(Overall_Per_STEM) construct was created by summarizing 26survey questions measuring perceptions and likeliness of Engineering, Science, and Math classesby weighing each of these subject matters equally (See Appendix A). The variable Overallperception of STEM (Overall_Per_STEM) was calculated by taking the arithmetic mean of threesub parameters. These are Overall Perception of Math, Overall Perception of Science and OverallPerception of Engineering (Appendix A: Construct a, b and c respectively). These variables werecalculated by taking the simple, equally weighted means of Likert scaled questions in a surveydata set. For each variable, related survey questions and descriptive statistics are given in AppendixA. The students who were enrolled in a STEM club
provocative software make a difference in the classroom? EdSurge. Retrieved December 5, 2016 from https://www.edsurge.com/research/special-reports/adaptive-learning/13. Balakrishnan Chandrasekaran, Todd R. Johnson, and Jack W. Smith. 1992. Task-structure analysis for knowledge modeling. Communications of the ACM 35, 9: 124–137.14. Harry Collins. 2010. Tacit and explicit knowledge. University of Chicago Press.15. Albert T. Corbett and John R. Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction 4, 4: 253–278.16. Jennifer B. Daines, Tonya Troka, and John M. Santiago Jr. 2016. Improving Performance in Trigonometry and Pre-Calculus by Incorporating Adaptive Learning
workshop were more hands-on with electronics/electromechanical systems which requiredsome knowledge of circuits.Pre/Post Survey: Concept QuestionsFor workshop 1, the answers Pre/Post survey multiple-choice questions show a trend ofimprovement in understanding of the concepts after the workshop (Appendix B). The multiple-choice questions were mainly focused on concepts on basics of electricity, circuits, motors, andquadcopters. From the results, we concluded that even though concepts in circuits, motors andquadcopters had shown positive trend (18 out of 23 questions), answers to electricity-relatedquestions did not show an improvement. There could be several reasons: a) Since the videoswere viewed at the participants’ discretion, they skipped the
). Sustainable engineering education in the United States. Sustainability Science, 4(1), 7–15. https://doi.org/10.1007/s11625-009-0065-5Anderson, A. (2010). Combating climate change through quality education. Retrieved from http://dspace.cigilibrary.org/jspui/handle/123456789/29684Andersson, B., & Wallin, A. (2000). Students’ understanding of the greenhouse effect, the societal consequences of reducing CO2 emissions and the problem of ozone layer depletion. Journal of Research in Science Teaching, 37(10), 1096–1111. https://doi.org/10.1002/1098- 2736(200012)37:10<1096::AID-TEA4>3.0.CO;2-8ASEE. (1999). ASEE Statement on Sustainable Development Education. Retrieved February 12, 2009, from http://www.asee.org
sources (e.g., Lande & Oplinger, 2014; Yilmaz & Daly,2016), gaining additional insight about the reviews and confirming or challenging our analysis.Case SelectionThe focus of this analysis is an in-depth exploration of two formative design reviews, one fromeach course. These reviews were selected on the criteria that they a) occurred before the projectwas completed (i.e., they were formative) and b) included the most types of power-relateddiscursive patterns displayed in the course they were selected from (i.e., they wererepresentative). To determine representativeness, we watched and read each of the threeformative ME and 17 formative ID design reviews in the dataset. For each review, we performedan initial coding of major power-related
motivation, non-academic competencies, and commitmentto STEM disciplines.14 Other scholars argue that service learning opportunities enhance students’engagement through, and well beyond, the college experience.15Research on SCB learning experiences in engineering suggests similarly robust studentoutcomes, as well as other engineering-specific learning outcomes mandated by theAccreditation Board of Engineering and Technology (ABET). For example, Ropers-Huilman etal. found that SCB learning experiences promote students’ ability to (a) design systems to meetreal-world needs, (b) perform on multi-disciplinary teams, and (c) communicate their workeffectively.16 Other studies of service-learning experiences in engineering suggest participantsdevelop a
Delta ’00 Symposium on Undergraduate Mathematics, Toowoowba.Dunne, B. E., Blauch, A. J., Sterian, A., “The Case for Computer Programming Instruction for all Engineering Discliplines,” ASEE Annual Conference and Exposition, Conference Proceedings, 2005, pp. 1525-1537. Portland, OR.Environmental Protection Agency: https://www3.epa.gov/region10/pdf/sites/boomsnub_airco/2010annual_status_report_boomsnub_airco.pdfMaase, Eric. 2007. “Kangaroo Thinking: Mathematics, Modeling, and Engineering in Introductory Computer Programming for Engineers,” ASEE Annual Conference and Exposition, Conference Proceedings, 2007, Honolulu, HI.Ogata, A., and Banks, R. B. 1961. A Solution of the Differential Equation of Longitudinal
Paper ID #19347Defining the Frontiers of Bioengineering Education at Illinois and BeyondDr. Jennifer R Amos, University of Illinois, Urbana-Champaign Dr Amos joined the Bioengineering Department at the University of Illinois in 2009 and is currently a Teaching Associate Professor in Bioengineering and an Adjunct Associate Professor in Educational Psychology. She received her B.S. in Chemical Engineering at Texas Tech and Ph.D. in Chemical En- gineering from University of South Carolina. She completed a Fulbright Program at Ecole Centrale de Lille in France to benchmark and help create a new hybrid masters program
laboratory experiments.The first pre-laboratory exercise involved designing an experiment that characterized the surface energyof polystyrene (PS) and polytetrafluoroethylene (PTFE). In the second pre-laboratory exercise, a protocolwas developed for preparing polyvinyl alcohol (PVA) hydrogels that matched the compressive propertiesof native cartilage. Based on your experiences with these pre-laboratory exercises, please indicate yourdegree of agreement with the following statements.Additional Questions:How did the pre-laboratory exercises affect your understanding of the material taught in the lab?How could the pre-laboratory exercises or laboratory experiments be changed to enhance your learningexperience?Any other comments?Appendix B
that the proposed solution will fail. Consider these possible actions: a) Have the entire team approach the manager together. b) Bring up your concerns in the meeting. c) Set aside your concerns and follow the manager’s lead. d) Discuss the issue with the manager later, in a private meeting. e) Consult your Chinese team members about appropriate actions to take. f) Discuss your concerns with a higher-ranking manager. Which of these actions (a-f) would you MOST likely take? Which of these actions (a-f) would you LEAST likely take?Figure 4 - Example Vignette and Situation Judgement Test [1]Evaluation of learning programsIt is also important to be able to evaluate and compare different models of learning in terms of theirinternational factors
the Blackstone River: Hearings Beforethe Joint Standing Committee on Public Health, on the Matter of Restraining the City ofWorcester from Polluting the Blackstone River (1882). This document contains residentand witness statements and legal arguments by a) municipal representatives and otherswho argued against taking action to purify the water that empties into the BlackstoneRiver and Canal, and, b) agents in favor of taking action (i.e., downstream industrialistsand residents, doctors, social activists, laborers, etc.).In-class, in-character debate:Students were instructed to take a position on the question: “Should Worcester have toclean up the water it puts into the Blackstone River?” In an in-class debate, studentsrepresented their
Paper ID #19588First-Year Engineering Student Perspectives Of Google Docs For Online Col-laborationMs. Natasha Perova-Mello, Oregon State University Natasha Perova-Mello is currently a Postdoctoral researcher at Oregon State University in the School of Civil and Construction engineering. She recieved Ph.D. in Engineering Education from Purdue University. She previously worked at the Harvard Graduate School of Education as a Research Assistant focusing on students’ learning algebra and also taught an introductory physics course at Suffolk University, Boston, Mass. Before that, she worked as a Graduate Research Assistant at
game and 10for DZ-Man game) in all the cases. The significance levels are 0.0051 for 2014 DZ-Man data,0.0006 for 2015 DZ-Man data, and 0.0006 for 2015 Angry Curves data. This means the increasesof students’ understanding on the targeted concepts (reflected by the quiz scores) are statisticallysignificant. a) 2014 DZ-Man data b) 2015 DZ-Man data c) 2015 Angry Curves data Figure 7. Matched Pairs T-Tests for Different Experiments Pre/Post Scores4. Beyond the CampusAt this stage of the project, we allow users from all over the world to have access to the games.This means the users of the games will no longer be limited within campus. The players’ datawill still be collected for further research purpose. The paper
conclusion, the Youngstown State University (YSU) “Gateway Project,” raingarden analysisand redesign has been a YSU student problem/project based learning success. It has evolved intoan inter-departmental research and beautification project that will involve communitystakeholders. We expect the redesigned garden to be a University showpiece indicating therelationship between faculty and student research and community engagement.Works Cited: 1. Bannerman, R., E. Considine. “Rain Gardens: A How-to Manual for Homeowners. University of Wisconsin Extension. Board of Regents of University of Wisconsin, 2003. Accessed June 2014. http://learningstore.uwex.edu/assests/pdfs/GWQ037.pdf. 2. Dorsey, J., and B. Puntu. 2014. “Site
different representations can easily translate between them, and can assess theusefulness of a particular representation in different situations. Similarly, Spiro (1992) found thatwhen learners develop multiple representations they are better able to transfer knowledge to newdomains with increased cognitive flexibility (Spiro, 1992). Representational fluency in theSTEM fields can include: a) visualizing and conceptualizing transformation processes abstractly;b) understanding systems that do not exhibit any physical manifestations of their functions; c)transforming physical sensory data to symbolic representations and vice versa; d) quantifyingqualitative data, e) qualifying quantitative data; f) working with patterns; g) working withcontinuously
Paper ID #19752Engineering Education for Visually Impaired StudentsDr. Deborah M. Grzybowski, The Ohio State University Dr. Deborah Grzybowski is a Professor of Practice in the Department of Engineering Education and the Department of Chemical and Biomolecular Engineering at The Ohio State University. She received her Ph.D. in Biomedical Engineering and her B.S. and M.S. in Chemical Engineering from The Ohio State University. Her research focuses on making engineering accessible to all students, including students with visual impairments, through the use of art-infused curriculum and models. Prior to becoming focused
willbe utilized by both the industry for training and development purposes and by the MET students.The authors will share the results of the study and the process of development andimplementation of risk assessment in hydraulics and pneumatics lab activities.MethodologyTo achieve aforementioned goals, a faculty member from Organizational Leadership andSupervision (OLS) and a faculty member from in Mechanical Engineering Technology (MET),who was also the instructor of the fluid power course, developed a survey (Appendixes A and B)and the students in the course took this survey in fall of 2016 and spring of 2017. The purpose ofthe survey was to learn about how much the students were being exposed to safety concerns priorto taking the course
essentially only the opinion of the researcher.Element B: Documentation and analysis of prior solution attempts5 Documentation of plausible prior attempts to solve the problem and/or related problems isdrawn from a wide array of clearly identified and consistently credible sources; the analysis ofpast and current attempts to solve the problem—including both strengths and shortcomings— isconsistently clear, detailed, and supported by relevant data.4 Documentation of existing attempts to solve the problem and/or related problems is drawnfrom a variety of clearly identified and consistently credible sources; the analysis of past andcurrent attempts to solve the problem—including both strengths and shortcomings—is clear andis generally detailed and
to consciously and deliberately monitor and regulate one’s knowledge, processes,and cognitive and affective states” (Hacker, 1998, p. 3). This definition identifies bothdeclarative and procedural components of metacognition (see Figure 1). Metacognitivedeclarative knowledge consists of a person’s knowledge or beliefs about: (a) one’s cognitive andaffective states and the states of others; (b) a task, its demands, and how those demands can bemet under varying conditions; and (c) strategies for accomplishing the task and how and when touse them (Flavell, 1979). Metacognitive procedural knowledge consists of both monitoring andcontrol components. Metacognitive monitoring refers to processes that are “directed at theacquisition of information
Figure 2. Emulator view of Mapbox’s “route navigation” and “making phone calls” featuresFigure 1 illustrates iTrust artifacts’ processing. Three Eclipse plug-ins are shown: Figure 1-Alists a set of to-be-indexed requirements, Figure 1-B outputs the indices of the input requirement,and Figure 1-D outputs the indices of a Java method that is selected as the to-be-indexed sourcecode artifact (cf. Figure 1-C). The development of Eclipse plug-ins was allocated to the first twolabs in the spring 2015 semester, as shown in Table 1. Similar development tasks were assignedin the spring 2016 semester, though the students worked on direct feature extensions of Mapbox(cf. Figure 2). In both semesters, the last two labs put more emphasis on
strategies for exploring participants’ professional identity formation werefairly logical decisions based on the personal nature of the research topic, the interview protocol,on the other hand, was rather difficult to develop. Acknowledging that an interview was anidentity intervention in itself, we needed to develop a protocol that was semi-structured andindirectly prompted participant discussion about identity formation. To accomplish this, wecreated a participant worksheet (Appendix B) in which participants defined civil engineeringthroughout three periods of their lives. As participants wrote down their definitions of civilengineering, we would ask them the follow-up questions using the protocol are as follows: 1. To get started, picture
“confirm, cross-validate, or corroborate findings within a single study” [p. 215]. b) Educational Activities A key aim of the educational activities is to strengthen practices by promoting the role ofmetacognition in design to engineering educators and students. To achieve that goal, three majoractivities are planned: i) Develop a Teaching Guide and Monitor New Teaching Intervention The principles for the intervention will come from the findings of the research activitiesat the secondary and post-secondary levels, focused on promoting SRL in engineering design.The aim is to build and field test particular interventions in an engineering context, to continue toadvance understanding through putting theories into practice
participanthas up to three hours to complete the task. The statement details constraints and encourages theparticipant to request information. The participant has access to a resource box withmiscellaneous tools (i.e., a calculator, post-it notes, pencils, pens, colored pencils, rulers, etc.).They have additional access to the facilitator and information binder (the participant must ask forspecific information) and an internet-connected computer. Refer to figure 1 for the design taskstatement. Figure 1: Study Design Task Statement B. Description of the DataEach design session lasts up to three hours. There is a scheduled ten-minute break and anapproximately 25-minute follow-up interview. Each session is video
-year institutions, Los Angeles: Higher Education Research Institute, UCLA.12. Myers, B. A. (2016). Evaluating admission practices as potential barriers to creating equitable access toundergraduate engineering education, Ph.D. Dissertation, University of Colorado Boulder.13. The University of Colorado Boulder 2008-2009 Course Catalog. Retrieved January 19, 2017.http://www.colorado.edu/registrar/sites/default/files/attached-files/ucb_2008-09_catalog.pdf14. The Accreditation Board of Engineering and Technology. (2014). 2015-16 Criteria for Accrediting EngineeringPrograms. Retrieved September 4, 2015. http://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineering-programs-2015-2016/15. Van Treuren, Ken and Eisenbarth
easy and straightforward. If the student has not worked through the assignment,however, it is unlikely that they will be able to figure it out in the time allotted. As noted, thequiz answers are discussed immediately after the quizzes are collected, while the solutions to theentire homework set are posted just after class, as was done previously just after homeworkassignments were collected on the due date.Student Survey ResultsTo ascertain some student perspectives on the use of quizzes in lieu of graded homework, thestudents in the senior elective course were given a short questionnaire. Of the 29 students, 25expected to make either an A or B in the class, while only one anticipated a D. They were askedto give scaled responses to three
a greater impact to graduationrates online or off-line.References[1] I. F. Liu, Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. , "Extending the TAM Model to Explore the Factors that Affect Intention to Use an Online Learning Community," Computers & Education, vol. 54, pp. 600-610, 2010.[2] M. Chmura, "Babson Study: Distance Education Enrollment Growth Continues," ed. Wellesley, MA: Babson University, 2016.[3] P. Blau, Inequality and Heterogeneity. New York, NY: Free Press, 1977.[4] S. B. Eom, Wen, H. J., & Ashill, N. , "The Determinants of Students' Perceived Learning Outcomes and Satisfaction in University Online Education: An Empirical Investigation.," Decision Sciences Journal of
. Coll. Sci Teach. 18:118-12014. Hunter, A-B., S. L. Laursen, and E. Seymour. (2007). Becoming a scientist: The role of undergraduate research in students’ cognitive, personal, and professional development. Science Education 91: 36-74.15. Guterman, L. (2007). What Good is Undergraduate Research, Anyway? The Chronicle of Higher Education, 53(50) A12.16. Nagda, B. A., S. R. Gregerman, J. Jonides, W. von Hippel, and J.S. Lerner. (1998). Undergraduate student-faculty research partnerships affect student retention. The Review of Higher Education 22: 55-72.17. Hathaway, R., B.A. Nagda, and S. Gregerman. (2002). The relationship of undergraduate research participation to graduate and professional education pursuit: An empirical study
Paper ID #19816Comparative Analysis of Technologies Used in Responsive Building FacadesMs. Negar Heidari Matin, Eastern Michigan University Negar Matin is currently a Ph.D. candidate in Technology at Eastern Michigan University (EMU), Yp- silanti, Michigan. Ms. Matin received her Master’s Degree in architecture from Tabriz Art University, Tabriz, Iran. She has been a doctoral fellow working on responsive fac¸ade systems since 2015. Her research interests are in interdisciplinary areas of cultural identities, architectural technology, building envelopes, responsive autonomous intelligent fac¸ade systems and smart
Communication (DTC) is a project based design course taken twice duringfreshmen year, with DTC I taken in the fall or winter, and DTC II taken in the spring. First-yearstudents also take calculus and a basic science course determined largely on their major ofchoice. Below, we look specifically at the math and engineering analysis curricula, advisingpractices, and related student outcomes.Student Preparation in Math and Science and OutcomesFigure 1 shows the percentage of the freshmen class with varying amounts of incoming math andscience credit (Chemistry, Calculus AB, Calculus BC, Physics B, Physics C [Mechanics],Physics C [Electricity and Magnetism], Physics 1, Physics 2, Biology, Environmental Science)for 2006 to 2016, binned into categories from
1.5 1 1 0.5 0.5 0 0 1 2 3 4 5 6 1 2 3 4 5 6 7 Question Indexes Question Indexes (A) (B)Figure 1, the survey result for Table 1 on evaluating students’ improvement in MATLAB skillsand MFC knowledge (A); and the survey result for Table 2 on students’ evaluation of the web-based training approach in this project.IV. Bibliography[1] webpage: http