could indicate apreference for specific topic(s) on their application. The grant team reviewed studentapplications and assigned qualified students to faculty mentors, following student preferences ifthere was sufficient room available in that project. Students selected for the four-week researchexperience were expected to complete the appropriate first-year curriculum for their major beforeparticipation in the program. Students who were not on track to complete the first-yearcurriculum were referred to another summer program at CSUB for first year students who werestruggling with their first-year curriculum. First year transfer students were also accepted if theywere at the lower-division curriculum level within the major. First year transfer
category is defined to characterize the different perspectives on engineeringfrom the participants on what engineering is. As shown in the quotes under “different ideas aboutengineering”, T1 talked about engineering at the system level, while T2 talked about applyingconcepts to build something. In addition, each counselor also had unique ideas: C1 talked about“manipulating things for a certain outcome” and C2 mentioned that engineering is “mathy” andsciency”. These show that there are differences in how the four participants thought aboutengineering. T1, T2 and C1 had specific definitions about engineering. These are all in contrastwith C2’s comments that engineering is “mathy” and “sciency”. For context, both T1 and T2were teachers that expose
recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National ScienceFoundationReferences [1] C. Conrad and M. Gasman. Educating a Diverse Nation: Lessons from Minority Serving Institutions. Cambridge, MA: Harvard University Press, 2015. [2] National Science Foundation (NSF), “Science and engineering indicators 2014,” 2014, Available: http://www.nsf.gov/statistics/seind14/ [Accessed: October, 15, 2018]. [3] S. L. Colby, and J. M. Ortman, “Predictions of the size and composition of the U.S. population 2014 to 2060: Population estimates and projects,” U. S. Census Report #P25-1143. Washington, DC
the same subject.Second, build a spreadsheet model to solve the calculated problem to test out your formulasbefore you put them into the LMS question. Have a data block in the spreadsheet that shows thelabels for all problem variables, identifies the randomized parameters by name, and includesyour settings for those parameters’ minimum value(s), maximum value(s), and number ofdecimal places. If your LMS has other potential settings for algorithmic parameters, includethose as well. While the formulas and functions are obviously different for a spreadsheet than anLMS formula answer, this step is still valuable for building the question.Having the parameter value settings worked out in advance makes constructing the calculatedquestion in the LMS
; Sheppard, S., & Atman, C. J., & Chachra, D. (2011, June), Motivation Makes a Difference, but is there a Difference in Motivation? What Inspires Women and Men to Study Engineering? Paper presented at 2011 Annual Conference & Exposition, Vancouver, BC. https://peer.asee.org/1881613. Canney, N. E. and Bielefeldt, A. R. (2015). Differences in Engineering Students’ Views of Social Responsibility between Disciplines. Journal of Professional Issues in Engineering Education and Practice, 04015004.14. Watson, M., Ghanat, S., Michalaka, D., Bower, K., Welch, R. (2015) Why Do Students Choose Engineering? Implications for First-Year Engineering Education. Paper presented at 2015 FYEE Conference, Roanoke, Virginia
-Mona, I. & Abd-El-Khalick, F. (2006). Argumentative discourse in a high school chemistry classroom. School Science and Mathematics, 106(8), 349–361. http://doi.org/10.1111/j.1949- 8594.2006.tb17755.x18. Latour, B. & Woolgar, S. (1986). An anthropologist visits the laboratory. In Labor life: The construction of scientifc facts (pp. 43–103). Princeton University Press.19. Fink, F. K. (2001). Integration of work based learning in engineering education. In Frontiers in Education Conference, 2001. 31st Annual. Reno, NV: IEEE. http://doi.org/10.1109/FIE.2001.96374720. Jonassen, D. & Shen, D. (2009). Engaging and supporting problem solving in engineering ethics. Journal of Engineering Education, 98(3), 235
factors that affect the schedule. The learners’ motivation was measured through the useof an adapted pre- and post-test called the OnLine Motivation Questionnaires.[21] The assessmentresults have proven the VCS3’s capability to motivate the students and increase their generalknowledge of the construction planning process.[2] However, while the VCS positively affectedstudents’ overall learning and motivation, the results still do not fully reveal the VCS3simulator’s ability to promote higher order thinking skills.3. Instructional design of the virtual construction simulator 4 The past experiences of the VCS3 have demonstrated that the game has great educationalpotential. This potential is being addressed with a new phase of research and
.'#:(*'# .%4,%..',%4#3&*):< !>"#G-6.#:(*#2.1-+-).;,+-11:#0'.0-'.)#2('#&5.#.%4,%..',%4#+(*'3.3# -&5.#B%6.'3,&:#(2#C,'4,%,-< !D"#?(#:(*#2..1#-#0-'(2#&5.#B%,6.3,&:#(2#C,'4,%,-A3#R+5((1#(2# S%4,%..',%4#-%)#@001,.)#R+,.%+.#+(;;*%,&:< !F"#$2#&5.'.#7-3#(%.#&5,%4#:(*#+(*1)#+5-%4.#-L(*&5.#&'-%32.'# 0'(+.33#:(*#./0.',.%+.)J#75-(*1)#&5-L.< Findings Students interviewed represented John Tyler and Piedmont Valley, both community colleges of the Virginia Community College System. Each community college has a Guaranteed Admission Agreement in place
students to 4. Re-tell the performance of a possible solution. 5. Analyze possible solution(s) according to several types of evidence, including results of physical tests, data from scientific investigations, information from external sources, and critique by other children or adults. 6. Purposefully choose how to move forward to improve the proposed solution.Table 1. Alignment of proposed definition of reflective decision-making in engineering withsupporting research and elementary engineering curriculum learning tasksElements of reflective decision- How engineering design practitioners Related learning tasks in the EiEmaking exhibit the element curriculumDuring initial
addition of a single cubic term whosecoefficient is a . This fact renders the cubic law as a simple extension of the traditional result.Some sample trajectories are displayed in Figs. 4 and 5 for (respectively) o 45 and 60 . Thetrajectory cases correspond to 1.5 , 1.0 , 0.5 , and 0.0 in each figure. Also, vo 10 m/s andg 9.81 m/s 2 were utilized to generate these particular results. These figures were created withthe chart-production capabilities available within an EXCEL® workbook. The solid and dashedcurves identify results generated with the approximate and exact solutions (respectively), but itwas not possible to obtain experimental results for a comparison with the exact and approximateresults, given the limitations imposed for
-generation peers when a given situation causes opposing valuesto confront, such as prioritizing familial responsibilities versus individual responsibilities.Further analyses of the survey and other measures, such as the VAI, will help better understandthese connections.Many of the FIG mentors commented how much they enjoyed incorporating the DEI panel torepresent a more diverse group of students. Though many of the same themes reoccurred fromone panel to another, such as seeking tutoring services and getting involved on campus, eachgroup of panelists was dynamic and unique.The panelists were interviewed to get their perceptions about the DEI panel(s) and suggestionsfor future panels. Interesting subjects emerged from the interviews that offered some
rate is controlled by changing the position of a ball valvemounted before the meter. Calibration showed that this meter’s rotation rate increases linearlywith increased flow rates within this range tested. Details of the experimental apparatus areprovided in Appendix A and the lab manual is provided in Appendix B. Mass Flow Rate vs Electronic Meter Reading 0.40 0.35 Mass Flow Rate (kg/s) 0.30 0.25 0.20 0.15 0.10
pastwork [1] to communicate the same ideas about types of behavior to the participants. How canwe know whether participants understood the same concepts that the vignettes were intendedto portray? Each time a participant indicated that a particular individual on their teamexhibited a particular behavior based on their reading of the vignette, one of the open-endedquestions asked was: “In what way do you feel ______'s behavior during the project is/wassimilar to the passage above?” These responses were randomized and de-identified. The web-based computer software Dedoose was used to code all 366 excerpts based on the 11 originalbehavior definitions independently of which behavior the participant had intended to indicate.A given excerpt could be
Electrical Engineering at LJMU, for his support of this project.References1. Amabile, T. M. (1996). Creativity in context: Update to the social psychology of creativity. Boulder, CO: Westview Press.2. Charyton, C., & Merrill, J. (2009). Assessing general creativity and creative engineering design in first year engineering students. Journal of Engineering Education, 98(2), 145–156.3. Howard, T. J., Culley, S., & Dekoninck, E. (2008). Describing the creative design process by the integration of engineering design and cognitive psychology literature. Design Studies, 29(2), 160– 180.4. Mumford, M. D. & Gustafson, S. B. (1988). Creativity syndrome: E-integration, application, and innovation. Psychological Bulletin, 103(1), 27
, “Engineering Major Selection: An Examination of Initial Choice and Switching Throughout the First Year,” in Proceedings of the American Society for Engineering Education Annual Conference & Exposition, 2016.[2] S. M. Lord, M. W. Ohland, R. A. Layton, and M. M. Camacho, “Beyond Pipeline and Pathways: Ecosystem Metrics,” J. Eng. Educ., vol. 108, no. 1, pp. 32–56, 2019.[3] M. K. Orr, C. E. Brawner, M. W. Ohland, and R. A. Layton, “The Effect of Required Introduction to Engineering Courses on Retention and Major Selection,” in Proceedings of the American Society for Engineering Education Annual Conference & Exposition, 2013.[4] M. K. Orr, C. E. Brawner, S. M. Lord, M. W. Ohland, R. A. Layton, and R. A. Long
Maturity Model (CMM) intosoftware engineering was developed by the Software Engineering Institute of CarnegieMellon University in 1987. The integrated version (CMMI) evolved from this early work.ABET’s Criteria 2000 was inexorably linked to the quality assurance fervor of the 1990’s[2-7]. However, the work involved in preparing for accreditation is enormous, and facultymembers do not always find the direct benefit of such work. As a result, some nontechnicalfaculty members have even resorted to excoriating the entire outcomes-based approach ofthe accreditation process publicly [8].The classroom instructors of many undergraduate courses are burdened with severalchallenges such as large class sizes, dwindling instructional support and the need to
Paper ID #28572How Extra Credit Quizzes and Test Corrections Improve Student LearningWhile Reducing StressDr. Brian Scott Rice, Rochester Institute of Technology Dr. Brian S. Rice is an assistant professor in the Manufacturing and Mechanical Engineering Technology Department at Rochester Institute of Technology since 2016. He joined the RIT faculty after spending over 25 years in applied research while working at University of Rochester Laboratory for Laser Ener- getics, Lockheed Martin Corporation, and Eastman Kodak Company. Areas of applied research include system dynamics and controls, solid mechanics, heat transfer, and
Task 4 status bar in Figure 1. It has been a fulfilling journey for all the instructors and thestudents on both trips. However, there have been multiple instances throughout the journey whenwe would almost have to give up when significant challenges emerged.The strategies that we have used to overcome those challenges to enable this successful initiativeof an engineering faculty-led course will be shared in this paper. Being prepared to be flexibleand responsive to each situation is a must. It should also be pointed out that one can only beresponsible for the things s/he can have an effect on, but s/he cannot and should not feelresponsible for external discouraging factors, such as local political situations, change inleadership support
climate, including norms, values, and prac�ces,the study suggests that the experiences of Black engineers are shaped by the prevailing a�tudes withintheir workplace. This includes the extent to which diversity and inclusion are embraced, the degree ofsupport provided by leadership, and the presence of inclusive policies that consider the uniquechallenges faced by Black professionals (Lukas, Goodman, 2015; Ray, 2019).S�ll, Black wellness is not priori�zed. Research by Dobbin and Kalev (2016) sheds light on the persistentchallenges related to diversity ini�a�ves within organiza�onal structures, par�cularly regarding Blackwellness. The study iden�fies systemic shortcomings that contribute to the neglect of Black well-being,exacerba�ng challenges
, students, workers, or something in between? Though Ipersonally believe that doctoral engineering students exist outside of this binary discussionbecause of their important societal role in contributing to knowledge, within the binary they docontribute economically and do work that they are not sufficiently compensated for. They dowork, plain and simple.I am not alone in this line of thinking. Legally, the classification of graduate students asemployees has gone back and forth since the 1990’s. At private institutions, this dispute fallsunder the National Labor Relations Act. Specifically, the decision as to whether or not graduatestudents at private institutions are employees falls under the National Labor Relations Boardwhich is a board made up
, when the subjects rural teachers taught was liberal arts, the teachers’ teaching beliefs significantly positively influenced classroom evaluation practice(β=0.38, SE=0.09, t(196)=4.45, p<0.001); when teaching sciences subjects, the influence of teaching beliefs on classroom assessment was further strengthened, indicating that for rural teachers teaching sciences subjects, the impact oftheir teaching beliefs on classroom evaluation was more significant (β=0.69, SE=0.08,t(196)=9.11, p<0.001). Figure 3 Moderating Effect Model of the Type of Subject Study 3’s findings indicate that the influence of rural teachers’ teaching beliefs ontheir classroom evaluations within a STEM education context is dependent on
developspatial skills and interest in engineering through play, which may ultimately encourage them topursue engineering pathways in the future.IntroductionThere are many research reports and studies that highlight the gender gap between men andwomen in engineering fields [1]. In 2018, 22.2% of bachelor’s degrees in engineering wereearned by women [2]. Additionally, in 2019, among those with science and engineering (S&E)degrees, 15.98% of women worked in S&E occupations (compared to 35.38% of men) [2]. Thegap between the number of women and men earning engineering degrees as well as the numberof women pursuing science and engineering careers suggests the need to improve and supportwomen’s participation in engineering fields.One factor that may
Disengagement in Engineering Education?," Science Technology Human Values, vol. 39, no. 1, pp. 42-72, 2014.[2] E. A. Cech and H. M. Sherick, "Depoliticization and the Structure of Engineering Education," in International Perspectives on Engineering Education, S. H. Christensen, C. Didier, A. Jamison, M. Meganck, C. Mitcham and B. Newberry, Eds., 2015, pp. 203-216.[3] W. Faulkner, "Dualisms, hierarchies, and gender in engineering," Social Studies of Science, vol. 30, no. 5, pp. 759-792, 2000.[4] W. Faulkner, "'Nuts and Bolts and People': Gender-Troubled Engineering Identities," Social Studies of Science, vol. 37, no. 3, pp. 331-356, 2007.[5] N. P. Gaunkar, N. Fila and M. Mina, "Broadening Engineering Perspectives by Emphasizing
provide the average duration of their commute, and those wholive off-campus are asked whether they live with the individuals who raised them as children andwhether they have responsibilities to care for children themselves. This demographic informationTable 1: Dependent Variables Variable Construct and Source Survey Items (Adapted) Classroom “Perceived Classroom Please rate your agreement with the following statements, which Comfort Comfort” from Hoffman et relate to your comfort levels about having discussions, academic, al.’s “Sense of Belonging personal, or otherwise, with members of the Cal State LA Scale” [12] community
innovations in project management practices. Aligning academic approaches with industry usage is crucial for bridging the gap and fostering a workforce ready to harness the potential of AI in project management. References[1] S. Makridakis, “The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms,” Futures, vol. 90, pp. 46–60, Jun. 2017, doi: 10.1016/j.futures.2017.03.006.[2] T. Brown et al., “Language Models are Few-Shot Learners,” Adv. Neural Inf. Process. Syst., vol. 33, pp. 1877–1901, 2020.[3] N. Glaser, “Exploring the Potential of ChatGPT as an Educational Technology: An Emerging Technology Report,” Technol. Knowl. Learn., vol. 28, no. 4, pp. 1945–1952, Dec. 2023, doi: 10.1007/s10758-023-09684-4.[4] A
CSP framework. Secondly, a framework for the use of CSPin praxis and research within the SSA context is proposed drawing on Onwuegbuzie et al.’s(2012) methodology literature analysis. Findings present an adapted CSP framework for SSA,comprising 11 tenets for asset-based research. They highlight CSP’s adaptability across contexts,underscoring its importance in SSA STEM education.Keywords: culturally sustaining pedagogy, asset-based education, exemplary teaching,inclusion, transferability, Sub-Saharan AfricaBackgroundResearch shows that teaching practices that are more contextual and inclusive of students’cultural backgrounds and experiences enhance students' learning [1]. It is this awareness ofimproved learning outcomes that paved the way for
–22, 1996.[3] J. Engle, “Postsecondary access and success for first-generation college students,” in American Academic, vol. 3, 1 vols., 2007, pp. 25–48.[4] D. C. York-Anderson and S. L. Bowman, “Assessing the college knowledge of first- generation and second-generation college students,” J. Coll. Stud. Dev., vol. 32, no. 2, pp. 116–122.[5] P. Terenzini et al., “The transition to college: Diverse students, diverse stories,” Res. High. Educ., vol. 30, pp. 301–315, 1994.[6] N. M. Stephens, S. A. Fryberg, H. R. Markus, C. S. Johnson, and R. Covarrubias, “Unseen disadvantage: How American universities’ focus on independence undermines the academic performance of first-generation college students,” J. Pers. Soc. Psychol., vol
theory model by usingTable 1: Summary of the studies selected to answer the RQs Works based on the conceptual framework of Perna’s model Implications for Author(s) / Year Purpose Population / Contexts Constructs / Methods practice or research Registrations information
, “Where is the engineering I applied for? A longitudinal study of students’ transition into higher education engineering, and their considerations of staying or leaving,” European Journal of Engineering Education, vol. 41, no. 2, pp. 154–171, Mar. 2016, doi: 10.1080/03043797.2015.1056094.[4] M. Meyer and S. Marx, “Engineering Dropouts: A Qualitative Examination of Why Undergraduates Leave Engineering,” Journal of Engineering Education, vol. 103, no. 4, pp. 525–548, 2014, doi: 10.1002/jee.20054.[5] B. Geisinger and D. R. Raman, “Why They Leave: Understanding Student Attrition from Engineering Majors,” International Journal of Engineering Education, vol. 29, no. 4, pp. 914–925, Jan. 2013.[6] National Academy of Engineering
7 References 1. Roberts, F. L., Kandhal, P. S., Brown, E. R., Lee, D. Y., & Kennedy, T. W. (1991). Hot mix asphalt materials, mixture design and construction. 2. Van Poel, C. D. (1954). A general system describing the visco‐elastic properties of bitumens and its relation to routine test data. Journal of applied chemistry, 4(5), 221-236. 3. Bari, J. (2005). Development of a new revised version of the Witczak E* predictive models for hot mix asphalt mixtures. Arizona State University. 4. Witczak, M. W., Quintas, H. V., Kaloush, K., Pellinen, T., & Elbasyouny, M. (2000). Simple performance test: Test results and