perceptions of grades: it’s simply ‘good’vs.‘bad,’” Assess. Eval. High. Educ., vol. 38, no. 3, pp. 253–259, 2013.[9] W. Harlen and M. James, “Assessment and learning: differences and relationships between formative and summative assessment,” Assess. Educ. Princ. Policy Pract., vol. 4, no. 3, pp. 365–379, 1997.[10] A. Hasnain and S. Bhamani, “Exploring Perceptions of University Students Pertaining to Grades over Knowledge and Skills.,” J. Educ. Educ. Dev., vol. 1, no. 2, pp. 101–115, 2014.[11] J. B. Adams, “What makes the grade? Faculty and student perceptions,” Teach. Psychol., vol. 32, no. 1, pp. 21–24, 2005.[12] M. L. Calhoun and J. Beattie, “Assigning grades in the high school mainstream
Paper ID #38239Identifying curriculum factors that facilitate lifelong learning inalumni career trajectories: Stage 2 of a sequential mixed-methods studyNikita Dawe, University of Toronto PhD student in the Department of Mechanical and Industrial Engineering at the University of Toronto, Collaborative Specialization in Engineering Education.Dr. Lisa Romkey, University of Toronto Lisa Romkey serves as Associate Professor, Teaching and Associate Director, ISTEP (Institute for Studies in Transdisciplinary Engineering Education and Practice) at the University of Toronto.Amy Bilton ©American Society for
, Song and Grabowski [32] investigated the relationship between goal orientation,problem−solving skills, and motivation by employing research on science students. Theypresented the students with an ill−structured problem in different goal−oriented contextsbecause they needed to consider multiple factors to find the solution. Results of the studyhave shown that students who were exposed to a learning−oriented context were moremotivated to solve ill−structured problems as compared to the performance−oriented context.Learning−oriented context included the instructional strategies to orient students towardslearning−goal orientation by incorporating three contextual factors “(a) task design (b)distribution of authority and (c) recognition or
it is divided into two sub-tests of equal difficultyto reduce the time needed to complete the test. Each of these tests consists of 12 questions. Onequestion was removed due to the excessive difficulty. These subtests are labeled A and B, andcurrently have a difficulty index of 54% and 53% respectively among the entire sample of BLVparticipants who have contributed to the project.The TMCT is administered in a controlled environment with proctors available to answerparticipants’ questions individually before beginning the test. The test was administered toanywhere from 1 to 6 people at a time. Participants are brought to a table that contains aturntable, binder, and post-it note dispenser. The turntable contains 12 slots that hold each of
different online databases including Google Scholar, Web of Science,Compendex/Engineering Village, EBSCOhost, ScienceDirect, IEEE Explorer, and Wiley OnlineLibrary. This process of searching played a pivotal role in curating the most applicable and reliablearticles in this review. The SLR process and structure/format used in this paper was referred fromseveral existing SLR studies [26-28]. The following questions served as a structured blueprint forresearch and the selection of publications. 1. How are the sampled articles distributed in terms of: a. Publication Year? b. Affiliated country of first author? c. Scholarly Resources? d. Disciplinary techniques? 2. How are the articles included selected in terms of
, who provided invaluableguidance in the early stages of this study.References[1] L. L. Baird, “Helping graduate students: A graduate adviser’s view,” New Directions for Student Services, vol. 1995, no. 72, pp. 25-32, Dec. 1995, doi: 10.1002/ss.37119957205.[2] I. Chirikov, K. M. Soria, B. Horgos, and D. Jones-White, “Undergraduate and Graduate Students’ Mental Health During the COVID-19 Pandemic,” UC Berkeley: Center for Studies in Higher Education, Berkeley, CA, USA, Aug. 2020. Accessed: Feb. 5, 2024. [Online]. Available: https://escholarship.org/uc/item/80k5d5hw[3] A. Edmondson, “Psychological safety and learning behavior in work teams,” Administrative Science Quarterly, vol. 44, no. 2, pp. 350-383, Jun
education as a catalyst for change,” Proceedings of the 128th American Society of Engineering Education, Virtual meeting, July 2021.[15] J. Trevelyan, “Reconstructing engineering from practice,” Engineering Studies, vol. 2, no. 3, pp. 175–195, 2010, https://doi.org/10.1080/19378629.2010.520135[16] N. S. Nasir, C.D. Lee, R. Pea, & M.M. de Royston, Handbook of the Cultural Foundations of Learning, In N. S. Nasir, C. D. Lee, R. Pea, & M. McKinney de Royston, Eds.; 1st ed., Routledge, 2020, https://doi.org/10.4324/9780203774977[17] B. A Brown, & E. Spang, “Double Talk : Synthesizing Everyday and Science Language in the Classroom,” Science Education, vol. 92, no. 4, pp. 708–732, 2007, https://doi.org
higher non-technical self-assessment (communication, teamwork, andleadership [38]), expertise confidence [7], competitive participation (how often students participatedin competitive events or activities [38]), and ratings of importance of earning a high salary. On theother hand, the interview participants had lower levels of career-fit confidence [7], and socialbelonging confidence. Further, they rated their environment as less toxic (discrimination and unequaltreatment [38]), and they were less likely to be motivated to take an ML/AI course based on thepopularity of the subject. The sample had independent variable averages that were within thestandard deviation of the larger sample (see Appendix A for mean participant responses, andAppendix B
teacher’s belief system about science teaching and learning,” J. Res. Sci. Teach., vol. 40, no. 9, pp. 835–868, 2003, doi:10.1002/tea.10113.[21] P. N. Johnson-Laird, “Mental models and human reasoning,” Proc. Natl. Acad. Sci. U. S. A., vol. 107, no. 43, pp. 18243–18250, 2010, doi:10.1073/pnas.1012933107.[22] W. B. Rouse and N. M. Morris, “On looking into the black box: Prospects and limits in the search for mental models,” Psychol. Bull., vol. 100, no. 3, pp. 349–363, 1985, [Online]. Available: http://scholar.google.com/scholar?q=related:QM4p5zGC8jMJ:scholar.google.com/&hl=e n&num=30&as_sdt=0,5.[23] K. Carley and M. Palmquist, “Extracting, representing, and analyzing mental models,” Soc
NLP-based machinelearning to a human researcher, presumed to be an expert; (b) NLP-as-expert studies whichassume that results of the automated classification of text-based data are of value in and ofthemselves without the intervention or approval of a human expert; (c) NLP-in-the-loop studiesthat are based on traditional methods of qualitative analysis but employ NLP at some point in theprocess to increase speed and efficiency of analyses; and (d) human-in-the-loop studies thatbegin with NLP as expert but recruit a human researcher at some point in the analysis of data toaugment the capabilities of NLP.Human-as-expert studies assume that the human is the most accurate among human and artificialintelligence approaches to analyzing qualitative
influencing the success of the Singapore skills development system. Global Business Review, 1(1), 11-47. https://doi.org/10.1177/097215090000100102Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79-122. https://doi.org/10.1006/jvbe.1994.1027Lent, R. W., Brown, S. D., Schmidt, J., Brenner, B., Lyons, H., & Treistman, D. (2003). Relation of contextual supports and barriers to choice behavior in engineering majors: Test of alternative social cognitive models. Journal of Counseling Psychology, 50(4), 458-465. https://doi.org/10.1037/0022-0167.50.4.458Lent, R. W., Sheu
J. R. Herkert, “Engineering and humanities: bridging the gap,” in Technology-Based Re-Engineering Engineering Education Proceedings of Frontiers in Education FIE’96 26th Annual Conference, Salt Lake City, UT, USA: IEEE, 1996, pp. 1124–1128. doi: 10.1109/FIE.1996.567791.[3] N. A. of S. Medicine Engineering, and, P. and G. Affairs, B. on H. E. and Workforce, and C. on I. H. E. in the A. Medicine Humanities, Sciences, Engineering, and, The Integration of the Humanities and Arts with Sciences, Engineering, and Medicine in Higher Education: Branches from the Same Tree. National Academies Press, 2018.[4] N. Kellam, J. Walther, and T. Costantino, “Integrating the Engineering Curriculum through the Synthesis and Design Studio
, Oura Y. Commentary: Reconceptualizing School Learning Using Insight From Expertise Research. Educational Researcher. 2003 Nov 1;32(8):26–9.8. Atman CJ, Chimka JR, Bursic KM, Nachtmann HL. A comparison of freshman and senior engineering design processes. Des Stud. 1999;20(2):131–52.9. Di Stefano G, Pisano G, Staats BR. Learning by thinking: How reflection aids performance. In: Academy of Management Proceedings. 2015. p. 12709.10. Turns J, Shroyer K, Lovins T, Atman C. Understanding Reflection Activities Broadly. In: 2017 ASEE Annual Conference & Exposition Proceedings. ASEE Conferences; 2017.11. Turns J, Sattler B, Yasuhara K, Borgford-Parnell J, Atman C. Integrating Reflection into Engineering Education
Systems Skills Taught Certificates Clark College Mechatronics Fundamentals Certificate of Completion 3 Clover Park Technical College Mechatronics Co-Op Certificate A - Power 3 Clover Park Technical College Mechatronics Co-Op Certificate B - Control 1 Greenville Technical College Mechatronics I Certificate in Applied Science 4 Greenville Technical College Mechatronics II Certificate in Applied Science 2 Hofstra University Mechatronics Online
WTAs and instructors need to collaborate. The quality of suchcollaboration depends strongly on the engagement of teaching staff.ReferencesBaik, C., Larcombe, W., & Brooker, A. (2019). How universities can enhance student mentalwellbeing: The student perspective. Higher Education Research & Development, 38(4), 674-687.Chadha, D., Kogelbauer, A., Campbell, J., Hellgardt, K., Maraj, M., Shah, U., ... & Hale, C.(2021). Are the kids alright? Exploring students’ experiences of support mechanisms to enhancewellbeing on an engineering programme in the UK. European Journal of Engineering Education,46(5), 662-677.Joaquin, J. J. B., Biana, H. T., & Dacela, M. A. (2020). The Philippine higher education sector inthe time of COVID-19. In
; McEwen, S. A. (2014). A scoping review of scoping reviews: advancing the approach and enhancing the consistency. Research synthesis methods, 5(4), 371-385.Phillips, C. M., London, J. S., Lee, W. C., Van Epps, A. S., & Watford, B. A. (2017, October). Reflections on the messiness of initiating a systematic literature review on broadening participation in engineering and computer science. In 2017 IEEE Frontiers in Education Conference (FIE) (pp. 1-8). IEEE.Polanin, J. R., Espelage, D. L., Grotpeter, J. K., Ingram, K., Michaelson, L., Spinney, E., ... & Robinson, L. (2022). A systematic review and meta-analysis of interventions to decrease cyberbullying perpetration and victimization
assessment practices, at the very least. Various versions of ChatGPT have been shown unable to solve very simple reasoningproblems. For example, Valmeekam et al. [10] show it performs very poorly in solvinginstances of the blocksworld problem, which is a toy AI problem developed in the 70s toevaluate AI planning systems. In an instance of blocksworld an agent is given an initialconfiguration, in which towers of labeled blocks are located on a table. The agent is alsogiven a set of goal conditions, each of which expresses a statement of the form "block A isdirectly on top of block B", or "block C is directly on the table". The objective is to computea sequence of actions that allows the agent to transform the initial configuration into one
document. Two model parameters (the Dirichlet parameters) indicatehow many topics are represented in each document (0 < a < 1) and how many words arerepresented in each topic (0 < b < 1) where a high a value makes the document appear moresimilar to one another and high b value makes topics appear more similar to one another. For ourstudy, we chose lower a and b values because the documents are relatively small and we want tolook for nuances across the corpus for neurodivergence as the main topics discussed in the dataare autism, ADHD, and neurodivergence. Figure 1 demonstrates how the LDA model creates aprobabilistic model of the topics from documents. Fig. 1. The corpus contains m documents with n words (left). An LDA model
://doi.org/10.1177/00220574211053586[5] E. O. McGee, “Devalued Black and Latino Racial Identities: A By-Product of STEM College Culture?,” American Educational Research Journal, vol. 53, no. 6, pp. 1626–1662, Dec. 2016, doi: 10.3102/0002831216676572.[6] M. W. Ohland et al., “Race, gender, and measures of success in engineering education,” Journal of Engineering Education, vol. 100, no. 2, pp. 225–252, 2011, doi: 10.1002/j.2168- 9830.2011.tb00012.x.[7] T. L. Fletcher, J. P. Jefferson, B. N. Boyd, and K. J. Cross, “Missed Opportunity for Diversity in Engineering: Black Women and Undergraduate Engineering Degree Attainment,” Journal of College Student Retention: Research, Theory & Practice, Jan. 2021, doi: 10.1177
, choice, and performance,” Journal of Vocational Behavior, vol. 45, no. 1, pp. 79–122, 1994, https://doi.org/10.1006/jvbe.1994.1027[9] Lent, R. W., Brown, S. D., & Hackett, G., “Contextual Supports and Barriers to Career Choice : A Social Cognitive Analysis,” Journal of Counseling Psychology, vol. 47, no. 1, pp. 36–49, 2000[10] Bryan, E., & Simmons, L. A., “Family involvement: Impacts on postsecondary educational success for first- generation Appalachian college students,” Journal of College Student Development, vol. 50, no. 4, pp. 391–406, 2009, https://doi.org/10.1353/csd.0.0081[11] S. L. Dika, M. A. Pando, B. Q. Tempest, K. A. Foxx, and M. E. Allen, "Engineering self- efficacy, interactions
determined for a topic? (B) What aspects of advice or other recommendations can be gleaned for future literature review improvements using computational text analysis tools?Additionally, we felt it was important from a validity standpoint to do this work checking theLIWC methodology on the six applicable systematic literature review papers identified duringthe SPIDER search. By testing both LIWC analysis methods as well as our custom dictionary onthis smaller sample, if any modifications were needed in our methodology or dictionary, wecould adjust before embarking upon the thematic analysis of the twenty-seven papers [4, Ch. 10],[15, Ch. 6].2 MethodsThis section provides insight into the novel review analysis methodology using computerizedtext
responding to an item. A participant elaborated: It is a little bit confusing that it says in uncertain situations, because I have to think about what that really is. I believe this is asking me in situations with which I don't immediately know what I'm going to do. (Participant B)As part of the interview protocol, after the participants completed responding to the nine items,they were asked to state their interpretations of “uncertain situations,” “uncertainty,” and “newsituations.” Variations in how participants responded included some participants describing“uncertain situation” in a similar manner to how other participants described "a new situation”indicating an unclear distinction between the two.Upon delving further into
obtainedprior to data collection and analysis.The methodological approach is outlined through the following three study phases: (1) BeliefsInterviews, (2) Contextual Intervention, and (3) Reconciliation Interviews (Fig. 1). The interviewprotocols for phase 1 and phase 3 can be found in Appendix A and Appendix B, respectively. Fig. 1. Research Design Visualization (adapted from [34])Phase 1: Beliefs InterviewsThe first phase of this study involved conducting semi-structured interviews with students todetermine their espoused beliefs when it comes to making process safety judgements. Theinterview was broken into two main sections: (1) students ranked the six criteria from theconceptual framework according to their beliefs about making
., 2022, doi: 10.1002/jee.20456.[17] E. A. Mosyjowski, S. R. Daly, D. L. Peters, S. J. Skerlos, and A. B. Baker, “Engineering PhD Returners and Direct‐Pathway Students: Comparing Expectancy, Value, and Cost,” J. Eng. Educ., vol. 106, no. 4, pp. 639–676, Oct. 2017, doi: 10.1002/jee.20182.[18] A. Olewnik, Y. Chang, and M. Su, “Co-curricular engagement among engineering undergrads: do they have the time and motivation?,” Int. J. STEM Educ., vol. 10, no. 1, p. 27, Apr. 2023, doi: 10.1186/s40594-023-00410-1.[19] J. S. Eccles and A. Wigfield, “From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation.,” Contemp. Educ
are used throughout. Table I Participant demographic information. a, b Pseudonym Pronouns Race / AGAB Description of Institution Ethnicity Elio They/Them Chinese AFAB Public, four-year research institution in the Southeastern US Leon He/They Latino AFAB Public, four-year research institution in the Southeastern US Zayn They/Them Latina AFAB Private, four-year
able to accomplish on their own or will pose a significantchallenge. As students acquire specific knowledge and skills, those supports are eventuallyremoved as they can apply the learning skills independently.In the context of engineering education practice, providing students with scaffolding is highlyrecommended when the faculty is not available to provide help (i.e. while solving a homeworkassignment or projects outside of the classroom). Specifically, in the context of computationalassignments, scaffolding methods can involve (a) short video lectures explaining difficultconcepts, (b) worked-out examples demonstrating and explaining difficult calculations orimplementations of a particular function, (c) templates of code that can get
stuff together, which I appreciate. Like it's not strictly the REU 24/7, but when we do, we ask each other really good questions about our research, and I enjoyed that part.” -Teddy (rising junior)Survey findings. The REU students were also given a pre-/post-survey to ascertain quantitative levels ofdevelopment across a variety of validated scales, including Bieschke et al.’s Research Self-Efficacy Scale and Godwin’s engineering identity scale. Survey statements are available inAppendix B, with their pre- and post- survey means and p values. In support of the socialization and preparatory experiences within the REU, the Wilcoxonsigned-rank test of longitudinal survey data indicated improvement in confidence and
Paper ID #39942Organizational Barriers to Conducting Engineering Education Research inEducation-adjacent IndustriesDr. Nikitha Sambamurthy, zyBooks, A Wiley Brand Nikitha Sambamurthy is the Editorial Director at zyBooks, at Wiley Brand. She completed her Ph.D. in engineering education at Purdue University, and has since been dedicated to bridging engineering education research and engineering education industry. ©American Society for Engineering Education, 2023 Organizational Barriers to Conducting Engineering Education Research in Education-adjacent IndustriesAbstractEngineering
Paper ID #43555FIE 2023: An Aggregate and Statistical Analysis of the Results and Feedbackof the ASEE ERM Premier International Conference on Engineering EducationHillary E. Merzdorf, Texas A&M University College of EngineeringAnna Stepanova, Texas A&M University Dr. Anna Stepanova is a researcher at the Sketch Recognition Lab at Texas A&M University. She holds a Ph.D., Master’s and Bachelor’s in geology. Anna’s research interests are in geosciences, micropaleontology and education.Dr. Saira Anwar, Texas A&M University Saira Anwar is an Assistant Professor at the Department of Multidisciplinary Engineering
Conference and Exposition Proceedings, Seattle, Washington: ASEE Conferences, Jun. 2015, p. 26.138.1-26.138.19. doi: 10.18260/p.23477.[12] S. R. Davies, “Exclusion: Whatever it is females like to talk about,” in Hackerspaces: Making the maker movement, Polity Press, 2017, p. 92‒107.[13] K. M. Sheridan and A. Konopasky, “Designing for resourcefulness in a community-based makerspace,” in Makeology, 1st ed., K. Peppler, E. R. Halverson, and Y. B. Kafai, Eds., New York: Routledge, 2016.: Routledge, 2016, pp. 30–46. doi: 10.4324/9781315726519-3.[14] P. Blikstein, Z. Kabayadondo, A. Martin, and D. Fields, “An assessment instrument of technological literacies in makerspaces and FabLabs,” J. Eng. Educ., vol. 106, no. 1, pp. 149–175