felt they had gained new knowledge and skills, thatmade them, if not expert, then competent practitioners.Future work may include investigation of any connections between the self-directed learnercharacteristics of these groups and the use of educational technology or increased competency inthe data science technologies that are the focus of the research experience. Future work will alsoinclude quantitative evaluation of lesson plans and classroom implementation for evidence ofincreased practice in computational thinking and more student-centered, inquiry-based lessonplans.References[1] S. Chen, H. Xu, D. Liu, B. Hu, & H. Wang, “A vision of IoT: Applications, challenges, andopportunities with China perspective,” IEEE Internet of Things
), 275-294.Ambrose, S. (2013). Undergraduate engineering curriculum: The ultimate design challenge. TheBridge, 43(2), 16-23.Benson, D. & Zhu, H. (2015). Student Reflection, Self-Assessment, and Categorization ofErrors on Exam Questions as a Tool to Guide Self-Repair and Profile Student Strengths andWeaknesses in a Course. Proceedings of American Society of Engineering Education AnnualConference, Seattle, WA.Claussen, S. & Dave, V. (2017). Reflection and Metacognition in an Introductory CircuitsCourse. Proceedings of American Society of Engineering Education Annual Conference,Columbus, OH.Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hilsdale, NJ:Lawrence Earlbaum Associates.Dickerson, S., & Clark, R. (2018
. Pantazidou and I. Nair, “Ethic of Care: Guiding Principles for Engineering Teaching & Practice,” Journal of Engineering Education, vol. , pp. 205-212, Apr. 1999[4] L. S. Shulman, L. S., Foreword, in Educating Engineers: Designing for the Future of the Field, S. D. Sheppard, K. Macatangay, A. Colby, & W. M. Sullivan, Eds. San Francisco, CA: Jossey-Bass, 2009.[5] J. Tronto, Moral Boundaries: A Political Argument for an Ethic of Care. New York: Routledge, 1993.[6] L. Kohlberg, "Moral stages and moralization: The cognitive-developmental approach,” in Moral Development and Behavior: Theory, Research and Social Issues. T. Lickona, ed. New York: Holt, Rinehart and Winston, 1976.[7] N. Haan, et al., “Family
the angularorientation of residual machining marks, and much more [12]. In the past decade, significantefforts have been directed towards developing standard worldwide 3D parameters, the result ofwhich is a set of standard “S Parameters” in four general categories: amplitude, spatial, hybridand functional. Similar to 2D Parameters discussed earlier in this paper, the 3D parameterscommonly used now are,Amplitude ParametersBased on overall heights, (1) Root Mean Square Deviation, Sq- RMS of height distribution (2) Skewness, Ssk- the degree of asymmetry of a surface height distribution (3) Kurtosis, Sku – the degree of peakedness of a surface height distribution (4) Average Height, Sz – average of ten highest and lowest points.Spatial
peers who completed the step-by-step version (p<0.05, d=0.32). Students who are generally weaker on this material, as judged bytheir eventual overall score in this course, tended to be helped more by the open-ended version ofthe lab than students who are generally stronger on this material. This outcome suggests thathaving to design their own experimental protocol may make students more likely to understandor remember all steps involved in particular data reduction tasks. When possible, instructorsshould avoid giving students unnecessarily detailed instructions.References[1] J. S. Bruner, “The Art of Discovery,” Harvard Educational Review 31 (1961): 21–32[2] W. S. Anthony, “Learning to discover rules by discovery,” Journal of
. People, Equipment, Material, Environment, and Methods). It was emphasized to look for direct causes only at this point– not solutions and not indirect or root causes (Figure 4). b. 5-Whys: After completing the Ishikawa diagram, each team picked their top three to five causes and used the 5-Whys method to drill down to the potential root cause(s). From the Ishikawa diagram, the team identified three direct causes that could be contributing to the inconsistency in the distance. Using the 5-whys, the root causes were identified (Table 2). Figure 4: Brainstormed Causes of Inconsistency in Distance Table 2: Direct Causes vs. Root Causes
engineering from the University of Belgrade, Yugoslavia, in 1995. His research publications in computational and applied electromagnetics include more than 150 journal and conference papers. He is the author of textbooks Electromagnetics (2010) and MATLAB-Based Electromagnetics (2013), both with Pearson Prentice Hall. Prof. Notaros served as General Chair of FEM2012, Colorado, USA, and as Guest Editor of the Special Issue on Finite Elements for Microwave Engineering, in Electromagnetics, 2014. He was the recipient of the 1999 Institution of Electrical Engineers (IEE) Marconi Premium, 2005 Institute of Electrical and Electronics Engineers (IEEE) MTT-S Microwave Prize, 2005 UMass Dartmouth Scholar of the Year Award, 2012
. ● Cognitive training: instruction aimed to help students understand how systems and devices work, what principles govern the operation of these components, and describing case studies of prototypical failures that students may latter draw analogies from. ● Troubleshooting stations: instructional method where students are intentionally provided poor performing designs and scaffolded in identifying the cause(s) of the problems and asked to improve the performance of the component. ● Teacher modeling: a form of coaching in which a teacher demonstrates for students how they analyze a component that is not performing well. In addition to describing four teaching strategies that may address
91.9%, andthe percentage of correct classifications in each model is shown in Table 5. The high rate of errorin prediction noted in both models (approximately 22% incorrect classification of students whopredicted to be successful but are not) suggests that important variables could be missing fromthe analysis. Model Predicted S Predicted NS Predicted S Predicted NS Actually S Actually NS Actually NS Actually SCART-1 96.74% 77.42% 22.58% (Type II) 3.26 (Type I)CART-2 96.74% 77.42% 22.58 (Type II) 3.26 (Type I)S= success; NS
, VA: National Science Foundation; 2015 https://www.nsf.gov/statistics/2016/nsf16300/[2] Sowell R, Allum J, Okahana H. Doctoral initiative on minority attrition and completion. Washington, D.C. 2015 http://cgsnet.org/ckfinder/userfiles/files/DIMAC_2015_final_report_PR.pdf[3] Sowell, R. S., Zhang, T., Bell, N., & Redd, K. (2008b). Ph.D. completion and attrition: Analysis of baseline demographic data from the Ph.D. Completion Project. Washington, DC: Council of Graduate Schools.[4] A. Kezar and P. Eckel, “Examining the institutional transformation process: The importance of sensemaking, interrelated strategies, and balance,” Research in Higher Education, vol. 43, no.3, pp 295-328, June
collection.We would also like to thank the students, instructors, and teaching assistants of the course fortheir participation in the study.References:[1] Mennin, S. (2007). Small-group problem based learning as a complex adaptive system. Teaching and TeacherEducation, 23, 303-313.[2] Johnson, D. W., & Johnson, F. P. (1991). Joining together: Group theory and group skills. Prentice-Hall, Inc.[3] Bhavnani, S. H., & Aldridge, M. D. (2000). Teamwork across disciplinary borders: A bridge between collegeand the work place. Journal of Engineering Education, 89(1), 13-16.[4] Bahner, B. (1996). Report: curricula need product realization. Mechanical Engineering-CIME, 118(3), S1-S1.[5] Ford, M., & Morice, J. (2003). Using micro management techniques
law enforcementprofessionals. Teams had to craft an eighteen-minute presentation describing the who, what,where, when, why, how(s) of the crime as well as discussing privacy or moral issues. Judges wereable to then ask the teams questions for two minutes.For the 2017 competition, each of the sixteen teams were given a vehicle to search and seizephysical items and digital devices. A laptop was placed in an obvious location, as well as otherdigital devices such as an external hard drive, Ring doorbell, and Amazon Echo device. Otherdigital devices, such as an SD card, were placed in much more difficult places to find, but oftenwere hinted at by the digital evidence trail, such as Windows device connection logs. Digitalevidence was often
5 10 15 20 25 0 50 100 150 Time(s) Time(s) Figure 7. Time history of (a) Northridge earthquake and (b) Chi-Chi earthquake The parameter sets are applied to the one-bay-one-frame model to perform the dynamicanalysis, and the maximum story drift are recorded. Figure below shows the histogram of 10000dynamic analysis results. From Figure 8, it can be observed that the uncertainty of maximumstory drift ratio can be evaluated. (a
assessmentof different course designs. During the fall 2019 we would again ask students to assess differentcourse designs as well as compare resulting grades from two session of the same class.AcknowledgementsWe would like to thank Kimberly Gottula for her help in developing this paper. Her class projecton LMS course design and additional directed research, including suggestions for navigationdesigns, were invaluable in writing this paper.References[1] J. Dahlstrom, E., Brooks, D. C., & Bichsel, “The current ecosystem of learning management systems in higher education: Student, faculty, and IT perspectives,” Louisville, CO, 2014.[2] P. S. Muljana and G. Placencia, “Learning Analytics: Translating Data into ‘Just-in-Time’ Interventions
. Department of Education. Washington, DC. [3] Suárez-Orozco, C., Suárez-Orozco, M., Todorova, I., (2009). "Learning a New Land." Belknap Press of Harvard University Press. [4] Torche, F. (2011). "Is a college degree still the great equalizer? Intergenerational mobility across levels of schooling in the United States." American Journal of Sociology 117(3). P. 763-807. [5] Wine J, Janson N, Wheeless S., (2011). "2004/09 Beginning Postsecondary Students Longitudinal Study (BPS:04/09) Full-scale Methodology Report on grad rates (NCES 2012-246) " National Center for Education Statistics, Institute of Education Sciences. U.S. Department of Education; Washington, DC: 2011. Retrieved from http://nces.ed.gov
Universal DesignLearning principles. Our findings, and the systems we deployed, are examples of how newtechnologies can reshape engineering education for all, enable digital accessibility and provide aplatform for evidence-based research of engineering education.AcknowledgementsDevelopment of ClassTranscribe is supported in part by a Microsoft research gift to theUniversity of Illinois. We wish to acknowledge UIUC IT staff, the College of Engineeringcurrent and former undergraduate and graduate students, and Prof. Hasegawa-Johnson, who havecontributed to the development, support and direction of the ClassTranscribe project.References[1] R. S. Moog and J. N. Spencer, “POGIL: An overview,” Process Oriented Guided Inquiry Learning (POGIL), vol
not have been possible without the financial support. Furthermore, we would liketo acknowledge the technical contributions of the following SIUE students: Nicholas Coglianese,Hunter Meadows, Zachary Hauck, Pratik Lamsal, and Tyler Austin, who helped at differentstages of the experimental platforms’ development.References [1] I. Nourbakhsh, K. Crowley, A. Bhave, E. Hamner, T. Hsiu, A. S. Perez-Bergquist, S. Richards, and K. Wilkinson, “The robotic autonomy mobile robotics course: Robot design, curriculum design and educational assessment,” Autonomous Robots, vol. 18, no. 1, pp. 103–127, 2005. [2] A. Soto, P. Espinace, and R. Mitnik, “A mobile robotics course for undergraduate students in computer science,” in 2006 IEEE 3rd Latin
, and K. A. Smith, "Cooperative Learning Returns To College What Evidence Is There That It Works?," Change: The Magazine of Higher Learning, vol. 30, no. 4, pp. 26-35, 1998/07/01 1998, doi: 10.1080/00091389809602629.[5] K. L. Ruhl, C. A. Hughes, and P. J. Schloss, "Using the Pause Procedure to Enhance Lecture Recall," Teacher Education and Special Education, vol. 10, no. 1, pp. 14-18, 1987/01/01 1987, doi: 10.1177/088840648701000103.[6] S. Keshmiri, A. Blevins, and A. R. Kim, "Active Learning and Student Engagement in Flight Dynamics and Control Classes," 2018: 2018 ASEE Annual Conference & Exposition.[7] M. Prince and R. Felder, "The Many Faces of Inductive Teaching and Learning," Journal
95% confidence interval for the true mean being between 0.66 hours and 1.65 hours. Summary for hours M ean 1.1568 S tD ev 1.1157 V ariance 1.2448 S kew ness 2.26237 Kurtosis 6.27783
STEM Education: A STEM Teacher Preparation Program,” Journal of the National Association for Alternative Certification, Volume 10, Number 2, 2015, pp 3-16. [6] Bracey G, Brooks M, Marlette S, and Locke S, “Teachers 'n Training: Building Formal STEM Teaching Efficacy through Informal Science Teaching Experience,” 3-2, ASQ Advancing the STEM Agenda Conference, 2013. [7] Nathan MJ, Tran NA, Atwood AK, Prevost A, and Phelps LA, “Beliefs and Expectations about Engineering Preparation Exhibited by High School STEM Teachers,” Journal of Engineering Education, 2010, pp 409-426. [8] Yang J, Lee Y, Park S, Wong-Ratcliff M, Ahangar R, Mundy MA, “Discovering the Needs Assessment of Qualified STEM Teachers
the MRRT project. Finally undergraduate junior and senior students, Rachel Ross,Gerardo Rodriguez, Pathik Patel, Brandon Foster, and Zachary Schultheis in addition to one ofthe authors, Lance Sebesta, are greatly appreciated for their hard work and contribution to theproject.References[1] Kondracki, R., Collins C., Habbab, K. (2014). Solar Powered Charging Station. ProceedingsASEE 2014 Zone I Conference, April 3-5, 2014.[2] Qazi, S. (2017). Chapter 3 - Mobile Photovoltaic Systems for Disaster Relief and Remote Areas,Editor(s): Qazi, S., Standalone Photovoltaic (PV) Systems for Disaster Relief and Remote Areas, Elsevier,2017, pp. 83-112, ISBN 9780128030226, https://doi.org/10.1016/B978-0-12-803022-6.00003-4[3] W. R. Young (2008
of class-scale testing perassessment), as well as the specificity of the subject matter tested, it is not possible to makegeneral validity claims about our assessments. However we hope that other researchers andpractitioners can learn from the specific examples of the types of insights which may be drawnfrom think-aloud interviews and how they supplement statistical measures.References [1] D. Sands, M. Parker, H. Hedgeland, S. Jordan, and R. Galloway, “Using concept inventories to measure understanding,” Higher Education Pedagogies, vol. 3, no. 1, pp. 173–182, 2018. [2] D. Hestenes, M. Wells, and G. Swackhamer, “Force concept inventory,” The Physics Teacher, vol. 30, no. 3, pp. 141–158, 1992. [3] P. S. Steif and J. A. Dantzler, “A
/DataBooks.aspx.4. Energy Star, A guide to Energy-Efficient Heating and Cooling, 2009.5. M. Mujahid, P. Gandhidasan, S. Rehman, qnd L. Al-Hadhrami, “A review on desiccant based evaporative cooling systems,” Renewable and Sustainable Energy Reviews vol. 45, pp. 145–159, 2015. http://doi.org/10.1016/j.rser.2015.01.0516. D. Yan, W. O’Brien, T. Hong, X. Feng, B. Gunay, F. Tahmasebi, and A. Mahdavi, “Occupant Behavior Modeling for Building Performance Simulation: Current State and Future Challenges,” Energy and Buildings, vol. 107, pp. 264-278, 2015.7. T. Hong, S. Taylor-Lange, S. D’Oca, D. Yan, and S. Corgnati, “Advances in research and applications of energy-related occupant behavior in buildings,” Energy and Buildings, Vol. 116, pp. 694-702
: Chapter 1: Anthropology and Development: Challenges for the Twenty-First Century [21] Reading: Chapter 1: Freedom as Development [22]3-4 Goal 6 - Clean Water and Project: group work on case study Sanitation Writing: individual reflection on intersectionality of chosen SDG, Goal 6 and the project Guest lecture and discussion: lecture on inequality and health from professor in Social Sciences Department Reading: supplementary reading(s) determined by guest lecturer5-6 Goal 13 - Climate
Teacher, vol. 30, pp. 141-158, 1992.[2] D. P. Maloney, T. L. O'Kuma, C. J. Hieggelke, and A. Van Heuvelen, "Surveying students' conceptual knowledge of electricity and magnetism," Am. J. Phys., vol. 69, no. S1, pp. S12-S23, 2001, doi: 10.1119/1.1371296.[3] S. Krause, J. C. Decker, and R. Griffin, "Using a materials concept inventory to assess conceptual gain in introductory materials engineering courses," presented at the 33rd ASEE/IEEE Frontiers in Education Conference, Boulder, CO, 2003.[4] R. Rosenblatt and A. F. Heckler, "The development process for a new materials science conceptual evaluation," presented at the 2017 IEEE Frontiers in Education Conference (FIE), Indianapolis, IN, 2017.[5] S
proceedings and journal articles and others are still in progress [12-16]. It is expectedthat some of these projects will result in securing further external funding. When students areinvolved in conducting a research study, they also participate in writing the findings in the paperand therefore get a chance to become authors of scholarly research publications. 6. Student PerspectivesSeveral students that work on various projects in the lab were asked questions about the usabilityof the lab. Following are some of the questions and their responses. a) What are the benefits / dis-benefits of having the AERO lab on campus? “Providing a space for students to meet and have access to computer capable of CAD, a spacefor more advanced machine(s) to be
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problem-based learning and self-regulated learning,” Innov. Educ. Teach. Int., vol. 50, no. 2, pp. 121–132, May 2013.[3] E. M. Al-Madi, S. L. Celur, and M. Nasim, “Effectiveness of PBL methodology in a hybrid dentistry program to enhance students’ knowledge and confidence. (a pilot study),” BMC Med. Educ., vol. 18, no. 1, Nov. 2018.[4] K. H. Tseng, F. K. Chiang, and W. H. Hsu, “Interactive processes and learning attitudes in a web-based problem-based learning (PBL) platform,” Comput. Human Behav., vol. 24, no. 3, pp. 940–955, May 2008.[5] J. Straub, J. Berk, A. Nervold, and D. Whalen, “OpenOrbiter: An Interdisciplinary, Student Run Space Program,” Adv. Educ., vol. 2, no. 1, pp. 4–10, 2013.[6] G
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-added products and services, as well as toimprove productivity through the use of technology-based tools [14]. The REU program andapproach detailed in this study may serve as a framework for addressing this need for two-yearand four-year institutes.References[1] D. Baker, L. Wood, J. Corkins and S. Krause, "Tinkering and Technical Self-Efficacy of Engineering Students at the Community College", Community College Journal of Research and Practice, vol. 39, no. 6, pp. 555-567, 2015. Available: 10.1080/10668926.2014.902780.[2] Dayerizadeh, A., & Carpenter, P. P. (2017, June), Board # 54 : Wide Band Gap Academy— Education and Workforce Development for the 21st Century Power Electronics and Power Systems Industries Paper