qualitative approach for examining language mediated frames that highlight someaspects of social reality while obscuring other aspects [52]. These frames or discourses may bespoken or communicated through “texts”, including the multimodal texts like videogames [11],[53]. To identify any discourses in Iconoclasts, the author analyzed the recorded dialogue fromthe game and associated notes on the story or storytelling from the research journal. Particularattention was given to engineering and technology topics and what was being included or excludedabout the topic, what assumptions the discourse(s) carried and which characters were invoking thediscourse(s). Previous engineering education research was reviewed to help connect the discoursesin
myunderstanding of patterns within the queer experiences in STEM.ValidityUsing Walther et al.’s [31] framework for achieving validity in engineering educationqualitative research, I present multiple means with which validation was achieved throughoutthis project. By being a member of the GRSM community myself, this study possessessome aspect of communicative validity [32]. This presents me with the ability to filter myparticipants’ stories through my own experiences and knowledge about the community,positioning me as an individual with enough experience and community-specific knowledge toconduct research with this community. I also was forced to navigate challenges within the STEMinstitution as a direct result of my identities as queer and disabled, thus
theoretical data.In addition to enduring outcomes (Table 2) and the important-to-know topics (Table 3), the labactivities also promote “good-to-be-familiar with” topics as follows: Students are expected tolearn and demonstrate the following topics throughout all six labs: Teamwork, report writing,and communication. If we, for instance, take modeling as an example, being able to modelconstitutes an important and direct predictor of conceptual understanding of often-complicatedengineering topics, such as heat transfer [42]. To sum up on these “good-to-be-familiar with”topics, they are covered in all labs (Labs #1–#6) and will become a part of necessary skills as apracticing engineer in the future no matter what field of engineering s/he choose to
R. Yu, “Involvement in out-of-class activities: A mixed research synthesis examining outcomes with a focus on engineering students,” Journal of STEM Education: Innovations and Research, vol. 18, no. 2, 2017.[3] A. L. Miller, L. M. Rocconi, and A. D. Dumford, “Focus on the finish line: Does high-impact practice participation influence career plans and early job attainment?,” Higher Education, vol. 75, no. 3, pp. 489–506, 2018, doi: 10.1007/s10734-017-0151-z.[4] G. Lichtenstein, A. C. McCormick, S. D. Sheppard, and J. Puma, “Comparing the Undergraduate Experience of Engineers to All Other Majors: Significant Differences are Programmatic,” Journal of Engineering Education, vol. 99, no. 4, pp. 305–317, 2010, doi: 10.1002/j
interested in STEM majors atTAMU and community college representatives interested in building relationships for theirstudents to transfer into STEM majors at TAMU. The STEM Conference program includedresource roundtables for students and one-on-one sessions with faculty and staff forrepresentatives.Other activities such as the NSF Scholarship in Science, Technology, Engineering, andMathematics (S-STEM) were used as both recruitment and retention strategies. The S-STEMsprovided community college transfer students research opportunities, academic and professionaldevelopment seminars, scholarship money to assist in funding students’ education without themhaving work commitments, and establishment of cohorts and the resulting social community tohelp
understanding this community. c American Society for Engineering Education, 2017 Quantifying and Assessing Trends on National Science Foundation’s Broader Impact Criterion The American Innovation and Competitiveness Act (S.3084) reapproved the NationalScience Foundation’s (NSF) merit review criteria i.e. Intellectual Merit and Broader Impacts,called for an update of the policy guidelines for NSF staff members and merit review processparticipants, and emphasized the importance of transparency and accountability. EvaluatingProject Summaries based on Intellectual Merit and Broader Impacts has been the standard ofmaintaining excellence and accountability since 1997. Intellectual
Characterize Reform-Oriented Instruction: The Scoop Notebook and Rating Guide. CSE Technical Report 707. National Center for Research on Evaluation, Standards, and Student Testing (CRESST).10. Chambers, J.M., Carbonaro, M., Rex, M., and Grove, S. (2007). Scaffolding knowledge construction through robotic technology: A middle school case study. Electronic Journal for the Integration of Technology in Education, 6, 55-70.11. Eguchi, A. (2010). What is educational robotics? Theories behind it and practical implementation. Proceedings of Society for Information Technology & Teacher Education International Conference, Chesapeake: AACE, pp. 4006–4014.12. Papert, S. (1993). The Children’s Machine: Rethinking Schools in
the National Science Foundation.References Atman, C. J., Kilgore, D., & McKenna, A. (2008). Characterizing design learning: A mixed-‐ methods study of engineering designers' use of language. Journal of Engineering Education, 97(3), 309-326. Bielaczyc, K., & Ow, J. (2014). Multi-player epistemic games: Guiding the enactment of classroom knowledge- building communities. International Journal of Computer-Supported Collaborative Learning, 9(1), 33-62. Bloome, D., Carter, S. P., Christian, B. M., Otto, S., & Shuart-Faris, N. (2004). Discourse analysis and the study of classroom language and literacy events: A microethnographic perspective. Routledge. Cohen, E. G., & Lotan, R. A. (2014). Designing groupwork
-91, 2014.[2] A. McKenna, R. Linsenmeier, and M. Glucksberg, "Characterizing computational adaptive expertise," in 2008 ASEE Annual Conference and Exposition, 2008.[3] J. S. Zawojewski, H. A. Diefes-Dux, and K. J. Bowman, Models and modeling in engineering education: Designing experiences for all students. Sense Publishers, 2008.[4] J. M. Wing, "Computationalthinking," in Communications of the ACM, vol. 49, no. 3, p. 33-35. 2006.[5] U. Ilic, H. I. Haseski, and U. Tugtekin, "Publications trends over 10 years of computational thinking research," in Contemporary Education Technology, vol. 9, no. 2, p. 131-153, 2018.[6] R. Lesh and H. M. Doerr (Eds.). Beyond constructivism: Models and modeling
convincing research gap in the introductory sections of the documents. Table 3shows the themes designated as Broader Impacts. These were determined by the NSF definitionof Broader Impacts, which was included in the Introduction, as well as open coding from theactivities that the participant described as contributing to the broader impact.Table 3: Broader Impacts Themes, Definitions, and Participant Examples Evaluation Criteria: Broader Impacts Theme Definition Example(s)K-12 Education Mention of outreach to “As I did as an undergrad during Engineering for Kids, I willand Outreach school-aged children, expose basic aspects of my
contesting identities of expertise in a heterogeneous learning context. In S. Wortham & B. Rymes (Eds.), Linguistic Anthropology of Education (Vol. 37, pp. 61–91). Westport, CT: Praeger.5. Bowker, G. C., & Star, S. L. (1999). Sorting things out: Classification and it consequences. Cambridge, MA: MIT Press.6. Star, S. L., & Bowker, G. C. (1997). Of lungs and lungers: The classified story of tuberculosis. Mind, Culture, and Activity, 4(1), 3-23.7. Greeno, J. G. & The Middle School Mathematics Through Applications Project Group (1997). Theories and practices of thinking and learning to think. American Journal of Education, 106, 85– 126.8. Johri, A., Olds, B.M., and O’Connor, K. (2014). Situative frameworks for
in SoTL.References[1] A. M. Lucietto, and L. A. Russell, “STEM Educators: How They Teach,” Journal of STEM Education: Innovations and Research, no. Summer 2018, 2018.[2] C. R. Thomas, “Personality in Engineering Technology,” Journal of Engineering Technology, vol. 31, no. 2, pp. 16-20, Fall2014, 2014.[3] E. R. Kahu, and K. Nelson, “Student engagement in the educational interface: understanding the mechanisms of student success,” Higher education research & development, vol. 37, no. 1, pp. 58-71, 2018.[4] R. M. Felder, and R. Brent, “Understanding student differences,” Journal of engineering education, vol. 94, no. 1, pp. 57-72, 2005.[5] J. A. Gasiewski, M. K. Eagan, G. A. Garcia, S. Hurtado
. Vitak et al. critique the IRB process for applying strict requirements forlow-risk research [18]. While our study was low-risk, we successfully underwent the IRBprocess and received approval exempt from full board review. However, we found that twocommunity colleges would not recognize our qualifying IRB. Each college's IRB requested thatthe research study go through their college’s IRB qualification before allowing their faculty toreceive the recruitment message. In one instance, coauthor 1 asked to forward the recruitmentmessage from coauthor 2's initial postings and was told to submit the survey to coauthor 1's IRBbefore doing so. In the second instance, after someone had forwarded our survey invitation totheir colleagues, a community
periods are the focus of this work. A visualization of thismodel is presented below in Figure 1. Figure 1: Conrad et al.’s (2006, p. 257) Model of Undergraduate SocializationStrayhorn [23] argues that feelings of belonging are a fundamental human need that are alsosufficient to drive behavior. Individuals that feel cared for, supported, and that they matter tothose around them in a given environment subsequently feel that they belong in thatenvironment. Belonging takes on heightened importance during uncertain or stressful periods oftime, and in contexts where an individual feels like an outsider. For most traditional prospectivestudents, the college application process is stressful and takes place during late adolescence: acritical period
traditional linear regression and thus necessitatesa regression method that accounts for clustering within a sample. ICC values can range from 0 to1, with higher values indicating stronger intergroup correlations and indicating the need forHierarchical Linear Modeling (HLM) methods. While the interpretation of ICC depends on thecontext of the study and the research question being addressed, ICC values greater than 0.1generally indicate that there is a significant amount of clustering in the data and that HLM maybe appropriate [40]. It is also important to note that the interpretation of ICC values should bedone in conjunction with other information about the study, such as the sample size andcharacteristics, the instrument(s) used, and the research
HSGPAranges.Continuing from the insights provided by the KDE analysis, we further examine the variability inprogram complexity among universities. This part of the exploratory data analysis focuses on howthe structural aspects of university curricula influence student enrollment decisions. As highlightedin Figure 3, the distribution of program complexity varies notably between different institutions,such as University ’1’ and University ’3’. This variability is not merely incidental but indica-tive of these institutions’ diverse academic cultures and curricular frameworks. The KDE plot forUniversity ’1’, with a multi-peaked distribution, suggests a curriculum that offers a wide array ofprograms ranging from less to more complex. In contrast, University ’3’s
Fellow role(s) interested them and why. All of the candidates wereinterviewed and, based on those conversations, we decided to add two more Fellowship roles: The EnSURE Fellow would help organize the Engineering Summer Undergraduate Research Experience (EnSURE) program The Recruiting Fellow would assist in identifying and connecting with prospective graduate students through on- and off-campus recruiting activitiesIn addition to these six Engineering Graduate Leadership fellows, we decided to partner with theGraduate School’s Leadership Fellows program to co-sponsor two additional roles: a GraduateStudent Life and Wellness Fellow, focusing specifically on the needs of Engineering graduatestudents, and a Women in STEM
of applications that were introduced in the workshop.Upon completion of the workshop, the participants were given an eight-question exit post-trainingsurvey shown in Figure 2. There were six quantitative questions using a five point or a three-pointLikert scale as well as two qualitative questions. The two qualitative questions were also used aspedagogical tools based on experiential learning best practices. Question 7’s goal was to elicit apositive self-reflection while Question 8 reinforced learning through internalization andsummarization. 1. Exiting this workshop, I learned something new about AI concepts, applications, and ethics (1 - strongly disagree to 5 - strongly agree). 2. I have a better understanding of AI and how to
, how to dress, eat and hold a professional conversation at a formal meal during aninterview; and how to network and follow-up after meeting people professionally. The guestspeakers, veterans themselves, were excited to present to these highly motivated student veteransand to share their stories, and in the process, they inspired this next generation of engineers andengineering technologists.Keywords: adult learners, engineering, learning communities, STEM workforce preparationIntroductionThe goal of the National Science Foundation S-STEM project, A Pathway to Completion forVeterans Pursuing Engineering and Engineering Technology Degrees, is to provide professionaldevelopment and scholarships to student veterans who are attending Old Dominion
] E. Salas, N. J. Cooke, and M. A. Rosen, “On Teams, Teamwork, and Team Performance: Discoveries and Developments,” Human Factors, 50(3), 540-547, 2008.[4] E. Salas, E., D. L. Reyes, and A. L. Woods, “The Assessment of Team Performance: Observations and Needs” Innovative Assessment of Collaboration, 21-36, 2017.[5] G. Wu, C. Liu, X. Zhao, and J. Zuo, “Investigating the Relationship between Communication- Conflict Interaction and Project Success Among Construction Project Teams,” International Journal of Project Management, 35(8), 1466-1482, 2017.[6] A. J. Garcia, and S. Mollaoglu, “Individuals’ Capacities to Apply Transferred Knowledge in AEC Project Teams,” Journal of Construction Engineering and Management
thermodynamics; Carnot Cycle; thermodynamic, overall, and isentropicefficiencies; effectiveness of heat exchangers; refrigeration and heat pump cycles, includingabsorption and cascade refrigeration, and other advanced cycles; air-conditioning processes ofhumid air; Reheat Rankine cycle including means to improve its efficiency; Otto and Dieselcycles; Brayton with intercooling, reheating, and regeneration; property diagrams, p-v, T-v, T-p,T-s, h-s, p-h, and Psychrometric chart.This paper examines course offerings in the fall of 2019, 2020, and 2021. The three offeringsdiffered in content delivery methods. Course in 2019 had one-third of the lectures flipped and alllabs were in person. Course in 2020 had completely flipped lectures and all instruction
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these constructs; Intrinsic goalorientation, Task value, Expectancy component and Metacognition increased after theyparticipated in the experiment whereas Test Anxiety reduced after the students were taught usingECP (mean = -0.21, test anxiety is expected to continuously decrease due to the intervention). Thisshows that the students are now confident in the biology concept they have learned.As previously mentioned, Table 2's results provide the summary statistics (mean, standarddeviation, and mean difference) as well as the p-values of paired t-tests of students' pre- and post-test scores for each MLSQ domain.Other notable improvements in the domain were in students’ Task value (subdomains: I am veryinterested in the content area of this course
circumstances, such as poor acoustics, room size, temperature, or aglaring blackboard, that could interfere with teaching and learning, are noted. The classobservation is completed before the end of the tenth week of the semester.Post Observation: This meeting is essential to share the observation outcomes. A post-observation form with guiding questions ensures that peer-observers follow a standardizedprocess. Faculty members also ask their own questions to the peer-observer(s). The discussioninvolves sharing notes and comments with the instructor, highlighting the positive aspects of theinstructions, and providing suggestions to further improve the quality of teaching. Either thecommittee member or the instructor may bring up any issue that needs
. Evans, F. Jentsch, and J. Keebler, “Constructs of Spatial Ability and Their Influence onPerformance with Unmanned Systems,” Hum. Factors Issues Combat Identif., Jan. 2010.[3] A. Ramful, T. Lowrie, and T. Logan, “Measurement of Spatial Ability: Construction and Validation of theSpatial Reasoning Instrument for Middle School Students,” J. Psychoeduc. Assess., vol. 35, no. 7, pp. 709–727, Oct.2017, doi: 10.1177/0734282916659207.[4] J. Buckley, N. Seery, and D. Canty, “Investigating the use of spatial reasoning strategies in geometricproblem solving,” Int. J. Technol. Des. Educ., vol. 29, no. 2, pp. 341–362, Mar. 2019, doi: 10.1007/s10798-018-9446-3.[5] N. S. Newcombe, “Picture This: Increasing Math and Science Learning by Improving