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
neutron flux data (from Neutron Monitoring Stations11) are shown in Figure 2 Neutron hourly flux (c/s) start 6 Sep 2017 3500 3300 3100 2900 2700 2500 0 50 100 150 200 Figure 2: The neutron 1-hr flux data (Counts/sec) with the same time duration of Figure 1. The Athens Centerneutron data (upper curve) was multiplied by 2 for easy display with the Newark Center neutron data (lower curve). 2018 ASEE Mid-Atlantic Spring Conference, April 6-7, 2018 – University of the District of ColumbiaThe Newark
within six months of first use.15 Some improvements that couldaid in this venture are better data collection, and continued miniaturization. Currently there are some downfalls for wearables in their effectiveness in monitoringsignals due to their use in uncontrolled settings. One such example presented by Nicholas Lowreset al. is in noisy electrode reading due to motion artefacts in products like Kardia Mobile.7 Dirtyelectrodes, bad electrode placement, and outside electrical interference can worsen the ECGreading of a wearable. In addition, traditional silver chloride electrodes may cause skin irritationduring long-term monitoring and require skin preparation, seen in the SEEQ patch and Zio patch.Atte S. Joutsen et al present one
fabrication and replacement. Heart valvesact as one-way valves for blood in the heart. As the heart contracts and relaxes, the heart valvesopen and close to ensure the correct flow of blood in the heart. Heart valves are extremelyimportant as they ensure the correct volume and pressure of blood is being released with eachcontraction of the heart.Heart valve disease occurs if one or more of the heart’s four valves: the tricuspid, pulmonary,mitral, and aortic valves do not work well, or function poorly.12 It is typically caused by heartconditions and disorders, age-related changes, rheumatic fever or infections.12 There are two maintypes of heart valve disease: regurgitation and stenosis.13 Regurgitation occurs when the valve(s) 2018 ASEE Mid
interviewed, hadgraduated over one thousand women doctorates but had hired only seven in the period in question. Apartfrom recruitment challenges, underrepresentation in engineering may also be due to poor science degreecompletion rates among these groups as racial minority students have much lower STEM completionrate than their white counterparts (Higher Education Research Institute [HERI], 2010). This createsscarcity of minority Ph.D.’s that feed the faculty ranks in engineering and related fields.The literature discusses many factors that affect recruitment of diverse faculty. Implicit bias is one ofthem. Greenwald and Krieger (2006) introduces implicit bias as “an aspect of the new science ofunconscious mental processes that has substantial
We formed divisions as per entrance examination scores and allocated better teachersto divisions with poor performers. The teachers were asked to follow the mastery approach i.e.focus more on understanding. We kept the same divisions for all courses. s based on consistentstudent evaluations of teaching effectiveness and performance of their students in universityexaminations. Kulik et al. [12] did meta-analysis of findings from 108 controlled evaluationsto conclude that mastery learning programs have positive effects on the examinationperformance of students in colleges. Further, they found that the effects appear to be strongeron the weaker students in a class, and they also vary as a function of mastery procedures used,experimental designs
Preschool TeacherCandidates", Universal Journal of Educational Research, vol. 4, no. 11, pp. 2533-2540, 2016.[8] D. Jonassen, J. Strobel and C. Lee, "Everyday Problem Solving in Engineering: Lessons forEngineering Educators", Journal of Engineering Education, vol. 95, no. 2, pp. 139-151, 2006.[9] S. Loyens, J. Magda and R. Rikers, "Self-Directed Learning in Problem-Based Learning and itsRelationships with Self-Regulated Learning", Educational Psychology Review, vol. 20, no. 4, pp. 411-427,2008.[10] M. Gick and K. Holyoak, “The cognitive basis of knowledge transfer”, Transfer of learning:Contemporary research and applications, Elsevier, pp. 9-46, 1987.[11] D. Jonassen, "Instructional design models for well-structured and III-structured problem
and bottom three motivational attitudes along with the student’s rating.Further, it depicts the average intrinsic and extrinsic scores allowing the student to comparehis/her motivation with that of the whole class. Finally, there is a short summary explaining thestudent’s motivational attitudes category together with the attitude items with which s/he wasleast and most motivated. Example report cards for students intrinsically and extrinsicallybalanced, predominantly intrinsic, and predominantly extrinsic in nature are shown in Figs. 1-3.Figure 1 is an example report card for an intrinsically and extrinsically balanced student with anaverage intrinsic score of 7.4 and average extrinsic score of 8.1. This student provided the lowestrating for
process, the ISE-2 project team will compare student reports of engagement and classroom climate in classrooms taught by ISE-2 faculty versus comparison classes. A survey for junior students was also administered in Spring 2017 and will be administered in the Spring semesters of subsequent years. This survey broadly examines student engagement and classroom climate in the College of Engineering. The goal is to determine if there are changes in juniors’ experiences pre-/post-implementation of ISE-2. Student engagement in the classroom is measured by the Student Experience in the Research University Survey (SERU-S)2. Classroom climate is measured by the Critical Incidents Questionnaire (CIQ)3, items from the Diversity
Recruitment, Mentoring and Retention through the Aerospace and Industrial Engineering (ASPIRE) Scholarship Program1. IntroductionThe overarching goal of the Aerospace and Industrial Engineering (ASPIRE) Scholarshipprogram is to improve recruitment and retention of aerospace engineering (AE) and industrial(IE) engineering students. With support from the NSF S-STEM program, the ASPIRE programprovides scholarships to academically talented, full-time AE and IE students with demonstratedfinancial need. The ASPIRE program enhances the educational experience of ASPIRE studentsthrough mentoring and networking events. The objectives of the ASPIRE program are to: • Prepare students for the workforce. • Provide educational
engagement of industry mentors with the students has increased the number ofinternships with the region. The interaction of students in competitions motivates the students totake on more challenging projects in STEM areas than they would engage in with traditionalcourses. Finally, having students carry out lessons and activities builds self-confidence andspeaking skills.References1. Jolly, Campbell, and Perlman, “Engagement, Capacity and Continuity: A Trilogy for StudentSuccess” (GE Foundation, September 2004)2. Chun-Mei Zhao and George D. Kuh, “ADDING VALUE: Learning Communities and StudentEngagement”, Research in Higher Education, vol. 47, 2006, pp 89-1093. Georgiopoulos, M., Young, C., Geiger, C., Hagen, S., Parkinson, C., Morrison-Shetlar, A
Engineering Education, vol. 104, no. 1, pp. 74–100, 2015.[6] J. C. Hilpert, J. Husman, G. S. Stump, W. Kim, W. T. Chung, and M. A. Duggan, “Examining students’ future time perspective: Pathways to knowledge building,” Jpn. Psychol. Res., vol. 54, no. 3, pp. 229–240, 2012.[7] E. Godfrey and L. Parker, “Mapping the Cultural Landscape in Engineering Education,” Journal of Engineering Education, vol. 99, pp. 5–22, 2010.[8] E. Crede and M. Borrego, “From Ethnography to Items: A Mixed Methods Approach to Developing a Survey to Examine Graduate Engineering Student Retention,” J. Mix. Methods Res., vol. 7, no. 1, pp. 62–80, Aug. 2012.[9] B. E. Lovitts and C. Nelson, “The Hidden Crisis in Graduate Education: Attrition From Ph.D
research is needed.AcknowledgementsThe authors thank the reviewers for their helpful comments and suggestions. We would also liketo gratefully acknowledge the NSF for their financial support (through the DUE-1744407 grant).Any opinions, findings, and conclusions or recommendations expressed in this Report are thoseof the authors and do not necessarily reflect the views of the National Science Foundation; NSFhas not approved or endorsed its content.References[1] S. Freeman et al., “Active learning increases student performance in science, engineering, and mathematics,” PNAS, vol. 111, no. 23, pp. 8410-8415, June 10, 2014.[2] M. H. Dancy and C. Henderson, “Experiences of new faculty implementing research-based instructional strategies,” AIP
are typically notassociated with engineering by middle schoolers, a reality that this game confronts. This allowsAlgae City to have a greater audience and get a wider variety of people interested in algae andengineering. Future work involves testing this game with subject groups of various ages rangingfrom 5th to 8th grade, gathering feedback, and then making any necessary changes to the gamebased off that feedback. In the end, Algae City aims to challenge, excite, and educate the playerwith the overarching goal of promoting STEM education.References[1]. T. S. Online, “Students taking up STEM subjects on decline last 10 years,” Nation | The StarOnline, 15-Jul-2017. [Online]. Available:https://www.thestar.com.my/news/nation/2017/07/16/students
A. Bergman, T. Kf Caughey, Anastassios G. Chassiakos, Richard O. Claus, Sami F. Masri, Robert E. Skelton, T. T. Soong, B. F. Spencer, and James TP Yao. (1997). "Structural control: past, present, and future." Journal of engineering mechanics 123, no. 9: 897-971.[6] Spencer Jr, B. F., and S. Nagarajaiah. (2003). "State of the art of structural control." Journal of structural engineering 129, no. 7: 845-856.[7] Mahin, S. A., P. B. Shing, C. R. Thewalt and R. D. Hanson. (1989). "Pseudodynamic test method-current status and future directions." J. Struct. Eng. 115 2113–28.[8] Shing, P. B., M. Nakashima and O. S. Bursi. (1996). "Application of pseudodynamic test method to structural research." Earthq. Spectra 12 29–56.[9
Paper ID #22817Evaluating Learning Engagement Strategies in a Cyber Learning Environ-ment during Introductory Computer Programming Courses – an EmpiricalInvestigationMrs. Mourya Reddy Narasareddygari I am Ph.D student at North Dakota State University. My research work is to see how different Learning strategies affect the student learning.Dr. Gursimran Singh Walia Gursimran S. Walia is an associate professor of Computer Science at North Dakota State University. His main research interests include empirical software engineering, software engineering education, human factors in software engineering, and software quality. He is a
a 5-point rubric yielding total scores between 0 and 16for each. Cohen’s d (effect size) was calculated ([3]: (µ1-µ2)/s), and average post-quiz scoreswere compared by paired t-test or repeated-measures ANOVA. Students’ self-recorded videoswere coded for the quality of their interactions as described by [1]. Two factors were varied: (1) the scaffolding (instructions) given to the students and (2)whether students watched a dialogue video or monologue video. Statistical analyses of thenumber of interactive episodes for each group are performed (by coding interactions observed inthe students’ self-recorded videos) to test the hypothesis that students watching dialogue videoshave more interactive episodes and higher learning gains than
/xcell/Xcell32.pdf ,text [22], which was an outgrowth of the research presented https://en.wikipedia.org/wiki/Xilinxhere [1]. [10] https://www.xilinx.com/products/silicon-devices/fpga/artix-7.html Thus based on written student surveys and observing the [11] https://www.xilinx.com/products/design-tools/vivado/vivado-general delight of students when building the projects, we webpack.htmlbelieve this approach (spiral model plus themed labs across [12] Brown, S. and Vranesic, Z., Fundamentals of Digital Logic withthe four years), and
2017 and 2018. In addition, two student teams presented their work at the 2017ASEE Zone II Conference and one team, composed of engineering students and an art student,presented a design solution at the spring 2018 ASEE SE Conference.Project Substantiation and ImportanceIn the 1980’s, research introduced that disability is socially created rather than rooted in theindividual [1]. More recent studies indicate that persons with disabilities may move through aprocess of seven types of identities: isolated affirmation, apathy, resignation, situationalidentification, affirmation, crusadership, and normalization [2]. Studies also indicate that the arts,including the visual arts, can be a tool to aid transition through these identities to enhance
science and technology as well as students approach to the technological designprocess. These areas will be explored more fully in future papers. References[1] X. Chen, “STEM Attrition: College Students’ Paths into and out of STEM Fields. Statistical Analysis Report. NCES 2014-001.,” Natl. Cent. Educ. Stat., 2013.[2] C. Dweck, Mindset: The new psychology of success. Random House, 2006.[3] D. S. Yeager and C. S. Dweck, “Mindsets That Promote Resilience: When Students Believe That Personal Characteristics Can Be Developed,” Educ. Psychol., vol. 47, no. 4, pp. 302– 314, Oct. 2012.[4] A. Rattan, K. Savani, D. Chugh, and C. S. Dweck, “Leveraging Mindsets to Promote Academic Achievement Policy
retention in our engineering program over time. 2018 ASEE Mid-Atlantic Fall Conference, October 26-27, 2018 – Brooklyn Technical High SchoolReferences1. S. Sorby, “Educational Research in Developing 3-D Spatial Skills for Engineering Students,” International Journal of Science Education, vol. 31, no. 3, 2009, pp. 459-480.2. Norman, K.L., Spatial visualization – A gateway to computer-based technology. Journal of Special Educational Technology, XII(3), 1994, pp. 195–206.3. Smith, I.M., Spatial ability - Its educational and social significance. London: University of London, 1964.4. J. Wai, D. Lubinski, and C. P. Benbow, “Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its
creativity of student project proposals. Because of this addition, andthe added stipulation that pantries identify a problem for students to work on ahead of time, weare expecting to see higher levels of student and agency motivation and engagement. With thismonetary award at stake, we also anticipate an improvement in the quality of this year’s projectproposals.References[1] R. G. Bringle and J. A. Hatcher, “A service-learning curriculum for faculty,” Michigan Journal of Community Service Learning, pp. 112-122, 1995.[2] S. J. Peterson and M. J. Schaffer, “Service learning: A strategy to develop group collaboration and research skills,” Journal of Nursing Education, vol. 38, no. 5, pp. 208-214, 1999.[3] C. I. Celio, J. Durlak, and A
knowledge. References[1] S. Sheppard, A. Colby, K. Macatangay, and W. Sullivan, “What is engineering practice?,” Int. J. Eng. Educ., vol. 22, no. 3, pp. 429–438, 2006.[2] National Academy of Engineering, The Engineer of 2020: Visions of Engineering in the New Century. Washington D.C.: The National Academies Press, 2004.[3] American Association for the Advancement of Science, “Project 2061: Science For All Americans,” Washington D.C., 1989.[4] A. L. Costa and B. Kallick, Learning and Leading with Habits of Mind: 16 Essential Characteristics for Success. Alexandria, VA: Association for Supervision and Curriculum Development, 2008.[5] M. R. Louis, “Switching
Future Prospects,” Encycl. Human-Computer Interact., pp. 211–219, 2005.[10] Y. P. Xin, S., Kastberg, and Y. J., Chen, Conceptual Model-based Problem Solving (COMPS): A Response-to-Intervention Program for Students with Learning Difficulties in Mathematics.National Science Foundation funded project.2015.[11] S. S. Zentall, Students with Mild Exceptionalities (Characteristtics and Applications). 2014.
strategies to be better communicated Encourages innovative teaching Threatening to participants-- Encourages discussion what happens with the data? Encourages critical questions Feedback after observations? Creates awareness of teaching goals References CitedAmrein-Beardsley, A., & Popp, S. E. O. (2012). Peer observations among faculty in a college ofeducation: investigating the summative and formative uses of the Reformed TeachingObservation Protocol (RTOP). Educational Assessment, Evaluation and