Florida.5. Davishahl, E., Statics Modeling Kit: Hands-On Learning in the Flipped Classroom, in 2018 ASEE Annual Conference & Exposition. 2018: Salt Lake City, Utah.6. Sarker, M.R., et al., WIP: Hands-on Engineering Mechanics with a Three-dimensional Laboratory Unit. ASEE Conferences: Virtual On line.7. Md Rashedul Hasan, S., et al
pursuing his doctoral studies in the School of Education & Human Development at the CU Denver with a focus on higher education leadership. His research interests include educational access and equity, particularly as it pertains to historically marginalized students in engineering.Karen C Crouch, University of Colorado Boulder Karen Crouch, Instructional Design and Technology Consultant at the University of Colorado Boulder. She holds a master’s in education with a specialization in international and comparative education from Northcentral University and a bachelor’s degree in international affairs from CU Boulder. She brings 15 years of experience of working in education at international and K-12 levels and has been at
sessions, likely aimed at evaluating their experience or performance within the simulated scenario. A structured process for conducting user activities highlights key actions and monitors mechanisms involved throughout the simulation task for each participant. Figure 3. Participant’s activities in data collection Preliminary Data AnalysisAs illustrated in the flowchart (Figure 3) this study has started data collection from two hapticgloves. Both gloves are set up in a similar fashion within Unity Engine and similar computerswith identical hardware and software properties. Randomly selected students completed thegiven tasks in different time frames. The fastest time to pick up an object was
groups and write reports on experiments performed [1],[2].Historically, the emphasis on laboratory in engineering education has varied [1] and relativelyless literature is available on laboratory. A review of the Journal of Engineering Educationliterature shows that during the first five years of the journal’s history (1993-1998) only 6.5percent of the papers used laboratory as a keyword. This number reduced to an even lower 1percentage of 5.2 in the next five years (1998-2002) [3]. Laboratory curricula are often designedwith the goals of 1) relating theory to practice [4-8] and 2) increasing the motivation of studentsto pursue engineering education [9,10]. One method of assessing laboratory goals
health, a lack of self-awareness or dysfunctional self-awareness can exacerbate issueswithin the mentoring relationship. This may occur if they are misinterpreting or ignoring howothers perceive them [2], [5]. If self-awareness is our ability to see ourselves by becoming theobject of our attention [3], a lack of self-awareness may suggest that one cannot “see” orunderstand themselves internally. They cannot perceive how others may be perceiving thembecause they are unaware of the influence of their thoughts, emotions, and behaviors on theirenvironment. STEMM (science, technology, engineering, mathematics, and medicine) graduatestudents who engage in research mentoring relationships need to exercise a healthy degree ofself-awareness while
people around me expectme to succeed at everything I do.” 3. Other-oriented Perfectionism (OOP), e.g., “I do not have veryhigh standards for those around me.” The internal consistency reliability for SOP, SPP and OOP is0.86, 0.87, and 0.82, respectively [8].Hewitt and Flett’s Multidimensional Perfectionism Scale (MPS) represents a holistic instrumentof perfectionism across three dimensions of self-oriented, other-oriented, and socially prescribedperfectionism. The instrument has been widely adopted in the literature and has been cited morethan 5,000 times according to the Google Scholar search. Its strength lies in its ability to capturethe nuanced ways that perfectionism can manifest, allowing for a deeper understanding of itsimpacts on
. 35. , Cham: Springer International Publishing, 2020, pp. 277–349. doi: 10.1007/978-3-030-31365-4_4.[12] E. M. Holcombe, A. J. Kezar, N. Ueda, and D. Vigil, “Shared equity leadership: Working collectively to change campus cultures,” Journal of Diversity in Higher Education, Dec. 2023, doi: 10.1037/dhe0000536.[13] G. P. King, T. Russo-Tait, and T. C. Andrews, “Evading race: STEM faculty struggle to acknowledge racialized classroom events,” LSE, vol. 22, no. 1, p. ar14, Mar. 2023, doi: 10.1187/cbe.22-06-0104.[14] H. N. McCambly, “Rising tides don’t create racialized change: Analyzing institutional change projects in postsecondary philanthropy’s college completion agenda,” The Journal of Higher Education, vol. 95, no. 4, pp
predictive evidence ofvalidity for three help-seeking intention instruments: ISCI, GHSQ, and MHSIS,” Journal ofCounseling Psychology, vol. 65, no. 3. p. 394, 2018.[15] J. H. Hammer, M. C. Parent, and D. A. Spiker, “Mental Help Seeking Attitudes Scale(MHSAS): Development, reliability, validity, and comparison with the ATSPPH-SF andIASMHS-PO,” Journal of Counseling Psychology, vol. 65, no. 1. p. 74, 2018.[16] E. Lee, M. Y. Hu, and R. S. Toh, “Respondent non-cooperation in surveys and diaries: ananalysis of item non-response and panel attrition,” International Journal of Market Research,vol. 46, no. 3. pp. 311–326, 2004.[17] A. M. Gibson and N. A. Bowling, “The effects of questionnaire length and behavioralconsequences on careless responding,” European
theory then developed to become an essential part of fluid mechanics theory acrossdisciplines in engineering sciences [2]. The theory is described in many textbooks, for example[3, 4], and is briefly summarised in the Appendix A.Learning about boundary layers is challenging. We decompose the problem here into threesequential challenges which provide a kind of ‘problem definition’, to which this paper proposesand evaluates a solution.1.1 Three challenges in learning boundary layer theoryThe first challenge stems from the fact that boundary layers cannot be seen in everyday life.Despite technical information such measured data and theoretical results, the existence of thelayers is often not intuitively clear to students. Education
experimental platforms in chemistry laboratory education and its impact on experimental self-efficacy," INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION, vol. 17, no. 1, 07/09/ 2020, doi: 10.1186/s41239-020-00204-3.[10] D. May, L. T. Smith, and C. Gomillion, "Student motivation in virtual laboratories in bioengineering courses," in 2022 IEEE Frontiers in Education Conference (FIE), 2022: IEEE, pp. 1-5.[11] C.-H. Huang, "Using PLS-SEM Model to Explore the Influencing Factors of Learning Satisfaction in Blended Learning," Education Sciences, vol. 11, no. 5, p. 249, 2021. [Online]. Available: https://www.mdpi.com/2227-7102/11/5/249.[12] I. D. Dunmoye, D. Moyaki, A. V. Oje, N. J. Hunsu
course is that sharing the pool ofprojects across both courses reduces the total number of projects required to be solicited andapproved each cycle, allowing faculty to focus on recruiting a smaller number of higher qualityprojects. Another benefit in combining the courses is the increased alignment between the goalsof the M.Eng. program and the undergraduate program. Whereas the undergraduate programfulfills many ABET criteria through the technical execution of a capstone project [27,28], theM.Eng. program objectives are amenable to providing management experience morerepresentative of the kinds of industry positions M.Eng. graduates typically seek [22,25]. Thesetwo major objectives complement well and lend themselves to more complex team
Boulder, where she helped develop the first large-enrollment introductory physics course-based research experience (CURE). ©American Society for Engineering Education, 2025Faculty Espoused versus Enacted Beliefs on Teamwork in Engineering Education: Results froma National Faculty SurveyIntroductionTeamwork is a cornerstone of engineering education, equipping students with the necessary skillsand experiences to navigate the complexities of engineering practice [1], [2], [3]. While studiesshow the importance of imparting teamwork-based skills and processes to successfully collaborate,there is a notable gap in literature regarding specific teamwork-related motivations, objectives, andgoals beyond those outlined by
outlet of Fountain B tothe center of the target ring, and (3) the flowrate QB that they must achieve for Fountain B. Theyuse the remainder of the two-hour lab session to perform empirical tests on the supporting fountain.They may test two standard plastic spheres, various tube sizes, and various fountain angles. Foreach fixed set of these parameters, they should determine QA when the sphere is held at the desiredheight ysphere.Week 2. This week is used for experiments related to major losses through the tubes. Each teamis given 6-foot lengths of straight tubing in the four sizes, as well as special connectors that allowthem to measure the pressure drop through the tube. Flexible tubing is provided for them toconnect to one of two pressure
the pedagogy. This paperproposes a novel solution to address these challenges by leveraging the power of artificialintelligence (AI) and computer vision (CV) to automate and enhance the classroom observationprocess.Classroom observation is a widely used method for evaluating and improving teaching andlearning practices in STEM education, as it provides rich and detailed information on thebehaviors, interactions, and activities of students and instructors in the classroom. However,current observation protocols, such as the Laboratory Observation Protocol for UndergraduateSTEM (LOPUS) [3] and the Classroom Observation Protocol for Undergraduate STEM(COPUS), rely on human observers who manually record and code the data using paper-based
19% vague/unspecifiedFigure 4. Participants of troubleshooting studies For theoretical grounding (Figure 5), across all troubleshooting papers (whether they includedan empirical study component or not), most of them (60%) grounded their work in some establishedtroubleshooting or problem-solving theory. The most comprehensive and widely-cited among themincluded: 1) Johnson’s Technical Troubleshooting Model [14], [20], 2) Jonassen’s catalog ofproblem typologies and design theory of problem solving [15], [21], 3) Ross & Orr’s DECSARtroubleshooting method [22], and 4) Schaafstal & Schraagen’s task analysis troubleshootingapproach [16]. Other subsets of papers framed their work on troubleshooting using a mix
collection that captureswomen undergraduate students’ experiences of EIJ and their conceptualizations of personalepistemology. The impact of the piloting phase on the larger study includes instrumentrefinement and skill development to collect rich data through effective narrative interviewingtechniques. Future work will leverage this instrument to generate narratives of epistemicinjustice and educate engineers on how injustice manifests and can be countered to foster betterexperiences for women.IntroductionWomen are underrepresented in engineering [1], [2]. Women’s underrepresentation perpetuatesthe male domination of the engineering field and the subsequent oppression hegemony inflicts[3], including stereotypes against women [4], [5] and gender
. and Technol. Educ 15, no. 1: 12-18, 2017. [2] A. Hofstein, and V. N. Lunetta. "The laboratory in science education: Foundations for the twenty‐first century." Science education 88, no. 1: 28-54, 2004 [3] D. A. Bergin, "Influences on classroom interest." Educational Psychologist 34, no. 2: 87- 98, 1999. [4] N. Holstermann, D. Grube, and S. Bögeholz. "Hands-on activities and their influence on students’ interest." Research in science education 40: 743-757, 2010. [5] L. E. Carlson, and J. F. Sullivan. "Hands-on engineering: learning by doing in the integrated teaching and learning program." International Journal of Engineering Education 15, no. 1: 20-31, 1999. [6] A. Johri, and B. M. Olds, eds
greatly outweighs the capitalcost.The students have received the described device well, with countless possible applications.Current plans include modifying this device to incorporate a piezoelectric device andtemperature sensor so that students can control the internal temperature of the spectrometer. Thisaddition would allow the student to investigate the effects temperature may have on diffusionwhile increasing the versatility of the timelapse spectrometer. This would also allow thespectrometer to be utilized for kinetics lab experiments, both with and without enzymes, thatprovide a colorimetric response. There are also plans to 3-D print a camera holder capable ofsuspending the camera over a petri dish so the time-lapse experiments can be
) Next, instructors can ask students to brainstorm ideas and sketch their designs for their fish. Students should consider how the actuation chambers are integrated into the fish. ○ Figure 2b shows a hand sketch of the fishtail. 3) An initial prototyping process can include the fabrication of simple rectangular shapes that focus on understanding the basics of hydraulic actuation as demonstrated in the initial prototypes (see Figures 2a and 3). ○ The goal of these prototypes for our own process was to learn and understand how silicone behaves and expands when water or another fluid is pumped into the internal chambers. As seen in Figure 2a, the original fishtail prototype is
processing. A screenshot of the application developed as part ofthe project is shown in Figure 3. The application has 4 stages, demarked by the four tabs on theleft side of the application: 1. Calibration: to calibrate the camera and find the magnification factor, which is calculated as the ratio of the camera image size to the field of view (FOV). © American Society for Engineering Education, 2024 2024 ASEE Annual Conference Figure 3 - PIV application developed based on PIVlab. 2. Image Pre-processing: to minimize noise and adjust camera intensity recordings. A side- by-side comparison of a raw image before and after processing is shown in Figure 4
has been the co-director of the Consortium to Promote Reflection in Engineering Education (CPREE, funded by the Helmsley Charitable Trust), a member of the governing board for the International Research in Engineering Education Network, and an Associate Editor for the Journal of Engineering Education. Dr. Turns has published over 175 journal and conference papers on topics related to engineering education. ©American Society for Engineering Education, 2025 (WIP) Beyond Implementation: Exploring Research through Design to Elevate Everyday Educational Design in Engineering EducationThis work-in-progress methods paper contributes to seeing how Research
). Students with afixed mindset will fail a test and think “I am dumb” while students who have a growth mindsetwith think “I need to study harder”.There have been many studies looking at growth mindset and student success. In 2019, a largestudy of 15,000 students and 150 STEM faculty showed that the racial achievement gap waslarger in classes where the faculty had a fixed mindset [2]. A review study in 2021 found mixedresults in incorporating growth mindset interventions [3], while another study found that theseinterventions are most beneficial for students from lower socioeconomic backgrounds and forminority students [4]. Specifically, one growth mindset intervention study found that a growthmindset for students of lower-income families
,observable behaviors. This can be particularly useful in engineering courses, when theinstructor’s goals include active learning. Learning analytics can provide a passive, non-intrusive approach to collecting data on learners’ interactions with their learning contexts butintroduces the complex challenge of interpreting the data [2]. Understanding the complexity ofteaching practices requires more than simple statistics; it requires visualization that connects thedata to the lens through which it is being analyzed. To make sense of the data, an understandingof the pedagogical and technical context from which the data was generated is required [3].As a result, there is a missed opportunity to use information that could inform institutions abouthow
explore the long-term effects on student learning and faculty researchproductivity.INTRODUCTIONIntegrating research into undergraduate engineering pedagogy has many benefits, includingimproving both students’ technical skills and self-efficacy [1]. Studies have shown that studentswho participate in undergraduate research have more enthusiasm towards STEM research, reportincreased feelings of belonging in their field, and demonstrate improvement in their ability tothink like a scientist [2], [3]. These benefits are especially meaningful in the case of developingunderrepresented or minority students [4], [5], [6]. Undergraduate research experiences also leadto greater retention rates [7], more students pursuing graduate level education [8], [9
course in the summer but have itpaid for with their fall tuition. Students were then able to attend the class and begin research as atransition into their university careers.The class was structured as an intensive research experience. Students met three times per week formultiple hours per day over a three week period in late summer. The technical theme for the class waswater quality and wave energy and the main goal was for students to develop an engineeringsolution/prototype to address a problem in their selected research area. The transformation theme of theclass was STEM research identity formation. Our choice of technical topics was intentional with the goalof selecting projects that were tied to issues that were of interest to a wide
].Faculty communities of practice provide a promising pathway for addressing these systemicissues by creating collaborative spaces where educators can share practices, develop innovativeteaching strategies, and reflect on their professional growth. Unlike short-term professionaldevelopment programs, CoPs foster sustained, peer-driven collaboration that empowersfaculty to align their teaching practices with their values, overcome institutional challenges,and contribute to inclusive learning environments. Research highlights the transformativepotential of CoPs in fostering long-term change, particularly by focusing on faculty beliefs,values, and institutional cultures rather than short-term interventions [3]. For example, CoPscan help redefine
individualizedattention, which emboldens them to take up not only academics but also their career goals. Dr.Smith's commitment is one good example of how mentors can affect students.Subtheme 3: Building Confidence and Independence Through MentorshipMentorship also plays a significant role in shaping students' professional identities. Phil shareshow his professor, Dr. Patel, guided him not just through technical projects but also created a safespace for students to ask him for life advice. Phil explains: Outside of my peers in engineering, I think that Professor Patel is also supportive. He’s my favorite professor here. He's great. He does computer architecture classes here. I think he’s the most inspiring professor for me, and he's very
learning opportunities within their respectivecommunities.The program objectives aim to support accelerated math mastery, resulting in algebra readiness in middleschool, include the following: (1) Improved student attitudes towards mathematics and school, writ large;(2) Improved academic performance in mathematics; and (3) Achievement of a performance level thatmeets, or exceeds, proficiency for Common Core State Standards (CCSS).The Ab7G program framework is designed around four core tenets: Integration, Acceleration,Engagement, and Research. 1. Integration: Incorporation of enhanced math rigor and STEM exposure into the base programming of the host organization; 2. Engagement: Commitment to regular session attendance and fulfilling Ab7G
of the ENGR 111 course was analyzed with independent samples t-test to explore ifthere were significant differences in these key constructs that could be ascribed to the onlinemakerspace format vs. normal face-to-face.1. Course DescriptionIn the fall of 2014, the J. B. Speed School of Engineering (SSoE) at the University of Louisville(UofL) commenced an endeavor to overhaul the institution’s existing course(s) focused onintroducing students to the fundamentals and profession of engineering. After a nearly two-yearperiod of development, the resultant two-course sequence, required for all first-year engineeringstudents, was inaugurated in the Fall 2016 semester [1-3]. The first component of this sequence,Engineering Methods, Tools, &
to improve student persistence.1 IntroductionTechnological innovation depends on a qualified and diverse engineering workforce [1, 2]. Toremain internationally competitive, the US needs to improve recruitment, retention, and prepara-tion of undergraduate engineering students, focusing particularly on improving the representationof underrepresented minorities [3]. This paper considers a broad range of factors that have beenfound to predict students’ persistence through the first year of undergraduate engineering school, Figure 1: SEVT, as presented in [5]with the goal of identifying potential interventions for improvement. The work is grounded in Situ-ated Expectancy Value Theory (SEVT), which describes and