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
(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
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
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
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
(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
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
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
, 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
, 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
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
research at the graduate level. However, studying creativity at thegraduate level is essential because creativity is required to generate new knowledge throughresearch. This study seeks to address the gap in knowledge about graduate-level creativitythrough a thematic analysis of five semi-structured interviews with engineering graduatestudents. These interviews are part of a larger mixed-methods research project with the goal ofcharacterizing the creative climate of graduate-level engineering education. In the interviews, weasked participants about their creative endeavors, how they define creativity, and theirperceptions of creativity within engineering. We used Hunter et al.’s (2005) creative climatedimensions as a theoretical framework to
this, we quantify thecomplexity of the example problem as 26. We could choose to use other network centralitymeasures and an investigation into their suitability will be conducted in the future. Thehorizontal shear equation computation node is the most “central” to the computation, with adegree centrality of 5. Figure 3a-d: (a) Digraph of the correct solution. Steps to the two-part correct solution start at the "reaction forces" node. Solid circles show target nodes for achieving the two-part solution to the problem. (b) Student 1’s solution with solid and dotted circles showing parts of the solution achieved and unachieved, respectively. (c-d) Student 2’s and 3’s solutions, respectively, with dotted circles showing both
) conveniently suggested a 3-factor model, the three factorsaligned only partially with the three dimensions of Fila et al.’s [19] engineering for, with, and aspeople framework. The first factor, which contained items focused on students’ generalengineering attitudes (i.e., sense of belonging in engineering, academic self-confidence and self-efficacy, and attitudes toward persisting and succeeding in engineering), fits well with theengineering as people dimension. This dimension takes into account that engineers areindividuals who have their own skill sets and experiences in engineering, which contributes totheir feelings of belonging because there are certain values and skills that are more acceptablethan others [31, 58]. A diminished sense of
mental illness: an exploration of their experiences and challenges,” in 2019 IEEE Frontiers in Education Conference (FIE), 2019, pp. 1–5.[2] J. Meickle, “Beyond burnout: Mental health and neurodiversity in engineering,” 2018.[3] C. L. Taylor, A. Esmaili Zaghi, J. C. Kaufman, S. M. Reis, and J. S. Renzulli, “Divergent thinking and academic performance of students with attention deficit hyperactivity disorder characteristics in engineering,” J. Eng. Educ., vol. 109, no. 2, pp. 213–229, Apr. 2020.[4] C. L. Taylor and A. E. Zaghi, “Leveraging divergent thinking to enhance the academic performance of engineering students with executive functioning difficulties,” Thinking Skills and Creativity, vol. 45, p. 101109, Sep. 2022.[5] L
the NationalScience Foundation.References[1] D. F. Lohman, “Spatial Ability and G.” 1993.[2] K. S. McGrew, “CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research,” Intelligence, vol. 37, no. 1, pp. 1–10, Jan. 2009, doi: 10.1016/j.intell.2008.08.004.[3] H. B. Yilmaz, “On the Development and Measurement of Spatial Ability,” International Electronic Journal of Elementary Education, vol. 1, no. 2, pp. 83–96, Mar. 2009.[4] C. Julià and J. Ò. Antolì, “Enhancing Spatial Ability and Mechanical Reasoning through a STEM Course,” International Journal of Technology and Design Education, vol. 28, no. 4, pp. 957–983, Dec. 2018.[5] M. Stieff and D. Uttal, “How
of growth mindsets than their White peers,yet they also reported lower levels of fixed mindsets [13]. Said differently, Ge et al.’s [13] cross-sectional study showed that White engineering students demonstrate a higher predispositiontowards a growth mindset and a higher predisposition towards endorsing a fixed view of theirabilities. An exploratory study aimed at understanding the relationship between students’engineering identity and mindsets longitudinally found that both a fixed and a growth mindsetwere positive predictors of identity [14]. However, the authors did acknowledge that there may bemoderating effects not considered in the model, such as course difficulty, that may also helpexplain the positive relationships [14]. The studies
teaching courses.Therefore, instead of using coding to present commonness, our strategy is to present acomprehensive picture that can capture different ideas. The main qualitative tacticsused include noting patterns/relations, building logical evidence, and makingcontrasts [25]. The interviews were conducted in Chinese, quotations were selectedand translated into English. The translation was confirmed with the interviewees. Table 2. The information of participants Years of teaching Participants Major Teaching course(s) * University
state of the literature in aspecific area without using formal quality examination in the inclusion or exclusion criteria [6].An ScR may also indicate whether conducting a systematic review would be appropriate [7].2.1 The Scoping Review Protocol. During the initial phase of the ScR, the research team must becritically reflective of the process, re-visiting prior stages to ensure that the final review meetsthe project's desired scope and research questions. The research team currently consists of anengineering librarian, two literature reviewers, and one content expert. Arksey and O'Malley'smethodology informed thedevelopment of the scoping review ScR S age Ob ec e O c
. Bilec, A. Dukes, A. Nave, A. Landis, and K. Parrish, “Developing and Sustaining Inclusive Engineering Learning Communities and Classrooms.” In 2022 ASEE Annual Conference & Exposition, Minneapolis, MN, 2022.[3] D. T. Rover, M. Mina, A. R. Herron-Martinez, S. L. Rodriguez, M. L. Espino, and B. D. Le, “Improving the Student Experience to Broaden Participation in Electrical, Computer and Software Engineering,” in 2020 IEEE Frontiers in Education Conference (FIE), 2020, pp. 1–7.[4] L. Long and J. A. Mejia, “Conversations about Diversity: Institutional Barriers for Underrepresented Engineering Students,” J. Eng., vol. 105, no. 2, 2016.[5] M. E. Matters, C. B. Zoltowski, A. O. Brightman, and P. M. Buzzanell
to student success in engineering education,” EuropeanJournal of Engineering Education, vol. 42, no. 4, pp. 368–381, 2017.[5] M. Scheidt, A. Godwin, E. Berger, J. Chen, B. P. Self, J. M. Widmann, and A. Q. Gates,“Engineering students’ noncognitive and affective factors: Group differences from clusteranalysis,” Journal of Engineering Education, vol. 110, no. 2, pp. 343–370, 2021.[6] S.-M. R. Ting and R. Man, “Predicting academic success of first-year engineeringstudents from standardized test scores and psychosocial variables,” International Journal ofEngineering Education, vol. 17, no. 1, pp. 75–80, 2001.[7] B. F. French, J. C. Immekus, and W. C. Oakes, “An examination of indicators ofengineering students’ success and persistence
when accomplishing this purpose. Specifically, a largeamount of information is considered indirect knowledge, or knowledge only reasonablyaccessible to a learner through social contact [1]. Further, within the learning context,interactions are adapted reciprocally by the learning environment and learner [2]. These andrelated foundations indicate that understanding the social aspect(s) of the learning environment isessential for understanding and improving learning.To identify and optimize social variables related to student learning, recent engineeringeducation literature shows a growing awareness of and interest in peer support. Theseobservations of student interactions and outcomes indicate improved learning, motivation, andself-efficacy due
components ofspatial ability which may aid in the creation of more complete training.AcknowledgementsThis material is based upon work supported by the U.S. National Science Foundation underGrant No. 1712887. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the authors and do not necessarily reflect the views of the NationalScience Foundation.References[1] K. S. McGrew, “CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research,” Intelligence, vol. 37, no. 1, pp. 1–10, Jan. 2009, doi: 10.1016/j.intell.2008.08.004.[2] D. F. Lohman, “Spatial Ability and G.” 1993.[3] A. Ramful, T. Lowrie, and T. Logan, “Measurement of Spatial
system users andother practitioners. For example, the LSRM may enhance the CATME system by accuratelymodeling longitudinal social relations data, and thereby improving the evaluation of teamdynamics and identifying potential areas for improvement. Ultimately, this may help instructorsbetter support their students' collaborative learning experiences and foster a more inclusivelearning environment. ReferencesAgrawal, A. K., & Harrington-Hurd, S. (2016). Preparing next generation graduates for a global engineering workforce: Insights from tomorrow's engineers. Journal of Engineering Education Transformations, 29(4), 5-12.Alsharif, A., Katz, A., Knight, D., & Alatwah, S. (2022). Using
, mathematics, and physics. His current research interests are focused on educational innovation and educational technologies.Dr. Gibr´an Sayeg-S´anchez, Tecnologico de Monterrey (ITESM) Dr. Gibr´an Sayeg-S´anchez is professor – consultant in the Science Department in Tecnologico de Mon- terrey, Puebla campus. He studied a PhD in Financial Science in EGADE Business School (2016), a MSc in Industrial Engineering in Tecnologico de Monterrey (2011), and a BEng in Industrial and Systems En- gineering in Tecnologico de Monterrey (2006). Dr. Sayeg-S´anchez has more than 11 years of experience in teaching statistics, mathematics, and operations research; and more than 13 years of experience in Op- erational Excellence consulting
context.AcknowledgmentsThis work was made possible by a U.S. Department of Education Graduate Assistance in Areasof National Need (GAANN) Grant Number P200A210109 and by a NSF Innovations inGraduate Education (IGE) Program [IGE DGE#2224724] grant. 5 References[1] Gilmore, J. A., Wofford, A. M., & Maher, M. A. (2016). The Flip Side of the Attrition Coin: Faculty Perceptions of Factors Supporting Graduate Student Success. International Journal of Doctoral Studies, 11, 419–439. https://doi.org/10.28945/3618[2] S. Spaulding, L., & Rockinson-Szapkiw, A. (2012). Hearing their Voices