, such as desktops, laptops, and tablets, b) retrieving each of the web pagesaccessed by an user to solve a problem, c) retrieving user’s action information within a webpageto detect the various objects, such as YouTube videos, buttons, and parameter from drop-downmenus clicked by the user, d) retrieving information useful to detect the various devices used byan user and to identify the compatibility of the user- tracking system with various operatingsystems, browsers and device types/models, e) retrieving users’ location information to identifyfrom which part of the world a user is accessing the system, and f) retrieving users’ browserstatus at a regular interval of time (60 sec) to detect whether a user is actively using the OWLSbrowser or
Cumulative GPA Cumulative GPA (a) Traditional section (b) Redesigned section Figure 1: Distribution of student cumulative GPA in the two sectionsStudent performance in StaticsTable 4 shows the comparison of the passing rate of the traditional section and the redesignedsection of Statics. The redesigned section has a moderately higher passing rate compared withtraditional section. Table 4 Comparison of Statics passing rate Section Total no. of students No. of students get C or better
? a) What amount of change (increase/decrease) after the first year is there in student's selecting a major (i.e. leaving first-year engineering) after the introduction of this module as compared to before indicating more informed decision making? b) What amount of change (increase/decrease) after two years is there in student's selection of a major (i.e. leaving first-year engineering) after the introduction of this module as compared to before indicating more informed decision making? 2. To what extent does retention increase/decrease within the STEM College and in engineering after introducing the informed decision making module? 3. To what extent have disciplines students are selecting
Study and AnalysisThe objective of this study is to compare several classification models and determine whichalgorithm works efficiently with regard to a number of evaluation metrics. The steps involved inthe study are listed below: A. Data collection B. Data pre-processing C. Feature selection D. Training model process E. Model evaluationA. Data collectionData collection is one of the most important and time-consuming stages of this analysis. Thequality and integrity of the data have to be maintained to get real and accurate predictions. Thestudy began with the data collection of students’ access behavior from Blackboard Learn. Wemade use of 11 sections from IT341 and CYSE230 courses offered in Spring and Fall 2018semesters
, American Society for Engineering Education Annual Conference & Exposition,June 2013.Schaub, D., Legg, S., Svoronos, S., Koopman, B., and S. Bai. 1999. Applying Total QualityManagement in an Interdisciplinary Engineering Course. Journal of Engineering Education.88:1, 107-112. https://doi.org/10.1002/j.2168-9830.1999.tb00419.xSharma, A. 2009. Interdisciplinary Industrial Ecology Education: Recommendations for anInclusive Pedagogical Model. Asia Pacific Journal of Education. 29:1, 75-85, DOI:10.1080/02188790802655056Spanierman, L. B., Soble, J. R., Mayfield, J. B., Neville, H. A., Aber, M., Khuri, L., and B. DeLa Rosa. 2013. Living learning communities and students’ sense of community and belonging.Journal of Student Affairs Research and Practice
minors complementary to the major or participation in the Grand Challenge Scholar Program. Any faculty time not directly related to mentorship efforts is considered non-value-added. Examples include showing thestudents how to register for courses on Self Service, the course management system, orwhere to find the list of Humanities courses, a subset of which are graduation requirements.The overall duration and variance of advising sessions is reduced through two generalcategories of effort. The first focuses on value-added activities, with the goal of capturing thebest practices across the faculty. To demonstrate consider faculty members A and B whodiscuss the benefit of having a Mathematics minor with their advisees. Faculty member Aholds a
" Proceedings of the American Society for Engineering Education Annual Conference and Exposition, Chicago, IL, 2006.[36] M. Allen and A. Kelley, "Emphasizing teamwork and communication skills in introductory calculus courses," Proceedings of the American Society for Engineering Education Annual Conference and Exposition, Honolulu, HI, 2007: https://peer.asee.org/2166.[37] A. Bernal, J. J. Leader, and J. B. Ward, "Creating laboratories to aid student modeling ability in Calculus I," Proceedings of the American Society for Engineering Education Annual Conference and Exposition, Salt Lake City, UT, 2018: https://peer.asee.org/30235.[38] J. D. Desjardins, E. Breazel, M. Reba, I. Viktorova, J. B. Matheny, and T. R. Khan
not limited to thefollowing learning outcomes: (a) distinguish between closed systems and open control volumes;(b) apply the conservation of mass in consideration of transient analysis; and (c) apply the firstlaw of thermodynamics to a control volumes (CV) and in consideration of transient analysis.While there were various other learning outcomes which were a part of the Pre-requisite Exam,in general, understanding of “transient analysis” in thermodynamics was a primary focus for thisassessment. The format of the exam required students to solve one problem with multiple parts. Thiswas not a concept question. It was a question involving filling or emptying of a vessel. Theproblem selected was not particularly aligned with the subject
containing a fluid with mass Mf and heatcapacity Cf, initially at a temperature Tf(0). A value for the convective heat transfercoefficient h between the pellet and fluid is given. Students are asked to determine thetemperatures T of the pellet and Tf of the fluid as functions of time, ignoring any thermalinteractions between the cooling bath and surroundings. A diagram of the problem isshown in Figure 1a.Figure 1. Quenching of a pellet in a small bath (a) and in a large bath (b).Previously, students have been exposed to the fundamentals of heat transfer to a lumpedparameter system through the basic notion of conservation of energy (rate ofaccumulation of energy in the system = rate of energy entering – rate of energy leaving).In addition, they have
Technical Symposium on Computing Science Education, New York, NY USA, 2016.[3] C. Watson and F. W. B. Li, "Failure rates in introductory programming revisited," in Proceedings of the 2014 conference on Innovation & technology in computer science education, New York, NY USA, 2014.[4] T. Beaubouef and J. Mason, "Why the high attrition rate for computer science students: some thoughts and observations," ACM SIGCSE Bulletin, vol. 37, no. 2, pp. 103-106, June 2005.[5] D. Teague and P. Roe, "Collaborative Learning – Towards A Solution for Novice Programmers," in Proceedings of the Tenth Australasian Computing Education Conference, Wollongong, Australia, January, 2008.[6] P. Fotaris, T. Mastoras, R. Leinfellner and Y
,” Florida Association of Teacher Educators Journal, vol. 1, no. 14, pp. 1-9, 2014.[4] J. B. Labov, A. H. Reid, and K. R. Yamamoto, “Integrated biology and undergraduate science education: a new biology education for the twenty-first century?,” CBE-Life Sciences Education, vol. 9, no. 1, pp. 10-16, 2010.[5] E. Perignat, and J. Katz-Buonincontro, “STEAM in practice and research: An integrative literature review,” Thinking Skills and Creativity, vol. 31, pp. 31-43, 2019.[6] A. M. Lucietto, J. Moss, and M. French, “Examining Engineering Technology Students: How they perceive and order their thoughts,” in ASEE National Conference, Columbus, OH, 2017.[7] A. M. Lucietto, J. D. Moss, E. Effendys and R. M
, 2002. https://doi.org/10.17226/10250. 3. B. Richmond, The ‘Thinking’ in Systems Thinking: Seven Essential Skills. Waltham, MA: Pegasus Communications, 2000. 4. L. B. Sweeney and D. Meadows, The Systems Thinking Playbook. White River Junction, VT: Chelsea Green, 2010. 5. G.M. Weinberg, An Introduction to General Systems Thinking. New York: Dorset House Publishing, 2011. 6. D.P. Stroh, Systems Thinking for Social Change: A Practical Guide to Solving Complex Problems, Avoiding Unintended Consequences, and Achieving Lasting Results. White River Junction, VT: Chelsea Green, 2015. 7. W. Donaldson, Simple Complexity: A Management Book for The Rest of Us: A Guide to Systems Thinking. New York
case studies is also included in thetable. Table 2: Categories for Ethical Dilemmas and Relevant NPSE Code Listings BER Cases (n=154) Non-BER (n=17) Ethical Dilemma Relevant NPSE Code Total number, (%) Total number, (%) Misleading Information II.3 , II.5.a, III.1 , III.3.a 35, (23) 0, (0) Withholding Information III.1.b, III.3 8, (5) 4, (24) Disclosing Private Information II.1.c , III.4 8, (5) 0, (0) Public Safety Risk
hydraulic system.References1. Sullivan, J., Fluid Power Theory and Applications, Prentice Hall Inc., Upper Saddle River, New Jersey, 1998.2. Rydberg, K.; Energy Efficient Hydraulics – System solutions for loss minimization; National Conference on Fluid Power, Linkoping University, Sweden. March 2015.3. Choudhury, A. and Rodriguez, J.; Experimental Analysis for Energy-efficient Product Design, Journal of Engineering Technology, Volume 34(1), 2017.4. Choudhury, A., Rodriguez, P. Ikonomov, J. He, B. De Young, R. Kamm, S. Hinton, Human powered energy efficient vehicle design, Proceedings the American Society for Engineering Education Annual Conference, San Antonio, TX, June 2012.5. Borghi, M., Zardin, B. Pintore, F., and Belluzi, F.; Energy
, “Factors affecting response rates of the web survey: A systematic review,” Computers in Human Behavior, vol. 26, no. 2, pp. 132–139, 2010.[25] C. G. P. Berdanier, “Learning the Language of Academic Engineering: Sociocognitive Writing in Graduate Students.” Purdue University, 2016.[26] E. Lavelle and K. Bushrow, “Writing Approaches of Graduate Students,” Educational Psychology, vol. 27, no. 6, pp. 807–822, 2007.[27] B. J. Zimmerman and A. Bandura, “Impact of self-regulatory influences on writing course attainment,” American Educational Research Journal, vol. 31, no. 4, pp. 845–862, 1994.[28] K. Lonka, A. Chow, J. Keskinen, N. Sandstrom, and K. Pyhalto, “How to measure PhD. students ’ conceptions of academic writing – and are
show areas in Mississippi where (a) there is Fixed Broadband Deployment of3Mbps/768kbps, (b) percentage of residents below the poverty level, (c) percentage of minorityresidents, (d) high school graduate rates, and (e) medically served communities. There is a clearcorrelation between communities with limited resources for technology and healthcare, poverty,low graduation rates, and underrepresented groups. As a result, the groups where the opportunitybest exists to address the STEM deficit are the same groups that lack access to entry into thepipeline. Fig. 2: Correlation of Technology, Medically Underserved, Poverty, Race, and High School Graduation Rate in Mississippi as of 2016.Consider the case of the Leland High
at the end of the 10th week of a 15-week semester, as a rough draft before thefinal report (FR). The PR consisted of the first four sections of the FR, A) Problem Definition-5%, B) Brainstorming Alternatives-5%, C) Proposed Design-10%, and D) Construction & TestProcedures-10%. The syllabus briefly defined expectations for each section and referredstudents to website references for more information about the initial IMD prototype. Studentswere then directed to visit Canvas for a detailed procedure of construction and experimentation,including a rubric with evaluation criteria. The PR submitted by each project group wasreviewed by the instructor, assigned a tentative in-progress grade based on rubric criteria.Reviewed drafts of the PR were
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
, “Immersion in desktop virtual reality,” in Proceedings of the 10th annual ACM symposium on User interface software and technology, 1997, pp. 11–19.[10] T. Griffiths and D. Guile, “A connective model of learning: The implications for work process knowledge,” Eur. Educ. Res. J., vol. 2, no. 1, pp. 56–73, 2003.[11] B. Dalgarno and M. J. Lee, “What are the learning affordances of 3-D virtual environments?,” Br. J. Educ. Technol., vol. 41, no. 1, pp. 10–32, 2010.[12] H.-M. Huang, U. Rauch, and S.-S. Liaw, “Investigating learners’ attitudes toward virtual reality learning environments: Based on a constructivist approach,” Comput. Educ., vol. 55, no. 3, pp. 1171–1182, 2010.[13] S. E. Kirkley and J. R. Kirkley, “Creating next
. Response scales ranged from 1 to 5, and we consider any response of 4 orgreater to display positive sentiments. The questions are abbreviated on the graphic below butare shown with the exact wording in Appendix B. Overall, students expressed satisfaction withthe course and the degree to which the course improved their understanding of the material(Questions 1, 2, 3 & 5), but they communicated a slightly less positive sentiment regarding thevalue and relevance of the course as a whole (Questions 8 & 9). Still, given our experience withthe subject of engineering statistics and the fact that the course serves many programs, theseresponses were higher than we expected. Responses to the question of prerequisites (Question 4)suggest students
meaningful relationships. 10 Community I felt encouraged and supported by others in a way that 8 helped me grow.Appendix B includes specific quotes from portfolios that we categorized in each impact theme.DiscussionMost Meaningful Activities/ExperiencesSeveral things stand out to us in the data. First, we were surprised by the number of differentactivities or experiences that the students listed as being most meaningful to them, and that noactivity or experience was listed by more than 11 students. This suggests it is unlikely that wecan plan any one activity that will be meaningful to an entire cohort of students, and thatincluding a diverse group of activities will make it
(issue-basedinformation system) [11]. We will document characteristics of the problematic situation (i.e.,location, type of infrastructure, issue to be addressed), documents either exchanged and orproduced (i.e. photos, diagrams, plans, contracts, bids), information about the stakeholders (i.e.role, expertise), and actions performed by them. Through the preliminary courses, in the fieldstudents will collect information regarding performance aspects of buildings connected to designissues. Then, they will feed the information into an ad hoc repository. A major framing elementof the content in the database will be: a. the performance of the infrastructure under high environmental stress conditions, and b. how this performance can be either
-education, illuminating the hidden curricula that often disadvantages first-generation and low income students. The educational research questions tested during theimplementation of the CAPS program focus on studying (a) how these interventions affect thedevelopment of social belonging and engineering identity of CAPS scholars, and (b) the impactof Mentor+ on academic resilience and progress to degree. The findings will help enhance theCAPS program and establish a sustainable Scholars Support Program at the university that canbe transferred to similar culturally diverse institutions to increase success for students who havesocio-economic challenges, and can be used for all scholars in the College regardless of thesource of their scholarships.This
question has three requirements. The question must be1) clearly written, 2) error-free, and 3) answerable within 3 minutes of testing time for averagestudents. Faculty are asked to focus on one or two key concepts only to design the question.Otherwise the question is not posed as an MC question.Category A questions are those in which questions are well-posed, and 60% or more of the classcan answer them correctly. On figure 2, Q1 and Q4 fit this category. Category B questions arethose where the questions are well-posed but less than 60% of the class can answer themcorrectly. Here Q2 fits that category. The response in Q3 on the other hand shows a completelydifferent trend. Such responses may happen due to one of three reasons: 1) the question
whilepracticing their creative problem solving, hands-on lab work, and technical writing. Theseactivities fill the gap caused by lack of opportunities to work on engaging problems related to thehuman body, preparing students better to work in the medical field. Our recommendation is toperform a complete study with more students and the ability to conduct interviews.Implementation of these activities and labs could better prepare students to be creative andcritical thinkers, and therefore, better health professionals.References[1] Tobin, K. and Fraser, B. J. (1989), Barriers to higher-level cognitive learning in high schoolscience. Sci. Ed., 73: 659-682. doi:10.1002/sce.3730730606[2] NGSS Lead States, (2013). Next Generation Science Standards: For States
can be found in [14]. This frameworkguided the research, including the questions asked, methodology used, and analytic decisions wemade.Research QuestionsTo investigate optimization in our specific context, we set out to answer the following question: 1. How do students and their teacher collectively optimize a multi-objective design through modeling and analysis? A. What role does risk taking play in the process and in presenting their final prototype? B. What knowledge, tools, and approaches do they use to improve their designs?Research Methods Our study takes an ethnographic perspective that is informed by discourse analysis toinvestigate precollege engineering because classroom activity
. Archer, J. DeWitt, J. Osborne, J. Dillon, B. Willis, B. Wong.”“Balancing acts'': Elementary school girls' negotiations of femininity, achievement, and science”, Science Education, 96(6):967-89, Nov 2012.[2] C. Hill, C. Corbett, A. St Rose. “Why so few? Women in science, technology, engineering, and mathematics”, American Association of University Women, 1111 Sixteenth Street NW, Washington, DC 20036, 2010.[3] E. Smith. “Women into science and engineering? Gendered participation in higher education”, STEM subjects. British Educational Research Journal, 37(6):993-1014, Dec 2011.[4] Women, minorities, and persons with disabilities in science and engineering: 2017. Available: www.nsf.gov/statistics/wmpd.[5] A. Johnson, J. Brown
, thenregenerate the formatted document to verify that the correct edit was performed. Likewise,modifying the source code in Figure 2c requires a similarly laborious process. Minor textualedits become major chores. Finally, traditional development tools such as debuggers andprofilers are extremely difficult to deploy for WEB documents and their associated programs.Figure 2: Knuth's WEB system for LP transforms the input source document in (a) to theformatted output in (b) and the source code in (c) as illustrated by the large arrows.[5]Later LP implementations addressed the first problem in Knuth’s approach: weaknesses inlanguage support and formatting. Some variants support additional programming languages:CWEB (for C), FWEB (Fortran, C, and C++), xmLP
thesestudents into a course that requires mastery. Among other reasons, it is likely that they have notdeveloped proper study habits or the skills necessary to review and correct their work during anexamination. To account for this, multiple opportunities were provided on each of the midtermexams. For each of the midterm exams, the final score was the sum of the best scores in eachsection (described below) from any of the exam attempts. There was only one attempt on thefinal exam, which had a similar structure as the midterm exams.In version 1 of the assessment model, three attempts (A, B and C) at each exam were offered.There were four midterm exams, so a total of twelve exams plus the final exam were offeredduring the semester. With three chances to
Paper ID #25655The Moral Foundations of Chinese Engineering Students: A Preliminary In-vestigationDr. Rockwell Franklin Clancy III, University of Michigan-Shanghai Jiao Tong Joint Institute Rockwell F. Clancy is an Associate Teaching Professor in engineering ethics and philosophy at the Uni- versity of Michigan-Shanghai Jiao Tong University Joint Institute, Research Fellow in the Institute of Social Cognitive and Behavioral Science at Shanghai Jiao Tong University, and has acted as a long-term educational consultant, setting up a course and writing a corresponding textbook with Heinz Luegen- biehl, entitled Global