be easily implementable. Its content addresses thekey drivers of attrition, which reorganized include the expectations of the major and profession,and other competencies for the major. Students are exposed to skills and strategies aroundlearning and belonging, but in a limited manner and without discussion of SRL and SOB socio-cognitive theories, as shown in the topics and learning objectives given next. Topics Learning Objectives1. Student responsibility: know a. Take full responsibility for learning what is expected, do the b. Manage your own time work, manage time c. Know pre-requisites of new course - 1 class d
Affecting PhD Student Success. International journal of exercise science, 12(1), 34–45.[5] Cass, C., Kirn, A., Tsugawa, M. A., Perkins, H., Chestnut, J. N., Briggs, D. E., & Miller, B. (2017, June), Board # 18 : Improving Performance and Retention of Engineering Graduate Students through Motivation and Identity Formation Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--27798[6] Perkins, H., Tsugawa-Nieves, M., Bahnson, M., Satterfield, D., Parker, M., Kirn, A., & Cass, C. (2019). Motivation Profiles of engineering doctoral students and implications for persistence. 2019 IEEE Frontiers in Education Conference (FIE). https://doi.org/10.1109
, a set of steps (labeled a-f) repeat; (a) the instructor asks a questionof the whole class (verbally or visually), (b) students answer individually, and (c) the instructorpresents and/or describes the distribution of responses. Next, (d) students discuss the question inpairs, (e) answer individually again, and (f) the instructor again presents or describes thedistribution of responses. Table 1: FASTOP Codes Code Student Focus Teacher (Instructor) Focus Solo Students act alone. Teacher with one student. Pair Students act in pairs. Teacher with pair of students. Team Students act in teams/groups
Engineers (ASME). He current serves as an Editor for the Electrophoresis.David B. Thiessen, Washington State University David B.Thiessen received his Ph.D. in Chemical Engineering from the University of Colorado in 1992 and has been at Washington State University since 1994. His research interests include fluid physics, acoustics, and engineering education.Jacqueline Gartner Ph.D., Campbell University Jacqueline Gartner is an Associate Professor at Campbell University in the School of Engineering, which offers a broad BS in engineering with concentrations in chemical, mechanical, and electrical.Kitana Kaiphanliam, Washington State University Kitana Kaiphanliam is a doctoral candidate in the Voiland School of Chemical
into a tactile test, called the Tactile MentalCutting Test (TMCT), designed to allow for tactile interpretation, instead of visual interpretation,of 3-D objects and their planar cuts. The TMCT allows all persons, including BLV populations,access to a tool that can quantify spatial ability. To increase the TMCT’s utility, the originalformat of the 25-question TMCT was split into two subtests (A & B), each containing 12questions. In 2021, the TMCT’s reliability in measuring spatial constructs of rotation, cuttingplane, and proportion in BLV populations was found to be good [1]. However, to increase theprecision of the results found in our pilot analysis, the research team desired a larger sample size.This paper presents a continued
frequently. Only twoTMCT items from the high scoring group had a non-correct answer selected the most frequently.Four TMCT items had particularly high percentages of wrong answers picked and were selectedfor further analysis. These items are problems 118, 124, 131, and 140. Images of each selecteditem with its corresponding answer choice page are given in appendix A. Of the 35 participantswho answered item 118, 17 (49%) participants picked answer choice B. Only two out of 24 highscoring participants picked answer B. The correct answer to problem 118 is answer choice C. Forproblem 124, 19 of the 27 (70%) participants who answered this item picked answer choice B.None of the low scoring participants chose the correct answer, D, while all 30 of the
small, midwestern university. While the university itself is quite small,the engineering department is even smaller, with an average of only 25-30 incoming first-yearstudents each year. These first-year engineering students all enroll in one of two sections of anintroductory engineering fundamentals course (that includes both a lecture and a lab) thatfamiliarizes them with engineering concepts and tools they will use throughout their four yearsof engineering coursework and in their engineering careers. One section of this course wastaught by a professor who has taught this course for many years (Instructor A) and the othersection of this course was taught by a new faculty member teaching it for the first time(Instructor B). Since the goal is to
: una herramienta en la formación de estudiantes universitarios en el sureste de México,” RIDE Revista Iberoamericana Para La Investigación Y El Desarrollo Educativo, vol. 11, no. 22, May 2021, doi: 10.23913/ride.v11i22.937.[4] J. García‐Alandete, E. R. Martínez, P. S. Nohales, and B. S. Lozano, “Sentido de la vida y bienestar psicológico en adultos emergentes españoles,” Acta Colombiana De Psicología, pp. 196–216, Jan. 2017, doi: 10.14718/acp.2018.21.1.9.[5] S. Nyholm and M. Rüther, “Meaning in Life in AI Ethics—Some Trends and Perspectives,” Philosophy & Technology, vol. 36, no. 2, Mar. 2023, doi: 10.1007/s13347-023-00620-z.[6] J. Fereday and E. Muir‐Cochrane, “Demonstrating rigor using thematic analysis: a hybrid approach
to knowledge, academic engagement and motivation, and self-regulation.Dr. Sheryl A. Sorby, University of Cincinnati Dr. Sheryl Sorby is currently a Professor of STEM Education at the University of Cincinnati and was recently a Fulbright Scholar at the Dublin Institute of Technology in Dublin, Ireland. She is a professor emerita of Mechanical Engineering-Engineering MecDr. Clodagh Reid, Technological University of the Shannon PhD in spatial ability and problem solving in engineering education from Technological University of the Shannon: Midlands Midwest. Graduated in 2017 from the University of Limerick with a B. Tech (Ed.). Member of Technology Education Research Group (TERG).Dr. Gibin Raju, University of
in science-related fields and atransformation of identity from student to professional [9, 10]. ARG participation promotes self-efficacy. ARG has been designed as a situated learning approach involving an apprentice-styleresearch experience [6, 10]. The focus is on inviting students with potential but who lackconfidence, low self-efficacy, or a sense of belonging [11]. Research on the model hasdemonstrated several positive outcomes for traditionally underrepresented students, including:a) Increased retention and participation in undergraduate computing majors [12].b) Increased likelihood of participants going to graduate school [13].c) Increased GPA for engineering students with moderate academic performance [14].d) Higher student
/dhe0000115.[7] I. Ajzen, “The theory of planned behavior,” Organizational Behavior and Human Decision Processes, vol. 50, no. 2, pp. 179–211, Dec. 1991, doi: 10.1016/0749-5978(91)90020-T.[8] B. Latané and J. M. Darley, The Unresponsive Bystander: Why Doesn’t He Help? New York, NY: Appleton-Century Crofts., 1970.[9] T. S. Harding, M. J. Mayhew, C. J. Finelli, and D. D. Carpenter, “The Theory of Planned Behavior as a Model of Academic Dishonesty in Engineering and Humanities Undergraduates,” Ethics & Behavior, vol. 17, no. 3, pp. 255–279, Sep. 2007, doi: 10.1080/10508420701519239.[10] M. E. Matters, C. B. Zoltowski, A. O. Brightman, and P. M. Buzzanell, “An Engineering Faculty and an Intention to Make Change for Diversity and
participatedin a robotic unit during their teacher preparation course, designed a lesson plan incorporatingrobots, and completed approximately 30-minute structured interviews. In the interviews,informants were invited to reflect on their processes of: (a) robot design, (b) robot assembly, (c)robot programming, and (d) lesson design, as well as on the challenges they faced and how suchwere overcome, and what they learned about STEM learning and teaching. As we used aninterview guide approach in which broad categories of interview questions along with specificinterview questions are specified before conducting the interview, but allowance can be made fortailoring questions to probe deeper into areas of interest [41]. The six study cohorts share manysame
course doubled the likelihood of persisting in engineering compared to starting belowcalculus. Moreover, those earning A/B grades demonstrated a 6.5 times higher likelihood ofpersistence. However, the study cautiously acknowledged potential limitations ingeneralizability.The role of mathematics proficiency has been suggested as a key factor affecting persistence inthe science, technology, engineering, and mathematics (STEM) fields, with particular emphasison engineering [34]. Performance in courses that serve as a prerequisite to many other courses –called variously barrier courses, gateway courses, and other terms – is a critical factorinfluencing a student’s persistence in the field of engineering, leading some students to doubttheir
approach to examining students’ epistemologies in thecontext engineering design. Studies of students’ epistemologies suggest a gap between theirprofessed epistemologies (i.e., their stated beliefs about knowledge) and their enactedepistemologies (i.e., what one might deduce about their beliefs from their behaviors). Thisresearch examines that gap in the context of design problem solving. We conducted focus groupsin which we asked students to (a) discuss their responses to a paper survey (professedepistemologies) and (b) evaluate engineering concepts, as well as the rationales behind theconcepts, in a set of engineering vignettes (enacted epistemologies). Preliminary findings suggeststudents often expressed hesitance for dominant epistemologies in
methodological paradigm.Such an exercise can further help us develop some contextual knowledge that will prepare us toconduct qualitative research in Chinese engineering classrooms.Reference[1] B. M. Olds, B. M. Moskal, and R. L. Miller, “Assessment in Engineering Education: Evolution, Approaches and Future Collaborations,” Journal of Engineering Education, vol. 94, no. 1, pp. 13– 25, Jan. 2005, doi: 10.1002/j.2168-9830.2005.tb00826.x.[2] A. W. Astin, Assessment for excellence: The philosophy and practice of assessment and evaluation in higher education. Rowman & Littlefield Publishers, 2012.[3] D.-M. Duşe and C. Duse, Engineering education in a highly globalised world. 2008.[4] S. O. Shaposhnikov and E. Yu. Yatkina
-Schaw, C. (2012). Introduction to quantitative research. In G. M. Breakwell, J. A. Smith, & D. B. Wright (Eds.), Research methods in psychology (4th ed.) (pp. 17-37). Sage.Goldin, I. M., Pinkus, R. L., & Ashley, K. (2015). Validity and reliability of an instrument for assessing case analyses in bioengineering ethics education. Science and Engineering Ethics, 21, 789-807. https://doi.org/10.1007/s11948-015-9644-2Herkert, J. R. (1999). Ethics and professional responsibility. In J. G. Webster (Ed.), Wiley Encyclopedia of Electrical and Electronics Engineering, Vol. 7 (pp. 73-182). Wiley.Herkert, J. R. (2005). Ways of thinking about and teaching ethical problem solving: Microethics and macroethics in
,” Group Organ. Manag., vol. 44, no. 1, pp. 165–210, Feb. 2019, doi: 10.1177/1059601118776750.[7] F. R. C. de Wit, L. L. Greer, and K. A. Jehn, “The paradox of intragroup conflict: A meta- analysis.,” J. Appl. Psychol., vol. 97, no. 2, pp. 360–390, 2012, doi: 10.1037/a0024844.[8] D. A. Harrison and K. Klein, “What’s the Difference? Diversity Constructs as Separation, Variety, or Disparity in Organizations,” Acad. Manage. Rev., vol. 32, no. 4, pp. 1199–1228, 2007.[9] J. Field and N. Morgan-Klein, “Studenthood and identification: higher education as a liminal transitional space,” presented at the 40th Annual SCUTREA Conference, Jul. 2010. Accessed: May 27, 2021. [Online]. Available: http://dspace.stir.ac.uk/handle/1893/3221[10] B
its increasing use among students and scholars, alongsideLatin* [see also 23].B. Sample and Settings Our analytic sample consists of multiple cohorts of undergraduate students who participated in materialsscience summer research internship programs between 2019 and 2023 in a diverse historically black collegesetting. Table 1 displays self-reported background information for the participants included in this study. Theparticipants were diverse in terms of race, ethnicity, socioeconomic status, and home college/university, aseach cohort included students from the engineering college host site as well as students recruited nationallyfrom other campuses including research-intensive universities, other HBCUs, and most recently alsocommunity
). Research characteristics and patterns in engineering education: Content analysis 2000-2009. World Transactions on Engineering and Technology Education, 8(4), 462-470.Chou, P. N., & Chen, W. F. (2014). Global resources in engineering education: A content analysis of worldwide engineering education journals. International Journal of Engineering Education, 30(3), 701–710.Counsell, A., & Harlow, L. L. (2017). Reporting Practices and Use of Quantitative Methods in Canadian Journal Articles in Psychology. Canadian psychology, 58(2), 140–147. https://doi.org/10.1037/cap0000074Godwin, A., Benedict, B., Rohde, J., Thielmeyer, A., Perkins, H., Major, J., Clements, H., & Chen, Z. (2021). New epistemological
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
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
effectiveness is used as one of the measures to evaluateinstruction quality [2]. In addition, learning effectiveness measures can also include an efficiencymeasure (i.e., time on task) [4, 10], which became an additional concept we explored in ourstudy.Influencing Factors of Learning Effectiveness in Online SettingsLiterature shows that learning effectiveness has been studied in three contexts: (a) when newteaching methods were introduced [6]; (b) when online or blended teaching was employed [7];and (c) when comparing different learning modes [4, 5]. The first two contexts suggest that usingalternative course delivery modes can provoke thinking and prompt studies on learningeffectiveness in different instructional settings. The instructional changes
Jared Markunas who assisted in the development of the survey that will inform the engagementguide prototype.References[1] D. R. Fisher, A. Bagiati, and S. Sarma, “Developing Professional Skills in Undergraduate Engineering Students Through Cocurricular Involvement,” J. Stud. Aff. Res. Pract., vol. 54, no. 3, pp. 286–302, Jul. 2017, doi: 10.1080/19496591.2017.1289097.[2] G. Young, D. B. Knight, and D. R. Simmons, “Co-curricular experiences link to nontechnical skill development for African-American engineers: Communication, teamwork, professionalism, lifelong learning, and reflective behavior skills,” in 2014 IEEE Frontiers in Education Conference (FIE) Proceedings, Madrid, Spain, Oct. 2014, pp. 1–7. doi: 10.1109/FIE
enough: Early prediction of student success and event-level difficulty during a novice programming task.,” International Educational Data Mining Society, 2019. [3] K. Rivers and K. R. Koedinger, “Data-driven hint generation in vast solution spaces: a self-improving python programming tutor,” International Journal of Artificial Intelligence in Education, vol. 27, pp. 37–64, 2017. [4] Y. Dong, S. Marwan, P. Shabrina, T. Price, and T. Barnes, “Using student trace logs to determine meaningful progress and struggle during programming problem solving,” International Educational Data Mining Society, 2021. [5] A. Emerson, M. Geden, A. Smith, E. Wiebe, B. Mott, K. E. Boyer, and J. Lester, “Predictive student modeling in block
, vol. 6, pp. 105–110, 2013.[10] A. L. Zydney, J. S. Bennett, A. Shahid, and K. W. Bauer, “Impact of Undergraduate Research Experience in Engineering,” Journal of Engineering Education, vol. 91, no. 2, pp. 151–157, 2002, doi: https://doi.org/10.1002/j.2168-9830.2002.tb00687.x.[11] D. F. Carter, H. K. Ro, B. Alcott, and L. R. Lattuca, “Co-curricular connections: The role of undergraduate research experiences in promoting engineering students’ communication, teamwork, and leadership skills,” Research in Higher Education, vol. 57, no. 3, pp. 363– 393, 2016.[12] T. D. Sadler and L. McKinney, “Scientific Research for Undergraduate Students: A Review of the Literature,” Journal of College Science Teaching, vol. 39, no. 5
Paper ID #43149Identifying Curriculum Factors that Facilitate Lifelong Learning in AlumniCareer Trajectories: Stage 3 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.Amy Bilton, University of TorontoMs. 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. Her research focuses on
Paper ID #42944Unmasking Cognitive Engagement: A Systematized Literature Review of theRelationships Between Students’ Facial Expressions and Learning OutcomesMr. Talha Naqash, Utah State University, Logan Mr.Talha Naqash is currently pursuing his doctoral studies in Engineering Education at Utah State University. With a profound educational background spanning multiple disciplines, he holds an MS in Telecommunication and networking. His extensive research contributions are reflected in numerous publications and presentations at prestigious IEEE; ASEE conferences, Wiley’s & Springer Journals. His research primarily
Journal of Engineering Education, 43(6), 927–949. https://doi.org/10.1080/03043797.2018.1462766Faber, C., & Benson, L. C. (2017). Engineering students' epistemic cognition in the context of problem- solving. Journal of Engineering Education, 106(4), 677–709. https://doi.org/10.1002/jee.20183Gillborn, D., Warmington, P., & Demack, S. (2018). QuantCrit: Education, policy, 'big data' and principles for a critical race theory of statistics. Race Ethnicity and Education, 21(2), 158–179. https://doi.org/10.1080/13613324.2017.1377417Godwin, A. (2017). Unpacking Latent Diversity. 2017 ASEE Annual Conference & Exposition Proceedings, Columbus, OH. https://peer.asee.org/29062Godwin, A., Benedict, B., Rohde
: 10.1080/13613324.2021.1924137.[15] B. A. Burt, “Toward a Theory of Engineering Professorial Intentions: The Role of Research Group Experiences,” American Educational Research Journal, vol. 56, no. 2, pp. 289–332, Apr. 2019, doi: 10.3102/0002831218791467.[16] J. Seniuk Cicek, P. Sheridan, L. Kuley, and R. Paul, “Through ‘Collaborative Autoethnography’: Researchers Explore Their Role as Participants in Characterizing the Identities of Engineering Education Graduate Students in Canada,” in 2017 ASEE Annual Conference & Exposition Proceedings, Columbus, Ohio, Jun. 2017, p. 29029. doi: 10.18260/1-2--29029.[17] J. B. Main, L. Tan, M. F. Cox, E. O. McGee, and A. Katz, “The correlation between undergraduate student
universitarios,” Revista Electr´onica de Psicolog´ıa Iztacala, vol. 20, no. 1, mar. 2017. [Online]. Available: https://www.revistas.unam.mx/index.php/repi/article/view/58921 [5] F. Sempere-Ripoll and A. Rodr´ıguez-Villalobos, “La emoci´on como clave del e´ xito para el desarrollo de competencias en la direcci´on de operaciones,” Direcci´on y Organizaci´on, p. 73–84, Jul. 2019. [Online]. Available: http://dx.doi.org/10.37610/dyo.v0i68.553 [6] B. Giangrasso, S. Casale, G. Fioravanti, G. L. Flett, and T. Nepon, “Mattering and anti-mattering in emotion regulation and life satisfaction: A mediational analysis of stress and distress during the covid-19 pandemic,” Journal of Psychoeducational Assessment, vol. 40, no. 1, p. 125–141, Dec