some members of the team, you know I deal with Page 14.1343.11 that and clearly my evaluation of the project as a whole is dependent to some extent.”Grading, however, was not about whether the team produced a great product; but also on theteam’s performance on the design process. As another faculty member said: “It is the whole process. So I mean if they tried and they did an amazing job and happen not to produce the final product, then they might still get an A. If they had an easy task and did it and then didn’t try than challenge themselves more, then they might not get an A. They might not get a B.”For
, enabling students to be instructors is a viableapproach for improving student motivation in introductory engineering courses.References[1] French, B. F., Immekus, J. C., & Oakes, W. C. (2005). An examination of indicators of engineering students' success and persistence. Journal of Engineering Education, 94(4), 419-425.[2] Montoya, Y., Pacheco, A., Delgado, E., Webb, I. & Vaughan, M. R. (2015) Developing Leaders by Putting Students in the Curriculum Development Driver Seat. 2015 ASEE Annual Conference and Exposition, Seattle, WA, June 2015.[3] Barkley, E. F., Cross, K. P., & Major, C. H. (2014). Collaborative learning techniques: A handbook for college faculty. John Wiley & Sons.[4] Fagen, A. P
observation (RO) are not really intuitive. Before diving into the statisticalanalysis, it will be helpful to more clearly define these terms (visualized in Figure 1). Figure 1: LSI Learning PreferencesThe following list contains statements to help define each of these terms (Kolb, 1993): 1. Abstract conceptualization (a) To learn, I’d rather think about ideas. (b) I like to reason things out. (c) I want to analyze things. (d) I’m rational. (e) I rely on my ideas. 2. Concrete experience (a) Thinking about my feelings affects how I learn. (b) I trust my feelings and intuition. (c) I’m open to experiencing new things. (d) I like to learn from
Turkey.Dr. Dennie L. Smith, Texas A&M University Dennie Smith is a Professor in Teaching, Learning and Culture at Texas A&M University.Dr. Christine Ehlig-Economides, Texas A&M University Dr. Ehlig-Economides has been full professor of petroleum engineering at Texas A&M University in the Albert B. Stevens endowed chair since 2004. Before that she worked for Schlumberger for 20 years in well test design and interpretation, integrated reservoir characterization, modern well construction design, and well stimulation. She has worked in more than 30 countries and authored more than 60 papers. Dr. Ehlig-Economides has received a number of technical awards in including the SPE Formation Evaluation and Lester C
otherpopulations and critical time periods. 12 ReferencesBabapour Chafi, M., Rahe, U., & Pedgley, O. (2012). The Influence of Self-reflective Diaries on Students’ Design Processes. In DesignEd Asia Conference 2012.Bauer, T. N., & Erdogan, B. (2012). Organizational socialization outcomes: Now and into the future. The Oxford Handbook of Organizational Socialization, 97–112.Boud, D. (2001). Using journal writing to enhance reflective practice. New Directions for Adult and Continuing Education, 2001(90), 9–18.Brunhaver, S., Gilmartin, S. K., Grau, M. M., Sheppard, S., & Chen, H. L. (2013). Not all the same: A look at early career engineers
.4 In acomplex system, this has a number of implications regarding the iterative development ofmultiple models. In particular, for a target model A and a peripheral model B, changes in modelB can effect model A without the mathematical or descriptive form of model A being adjusted.This is due to the change in focus of the user’s attention. When new features or relationshipsbecome apparent, different aspects of the applicability of model A may become apparent.In the next stage of modeling, evaluation, any number of influences cause a user to considerchanging the target model. Interactions with the target system (use of the model or otherinteraction) or other representations can prompt the evaluation process.32,33
students? on the use of the Engineering Student Identity Survey. In: Frontiers in Education Conference (FIE), 2016 IEEE.; 2016:1-6.18. Curran PJ, West SG, Finch JF. The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychol methods1. 1996;1(1):16-29.19. Muthen B, Kaplan D. A comparison of some methodologies for the factor analysis of non-normal Likert variables: A note on the size of the model. Br J Math Stat Psychol. 1992;45(1):19-30.20. Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ. Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods. 1999;4(3):272.21. Tabachnick BG, Fidell LS. Using multivariate statistics. 2001.22
Taxa, Figure 1(a) apply knowledge of mathematics, science, and engineering II & III(b) design and conduct experiments, and analyze and interpret data I & III(c) design a system, component, or process to meet desired needs V & VI(e) identify, formulate, and solve engineering problems III, IV & V(i) a recognition of the need for, and an ability to engage in life-longlearning(j) a knowledge of contemporary issues VI & VII(k) an ability to use the techniques, skills, and modern engineering toolsnecessary for engineering practice Table 3. Comparison between ABET Technical Outcomes and the Proposed
was correct and complete on 3/24/04.30% 50% 80% 100%I expect the project to be graded as ____________________.I am ___________ confident that I know the material to be covered on the exam.30% 50% 80% 100%I expect to earn a grade of ______________________ on the midterm.Write a brief comment on how the class and project is going for you, if you want. Page 11.136.14Appendix B: Assessment Survey 6 from Study 1 Name: MW or W COMP 432 Assessment 6Circle or write the appropriate response. If the question is not applicable write "NA".I have
, measurement, and theory-focused approaches," in Cambridge Handbook ofEngineering Education Research, 1st ed., A. Johri and B. Olds, Eds. Cambridge UniversityPress, 2014, pp. 83-101.[9] C. Venters, L. McNair and M. Paretti, "Using writing assignments to improve conceptualunderstanding in statics: Results from a pilot study," in ASEE 112th Annual Conference andExposition, San Antonio, TX, 2012.[10] D. Montfort, S. Brown and D. Pollock, "An Investigation of Students' ConceptualUnderstanding in Related Sophomore to Graduate-Level Engineering and Mechanics Courses,"Journal of Engineering Education, vol. 98, (2), pp. 111-129, 2009.[11] R. Taraban et al, "First Steps in Understanding Engineering Students' Growth of Conceptualand Procedural Knowledge in an
Paper ID #5942”You choose between TEAM A, good grades, and a girlfriend - you get tochoose two!” - How a culture of exclusion is constructed and maintained inan engineering design competition teamMs. Cindy E Foor, University of Oklahoma Cindy E. Foor is the Associate Director/Research Associate for the Research Institute for STEM Ed- ucation (RISE) at the University of Oklahoma. Her contribution to the multi-disciplinary team lies in qualitative methodologies, cultural theory and the belief that outliers offer great insight into the workings of power. Her research interests include cultural theory, the cultural/historical
Engineering, Leuven, Belgium, pp. 173-176.3 Allen, D.T., Murphy, C.F., Allenby, B., & Davidson, C. (2006). Sustainable engineering: A model for engineering education in the twenty-first century? Clean Technology and Environmental Policy 8:70-71.4 Nair, I. (1998). LCA and Green Design: A Context for Teaching Design, Environment and Ethics, Journal of Engineering Education.5 Allen, D., Allenby, B., Bridges, M. et al. (2008). Benchmarking Sustainable Engineering Education: Final Report. Available online: http://www.csengin.org/BSEE_Final_Report_31Dec08_No_Appen_D.pdf6 Kilgore, D., Atman, C.J., Yasuhara, K., Barker, T.J., & Morozov, A. (2007). Considering Context: A Study of First-Year Engineering Students
community do; • Knowledge: the understandings that people in the community share; • Identity: the way that members of the community see themselves; • Values: the beliefs that members of the community hold; • Epistemology: the warrants that justify actions or claims as legitimate within the community [...]The epistemic frame hypothesis claims that: (a) an epistemic frame binds together the skills, knowledge, values, identity, and epistemology that one takes on as a member of a community of practice; (b) such a frame is internalized through the training and induction processes by which an individual becomes a member of a community; and (c) once internalized, the epistemic frame of a community is used when an individual
signals of anelectrocardiogram (ECG) in biomedical applications; Romero, Touretzky, and Thibadeau11applied PNNs to Chinese Optical Character Recognition (OCR). Haque and Sudhakar12 appliedANN Back-propagation (BP) to predict fracture toughness in micro alloy steel.The author believes that an artificial neural network (ANN) model can similarly be trained toclassify the correlation between student performance (pass/fail or grades A, B, C, D, F) andexternal factors. Hence, the author’s objective of this research was to implement a ProbabilisticNeural Net (PNN) based Genetic Algorithm model to determine the effect of absenteeism onoverall student grade performance in his Structural Systems II course.Research MethodologyCourse and Study PopulationThe
results of this research, first-year and second-yearstudents found online learning as the worst outcome of the pandemic compared to social distancingand unemployment. Hence, integrating self-discipline training or courses into curriculumespecially for new college students will be a game changer during these unprecedented times.Imbedding more active learning elements, group projects and assignments, optional in-person labsand meetings, and breakout rooms activities to online courses besides sending weekly updates canstimulate students and mitigate the effect of social isolation.References[1] J. Crawford, K. Butler-Henderson, J. Rudolph, B. Malkawi, M. Glowatz, R. Burton, P. A. Magni, S. Lam, “COVID-19: 20 countries’ higher education intra
grades, a small but significant proportion, when considering the multiplicity ofvariables that affect course performance, ∆R2 = .06, F(1, 110) = 7.35, p = .008.Table 2Results from Linear Regression Model to Predict Exam Average (N = 113) B S.E. β t Sig 95% CI (unstandardized) (standardized) ACT-M .005 .006 .085 0.92 .360 [-.006, .017] Belonging Uncertainty -.036 .013 -0.25 -2.71 .008* [-.062, -.010]Notes. CI = confidence interval*p < .05 These results indicate that students’ insecurities about belonging in college negativelyimpact course
Conference and Exposition, 2013.[12] H. B. Carlone and A. Johnson, “Understanding the science experiences of successful women of color: Science identity as an analytic lens,” J. Res. Sci. Teach., vol. 44, no. 8, pp. 1187–1218, Oct. 2007.[13] H. G. Murzi and L. D. McNair, “Comparative dmensions of disciplinary culture,” in ASEE Annual Conference and Exposition, 2015.[14] M. Eliot and J. Turns, “Constructing professional portfolios: Sense-making and professional identity development for engineering undergraduates,” J. Eng. Educ., vol. 100, no. 4, pp. 630–654, 2011.[15] D. M. Riley, “Aiding and ABETing: The bankruptcy of outcomes-based education as a change strategy,” in ASEE Annual Conference and Exposition
-upfocus group discussions, external-raters provided insights into the criteria they used to score Page 12.168.7students’ work. Performance criteria are then updated for use in subsequent semesters. Theresults of external-rater evaluations are presented in the following sections. (a) (b) (c)Figure 3: House of Quality format for mapping objectives, outcomes and criteria.The benefits of using a graphic approach for tracking assessment data can be described in theexample of adding ABET criteria (i), a recognition of the need for, and an ability to engage inlife-long
and APPLES studies, the intrinsic psychological motivation variable is a modifiedversion of the intrinsic motivation subscale of the Situational Motivation Scale (SIMS)5 and iscomprised of three items (questions)ii: a) I feel good when I am doing engineering activities. b) Majoring in engineering is fun. c) I think engineering is interesting.Students were asked to rate the extent to which they agreed that each of the items was a reasonthat they were currently majoring in or considering majoring in engineering, and the options forthese items were “strongly disagree,” “moderately disagree,” “disagree,” “unsure,” “agree,”“moderately agree,” or “strongly agree.”The confidence in professional and interpersonal skills
examples research,” Rev. Educ. Res., vol. 70, no. 2, pp. 181–214, 2000.[41] J. Tuminaro and E. F. Redish, “Elements of a cognitive model of physics problem solving: Epistemic games,” Phys. Rev. Spec. Top. - Phys. Educ. Res., vol. 3, no. 2, Jul. 2007.[42] A. A. DiSessa, “Knowledge in Pieces,” in Constructivism in the Computer Age, G. Forman and P. B. Pufall, Eds. New Jersey: Lawrence Erlbaum Associates, In., 1988.[43] E. Yackel and P. Cobb, “Sociomathematical Norms, Argumentation, and Autonomy in Mathematics,” J. Res. Math. Educ., vol. 27, no. 4, p. 458, Jul. 1996.[44] K. Tatsis and E. Koleza, “Social and socio-mathematical norms in collaborative problem- solving,” Eur. J. Teach. Educ., vol. 31, no. 1, pp. 89–100, Feb. 2008.[45
).2 Crotty, M. The Foundations of Social Research. (Sage Publications, 2003).3 Schwandt, T. A. Dictionary of Qualitative Inquiry, Second Edition. (Sage Publications, 2001).4 Hutchinson, S. A., Wilson, M. E. & Wilson, H. S. Benefits of participating in research interviews. Image: Journal of Nursing Scholarship 26, 161-164 (1994).5 Kvale, S. InterViews: an introduction to qualitative research interviewing. (Sage Publications, Inc., 1996).6 Harper, D. Talking about pictures: A case for photo elicitation. Visual Studies 17, 13-26 (2002).7 Clark-Ibanez, M. Framing the social world with photo-elicitation interviews. American Behavioral Scientist 47, 1507-1527 (2004).8 Harrison, B. Photographic
). Creativity in the design process: the co-evolution of problem-solution. Design Studies, 22(5), 425-437. 10. Cinlar, E. (2013). Introduction to Stochastic Processes. Prentice Hall: Englewood Cliffs, NJ. 11. Daltrozzo, J., & Conway, C. M. (2014). Neurocognitive mechanisms of statistical-sequential learning: what do event-related potentials tell us? Frontiers in Human Neuroscience, 8. 12. Keele, S. W., Ivry, R., Mayr, U., Hazeltine, E., & Heuer, H. (2003). The cognitive and neural architecture of sequence representation. Psychological Review, 110(2), 316–339. 13. Clegg, B. A., DiGirolamo, G. J., & Keele, S. W. (1998). Sequence learning. Trends in Cognitive Sciences
brain learns”, Corwin, 2006.[18] U. Boser, ”Learn Better”, Rodale, 2017.[19] P. Brown, H. Roediger, and M. McDaniel, ”Make It Stick : the Science of Successful Learning”, Harvard University Press, 2014.[20] B. Carey, ”How We Learn: The Surprising Truth About When, Where, and Why It Happens”, Random House Trade Paperbacks, 2015.[21] E. Leung and E. Pluskwik, ”Effectiveness of Gamification Activities in a Project-Based Learning Classroom”, in Proceedings of ASEE Annual Conference and Expo, 2018.[22] D. Guest, ”The hunt is on for the Renaissance Man of computing,” The Independent, Sept. 17 1991.
meaning-making lens. Such a perspective of reflection helps align multiple bodies of literature around the topic.In light of our four cases, we explore two questions that were central to our collaborative inquiry: 1. What common strategies did we use and what common challenges did we face? This question is motivated by the assumption that features common to our cases suggest what may be relevant to future work of this variety. 2. What implications do our cases suggest for: a) individual researchers interested in trying to do this type of work, b) researchers wondering if this type of work is relevant to their topic, and c) a community trying to decide if and how to value this type of work?By sharing our
: Rethinking the racial wealth gap,” Social Currents, vol. 4, no. 3, pp. 199–207, Jun. 2017, doi: 10.1177/2329496516686620.[8] A. B. Abad, “Paying the Price: College Costs, Financial Aid, and the Betrayal of the American Dream by Sara Goldrick-Rab,” The Review of Higher Education, vol. 42, no. 1, p. E-7-E-10, 2018, doi: 10.1353/rhe.2018.0041.[9] A. M. Shahiri, W. Husain, and N. A. Rashid, “A Review on Predicting Student’s Performance Using Data Mining Techniques,” in Procedia Computer Science, Jan. 2015, vol. 72, pp. 414–422, doi: 10.1016/j.procs.2015.12.157.[10] N. Kronberger and I. Horwath, “The Ironic Costs of Performing Well: Grades Differentially Predict Male and Female
educationalexperience than track B or it does not. If it does, the success of the track should be monitoredthrough specific learning outcomes. If it does not, then there is really only one track andstudents’ choices are essentially meaningless. Page 13.1362.6While student learning outcomes are a useful set of requirements by which to define success ofan educational program, they are not easy to measure. Two examples of student learningoutcomes are: 1. Ability to function on multidisciplinary teams (ABET d); 2. Understanding of Professional and Ethical Responsibilities (ABET f)How does one measure these outcomes to determine if the engineering
-Participants noted an innate aptitude was necessary for continued science interest due to challenging materialEnvironmental FactorsThe data revealed four overall themes relating to environmental factors that impact bothdecisions to major in computer science and pursue a career in computer science. Themesincluded: a) prior experiences, b) pedagogy and immediate educational environment, c) cultureof the computer science field, and d) long term job prospects. While the literature suggests thatdifferences may exist between men’s and women’s experiences, such differences did not emergein the data from our study with the exception of the value associated with and likelihood ofparticipating in “tinkering” experiences.The environmental
grade: Exploring the judgement processes involved in examination grading decisions,” Eval. Res. Educ., vol. 23, no. 1, pp. 19–35, 2010.[10] W. B. Armstrong, “The association among student success in courses, placement test scores, student background data, and instructor grading practices,” Community Coll. J. Res. Pract., vol. 24, no. 8, pp. 681– 695, 2000.[11] N. M. Hicks and H. A. Diefes-Dux, “Grader consistency using learning objective based rubrics,” in The 124th ASEE Annual Conference & Exposition, 2017.[12] M. A. Stellmack, Y. L. Konheim-Kalkstein, J. E. Manor, A. R. Massey, and J. A. P. Schmitz, “An assessment of reliability and validity of
2006-187: CURRICULAR ELEMENTS THAT PROMOTE PROFESSIONALBEHAVIOR IN A DESIGN CLASSSteven Zemke, Gonzaga University Steven C. Zemke, an Assistant Professor of Mechanical Engineering at Gonzaga University in Spokane Washington teaches sophomore, junior, and senior level design courses. His research interests include enriched learning environments, non-traditional instructional methods, and design processes. Before changing careers to academia Steven was a design engineer and manager in industry for 20 years.Donald Elger, University of Idaho Donald F. Elger, a Professor of Mechanical Engineering at the University of Idaho in Moscow, has been actively involved with traditional research and
Design Theory and Methodology, Scottsdale, Arizona, 1992. 1992. pp. 277-281.[17] T. Kershaw, K. Holtta-Otto, and Y. S. Lee, "The effect of prototyping and critical feedback on fixation in engineering design," in Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Boston, Massachusetts, USA, 20-23 July 2011. Carlson, L., Ed. Cognitive Science Society, 2011. pp. 807-812.[18] P. Samuel and K. Jablokow, "Psychological inertia and the role of idea generation techniques in the early stages of engineering design," in Proceedings of the Fall 2010 Mid-Atlantic ASEE Conference, Villanova, Pennsylvania, USA, October 15-16, 2010. 2010. pp. 1-12.[19] I. Belski, A. Belski, V. Berdonosov, B