lab)introductory materials science course for students in Aerospace, Civil, Mechanical, andManufacturing Engineering; (b) allow flexibility for a variety of delivery formats (e.g., flipped,online, emporium, etc.); (c) require some minimum number of on-campus experiments in atraditional materials testing lab that would satisfy course objectives, yet provide a manageablesolution for online students or for institutions lacking traditional materials testing equipment.The initial curriculum and pilot implementation were designed around a flipped approach, inwhich students were expected to read from a textbook and view video lessons outside of class,and then use class time for group problem-solving sessions and laboratory experiments. In orderto
, and other tutoringsystems,” Educat. Psychologist 46, 197 (2011).2 C. D. Whitlatch, Q. Wang, and B. J. Skromme, “Automated problem and solution generation software forcomputer-aided instruction in elementary linear circuit analysis,” in Proceedings of the 2012 American Society forEngineering Education Annual Conference & Exposition (Amer. Soc. Engrg. Educat., Washington, D.C., 2012),Session M356.3 B. J. Skromme, C. D. Whitlatch, Q. Wang, P. M. Rayes, A. Barrus, J. M. Quick, R. K. Atkinson, and T. Frank,“Teaching linear circuit analysis techniques with computers,” in Proceedings of the 2013 American Society forEngineering Education Annual Conference & Exposition (Amer. Soc. Engrg. Educat., Washington, D.C., 2013),paper 7940.4 B. J
Experiential Case Study of Data Sharing and Reuse,” Adv. Eng. Educ., vol. 5, no. 2, 2016.[6] D. C. Miller and J. P. Byrnes, “To achieve or not to achieve: A self-regulation perspective on adolescents’ academic decision making,” J. Educ. Psychol., vol. 93, no. 4, pp. 677– 685, 2001.[7] M. K. Orr, K. M. Ehlert, M. Rucks, and M. Desselles, “Towards the Development of a Revised Decision-Making Competency Instrument,” in Proceedings of the American Society for Engineering Education, 2018, vol. 2018-June.[8] K. M. Ehlert, M. L. Rucks, B. A. Martin, M. Desselles, S. J. Grigg, and M. K. Orr, “Expanding and Refining a Decision-Making Competency Inventory for Undergraduate Engineering Students,” in Proceedings of the
enhanced with machine learning techniques will becombined. The goal of this interdisciplinary partnership is to initiate a boundary spanning research programto identify and validate novel research methods and formative and summative assessmentmechanisms. The efforts center on enhancing qualitative and quantitative educational researchand assessment methods with machine learning techniques such as automatic data clustering.Our specific goals are to (a) provide a context for an exploratory study to be used as a baselinefor future efforts in engineering education research methods and assessment; (b) addresschallenges in cultivating a culture of lifelong learning among professional and future engineersvia scientific habits of mind in an engineering
fewest at 337. Figure 1(b)provides insights into the number of undergraduate engineering students per teaching faculty(both tenure track and non-tenure track). Western Kentucky University has 46.3 students perteaching faculty member and both Tufts University and Olin College have only 6.0.An indication of the resources available per student at each of the institutions is shown in Fig.1(c), which is a graph of endowment per total number of undergraduate students. The figureshows a range from $3.1M per student at Stanford down to $92 per student at Western KentuckyUniversity. Obviously, there is a wide range (4 orders of magnitude) along this particular axis.The variation in the 4-year institutional graduation rate (not just in engineering) is
Paper ID #17920A PATTERN RECOGNITION APPROACH TO SIGNAL TO NOISE RA-TIO ESTIMATION OF SPEECHMr. Peter Adeyemi Awolumate P.AMr. Mitchell Rudy, Rowan University Rowan University Electrical and Computer Engineering student.Dr. Ravi P. Ramachandran, Rowan University Ravi P. Ramachandran received the B. Eng degree (with great distinction) from Concordia University in 1984, the M. Eng degree from McGill University in 1986 and the Ph.D. degree from McGill University in 1990. From October 1990 to December 1992, he worked at the Speech Research Department at AT&T Bell Laboratories. From January 1993 to August 1997, he was a
consciousness be "uploaded" into robots to extend our lives indefinitely? What would this mean for humanity? How accurate have fiction and futurists proved in predicting the future of robots so far? Is the direction in which technology develops inevitable, or do we have choices?GradingThe course is graded on the scale ≥ 90 = A, 90-80 = B, 80 - 70 =C, 70 - 60=D, <60 =F. Thecourse grade is the grades on the various activities as follows: 20% Participation 10% Surveys 15% Quizzes 20% Research Project (initial discussion 5%, research paper 15%) 15% Midterm exam 20% Final examThe participation grade is based on the discussion boards, one per module. Grading takes intoaccount engagement with other
Conference (FIE), pages 1–8. IEEE, 2014. [8] Mica Estrada, Paul R Hernandez, and P Wesley Schultz. A longitudinal study of how quality mentorship and research experience integrate underrepresented minorities into STEM careers. CBE—Life Sciences Education, 17(1):ar9, 2018. [9] Heather Wright and Burcin Tamer. Path Ambassadors to High Success (PATHS): Comparative evaluation of pilot and cohort 1 to a national sample, 2019.[10] Linda J Sax, Hilary B Zimmerman, Jennifer M Blaney, Brit Toven-Lindsey, and Kathleen Lehman. Diversifying undergraduate computer science: The role of department chairs in promoting gender and racial diversity. Journal of Women and Minorities in Science and Engineering, 23(2):101–119, 2017.[11] Linda J Sax
E. McCave, “Best Practices for Developing a Virtual Peer Mentoring Community,” in American Society for Engineering Education Annual Conference, 2017.[4] C. M. Campbell and K. A. O’Meara, “Faculty agency: Departmental contexts that matter in faculty careers,” Res. High. Educ., vol. 55, no. 1, pp. 49–74, 2014, doi: http://doi.org/10.1007/s11162- 013-9303-x.[5] N. K. Schlossberg, E. B. Waters, and J. Goodman, Counseling adults in transition: Linking practice with theory, 2nd ed. New York, NY: Springer Publishing Company, 1995.[6] A. Coso Strong, C. S. Smith-Orr, C. A. Bodnar, W. C. Lee, C. J. Faber, and E. J. McCave, “Using a Critical Incident-centered Transition Theory Framework to Explore
-11].Each unit contained a lesson plan, in-class activities, an infographic fact sheet, and homeworkassignments (with answer keys for instructors). The in-class activities engaged students with oneof three data sets: • A student writing data set, which included 99 files of student technical and scientific writing, including abstracts, critical reviews, process explanations, progress reports, proposals, and white papers. All the texts earned a grade of “A” or B” from the instructors of record. • A professional writing data set, which included 240 files of published writing in cell biology, electrical engineering, mechanical engineering, applied linguistics, marketing, philosophy, and physics. All of the
required toparticipate in the project activities and in presenting the project results. Each team member wasexpected to have a thorough understanding of the project, make a presentation and assumeleadership responsibility for their portion of the project.Soft skills can be seen in the Technology Accreditation Criteria of ABET. For example,TAC/ABET Criterion 23 lists the eleven areas of expertise a graduate must possess uponprogram completion, known as the “a-k” criterion. Under this standard an engineeringtechnology program must demonstrate that graduates have: 2a. an appropriate mastery of the knowledge, techniques, skills and modern tools of theirdisciplines,b. an ability to apply current knowledge and adapt to emerging applications of
covering sevenbroad categories: A. Did the students think the VOLTA is useful for their learning? (Learning environment) B. Did the students find the software motivating? (Motivational value) C. Did the students find the VOLTA easy to use? (Ease of use) Page 26.449.9 D. Did the students perceive the usefulness of various features of the VOLTA? (Perception of usefulness) E. Did the students “buy into” the virtual laboratory environment? (Authenticity of virtual learning) F. What was the perceived quality of the VOLTA? (Quality assurance) G. What additional features or learning situation the students
information processing activities may go on. Metacognitionrefers, among other things, to the active monitoring and consequent regulation and orchestrationof these processes in relation to the cognitive objects or data on which they bear, usually in serviceof some concrete goal or objective." As stated by Pintrich [13], “Although there are manydefinitions and models of metacognition, an important distinction is one between (a) knowledgeof cognition and (b) the processes involving the monitoring, control and regulation of cognition.”Often students in gateway STEM courses demonstrate poor knowledge of cognition as they,“confuse their ability to recognize vocabulary with mastery of material” [14] and thus clearly areunaware of their knowledge deficits.In
Paper ID #8982A Summer Program to promote an Integrated Undergraduate Research andGroup Design ExperienceDr. Chiang Shih, Florida A&M University/Florida State University Dr. Chiang Shih is a Professor of Mechanical Engineering Department, FAMU-FSU College of Engineer- ing, Florida State University. He received his Ph.D. degree from the Aerospace Engineering Department at University of Southern California in 1988. He has served as the department Chair from 2002 until 2011 and is currently the Director of the Aeropropulsion, Mechatronics and Energy Center established in 2012. He is also the PI of the NSF REU program on
, D. B., Brown, E. R. (2015). To grab and hold: cultivating communal goals to overcome cultural and structural barriers in first-generation college student’s science interest. Transl Issues Psychol Sci, 1, 331–341.Azungah, T. (2018), "Qualitative research: deductive and inductive approaches to data analysis", Qualitative Research Journal, Vol. 18 No. 4, pp. 383-400. https://doi.org/10.1108/QRJ-D-18-00035Estrada, M., Burnett, M., Campbell, A. G., Campbell, P. B., Denetclaw, W. F., Gutiérrez, C. G., Hurtado, S., John, G. H., Matsui, J., McGee, R., Okpodu, C. M., Joan Robinson, T., Summers, M. F., Werner-Washburne, M., & Zavala, M. E. (2016). Improving underrepresented minority student persistence in
twocohorts of 14 scholars from their sophomore to senior years. This paper presents CAPS programimplementation progress during the first two project years (fall 2018 – fall 2019). In particular,we will share the changes that we have made after the first project year (fall 2018 – summer2019) to improve several key components of the program - recruitment, cohort building, andmentor training. We will also report findings of the following CAPS research questions: (a) howdid CAPS interventions affect the development of social belonging and engineering identity ofCAPS scholars, and (b) what was the impact of Mentor+ on academic resilience and progress todegree. The program conducted qualitative data collection and analysis via focus group meetingsand
representedtwelve different rural, urban and suburban Grade 5-12 schools. These schools have a percentageof students on free/reduced lunch that ranges from 4.3% to 100% and a non-white populationthat ranges from less than 1% to greater than 95%. In an effort to increase the impact to minorityserving schools, targeted recruiting will be done for the 2016 cohort.Objective B: Develop inquiry- and team-based STEM curriculum and innovative pedagogy toencourage interest in STEM and, in particular, engineering: Participants worked on usinginnovative ways to design curriculum that incorporated the interrelatedness of different topicsand were challenging for students. They also learned to construct weekly lesson plans to enhancethe educational process. Teachers
? Positive Ranks 0 0.00 0.00 Ties 3 Total 8 a. Post-Survey < Pre-Survey b. Post-Survey > Pre-Survey c. Post-Survey = Pre-SurveyDiscussing Survey StatisticsThe survey data reflects the goals achieved by the REU program. For the most part, the surveyquestions reflected an improvement in the self-confidence of the participants as well as theirinterest in attending graduate school post-graduation. Survey questions that did not follow thistrend generally had the responses that had been hoped for in the pre-survey.A main goal of the REU experience was in increase the participants’ self-confidence
Outputscategory of blocks. Drag a Space2D block to the work area and resize it. Connect the outputnode of the Array Input block to both input nodes of the Space2D block as an attempt to plot thedata. a. Is there a problem with the final graphical output? Explain. b. Fill in the table below in describing each action you did in trying to fix the problem: List of things done Reasoning for each State what you learned step from each stepRefining Debugging RubricsTwo of the researchers coded both Python and iFlow questions together to establish interrateragreement. Although the sample size was too small to establish rigorous scoring reliability, thetwo researchers
/S0033291703001624.[16] R. N. Spreng, M. C. McKinnon, R. A. Mar, and B. Levine, "The Toronto Empathy Questionnaire: Scale Development and Initial Validation of a Factor-Analytic Solution to Multiple Empathy Measures," Journal of Personality Assessment, vol. 91, no. 1, pp. 62- 71, 2009/01/01 2009, doi: https://10.1080/00223890802484381.[17] J. A. Johnson, J. M. Cheek, and R. Smither, "The structure of empathy," Journal of Personality and Social Psychology, vol. 45, no. 6, pp. 1299-1312, 1983, doi: https://10.1037/0022-3514.45.6.1299.[18] S. King Jr. and M. J. Holosko, "The development and initial validation of the empathy scale for social workers," Research on Social Work Practice, vol. 22, no. 2, pp. 174-185
program, they now had enoughinformation to (a) ask more and better questions and have conversations in their schools; (b)better infuse things into their own CS curriculum; and (c) help others better understand theneed for technology and computer science.3.2. Cohort 2Similar to procedures for Cohort 1, Cohort 2 teachers completed a comprehensive end-of-courseevaluation. Quantitative results are shown below in Figure 10. In addition to the morequantitative ratings, open-ended questions probed individual reflections. Respondents wereappreciative of the opportunities to work with colleagues in small groups with the assistance andguidance of group facilitators along with the help provided for them outside of class time.Respondents indicated they
Communities: Creating Connections Among Students, Faculty, and Disciplines, Jossey-Bass, San Francisco: CA.3. Kline, A., Aller, B., and Tsang, E (2011), “Improving Student Retention in STEM Disciplines: A Model That Has Worked,” Proceedings of the American Society for Engineering Education Annual Conference, Vancouver, B.C., Canada, June 26-29, 2011.4. Stanford, C., Cole, R. S., Froyd, J., Henderson, C., Friedrichsen, D., Khatri, R. (2017). “Analysis of Propagation Plans in NSF-Funded Education Development Projects,” Journal of Science Education and Technology, 26 (4), pp.418-437.5. Foote, K., Knaub, A., Henderson, C., Dancy, M., & Beichner, R. J. (2016). Enabling and challenging factors in institutional reform: The case of SCALE-UP
students and (b) identify factors related to effectiveness of interventions to promotetransfer, retention and graduation of Hispanic STEM undergraduates. Given the urgency toimprove transfer of Hispanic students to four-year engineering programs, the likelihood of large-scale programs to improve the success of minority students in STEM, and the lack ofcomprehensive syntheses, the need for such syntheses is imminent and important. Therefore, wepropose to develop a comprehensive framework for promoting success of Hispanic STEM transferstudents by applying established systematic review methods [4] to existing primary studies inseveral areas. Results of the synthesis will be extensively shared with multiple stakeholders in thesuccess of Hispanic STEM
today and tomorrow’s tech savvy youth. Tech Trends, 49(3), 33-41. http://dx.doi.org/10.1007/BF02763645Kritzer, K. L., & Pagliaro, C. M. (2013). An Intervention for Early Mathematical Success: Outcomes from the Hybrid Version of the Building Math Readiness Parents as Partners (MRPP) Project. Journal of deaf studies and deaf education, 18(1), 30-46. https://doi.org/10.1093/deafed/ens033Lewis, A. B., & Mayer, R. E. (1987). Students' miscomprehension of relational statements in arithmetic word problems. Journal of educational psychology,79(4), 363.Lunce, L. (2006). Simulations: Bringing the benefits of situated learning to the traditional classroom. Journal of Applied Educational Technology, 3(1), 37
Update Proposed Revisions to EAC General Criteria 3 and 5". 2016 EDI, San Francisco,CA, 2016, March. ASEE Conferences, 2016.3. Denecke, D., K. Feaster, and K. Stone. "Professional development: Shaping effectiveprograms for STEM graduate students." Washington, DC: Council of Graduate Schools(2017).4. Trevelyan, J. The Making of An Expert Engineer. (Taylor and Francis, 2014).5. Ahlqvist, S., London, B. & Rosenthal, L. Unstable Identity Compatibility How GenderRejection Sensitivity Undermines the Success of Women in Science, Technology, Engineering,and Mathematics Fields. Psychological Science 24, 1644-1652 (2013).6. Wieman, C., & Gilbert, S. (2014). The Teaching Practices Inventory: a new tool forcharacterizing college and university
Plant: Generation pollution and Use electricity a) oxy- control generation. combusti devices for on, and power b) alt plant. fuels. Change Chem is restricted to the discussion component of the course with the lectureserving as a complement. A team of graduate teaching assistants (TAs) from engineering andchemistry teach the discussion sections. The emphasis on the discussion is based upon the resultsof previous work demonstrating that the discussion and
Paper ID #12083Enacting Video-Annotated Peer Review (VAPR) of Faculty in a First-YearEngineering DepartmentDr. James J. Pembridge, Embry-Riddle Aeronautical Univ., Daytona BeachDr. Yosef S. Allam, Embry-Riddle Aeronautical University, Daytona Beach Yosef Allam is an Assistant Professor in the Freshman Engineering Department at Embry-Riddle Aero- nautical University. He graduated from The Ohio State University with B.S. and M.S. degrees in Industrial and Systems Engineering and a Ph.D. in Engineering Education. Dr. Allam’s interests are in spatial visu- alization, the use of learning management systems for large-sample
and Planning created acohort of students that • Were Full-time (i.e. taking at least 12 credit hours), • Had begun enrollment at COD relatively recently (i.e. after the fall term of 2014), • Have a composite ACT score of at least 214, • Received financial aid in some form and • Appeared to be a STEM student either because (a) they have declared they intend to graduate with either an associate of science (AS) or an associates of engineering (AES) degree or (b) are taking a significant load of STEM courses (e.g. calculus). The above criteria for the control group are an attempt to make the control group as similaras possible to the S-STEM student group. Table 2 shows the average composite and math ACTscores for students
resulting outline contains 1,242 keywords categorized into 38 first level terms. 2. Outline B was based on 2,216 peer-reviewed journal articles in five international engineering education journals from 1959 to 2012. The author studied the titles to identify an initial set of keywords then applied network analysis to identify underlying themes and relationships between them. The outline includes 256 keywords, arranged in 46 first-level terms and multiple second- level terms, as well as a time dimension and a level of connectedness (frequency of occurrence). 3. Outline C involved a keyword analyses of three separate sources: (1) the full text of research or discussion papers from journal articles during 2006-2012
Paper ID #40996Board 320: Integrating Playful Learning: A Mixed-Reality Approach to EnhanceComputational Thinking in Young LearnersDr. Jaejin Hwang, Northern Illinois University Dr. Jaejin Hwang, is an Associate Professor of Industrial and Systems Engineering at NIU. His expertise lies in physical ergonomics and occupational biomechanics and exposure assessment. His representative works include the design of VR/AR user interfaces to minimize the physical and cognitive demands of users. He specializes in the measurements of bodily movement as well as muscle activity and intensity to assess the responses to physical and