interventions to improve student success. He was most recently recognized by INSIGHT Into Diversity Magazine as an Inspiring STEM Leader, the University of Illinois at Urbana-Champaign with the College of Liberal Arts & Sciences (LAS) Outstanding Young Alumni Award, Career Communications Group with a Black Engineer of the Year Award for college-level promotion of engineering education and a National Science Foundation CAREER Award in 2023 to advance his work that centers engineering identities of Black men in engineering.Dr. Sherri S Frizell, Prairie View A&M University Sherri S. Frizell is a Professor in the Computer Science Department at Prairie View A&M University. She has a B.S. in Computer Science from
, Texas A&M University Blaine is currently a graduate student earning his Ph.D. in Educational Psychology with an emphasis in Research, Measurement, and Statistics at Texas A&M. His research is primarily focused on issues of equity in STEM education.Camille S. Burnett, Prairie View A&M University Camille S. Burnett, Ph.D., ACUE, is Assistant Professor of Mathematics Education and Director of the SMaRTS (Science, Mathematics, Reading, Technology, and Social Studies) Curriculum Resource Lab in the Department of Curriculum and Instruction at Prairie View A&M University. She has almost 20 years of combined experience in the K-12 and higher education settings. She is also the principal investigator for
of competence ● Validated and reliable student performanceTo answer the Research Question, teaching can be redesigned to support students in transfertheir knowledge and skills by integrating the transfer of learning and authentic assessmentconcepts displayed in Table 1.MethodologyBased on an undergraduate engineering program at the Singapore Institute of Technology,students are exposed to simulations using finite element analysis (FEA) and computationalfluid dynamics (CFD) as part of their Year 1 engineering foundation [15], [16]. In Year 2, thestudents are taught the Mechanical Simulation (M&S) module to learn how to solve ill-structured
students’ learning. The students were also encouraged to ask questions and interactwith their peers.InstrumentsThis study comprised multiple data sources: an open-ended questionnaire, classroomobservation, and an S-STEM survey. The open-ended questionnaire consisted of five questionsdesigned to probe students to share their experiences of the problem-based learning environment.The students were provided the opportunity to address their likes and dislikes regardingengineering learning through PBL and describe the strategies they used to solve each problemscenario [10]; [34].Classroom observations were conducted throughout the duration of the study. The commentsentailed the teacher and the students. The implementation of the lessons, pedagogy, and
. Educ. Psychol., vol. 99, no. 2, pp. 397–420, 2007, doi: 10.1037/0022-0663.99.2.397.[5] V. Simms, S. Clayton, L. Cragg, C. Gilmore, and S. Johnson, “Journal of Experimental Child Explaining the relationship between number line estimation and mathematical achievement : The role of visuomotor integration and visuospatial skills,” J. Exp. Child Psychol., vol. 145, pp. 22–33, 2016, doi: 10.1016/j.jecp.2015.12.004.[6] V. Crollen and M. Noël, “Journal of Experimental Child Spatial and numerical processing in children with high and low visuospatial abilities,” J. Exp. Child Psychol., vol. 132, pp. 84–98, 2015, doi: 10.1016/j.jecp.2014.12.006.[7] P. G. Clifton et al., “Design of embodied interfaces for
the engineeringeducation context which included SNA. To guide our research toward the study purpose, weprepared the following Research Questions (RQs):RQ1: What is the current breadth of SNA in the engineering education context?RQ2: What areas of SNA in engineering education warrant systematic review(s)?For this research brief, we present key publication, study context, and methodological trends inthe data through an analysis of code frequency. Specifically, we will focus on findings related toRQ1 by identifying the number of records that included each code.MethodologyA scoping review, as presented by Grant and Booth “provides a preliminary assessment of thepotential size and scope of available research literature” [30, p. 95]. We selected
, he completed his MSc Degree in Construction Management from Istanbul Technical University in 2019. For his MSc thesis, he focused on the integration of Building Information Modeling (BIM) in facilities management. Before joining MSU, he worked as a research & teaching assistant at ITU from 2017 to 2021.Andreana Louise RoxasDr. Kristen Sara Cetin P.E., Michigan State University Dr. Kristen S Cetin is an Associate Professor at Michigan State University in the Department of Civil and Environmental Engineering.Dr. Annick AnctilGeorge Berghorn, Michigan State UniversityRyan Patrick Gallagher ©American Society for Engineering Education, 2023 Developing and Evaluating a Virtual Training
practices in US classrooms," Teach. Teach. Educ., vol. 99, p. 103273, Mar. 2021, doi: 10.1016/j.tate.2020.103273[3] M. J. Hannafin, J. R. Hill, S. M. Land, and E. Lee, "Student-centered, open learning environments: Research, theory, and practice," Handbook of Research on Educational Communications and Technology, pp. 641-651, May 2013, doi: 10.1007/978-1-4614- 3185-5_51[4] B. L. McCombs and J. S. Whisler, The Learner-Centered Classroom and School: Strategies for Increasing Student Motivation and Achievement. The Jossey-Bass Education Series. San Francisco, CA: Jossey-Bass Inc., 1997.[5] J. N. Agumba¹ and T. Haupt, "Collaboration as a strategy of student-centered learning in construction technology
, DC, pp. 1– 77, 2012.[5] National Research Council, “Promising Practices in Undergraduate Science, Technology, Engineering, and Mathematics Education: Summary of Two Workshops,” The National Academies Press, Washington, DC, 2011. Accessed on 13 June 2016 from http://www.nap.edu/catalog.php?record_id=13099[6] T. A. Litzinger and L. R. Lattuca, “Translating Research into Widespread Practice in Engineering Education,” in A. Johri and B. Olds. (Eds.), Cambridge Handbook of Engineering Education Research, Cambridge University Press, New York, pp. 375–392, 2014.[7] S. Zappe, K. Hochstedt, E. Kisenwether, & A. Shartrand, “Teaching to innovate: Beliefs and perceptions of instructors who teach
educators achieve this much-needed broader vision.References[1] M. E. Cardella, “Early childhood engineering: Supporting engineering design practices with young children and their families,” presented at the NARST 2020 Annual International Conference, Portland, OR, Mar. 2020. [Online]. Available: https://www.researchgate.net/publication/340234317_Early_Childhood_Engineering_Supp orting_Engineering_Design_Practices_with_Young_Children_and_Their_Families[2] National Academies of Sciences, Engineering, and Medicine, Science and engineering in preschool through elementary grades: The brilliance of children and the strengths of educators. Washington, DC: National Academies Press, 2021, p. 26215. doi: 10.17226/26215.[3] S. A
double coded. A Cohen’sKappa value of κ = 0.72 across all three rounds was achieved, indicating strong inter-raterreliability and agreement beyond chance [39].3. ResultsSince this is a research brief, only the results of the Delphi study after round 3 will be presentednext. The full set of results from rounds 1 through 3 will be presented in a forthcoming article.Relevance Ratings. The average relevance ratings (on a 1-5 scale) and standard deviations (s)for the various conceptions of judgment given in Table 1 during the final Delphi round rangedfrom 3.00 (s=1.17) to 4.88 (s=0.33) based on n=17 responses. Decision making (item 7 in Table1) had the highest average relevance of 4.88 and the smallest standard deviation of s=0.33,indicating the
(3–5). Teacher with student team. Teams Students act across or between teams. Teacher with multiple teams. Class Students act as whole class. Teacher with whole class. Code Student Action or Teacher (Instructor) Action Answer Answer question(s) posed by other(s). Ask Ask question(s) and wait for other(s) to answer. Discuss Talk back and forth (more than one question and answer). Speak Talk by one person with no interaction. Manage Pass out or collect papers, assign groups, take attendance. Distracted Distracted or off task. Watch/Listen Watch or listen (e.g., to lecture or presentation). Work Write, take notes, work on
questions that relied heavily within the applying, analyzing,and evaluating levels of knowledge from Bloom’s revised taxonomy, building upon the lowerlevels of knowledge like remembering and understanding, but not asking questions that focusedwithin those lower levels. Differences between these collected domain-specific studies are basedheavily on the intentions of the surveys. Turner et al. [31]’s survey is intended for a widerpopulation of US adults and to establish a concept inventory for energy and power grid knowledge.Basic energy knowledge questions are included in Turner et al.’s survey, but a majority of thequestions require higher-level energy knowledge applied specifically to power grid use andinfrastructure. While Prince et al. [32]’s
diverse student populations, ultimately enhancing their sense ofbelonging and academic performance in STEM fields.AcknowledgementsThis work was supported through funding by the National Science Foundation IUSE Grant No.2111114/2111513. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the National ScienceFoundationReferences[1] National Center for Science and Engineering Statistics (NCSES), “Diversity and STEM: Women, Minorities, and Persons with Disabilities,” Alexandria, VA, 2023. Accessed: Oct. 24, 2024. [Online]. Available: https://ncses.nsf.gov/pubs/nsf23315/[2] R. M. Felder, G. N. Felder, and E. J. Dietz, “A
gratefully acknowledge the alumni participants in this study and the contributions ofour research team. Finally, we acknowledge the generous support of this work from theHasso Plattner Design Thinking Research Program.References1. National Academy of Engineering, U. S. (2004). The engineer of 2020: Visions of engineering in the new century. Washington, DC: National Academies Press.2. Wigner, A., Lande, M., & Jordan, S. S. (2016). How can maker skills fit in with accreditation demands for undergraduate engineering programs?. In 2016 ASEE Annual Conference & Exposition.3. Trilling, B., & Fadel, C. (2009). 21st century skills: Learning for life in our times. John Wiley & Sons.4. ABET Student Learning Outcomes, Retrieved from
improveretention, researchers have applied asset-based perspectives to studying retention of marginalizedstudents. This approach often emphasizes the role of social capital [1], [11] and socializers [12]–[14] as primary drivers of motivation to pursue STEM education and careers. This present paperbegins to unpack the unique relationship between socializers and the decision students atminority serving institutions (MSIs) make to pursue STEM. We report on the experiences ofstudents gathered using qualitative methods and examined through the lens of expectancy valuetheoretical framework.Theoretical Framework: Expectancy-ValueMotivation to pursue a career in STEM can be modeled through Eccles et al.'s Expectancy-Valuetheory (EV) [15]. EV establishes a direct
. She is also serving as the Principal Investigator on the college’s NSF S-STEM grant, Building an Academic Community of Engineering Scholars.Carrie Kortegast, Northern Illinois University ©American Society for Engineering Education, 2025 Guides on the transfer journey: A qualitative study exploring the academic and social supports of community college transfer studentsIntroductionThis research brief explores the community college student’s transfer journey guided by thetransfer student capital and engineering identity frameworks. Academic supports, socialrelationships, and experiential learning are common programmatic approaches to fostering asense of belonging and engineering identity
definition highlights the depth and complexity of successful mentoring. After a close review of theliterature, we opted for sticking to [31]’s identification of 4 latent variables that were validated by [32] in 2009 forthe College Student Mentoring Scale. The variables underlying the mentor-protégé relationship at the collegiatelevel involve (a) Psychological and Emotional support, (b) Degree and Career Support, (c) Academic SubjectKnowledge Support, and (d) the Existence of a Role Model. While more testing is needed to validate theseconstructs in a variety of settings, it provides an important starting point for a contextually sensitive mentoringstudy. A definition with this level of theoretical specificity can be helpful for assessing program
missed some important articles published before 2017, which could haveprovided some more critical insights into this study. A potential direction for future researchwould be exploring the use of all social media platforms in engineering and its impact on studentlearning.REFERENCESThe articles included in the preliminary review are marked with an asterisk (*).[1] N. S. Hawi and M. Samaha, "The relations among social media addiction, self-esteem, and life satisfaction in university students," Social Science Computer Review, vol. 35, no. 5, pp. 576-586, 2017.[2] I. C. Drivas, D. Kouis, D. Kyriaki-Manessi, and F. Giannakopoulou, "Social Media Analytics and Metrics for Improving Users Engagement," Knowledge, vol. 2, no. 2, pp
(S-STEM) grant to increase engineering degree completion of low-income, high achievingundergraduate students. The project aims to increase engineering degree completion byimproving student engagement, boosting retention and academic performance, and enhancingstudent self-efficacy by providing useful programming, resources, and financial support (i.e.,scholarships). This work is part of a larger grant aimed at uncovering effective strategies tosupport low-income STEM students’ success at HBCUs. The next section will discuss thebackground of this work.Keywords: Historically black colleges/universities (HBCUs), learning environment,undergraduate, underrepresentationBackgroundA public historically black land-grant university in the southeastern
computing education and its longitudinal impact on ethical decision making. Futurestudies could also measure the framework’s impact on students’ problem-solving abilities,especially when dealing with even more complex, real-world security or privacy challenges.AcknowledgementThis research is supported by the National Science Foundation (Award #: 2335681).AppendixS. Shin, J. Lee, S. Lim, and S. Shin. “Draft of ethical motivation and behavioral intention surveyin engineering education,” American Society for Engineering Education Annual Conference,June 22-25, 2025, Montreal, Canada, 2025.Sample Survey Items for Ethical Motivation and Behavioral IntentionWe used a 6-point Likert scale (including “Don’t Know” as an option) for this survey. Thesurvey is
based learning environment. She was previously an engineering education postdoctoral fellow at Wake Forest University supporting curriculum development around ethics/character education.Dr. Diana Bairaktarova, Virginia Tech Dr. Diana Bairaktarova is an Assistant Professor in the Department of Engineering Education at Virginia Tech. Through real-world engineering applications, Dr. Bairaktarovaˆa C™s experiential learning research spans from engineering to psychology to learning ©American Society for Engineering Education, 2023 Empathy and mindfulness in design education: A literature review to explore a relationshipAbstractLearning to design in undergraduate
publications in the context of US No data range Include all publications until the date of the literature searchAbstract Review To test the initial inclusion criteria, a pilot abstract review was conducted. Thisabstract review was conducted using Rayyan (https://www.rayyan.ai), a collaborative systematicliterature review software for organizing, sharing, managing, and preserving records and data.Following Polanin et al.'s (2022) best practice guidelines, only 10% of the retrieved literature wasreviewed for the pilot study. The objectives were two-fold: 1) Search strategy refinement, aimingto further refine the inclusion criteria and their working definitions, and 2) Project management,to estimate an approximate number or
, pp. 151–185, 2011.[6] Elementary science teachers’ sense-making with learning to implement engineering design and its impact on students’ science achievement[7] C. M. Cunningham and G. J. Kelly, “Epistemic Practices of Engineering for Education,” Science Education, vol 1010, no. 3, pp. 486–505, 2017.[8] T. J. Moore, A. W. Glancy, K. M. Tank, J. A. Kersten, K. A. Smith, and M. S. Stohlmann, “A Framework for Quality K-12 Engineering Education: Research and Development,” Journal of Pre-College Engineering Education Research (J-PEER), vol. 4, no. 1, 2014.[9] American Society for Engineering Education and Advancing Excellence in P12 Engineering Education. Framework for P-12 Engineering Learning, 2020
other contexts were not considered.• The research should incorporate at least one significant finding related to the discrimination encountered by Asian engineering students, even if this is not the primary research question the study aims to address. After refining the search criteria, we identified nine studies. These studies arelisted in Table 1.Table 1Selected Studies 1 Bahnson, M., Hope, E., Satterfield, D., Alexander, A., Briggs, A., Allam, L., & Kirn, A. (2022). Students’ Experiences of Discrimination in Engineering Doctoral Education. 2022 ASEE Annual Conference & Exposition. https://peer.asee.org/41006.pdf 2 Lee, M. J., Collins, J. D., Harwood, S. A., Mendenhall, R., & Huntt, M. B
, I think, because anybody can use the tool to give me a summary. I guess my view on that would be that maybe assessments can start looking at students' ability to critically analyze these summaries that GenAI tools provide, to reason about what is accurate, what is not accurate.’ (George)Our findings also aligned with Nikolic et al.'s (2023) suggestion for a shift in assessment fromonline to oral or in-person exams. A similar conclusion was reached by Qadir (2023), whobelieved a shift in assessment methods towards oral exams or individual projects could reducethe risks posed by GenAI, while the traditional way of assessment can be used as daily exercisewith less focus on the students’ final grades.Hillary proposed
]. Both face and contentvalidity search to decide the degree to which a construct is accurately translated intooperationalization. Face validity examines the operationalization at face value to determinewhether it is a good translation of the construct [26], while content validity examines theoperationalization compared to the construct’s relevant content area(s) (i.e., the appearance thatthe instrument measures what it is intended to measure) [27].Survey items were written by the first author and then reviewed and critiqued by various groups.The authors’ research lab group initially provided feedback on the survey questions’ clarity andreadability, and whether the items are relevant and right for measurement. This research groupbrings expertise
factors that hidden curriculum stands on and use them to identify and understand themechanism of hidden curriculum. These key factors include emotions, self-efficacy, self-advocacy, and awareness [14], [15]. More specifically, Villanueva et al.’s model describes that anindividual recognizes hidden curriculum through hidden curriculum awareness, which isprocessed by emotions. Emotions are then regulated by self-efficacy, which ultimately sustainsand reinforces the individual’s self-advocacy. While Villanueva et al.’s conceptual model isfocused on the coping mechanism upon discovering hidden curriculum, our study usesVillanueva et al.’s work on identifying hidden curriculum in engineering classroom exams basedon the described mechanism.Examining
demographics are in Bolton [14] forthe early-career sample and Miskioğlu et al. [6] for the mid-to-late career sample. Allparticipants self-identified as women or men in an open-response text box.Data Collection is also described in detail in prior work [6], [14]. All interviews followed thesame previously tested protocol [1], [6], [14]. This protocol includes three main interviewsections: expertise, decision making, and intuition. In this paper, we are only interested in theintuition section of the interviews.Table 1 Pseudonyms categorized by years of experience with gender identity, racial/ethnicidentity, and degree discipline(s); tables adapted from Miskioglu et al. [6] and Bolton [14] Level of Reported Reported Years of
to be an important part of the life and activity of the class”. This definitionpresents SB as a unidimensional construct, which can be measured as a general SB.Alternatively, Freeman et al. [3] view SB as a multidimensional construct encompassing classbelonging, university belonging, professors’ pedagogical caring, and social acceptance,suggesting that measuring SB should be approached by asking questions that correspond to eachof these dimensions. Given the diversity of conceptual definitions of SB, it is reasonable toanticipate the presence of multiple measurement instruments for this construct. For example,Goodenow’s Psychological Sense of School Membership [PSSM] was created to measure ageneral SB, while William et al.’s Higher Education