of quality and source of received support for student wellbeing,” Student Success, vol. 10, no. 3, pp. 64–75, 2019, doi: 10.5204/ssj.v10i3.1407.[6] P. Pihkala, “Eco-anxiety, tradgedy, and hope: psychological and spiritual dimensions of climate change,” Zygon, vol. 53, no. 2, pp. 545–569, 2018, doi: 10.1111/zygo.12400.[7] S. Every-Palmer, S. Mcbride, H. Berry, and D. B. Menkes, “Climate change and psychiatry,” Aust. N. Z. J. Psychiatry, vol. 50, no. 1, pp. 16–18, 2016, doi: 10.1177/0004867415615946.[8] K. Usher, J. Durkin, and N. Bhullar, “Eco-anxiety: How thinking about climate change- related environmental decline is affecting our mental health,” Int. J. Ment. Health Nurs., vol. 28, no. 6, pp
expressed in this work are those of the author and do not necessarily representthose of the National Science Foundation.References[1] V. Hunt, S. Prince, S. Dixon-Fyle, and L. Yee, "Delivering through diversity," McKinsey & Company Report. Retrieved April, vol. 3, p. 2018, 2018.[2] ASEE, "Transforming Undergraduate Education in Engineering, Phase I: Synthesizing and Integration Industry Perspectives.," 2013.[3] J. L. Arminio et al., "Leadership experiences of students of color," NASPA journal, vol. 37, no. 3, pp. 496-510, 2000.[4] C. R. Romano, "A qualitative study of women student leaders," Journal of College Student Development, 1996.[5] A. Kezar and D. Moriarty, "Expanding our understanding of
Behavior, Lumen, 2019, pp. 1–18.[2] P. Adler, “Work Organization: From Taylorism to Teamwork,” Perspect. Work, vol. 1, no. 1, pp. 61–65, 1997.[3] R. B. Helfgott, “America ’ s Third Industrial Revolution,” Challenge, vol. 29, no. 5, pp. 41–46, 1986.[4] S. Lund, “AI , automation , and the future of work : Implications for Engineering Deans,” 2019.[5] T. Chowdhury and H. Murzi, “Literature Review : Exploring Teamwork in Engineering Education,” in Research in Engineering Education Symposium, 2019.[6] H. G. Murzi, T. M. Chowdhury, J. Karlovšek, and B. C. Ruiz Ulloa, “Working in large teams: Measuring the impact of a teamwork model to facilitate teamwork development in engineering students working in a real
more motivated to complete multiple rotations.Future WorkFuture work is to expand this data beyond a single institution to look for other contextualinfluences on student views. This will help build better descriptions or find additional uniquegroups. Additionally, this expanded work can help identify how institutional or corporatecultures could be impacting the co-op experience.References[1] R. S. Lindenmeyer, “A comparison study of the academic progress of the cooperative and the four year student,” J. Coop. Educ., vol. 3, no. 2, pp. 8–18, 1967.[2] B. F. Blair, M. Millea, and J. Hammer, “The Impact of Cooperative Education on Academic Performance and Compensation of Engieering Majors,” J. Eng. Educ., vol. 93, no. 4, pp. 333
large response rates (i.e. not skip logic based). Thisrestricts the number of responses used for analysis as well as the ability to test the surveyinstrument’s factor structure in its entirety. This means that there may be larger underlyingthemes that we cannot pull out or important themes present in these opt in items that will beoverlooked. References[1] S. Lipson, E. Lattie, & D. Eisenberg, “Increased rates of mental health service utilization by US college students: 10-year population-level trends (2007–2017),” Psychiatric Services, vol. 70, no. 1, pp. 60-63, 2019.[2] S. Lipson & D. Eisenberg, “Mental health and academic attitudes and expectations in university
V solid-state against a conventional low-frequency distribution transformer," 2014 IEEE Energy Convers Congress and Expo (ECCE), Pittsburgh, PA, 2014, pp. 4545-4552. doi: 10.1109/ECCE.2014.6954023.[3] C. Nan and R. Ayyanar, "Dual active bridge converter with PWM control for solid state transformer application," 2013 IEEEEnergy Conversion Congress and Exposition, Denver, CO, 2013, pp.4747-4753.doi: 10.1109/ECCE.2013.6647338.[4] L. Wang, D. Zhang, Y. Wang, B. Wu, and H. S. Athab, "Power and Voltage Balance Control of a Novel Three-Phase Solid-State Transformer Using Multilevel Cascaded H-Bridge Inverters for Microgrid Applications," in IEEE Transactions on PowerElectronics, vol. 31, no. 4, pp. 3289-3301, April 2016. doi: 10.1109/TPEL
University for reviewingthis paper and providing constructive feedback.References[1] W. Zhou and X. Shi, “Culture in groups and teams: A review of three decades of research,” Int. J. Cross Cult. Manag., vol. 11, no. 1, pp. 5–34, 2011.[2] A. S. Tsui, S. Nifadkar, and A. Y. Ou, “Cross-national, cross-cultural organizational behavior research: Advances, gaps, and recommendations,” J. Manage., vol. 33, no. 3, pp. 426–478, 2007.[3] S. Wei, D. M. Ferguson, M. W. Ohland, and B. Beigpourian, “Examining the cultural influence on peer ratings of teammates between international and domestic students,” in the American Society for Engineering Education Annual Conference & Exposition, 2019.[4] J. Wang, G. H.-L. Cheng, T
. Further analysis and modeling of the data areforthcoming, and will provide details of the competencies developed among the newcomers andhow they were developed. We anticipate that articulating the competency models of professionaland technical competence developed in this learning ecology will provide a deeper understandingof what newly hired engineers learn and how they learn as they develop into their careers.References[1] R. Korte, “Learning to practice engineering in business: The experiences of newly hired engineers beginning new jobs,” in The Engineering-Business Nexus: Higher Aims or Triumphant Markets? S. Christensen, B. Delahousse, C. Didier, M. Meganck, & M. Murphy (Eds), Cham, Switzerland: Springer, 2019, pp. 341
reviewed. Review of Nodes 3 and 1 is marginal in thesense that a review is determined by the context of the current node and its assessment. Forexample, node 8 is not directly related to node 3, but is indirectly related through node 6 and nodirect review is needed. Node 1, like nodes 5 and 7, is on the edge of this knowledge domainand no assessment can be made beyond this node(s). As additional knowledge domains becomeavailable, they can be interconnected through appropriate links. Figure 5: Typical KnowNet Student Review Scenario (R - Reviewed and N = Not Reviewed)This brute force approach to an intelligence tutor assures that any missing knowledge will becovered through the search. But it is not terribly efficient as demonstrated by the shaded
the same page.”Feelings toward AmbiguityStudents also expressed their feelings towards ambiguity. Bob expressed fear and ambiguitytogether by describing his experience as “I think generally overall speaking ambiguity would belike being in the unknown. Kind of like almost fear of the unknown then like, yeah, you're notsure what you need to do or what is going to be happening.” Jon discussed how taking the wrongpath for ambiguous problem increases his anxiety, “if something is too ambiguous…I know I getalmost like anxiety if it's ambiguous and I'll never really get going or never know if I'm going inthe right direction.” Jon’s anxiety also became evident when he discussed ambiguity in theworkplace versus academia, stating that he “believe[s
, 2007.[2] L. L. Bucciarelli, Designing Engineers. Cambridge, Massachusetts: MIT Press, 1994.[3] M. T. H. Chi, S. Kang, and D. L. Yaghmourian, “Why Students Learn More From Dialogue- Than Monologue-Videos: Analyses of Peer Interactions,” J. Learn. Sci., vol. 26, no. 1, pp. 10–50, 2016.[4] M. D. Koretsky, D. Gilbuena, S. B. Nolen, G. Tierney, and S. E. Volet, “Productively Engaging Student Teams in Engineering: The Interplay between Doing and Thinking,” in IEEE Frontiers in Education Conference (FIE) Proceedings, 2014.[5] S. Michaels and C. O’Connor, “Talk Science Primer,” Terc, pp. 1–20, 2012.[6] M. R. Banaji and A. G. Greenwald, Blindspot: Hidden Biases of Good People, 1st ed
incoming first-year students are placed in. However, engineering students are oftenunderprepared in several pre-calculus topics. To assist these underprepared students, a significantpercentage of first-year students at our midsize STEM University are placed into remedial pre-calculus courses. At our institution, the percentage of first-year students placed into pre-calculusis about 35%, averaged over the past five years. This distribution has only slightly improved overthe years despite a significant increase in the average student profile in terms of SAT/ACTscores and high school GPA. Furthermore, a large number of students placed into calculus fail orwithdraw from it, automatically leading to additional semester(s). An explanation for this can
understanding of empathy has also been pursued in the fields ofengineering and technology for purposes relating to the ability of robotic technologies to imitatehuman abilities [8]–[10]. In our study, we focus on the aspect of empathy research concernedwith the ability of people to consider how their decisions affect others.Service learning (S-L) is a well-studied approach to teaching and learning [11]–[16]. It is one ofseveral pedagogies for engaging students in learning. In this study, by service learning we meana learning environment where students are taking a course for credit, serving a community aspart of the course and reflecting on their experience also as a component of the course [12], [17].S-L has been identified as a helpful pedagogy for
Literature Review of the Upbringing Influence on Spatial Ability References 1) Bandura, A., “Self-efficacy: Toward a Unified Theory of Behavioral Change”, Psychological Review, Vol. 84, 1977, pp. 191-215. 2) Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman. 3) Berger, P., & Luckmann, T. (1966). The social construction of reality: A treatise in the sociology of knowledge. Garden City, NY: Doubleday. 4) Dabbs Jr, J. M., Chang, E. L., Strong, R. A., & Milun, R. (1998). Spatial ability, navigation strategy, and geographic knowledge among men and women. Evolution and Human Behavior, 19(2), 89-98. 5) DeLamater, J. D., & Hyde, J. S. (1998
more of the following characteristics: resilience self-organization, and hierarchy. • Focus on the mentor and mentee’s needs––two-way communication. Mentor should look to improve the mentee’s prospects while respecting the his/her personal life circumstances and perspective. • Pursue and use help and support from facilitators and program staff.References [1] S. A. Ginder, J. E. Kelly-Reid, and F. B. Mann, “Enrollment and employees in postsecondary institutions, fall 2017; and financial statistics and academic libraries, fiscal year 2017”, U.S. DEPARTMENT OF EDUCATION, Tech. Rep., 2019. [2] A. Radford, A. Bentz, R. Dekker, and J. Paslov, “After the post-9/11 GI bill: A profile of military service members and veterans
):63–85, 2000. [2] D. H. Jonassen. Learning to Solve Problems: An Instructional Design Guide. Instructional Technology and Training Series. Pfeiffer, San Francisco, CA, 2004. [3] D. H. Jonassen. Learning to Solve Problems: A Handbook for Designing Problem-solving Learning Environment. Routhledge, New York, NY, 2011. [4] D. R. Woods, A. N. Hrymak, R. R. Marshall, P. E. Woods, C. M. Crowe, T. W. Hoffman, J. D. Wright, P. A. Taylor, K. A. Woodhouse, and C. G. K. Bouchard. Developing problem solving skills: The McMaster problem solving program. Journal of Engineering Education, 86(2):75–91, 1997. [5] P. C. Wankat and F. S. Oreovicz. Teaching Engineering. Purdue University Press, 2nd edition, 2015. [6] D. R. Woods. An evidence-based
material are those of the author(s) and do not necessarily reflect the views of the NSF. References[1] M. F. Fox, “Women and men faculty in academic science and engineering: Social- organizational indicators and implications,” American Behavioral Scientist, vol. 53, no. 7, pp. 997–101, 2010.[2] M. Sabharwal and E. A. Corley, "Faculty job satisfaction across gender and discipline," The Social Science Journal vol. 46, no. 3, pp. 539-556, September, 2009.[3] Bureau of Labor Statistics, U. S. Department of Labor, Occupational Outlook Handbook, Postsecondary Teachers, on the Internet at https://www.bls.gov/ooh/education-training-and- library/postsecondary-teachers.htm
homogeneous group. A group is heterogeneouswith respect to a given question if students in the group select mostly different answers to the question. Agroup is homogeneous with respect to a given question if students in the group select mostly the sameanswers to the question. For these questions, the fitness measure is given by: 1 c n Xi, j = ∑ rs,k , n k=1 s=1where n is the number of students in the group, c is the number of choices for the question, and rs,k is 1when student s has selected option k and 0 otherwise. The expression ns=1 rs,k is the logical or operatorover values of rs,k as s
render more loss of life anddestruction of property. As an example, large fires have destroyed highly affluent neighborhoods acrossCalifornia, Texas, and Florida. Floods and flash floods have killed hundreds of people around the worldeach year, more than any other weather event. Catastrophic flooding, as a result of Hurricane Harvey,left many people stranded. Tornadoes cause widespread property damage, clearing slabs and flippingmobile homes. Tornadoes are also most common in the central part and Great Plains regions of theUnited States; thus, including Mississippi (U. S. Tornadoes, 2016).As researcher Quarentelli has predicted (1996 and 2001) the increase of disasters and the emergence ofnew and more impactful disasters, there would be an
. J. Atman and L. J. Shuman, "Characteristics of Freshman Engineering Students: Models for Determining Student Attrition in Engineering," Journal of Engineering Education, vol. 86, no. 2, pp. 139 - 149, 1997.[3] F. S. Laanan, "Transfer Student Adjustment," New Directions for Community Colleges, vol. 29, no. 2, pp. 5 - 13, 2001.[4] M. R. Laugerman, Academic and Social Integration Variables Influencing the Success of Community College Transfer Students in Undergraduate Engineering Programs, Ames: Iowa State University, 2012.[5] D. S. Doucette and D. J. Teeter, "Student Mobility among the Public Community Colleges and Universities in the State of Kansas," in Annual Forum of the Association for Institutional Research
representatives. While the ROV project is the highlight of the class, the maingoal of the class is to help students understand how to work in teams of four or five students andeffectively communicate both within the team and to external stakeholders.Faculty and StaffThe course is instructed by two or three co-instructors: one technical lecturer and one or twotechnical communication lecturers. They share lecture time, and the technical communicationlecturer(s) also act as the smaller twenty person discussion section lead(s). The labs are led by aprofessional lab manager and four instructional assistants (IAs) who are selected by the facultyfrom upper-class students who excelled in the class when they took it during their first year. Threeof the IAs are
le es ob Pr Structuredness 1. Did the problem have more than one solution? ✓ Task complexity 2. Do students need to use information/knowledge/skills from other concurrent ✓ course(s) in the term in order to successfully complete the task? 3. Please list the course/course
resistance. The study also hopes to provide answers of if students are actuallyresisting active learning, as well as the instructors’ perception of this resistance.AcknowledgmentsThis material is based upon work supported by the National Science Foundation under Grant NoDUE-1821488. Any opinions, findings, and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation.References[1] Dancy, M., Henderson, C., &; Turpen, C. (2016). How faculty learn about and implementresearch-based instructional strategies: The case of Peer Instruction. Physical Review PhysicsEducation Research, 12(1), 010110.[2] Gradinscak, M. (2011). Redesigning engineering
or design thinking29. More generally, thiswork follows the tradition of examining design actions to understand designers30. For this study,the specific platform used, Energy3D, recorded each student design action (e.g. Add Window,Edit Wall, or Annual Energy Analysis, Add Note) into a JSON format data-log. Researchers areable to use Energy3D to see all student design actions, their final design artifacts and relatedperformance of their artifacts on key design criteria. Three primary categories of action were thefocus of data analysis: Reflecting, Modeling, and Analysis. Table 1 summarizes our schema formapping action(s) to these categories. Importantly, each keystroke is logged as a separate add oredit note action, therefore on average every
the ground in advance”?A Plan for when you get hit –• What is the other perspective? Why does it have value? Why is the intended benefit of greater value than the value from the other perspective?• Are your allies on board?• Push forward constructively, make the case, and be politely but firmly persistent.A Plan for when you get knocked down –• Is it time to wait for a better opportunity/situation?• Do you need to build a better value case for the decision maker(s) for the benefit? Do you need to develop additional allies?• Can you maintain your cool and advance constructively?• If affirmative, then improve your plan, pick yourself up and keeping engaging constructively. Having the first two types of plan is generally wise.
students’ motivational factors that led them to choose and continue topursue an engineering baccalaureate degree(s).This studied used Eccles's (1983) expectancy-value theory of motivation as the guidingtheoretical framework to show the relationship between competence and value beliefs as themotivated actions towards earning an engineering degree. It relates competence to, “Can I earnan engineering degree?” and task value beliefs to, “Do I want to earn an engineering degree?”Twenty students (12 first-year and 8 second-year low-income engineering transfer students) wereinterviewed about their experiences in engineering. Additionally, these twenty studentscompleted a survey collecting data on their demographics, recognition, social belongingness
]. New SCCT models were developed to explain vocational satisfaction and well-being [10,11], and career management [9]. At the core of the original SCCT model, and most of the SCCTmodels that followed, are self-efficacy (i.e., confidence in the ability to successfully perform adomain-specific task, like a specific engineering skill), outcome expectations (i.e., anticipatedoutcomes of a particular behavior), interests (i.e., patterns of likes/dislikes for career activities),and goals (i.e., determination for a particular outcome). Taking this one step further, Lent etal.’s [9] integrative social cognitive model of academic adjustment, derived from both SCCT [1,2] and the social cognitive model of academic satisfaction [10, 11], explains how
appropriate realistic constraints, including consideration of health, safety, etc., to the engineering problem for the capstone design. Measure: Evaluated in final CPEN 3850 report • Competency: Students demonstrate ability to generate effective solution(s) to the capstone design problem formulated in CPEN 3850, including identified constraints. Measure: Evaluated in final CPEN 4850 report [1]Thus, in order to determine whether students can both identify and apply appropriate standardsand constraints, and apply these in an engineering design, it was decided that it was necessary toevaluate students continuously working on a project; therefore, measuring in sequentialsemesters was specified. Other required
throughout the search process. In addition, she runs a faculty develop- ment and leadership program to recruit and support diverse PhD students who wish to pursue academic positions in engineering or applied science after graduation. Dr. Sandekian earned B.S. and M.S. degrees in Aerospace Engineering Sciences at CU Boulder in 1992 and 1994, respectively. She went on to earn a Specialist in Education (Ed. S.) degree in Educational Leadership and Policy Studies in 2011 and a Ph.D. in Higher Education and Student Affairs Leadership in December 2017, both from the University of Northern Colorado. She is a Founding Leader of the American Society of Engineering Education (ASEE) Virtual Community of Practice (VCP) for LGBTQ
understanding of our overall data, we performed descriptivestatistical analysis. Shown below in Table 4 are the descriptive statistics for average noveltyscores by brainstorming group. Here, N represents the number of ideas generated in a givenbrainstorming session and mean represents the total novelty score of each design divided by thetotal number of designs generated. The groups are denoted by the gender composition andstructure (i.e., PM-S = Predominantly Male-Structured) We also present skewness and kurtosisto demonstrate the suitability of the dataset for subsequent statistical analysis. Based on thevalues shown in Table 4, we used standard statistical tests without violating assumptions ofnormality.Table 4: Overview of descriptive statistics for