learning objectives, instructional strategies, and assessments forsustainable infrastructure topics. Subsequent problem-based learning activities are being revisedand improved.AcknowledgmentsThis work was funded by the Scholarship of Teaching and Learning grant from the University ofNorth Carolina at Charlotte.References[1] A. Steinemann, "Implementing Sustainable Development through Problem-Based Learning:Pedagogy and Practice," Journal of Professional Issues in Engineering Education and Practice,vol. 129, no. 4, pp. 216-224, 2003, doi: 10.1061[2] S. A. Gallagher, B. T. Sher, W. J. Stepien, and D. Workman, "Implementing Problem-BasedLearning in Science Classrooms," School Science and Mathematics, vol. 95, no. 3, pp. 136-146,1995, doi: 10.1111/j
are now compiling concept inventoriesto be delivered and marked by OASIS. Such course concept inventories will make it possibleto track the level of student understanding through a course and to identify common studentmisconceptions, thus informing improvements to course design.References1. Hale, S.E., Report of the committee on university teaching method. 1964, HMSO: London.2. Robbins, L., Higher education. 1963, HMSO: London.3. Dill, D.D. and B. Sporn, The implications of a postindustrial environment for the university: An introduction, in Emerging patterns of social demand and university reform: Through a glass darkly, D.D. Dill and B. Sporn, Editors. 1995, Pergamon: Oxford. p. 1-19.4. Sporn, B., Adaptive
for other activities.The second major objective of the TExT is to provide learning activities to be used in the class-room along with detailed lesson plans describing how to conduct these activities. To the maxi-mum extent possible, this includes providing the resources necessary for conducting the in-classactivity. In cases where the resources cannot be provided, the lesson plan includes a list of all theitems the instructor will need in class along with an indication of those that must be obtainedfrom a source external to the TExT. The key points of this objective are to ensure (a) that eachactivity is well designed as a student learning experience, (b) that implementation of each activ-ity is straightforward and time-efficient and (c) that
to 50,644 records. Thus the first step towards data cleaning is completed.Validating the keyword search strategy It was decided to validate the keyword-based search using a post-hoc analysis in whichrecords of a single source i.e., Frontiers in Education (FIE) were taken for analysis. These FIErecords were taken from the complete article list and not just the cleaned version and hence acode was run on the 142,981 records to parse out FIE records. As a result 1869 records werefiltered out. Validation analysis is split in two parts as provided here: (a) Source/venue count , (b)Validation.(a) Source/venue counts To begin the source count analysis keyword phrases were extracted from the 1869 FIErecords. It was found that only 804 of
literature toidentify key components of the entrepreneurial mindset. As Figure 1 shows, this review was inthe disciplinary areas of a) education or learning theory b) engineering education and c) businessmanagement.Fig1: Disciplinary areas for literature reviewThe first stage yielded the identification of key components that define these three disciplinaryareas. These involved elements such as: risk tolerance, empathy, pro-activity, co-regulation, etc.We could find homologues in entrepreneurship and learning theory. Nonetheless, there weregaps as it related specifically to engineering education. Therefore, as Figure 2 shows, we neededto triangulate this information with sources derived from praxis like the perceptions of students,instructors, and
manageable scale. The smaller nature of the Tiny House projectallowed for greater collaboration between disciplines and required a fraction of the fundraisingand travel logistics presented by the Solar Decathlon – a much easier undertaking for aninstitution with limited resources to devote to such a project. (Schematics of the Tiny House canbe seen in Appendix B)After reviewing the successes, failures, and lessons learned from both the Solar Decathlon andTiny House competitions, the researchers have decided to continue to integrate student teamsfrom multiple disciplines in project-based-learning opportunities. Both the Tiny House, and theSolar Decathlon house are now back on the subject university campus. In deciding what to dowith the buildings
learning management system in an unedited format. The intention wasto provide a simple “facsimile” of the lecture for students to use in-lieu of attending the lectures,or as a review of the lecture. It should be noted that the videos were not intended to replace thein-class lectures, but rather, to provide an additional resource for students to support theirlearning.3.2 The classroom surveyThe method used to determine if the supplemental video lectures enhanced student performanceinvolved administering a very simple survey – in the form of a single question – at the end ofeach quiz: Which of the following best describes how you prepared for this quiz? a) I prepared primarily using class lectures. b) I prepared
). “An overview of computational thinking,” International Journal of Computer Science Education in Schools, 3 (1) 1-11.[3] V. Shute, C. Sun, & J. Asbell-Clarke (2017). “Demystifying computational thinking’” Educational Research Review, 22, 142-158.[4] M. Berland & U. Wilensky (2015). “Comparing virtual and physical robotics environments for supporting complex systems and computational thinking,” Journal of Science Education and Technology 24(5), 628-647.[5] M. Bers, L. Flannery, E. Kazakoff, & A. Sullivan, (2014). “Computational thinking and tinkering: Exploration of an early childhood robotics curriculum,” Computers & Education 72, 145–157.[6] B. Zhone, Q. Wang, J. Chen, & Y. Li (2016). “An exploration of
in order to helpMichael catch up to them. Finally, Michael exclaims, “God, that took forever. That was stupid.”He has finally reached the correct solution on part B. His exclamation reveals a negativeappraisal regarding the length of time it took to complete that portion of the problem wasunacceptable. This frustration leads Michael to move on to the next part of the problem withoutchecking to see if his partner, Gary, is caught up. He moves on to part C, while there is noindication that Gary is caught up to the rest of the group. By the time Michael moves on to partC of the first homework problem, Becca and Benjamin have already moved on to the secondproblem. The group continues working in a similar manner throughout the evening
differences in returning and direct-pathway students’ decisions to enroll and persist in engineering PhD programs. Consistent withthis literature and recent calls for a more in-depth theoretical focus on the dimensions cost withinthe EVT model and how cost relates to a number of student outcomes, in this paper we focusexclusively on the cost component of expectancy value theory. Specifically we ask: a) howreturning students differently perceive the costs associated with an engineering PhD and b) how,if at all, these costs shape their PhD work. We explore these questions using a mixed methodsapproach that draws on quantitative findings from a survey of returning and direct pathwaystudents in conversation with qualitative findings from interviews with
, 2011.10. B. M. Moskal, C. Skokan, D. Munoz and J. Gosink, "Humanitarian Engineering: Global Impacts and Sustainability of a Curricular Effort," International Journal of Engineering Education, vol. 24, no. 1, pp. 162- 174, 2008.11. S. Schwartz, "Normative influences on altruism," Advances in experimental social psychology, vol. 10, pp. 221- 279, 1977.12. S. H. Schwartz and J. A. Howard, "Helping and Cooperation: A Self-Based Motivational Model," in Cooperation and Helping Behavior: Theories and Research, New York, Academic Press, Inc., 1982, pp. 327- 353.13. J. Ramsey, "A Curricular Framework for Community-Based STS Issue Instruction," Education and Urban Society, vol. 22, no. 1, pp. 40-53, November 1989.14. J. Ramsey, "The
epistemic beliefs is to lay the groundwork for future studies toexplore a potential link between epistemology and teaching practices and to suggest ways toimprove pedagogy and increase self-awareness for faculty and graduate teaching assistants.ReferencesBaxter Magolda, M.B. (1992). Knowing and Reasoning in College. San Francisco: Jossey-Bass.Belenky, M. F., Clenchy, B. M., Goldberger, N. R., and Torule, J. M. (1986). Women’s Ways ofKnowing: The Development of Self, Voice and Mind. New York: Basic Books.Bendixen, L. D. & Rule, D. C. (2004). An Integrative Approach to Personal Epistemology: AGuiding Model. Educational Psychologist, 39(1), 69-80.Benson, L, Becker, K., Cooper, M., Griffin, H., & Smith, K. (2010). Engineering
being met through student learning and validated through the businessimpacts of training.The research method used ensured that the problems identified could be met through measurableobjectives and provided a framework for evaluating the following questions: a) did theknowledge transfer, b) did the knowledge impact behaviors, and c) did the behaviors impactproductivity and productivity of the product service offerings? This framework includedinstructor and student evaluations of the PLM course. This evidence based approach is a critical Page 13.236.5business component of most industrial training programs.Instructor Led Online Lectures and
methodological perspectives. Journal of Engineering Education. 6. Huff, J. L., Smith, J. A., Jesiek, B. K., Zoltowski, C. B., Graziano, W. G., & Oakes, W. C. (2014, October). From methods to methodology: Reflection on keeping the philosophical commitments of interpretative phenomenological analysis. In 2014 IEEE Frontiers in Education Conference (FIE) Proceedings (pp. 1-9). IEEE. 7. Kirn, A., & Benson, L. (In Review). Engineering Students' Perceptions of Problem Solving and their Future. Journal of Engineering Education. 8. Ross, M., & Godwin, A. (2015, October). Stories of Black women in engineering industry—Why they leave. In 2015 IEEE Frontiers in Education Conference (FIE), Proceedings (pp. 1-5
to cognitive assessment and the articulated thoughts in simulated situations paradigm." Journal of Consulting and Clinical Psychology, 65(6), 950.23. Haaga, D. A., Davison, G. C., McDermut, W., Hillis, S. L., and Twomey, H. B. (1993). "“States-of-mind” analysis of the articulated thoughts of exsmokers." Cognitive Therapy and Research, 17(5), 427-439.24. Schellings, G., Aarnoutse, C., and van Leeuwe, J. (2006). "Third-grader's think-aloud protocols: Types of reading activities in reading an expository text." Learning and Instruction, 16(6), 549-568.25. Gerloff, P. (1986). "Second language learners‟ reports on the interpretive process: Talk-aloud protocols of translation." Interlingual and intercultural communication. Tübingen
measurestudents' responses to the types of instruction delivered in the undergraduate engineeringclassrooms [7]. It consists of three main sections and eleven subscales, as seen in Table I. TABLE I SECTIONS OF THE StRIP QUESTIONNAIRE AND ITS SUBSCALES Instrument Sections Subscales 1. Interactive 2. Constructive A. Types of instruction 3. Active Student 4. Passive response to B. Strategies for using in- 5. Explanation
solutions addressed the five subtasks, and (b) the strategies that studentsemployed within each subtask.In all 100 student work products, we identified strategies for each of the 5 subtasks. Within eachsubtask, we identified 3-6 different specific strategies employed by student teams in their workproducts. Deep and shallow strategies in each of the 5 subtask areas were determined byconsidering aspects of expertise and cognitive difficulty.Comparisons of deep and shallow groupings in each subtask indicate significant differences inQAG Score for 3 subtasks - Determine a Weighting System, Apply Weightings, and DetermineFinal Rankings . There was no statistically significant difference in Overall Score betweengroups that applied deep and shallow
AP physicsclasses and, when they did, were 30% less likely than boys to sit for the culminating APPhysics B exam.22 The implications of the lower representation are clear, if engineeringadmissions decisions rely upon taking advanced physics as a proxy for the quality of the highschool course load, fewer females would be considered strong applicants. The U.S. Departmentof Education found that 39% of all high school boys had completed a physics class in 2009,versus 33% of girls.21 The participation difference by gender was striking among AP Physicsexam takers in 2013: females represented only 35% of Physics B, 23% of Physics C: Electricityand Magnetism and 26% of Physics C: Mechanics exam takers.20 So, if taking AP Physics is agatekeeper, as
Education, vol. 78, no. 7, pp. 674-681, 1988.[2] A. Kaw, R. Clark, E. Delgado, and N. Abate, "Analyzing the use of adaptive learning in aflipped classroom for preclass learning," Computer Applications in Engineering Education, vol.27, no. 3, pp. 663-678, 2019, doi: 10.1002/cae.22106.[3] G. Morgan, J.-M. Lowendahl, and T.-L. Thayer. Top 10 Strategic Technologies ImpactingHigher Education in 2016.[4] L. Yarnall, B. Means, T. Wetzel, “Lessons Learned from Early Implementations of AdaptiveCourseware,” SRI Education. SRI Project Nos. 21987 and 22997, 2016.
Educ Psychol. 1983;75(2):215-226. doi:10.1037/0022-0663.75.2.215.2. Tinto V. Dropout from higher education: A theoretical synthesis of recent research. Rev Educ Res. 1975;45(1):89-125.3. Ohland MW, Brawner CE, Camacho MM, et al. Race, Gender, and Measures of Success in Engineering Education. J Eng Educ. 2011;100(2):225-252.4. Cabrera AF, Nora A, Castaneda MB. The Role of Finances in the Persistence Process: A Structural Model. Res High Educ. 1992;33(5).5. Wohlgemuth D, Whalen D, Sullivan J, Nading C, Mack S, Yongyi W. Financial, academic, and environmental influences on the retention and graduation of students. J Coll Student Retent. 2006;8(4):457-475. doi:10.2190/86X6-5VH8-3007-6918.6. Rhoads B
.20054Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. J. (1993). Reliability and predictivevalidity of the motivated strategies for learning questionnaire (mslq). Educational andPsychological Measurement, 53(3), 801-813. doi:10.1177/0013164493053003024Seymour, E., & Hewitt, N. M. (1997). Talking about leaving: Why undergraduates leave thescience. Boulder, CO: Westview Press.The National Center for Academic Transformation. (2017). Who we are. In The national centerfor academic transformation. Retrieved March 7, 2017, fromhttp://www.thencat.org/whoweare.htmlZhang, G., Anderson, T. J., Ohland, M. W., & Thorndyke, B. R. (2004). Identifying factorsinfluencing engineering student graduation: A longitudinal and cross-institutional
a scoring guide, rubric, or answer key. For instance, Osgood andJohnston [25] developed a measure of design ability, which they operationalized as problemframing, evaluating alternatives, and communicating their design ideas. Their measure includedthree scenario-based multiple-choice items and five Likert-scale questions related to problemframing. The multiple-choice scenario involved designing a chair for someone over six feet talland posed questions such as "You just finished the first meeting with the client to discuss theproblem, which lasted 15 minutes. Of the following, the first task you should complete is: (a)Develop a schedule of all tasks to be completed. (b) Find out more about chair design andbackground information. (c
for posing open-ended engineering problems: Model-eliciting activities,” in Proceedings of the 34th ASEE/IEEE Frontiers in Education Conference, 2004.[11] H. A. Diefes-Dux, M. Hjalmarson, J. S. Zawojewski, and K. Bowman, “Quantifying aluminum crystal size part 1: The model-eliciting activity,” J. STEM Educ. Innov. Res., vol. 7, no. 1–2, pp. 51–63, 2006.[12] R. Lesh, M. Hoover, B. Hole, A. Kelly, and T. Post, “Principles for developing thought- revealing activities for students and teachers,” in The handbook of research design in mathematics and science education, A. Kelly and R. Lesh, Eds. Mahwah, NJ: Lawrence Erlbaum Associates, 2000, pp. 591–646.[13] D. Ifenthaler and N. M. Seel, “Model-based reasoning,” Comput. Educ
, whetherfemale attributes are viewed as prototypical when race/ethnicity is included, e.g. a White femalevs. URM female or URM male, requires additional research to directly probe these intersections.References[1] ASEE, "Transforming Undergraduate Education in Engineering, Phase I: Synthesizing and Integration Industry Perspectives.," 2013.[2] E. S. Ng, L. Schweitzer, and S. T. Lyons, "New generation, great expectations: A field study of the millennial generation," Journal of Business and Psychology, vol. 25, no. 2, pp. 281- 292, 2010.[3] K. Crenshaw, "Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics," U. Chi. Legal F., p. 139, 1989.[4] B
A. Sweetman, “A Course in Flow Visualization : the Art and Physics of Fluid Flow,” in Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition, 2004, p. 11.[2] K. Goodman, J. Hertzberg, T. Curran, and N. D. Finkelstein, “Expanding Perception : How Students ‘ See ’ Fluids (#12169),” in ASEE Annual Conference and Exposition, Conference Proceedings, 2015.[3] J. Hertzberg, B. R. Leppek, and K. E. Gray, “Art for the Sake of Improving Attitudes towards Engineering,” Am. Soc. Eng. Educ., 2012.[4] K. Goodman, J. Hertzberg, and N. Finkelstein, “Aesthetics and Expanding Perception in Fluid Physics,” in Frontiers in Education, 2015, pp. 1747–1751.[5] J. P. Mestre, A
engineering field. B. Teachers articulated characteristics which indicated students’ potential for pursuing engineering as a college major. C. Teachers felt a “responsibility” to teach engineering as a result of their new knowledge and perceptions.A. Perceptions of Engineers and Engineering FieldAll eleven teachers talked about the program in terms of bringing about positive changes in theirgeneral perception of engineering (n = 11; 38 references). While many already had favorableperceptions prior to the program, initial levels of knowledge varied greatly in accuracy. Whileinitially a few participants were aware of various engineering disciplines (particularly the“traditional” fields such as civil and electrical), others viewed
sustainability analysis in electronics lecture courses. ASEE 2011 Conference Proceedings,Vancouver, British Columbia. 10. Leiserowitz, A. A., Kates, R. W., Parris, T. M. (2006). Sustainability values, attitudes, and behaviors: A review of multinational and global trends. Annual Review of Environment and Resources, 31, 413-44. 11. Kates, R. W., Parris, T. M., Leiserowitz, A. A. (2005). What is sustainable development? Goals, indicators, values, and practice. Environment: Science and Policy for Sustainable Development, 47(3), 8-21. 12. Smith, M. B., & Laurie, N. (2011). International volunteering and development: Global citizenship and neoliberal professionalisation today. Transactions of the Institute of British
.2017.00236/full[5] C. Seron, S. S. Silbey, E. Cech, and B. Rubineau,“Persistence is cultural: Professional socialization and the reproduction of sex segregation,” Work and Occupations, vol. 43, no. 2, pp.178–214, 2015.[6] C. Seron, S.S. Silbey, E. Cech, and B. Rubineau, “‘I am not a feminist, but . . .’: Hegemony of a meritocratic ideology and the limits of critique among women in engineering,” Work and Occupations, vol. 45, no.2, pp.131-167, 2018.[7] B.W. Packard, J.L. Gagnon, O. LaBelle, K. Jeffers, and E. Lynn, “Women’s experiences in the STEM community college transfer pathway,” Journal of Women and Minorities in Science and Education, vol.17, no. 2, pp. 129-147, 2011.[8] P. Black and D. William
AC 2010-1501: SPECIAL SESSION: MODEL-ELICITING ACTIVITIES INENGINEERING: A FOCUS ON MODEL BUILDINGEric Hamilton, United States Air Force AcademyMary Besterfield-Sacre, University of PittsburghBarbara Olds, Colorado School of MinesNora Siewiorek, University of Pittsburgh Page 15.1081.1© American Society for Engineering Education, 2010 MEAs In Engineering: A Focus On Model BuildingAbstractThis paper addresses the importance of models and modeling in engineering education reform. Itfocuses specifically on model-eliciting activities, or MEAs, as research and curriculum tools todevelop complex reasoning skills, nurture transference and generalizability of problem
experienceThe following results in this section were obtain from the The University of MarylandBaltimore County GK-12 Teaching Enhancement Partnership Project Final Evaluation Page 12.825.7Report (2002-2005).In all three years of the program, the survey focused on four key areas: A) Fellows’ skills improvement. B) K-12 students’ enriched learning C) Teachers’ professional development D) Strengthened university-community partnershipsFor the purpose of this study, the fellow’s skill improvement section was focused onbecause of how it identified with the fellows thoughts on skill development in careerrelated areas. Particularly, how the TEPP program