Institutional Culture Change. Journal of Learning Analytics, 6(2), 86-94. Retrieved from https://eric.ed.gov/?id=EJ1224131[2] Chan Hilton, A., Blunt, S., and Mitchell, Z. (2022). Capacity-Building to Transform STEM Education Through Faculty Communities in Learning Analytics and Inquiry. ASEE 2022 Annual Conference and Exhibition, June 2022, Minneapolis, MN. Retrieved from https://peer.asee.org/42085[3] Barron, K. & Hulleman, C. (2014). Expectancy-Value-Cost Model of Motivation. In: International Encyclopedia of Social & Behavioral Sciences (Second Edition) (ed. J. D. Wright), 503-509. DOI: 10.1016/B978-0-08-097086-8.26099-6[4] Wigfield, A. & Eccles, J.S. (2000). Expectancy-value theory of achievement motivation
develop aresource efficient prediction model for any quantifiable data set. By continuing this research andtesting a wider variety of data sets, we can get a better understanding to the strengths andweaknesses of this system. While it is a relatively new method within the computationalsciences, LS-SVM is a very promising and exciting avenue for computer scientists interested inregression, function estimation, classification, and prediction.Acknowledgments:The authors would like to express thanks to the University of the District of Columbia STEMCenter (NSF/HBCU-UP/HRD-0928444) grant and DC Water Resources Research Institute(WRRI) Grant.References: 1. Potomac Conservancy, State of the Nation‟s River, Potomac Watershed. 2007. Available:http
thefilter in the discrete domain, is converted to the analog domain, and then the transfer function ofthe filter is calculated in the analog domain. Once, this is achieved the transfer function in theanalog domain is converted to the digital domain by performing a one to one mapping of thefunction in the s-domain to the function in the z-domain. Usually in order to obtain the stabilitycondition, it is necessary to make sure that all of the components in the left half side of the s-plane is mapped to the values inside the unit circle of the complex z-plane. II Design Methods FIR filters can be designed in a number of ways. The most common methods ofdesigning FIR filters are design by windowing, frequency sampling method and the
. Questions for Discussion We hope that sharing our in-progress unit at the ASEE conference will createopportunities for us to share our unit design with others seeking to embed engineering designexperiences into required high school science courses. Acknowledgement This material is based upon work supported by the National Science Foundation underGrant No. 2149782. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. References Banilower, E. R., Smith, P. S., Malzahn, K. A., Plumley, C. L., Gordon, E. M., & Hayes, M
who might not have had other chances to learn aboutengineering. One female counselor noticed that girl campers were less confident speaking if boycampers were present and worked with another female counselor to “all show each other girlscan do it”. Two counselors were interested in applying for the job as a means of challenginginjustice by providing the camp opportunity to “students like them”. Participants spoke about nothaving such camps available when they were in middle and high school, and how they wouldhave benefited from such programs. One shared that she chose to be a counselor to be a “spark ofinspiration” for “underrepresented kids” because she “really like[s] the message”. Anothershared what it meant to him to be able to be a
ASEE Annual Conference & Exposition Proceedings, Atlanta, Georgia: ASEE Conferences, Jun. 2013, p. 23.120.1-23.120.13. doi: 10.18260/1-2--19134.[2] K. J. Reid, D. Reeping, T. Hertenstein, G. Fennel, and E. Spingola, “Development of a Classification Scheme for ‘Introduction to Engineering’Courses,” in 2013 IEEE Frontiers in Education Conference (FIE), Oklahoma City, OK, USA: IEEE, Oct. 2013, pp. 1564– 1570. doi: 10.1109/FIE.2013.6685101.[3] B. D. Jones, M. C. Paretti, S. F. Hein, and T. W. Knott, “An Analysis of Motivation Constructs with First-Year Engineering Students: Relationships Among Expectancies, Values, Achievement, and Career Plans,” J. Eng. Educ., vol. 99, no. 4, pp. 319–336, Oct. 2010, doi: 10.1002/j
(Engeström), Daiute [48],[49] recognizes the social, dynamic nature of narratives to inform data collection and analysismethods. According to these theories, it is important to consider the interdependence inherent inthe broader context of experience and narration. This perspective aligns with this research as oursurvey reinforced the complexity of individual experiences of lifelong learning.In narrative research, the researcher needs to make plausible interpretations within the bounds ofthe narrative(s) because they capture complex experiences that are not aligned with hypothesistesting paradigms [50]. To bring forward meaningful evidence in interview approaches involvinghomogenous groups, 12 participants are typically sufficient for thematic
individual(s) involved in thematic analysis toengage in a phase of reflexivity [26] after the data have been classified. Reflexivity involvesquestioning the assumptions made during coding of the data to identify potential biases in thecoding and ultimately in the conclusions drawn from the data. Potential reflexivity bias isexplored in the Limitations section of this manuscript.In this study, thematic analysis was initially applied to identify broad themes in the data. Afterinitial patterns in the data were identified, one or more of these broad (primary) themes wereassigned to each student response. Once the dataset was broken down into this primary set ofthemes, responses within each theme were re-examined to determine whether secondary themeswere
new, marketable job skills,including IoT hardware, cloud technologies, cryptography, planning, budgeting, intellectualproperty rights, and networking. However, more importantly, the students delivered a productwith their newfound skills to help protect people's privacy. Team SIHDD (from left to right): Garrett Orwig, Nadaa Elbarbary, Krizia Ragotero, Hayden JonesReferences[1] S. Sami, B. Sun, S. Tan, and J. Han, "LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors," in SenSys '21, Coimbra, Portugal. November 15- 17, 2021. Available: https://dl.acm.org/doi/pdf/10.1145/3485730.3485941[2] Z. Yu, Z. Li, Y. Chang, S. Fong, J. Liu, and N. Zhang, "HeatDeCam: Detecting
autonomous agent that provides automated feedback on students' negotiation skills,” in Proc. of the 16th Conf. on Autonomous Agents and Multiagent Syst., pp. 410–418, May 2017.[4] M. Wheeler, “Introduction to special issue: Artificial Intelligence, technology, and negotiation,” Negotiation J., vol. 37, no. 1, pp. 5–12, 2021.[5] Reuters, “ChatGPT sets record for fastest-growing user base - analyst note,” 2023. [Online]. Available: www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base- analyst-note-2023-02-01/[6] D. Kolb, Experiential Learning: Experience as the Source of Learning and Development, Englewood Cliffs, NJ: Prentice Hall, 1984.[7] S. Brookfield, Understanding and Facilitating
Paper ID #44407Lighting a Pathway to Energy Transitions: Collecting, Interpreting and SharingEngineering Designs and Research Data Across a School-based AgrivoltaicsCitizen Science Network (Pre-College Resource/Curriculum Exchange)Dr. Michelle Jordan, Arizona State University Michelle Jordan is as associate professor in the Mary Lou Fulton Teachers College at Arizona State University. She also serves as the Education Director for the QESST Engineering Research Center. Michelleˆa C™s program of research focuses on social interactMs. Katie Spreitzer, Arizona State UniversitySarah Bendok ©American Society for
in Spring2024 and informal in-class feedback from students indicated that the activity improved theirtheoretical knowledge and problem-solving skills. Mechanisms to assess the effectiveness of thishands-on activity in improving student outcomes will be implemented in future semesters.References[1] J. R. Grohs, T. Kinoshita, B. J. Novoselich, and D. B. Knight, "Exploring learner engagementand achievement in large undergraduate engineering mechanics courses," in 2015 ASEE AnnualConference & Exposition, 2015.[2] J. W. Giancaspro, D. Arboleda, N. J. Kim, S. J. Chin, J. C. Britton, and W. G. Secada, "Anactive learning approach to teach distributed forces using augmented reality with guidedinquiry," Comput. Appl. Eng. Educ., vol. 32, no. 2
classroom problem based learning and design thinking, he also co-founded the STEPS program (funded through NSF S-STEM) to support low-income, high-achieving engineering students. Budischak holds a Doctorate in Electrical Engineering and enjoys outdoor activities with his family.Dr. Haritha Malladi, University of Delaware Haritha Malladi is an Assistant Professor of Civil and Environmental Engineering and the Director of First-Year Engineering at the University of Delaware. She received her Bachelor of Technology degree in Civil Engineering from National Institute of Technology, Warangal, India, and her MS and PhD in Civil Engineering from North Carolina State University. She is a teacher-scholar working in the
] Case study: Ayesha and the Trade Show [14] – addressing invisibility and “old-boy 6 network” in workplace 7 Continue the case study from the previous week 8 Panel: Women in Engineering 9 No lecture. Students attend Women+ in Biomedical Engineering Lunches 10 Wrap-upReferences[1] M. J. Johnson and S. D. Sheppard, "Relationships between engineering student and faculty demographics and stakeholders working to affect change," Journal of Engineering Education, vol. 93, no. 2, pp. 139-151, 2004.[2] G. Lichtenstein, H. L. Chen, K. A. Smith, and T. A. Maldonado, "Retention and persistence of women and minorities along the engineering pathway in the United States
3 higher; EGR 2600. Co-requisite(s): EGR 2710 EM 2900 - Advanced Machining (*) 3 MET 2800 EM 3100 - Additive Manufacturing Processes (*) 3 EM 2900 EM 3200 - Advanced Additive Manufacturing (*) 3 EM 3100 Total Credits Required 18 (*) New coursesThe courses EGR 1710, EGR 2710 and MET 2800 are currently part of the MechanicalEngineering Technology program and they will lay the foundation needed for students toprogress towards more complex subjects. The courses EM 2900, EM 3100 and EM 3200 are newand will be focused on advanced manufacturing
for the Course Design Institute and faculty development workshops on Equity in Collaborative Learning, Universal Design for Learning (UDL), and Specifications Grading. As former Director of Undergraduate Research for the UVA School of Engineering and Applied Science, Brian created Starting an Undergraduate Research Experience (SURE), a student-led program to lower barriers to entry in research experiences for 1st-year engineering students. Brian has received the Harold S. Morton Teaching Prize for excellence in 1st- and 2nd-year teaching in engineering, and he is a BMES Fellow. Brian is co-founder and Deputy Editor in Chief of the journal Biomedical Engineering Education. Brian’s science and engineering research
preliminary findings point towards a positive experience. Future researchwill include interview responses and response analysis, which will draw the study conclusionsand recommendations for enhancing practical, authentic learning experiences within engineeringcurricula.VII. References[1] AE Brooks, DL Ewert, "Discovery-Based Learning: A Bridge Between Research andTeaching." ISA Biomed. Sci. Instrum. Symp.. Vol. 53. 2017.[2] D. Gürdür Broo, O. Kaynak, and S. M. Sait, “Rethinking engineering education at the age ofindustry 5.0,” Journal of Industrial Information Integration, vol. 25, p. 100311, Jan. 2022, doi:10.1016/j.jii.2021.100311.[3] [S. R. Brunhaver, R. F. Korte, S. R. Barley, and S. D. Sheppard, “Bridging the Gaps betweenEngineering Education
for a number of years. In these classes, students were “learning bydoing” in a semi-professional environment.Software engineering is concerned with creating and maintaining software applications byapplying technologies and practices from computer science, project management, engineering,application domains, and other fields. In other words, Software Engineering encompasses “hardskills” that pertain to Computer Science, application domain(s) and process knowledge as well as“soft skills”, like thinking conceptually, attending to detail, working in a team, leading a team,etc. Unlike hard skills, soft skills are discipline-neutral.This paper reflects on experiences the author made with optimizing the composition of projectteams. Optimization of
of the differential equation in (??) (and disregarding any terms associatedwith initial conditions at time zero, i.e., t 0 ), we obtainG(s) X (s) L( 2wn x wn x) L(wn sin(wt )) U (s) and appropriate substitution (s=wj) we have the gain 2 2 x and phase functions | G ( jw) | wn2 (7) ( wn w2 ) 2 (2wwn ) 2 2
Comparison of Approaches VM AccomplishmentsJ. Rainwater - CpSc Department Object-Oriented Approach Traditional Procedural Approach ● Object Memory: Fully functionalS. E. Watkins - ECE Department ● Able to split code up into ● Code is monolithic with one ● Interpreter: Executes arithmetic Missouri S&T, Rolla, MO segments with goals to improve executable or several large and control flow bytecodesB. Cooper - Simplex Technologies efficiency, flexibility, and executables Rolla, MO ● Controller
improvements are needed.18/09/2018 Marszalek-2018 ASEE Midwest Section Conference 210 Helpful Hints (cont., Teles, 2011)4. While accountability is important, evaluation of impact and effectiveness is vital5. Evaluate both short‐ and long‐term goals, develop indicators to use to measure progress, and create timelines6. Develop the evaluation plan jointly with the evaluator(s)18/09/2018 Marszalek-2018 ASEE Midwest Section Conference 310 Helpful Hints (cont., Teles, 2011)7. Assign responsibilities for various components of the evaluation.8. Use the evaluation literature: • NSF’s web site • Online Evaluation Resource Library (OERl) • http://oerl.sri.com/ • Field‐Tested Learning
that has flight path correction.Subhasish Mitra, Philip H. S. Wong, “Nanotechnology-Carbon Nanotube (CNT)Electronics,” Stanford Nanofabrication Lab25-26This research effort epitomizes some of the best practices in nanoelectronics as it leveragesfundamental research in CNT science into useful nano-chip technology for high speedcomputing based on quarter-size CNT chips. CNTs are highly electrically conductive, andtheir small, nanometer size allows for wafer scale, smaller circuits than the conventionalsilicon circuits. In this, CNT instead of silicon is grown on quartz wafer facilitated by catalystnanoparticles at 900 oC for 17 hours. This growth process is carried out at optimal conditionsof density, length and uniformity to marginalize the
, = 0, we obtain a second order nonlinear ordinary dtdifferential equation, d 2θ (3) + ω 02 sin θ = 0. dt 2The units of physical parameters are emphasized that is, angular frequency, frequency and periodas summarized below in table 1. Name of Parameter Symbol Units Angular Frequency g rad ω0 = s l
encryption using a joint transform correlator architecture,” Optical Engineering, vol. 39, no. 8, pp. 2031–2035, 2000.[2] P. Refregier and B. Javidi, “Optical image encryption based on input plane and Fourier plane random encoding,” Optics Letters, vol. 20, no. 7, pp. 767 – 769, 1995.[3] B. Javidi and T. Nomura, “Polarization encoding for optical security systems,” Optical Engineering, vol. 39, no. 9, pp. 2439 – 2443, 2000.[4] Y. H. Doh, J. S. Yoon, K. H. Choi and M. S. Alam, “Optical security system for the protection of personal identification information,” Applied Optics, vol. 44, no. 5, pp. 742 – 750, 2005.[5] B. Javidi, L. Bernard and N. Towghi, “Noise performance of double-phase encryption compared to XOR encryption,” Optical
specification, the formula fororder calculation will be different.For a relative specification, the order N of a Butterworth lowpass prototype is given by: ⎡ log10 [(10 R p / 10 − 1) /(10 As / 10 − 1)] ⎤N =⎢ ⎥ , where R p denotes the passband ripple in dB, ⎢ 2 log10 (Ω p / Ω s ) ⎥As denotes the stopband attenuation in dB, Ω p is the passband edge angular frequency inradians/s, and Ω s is the corresponding stopband edge angular frequency in radians/s.In the case of absolute specifications in terms of 3-dB cutoff frequency Ω c , the order N is given ⎡ log10 [(1 / δ 22 ) − 1] ⎤by N = ⎢ ⎥ , where δ 2 is the absolute attenuation factor. ⎢ 2
students will design the experiment with purpose. The students need to take some courses like statistics, thermodynamics, experimental design etc. so that they can analyze the observed data in a more accurate way. Reference:1. National Research Council. “Engineering Undergraduate Education.” Nation Academy Press. Washington, D.C. pp. 8-15. 1986.2. Albrecht, H., and etc. (2002), Laser Doppler and Phase Doppler Measurement Techniques. Springer-Verlag Publishing.3. S. M. Maasutani,(1997). Laboratory Experiments to Simulate CO 2 Ocean Disposal.4. Albrecht, H., and etc., Laser Doppler and Phase Doppler Measurement Techniques, Springer-Verlag Publishing, December, 2002.5. Chukwulebe, B.Q. and S. Lee, “Laser-Based Investigation of
CD ROM drives.• Minimum 32 MB RAM (the program will still run with less then minimum RAM required, but you may not get the desired results in speed or video reproduction quality).• Any Windows Media Player programs, including the player that is always included in all standard MS Windows installations.• Any commercial speakers.Bibliography:Alessi, S. & Trollip, S. (1991) Computer Based Instruction: Methods & Development,2nd Edition. New Jersey: Prentice Hall Publishers.Arnold, S., Barr, N., & Donnelly, P. (1994). Constructing and ImplementingMultimedia Teaching Packages, Glasgow: University of Glasgow (TLTP).Blackmore, J. (1996) Pedagogy: Learning Styles [Online]. Available:http://granite.cyg.net/~jblackmo/diglib
solving sessions to engage students in a highly theoretical Random SignalAnalysis course.Research has shown that when students are in constructive and interactive modes of engagement,they gain deeper understanding of knowledge. To help students learn better, six interactive andactive problem solving sessions are incorporated in the Random Signal Analysis course. In eachproblem solving session, students are presented with one or multiple non-trivial problem(s).They work in teams of two while interacting with a table of eight students. While students areengaging with problem solving, the instructor and teaching assistants walk around the classroomanswering questions and giving feedback.At the end of each problem solving session, students complete a
curve, experimental – purple triangles).References[1] University of St. Thomas, 2002-2004 Undergraduate Catalog, St. Paul, MN, 2002.[2] Scofield, C., O’Brien, M., and Nelson, R., “Modeling the Kinematics of a GCA Par Systems DKP300V Robot Manipulator,” 15th Annual Winchell Undergraduate Research Competition, sponsored by the Minnesota Academy of Science and hosted by the University of St. Thomas, April 27-28, 2001. [3] McClelland, S. R., “Characterizing Slop in Mechanical Assemblies Using SolidWorks ,” ASME RSC Region VII Old Guard Competition, Wichita, KS, April 5, 2002.[4] Hennessey, M. P., Shakiban, C., and Shvartsman, M
emory and the distributed me emory models of parallel p programming g. The shareed memory m model is also known as the e symmetric multiprocess sing or SMP wwhere cost off data access by different sequential program insta y s ances of a parallel com mputation are the same. This e . assumptioon is not nece essarily alway