curriculum.References[1] Digilent Inc., Analog Discovery 2 https://digilent.com/reference/test-and-measurement/analog-discovery-2/start[2] Donald A. Neamen, “Microelectronics Circuit Analysis and Design,” McGraw-Hill, 4thedition, September 3, 2009, Chapter 9, pp. 621-686.[3] Giorgio Rizzoni, “Principles and Applications of Electrical Engineering,” McGraw-Hill, 3rdedition, September 3, 2001, Chapter 12, pp. 531-580.[4] Ron Mancini, “Op Amps for Everyone – Design Reference,” Texas Instruments, August 2002.[5] B. Verdin and R. V. Borries, P. A. Nava, and A. C. Butler, “An Experiment to Enhance Signalsand Systems Learning by Using Technology Based Teaching Strategies,” 2014 ASEE AnnualConference & Exposition, Indianapolis, Indiana. June 2014.[6] R
than80 percent correctness for post-lab surveys. The results of the self-identified assessment wereverified by analyzing results from multiple-choice questions.The first question was Figure 15 A, which evaluated students' knowledge of identifying drawnlogic gates. Surprisingly, 67 percent (Table 3) of students could identify the correct logic gate thefirst time without using the BEADLE curriculum. This stemmed from the fact that students hadbriefly covered this material in the lecture before completing the assignment. Nearly the entiretest population selected the answer correctly the second time, as 99 percent chose the correctsolution.The question from Figure 15 B tests students' knowledge of Boolean operator symbols, which issimilar to the
students were given kits containing 1 microfarad, a 2.2 kW resistor, and an ADALM1000 device. The connection layout shown in Figures 4a and 4b was used as a setup guide withthe software. The steps followed by the students to set up these circuits includes: Set the minimumand maximum values of channel A of the arbitrary waveform generator (AWG) to 0.5 V and 4.5V, respectively, to apply a peak-peak square waveform centered on 2.5 V as the circuit's inputvoltage. The source voltage measure current (SVMI) mode was chosen from the AWG A drop-down menu as a square waveform menu. Channel A's frequency was set to 15 Hz, and Hi-Z modewas chosen from the AWG B mode drop-down menu. CA-V and CB-V were chosen for displayfrom the ALICE curves drop-down menu
:” Average(a) “Because of the oral assessment, I am more aware of which concepts I am struggling 0.68 with.” (N = 26)(b) “Interaction with a Prof/TA/Reader during the oral assessment has increased my 0.58 motivation to learn.” (N = 26)(c) “I would have participated in the oral assessment, even if it did not involve any extra 0.39 credit.” (N = 26)(d) “I would have liked to be able to do an oral assessment, even if it did not involve any -0.05 extra credit.” [for students who did
contains the code of publisher client. Fig. 2(b) is the screenshot of running sub.js file that contains the code of subscribe client. It shows “HelloHaolin” message received sent from the publisher via the broker. Fig. 1 Diagram of MQTT Fig. 2 (a) Fig. 2 (b) Fig. 2 Student submission of MQTT implementation using Node.jsPractical hands-on experiences in engineering courses support student learning outcomes in boththe affective and cognitive domains (Windsor, 2017). To give students more hands-on experimentexperiences, the project assignment is extended to send and receive MQTT messages usingArduino WiFi 1010 in fall 2022. The following major steps are involved: (1) build a simplecircuit
presentations of their work processes, and overall poor communicationskills for college level learning. This is the new norm for them. B. Knowledge and understanding aspects This is not a new problem. Most educators had encountered students with poor foundationaland fundamental understanding of prior courses. This is the same for both of our classes, but thenumbers have grown since the pandemic. In our freshman class, we see students who cannotcomprehend high school algebra or trigonometry. In our EM class, students do not have muchrecollection of the basic circuits, physics, and calculus classes they took. From our conversation with the students and through their reflections, one common themebrought up was the harsh high school and freshman
creation zone more quickly. References[1] D. Rothstein and L. Santana, Make just One Change: Teach Students to Ask their OwnQuestions. 2011Available: http://eric.ed.gov/ERICWebPortal/detail?accno=ED524346.[2] B. Ferri et al, "Use of a MOOC platform to blend a linear circuits course for non-majors," inJun 15, 2014, pp. 24.1304.1.[3] B. Benson and F. Depiero, "Teaching Introduction to Electronic Circuits in a Studio Format,"2017 ASEE Annual Conference & Exposition Proceedings, . DOI: 10.18260/1-2--28919.[4] S. Northrup and J. Burke, "A hybrid approach to a flipped classroom for an introductorycircuits course for all engineering majors," in Jun 14, 2015, Available:https://search.proquest.com/docview
%, Pell enrollment ~50% of the total enrollment. Based on a total enrollment of about 1500 students per class. * African American average GPA gap is significantly higher than URM, typically 0.3-0.85, and enrollment is about 5% of the total. A 0.4 gap in GPA separates ‘B+’ and ‘A-’ grades, for example. Data provided by the California State University Student Success Dashboard [30]To overcome the GPA gap and the DFW disparities, we plan to redesign six critical-path, largeenrollment courses ENGR1 Introduction to Engineering, ENGR17 Introductory Circuit Analysis,EEE117 Network Analysis, EEE108 Electronics I, EEE161 Applied Electromagnetics, andEEE180 Signals & Systems, based on active
opportunity to explain their solutions (also referred toas video logs or vlogs for students who add visual components to their podcasts). The podcastassignments aim to: (a) motivate students to submit answers in an enjoyable way, (b) increase timeand effort that the students are encouraged to devote to a project, and (c) improve course outcomes.Background and OverviewThe students in the “Signals and Systems” base course (ECE2714) are enrolled in theundergraduate engineering program in the Bradley Department of Electrical and ComputerEngineering at Virginia Tech [1]. Emphasis was placed on analytical solutions to differential anddifference equations as well as facility in solving problems in both the time and frequency domains.Prior knowledge includes
furthercategorized the student experiences into “cognitive, affective, metacognitive, and pedagogicalcategories”, as influenced by Bloom’s taxonomy, Fink’s taxonomy, and the work explained in[53], [60], [61]. The categorization structure for codes representing challenging experiences isshown below in Figure 3.Figure 3. Categorization structure of codes representing challenging experiences into cognitive,metacognitive, and affective experiences, in addition to pedagogical factors. Categories labeledwith (B) are extracted from Bloom’s taxonomy, and categories labeled with (F) are extractedfrom Fink’s significant learning outcomes.With this structure, we were able to assess Kulkarni’s experiences as a BLV student in ENGR40M more comprehensively, and we also
Paper ID #41730Microelectronics Research and Global Competencies: Unpacking ResearchAbroad Experiences of Engineering StudentsChibuzor Joseph Okocha, University of Florida Okocha Chibuzor Joseph is an ambitious Ph.D. student at the University of Florida, specializing in the integration of Artificial Intelligence (AI) in engineering Education and computer science education with a significant focus on global competence. His pioneering research, guided by Professor Gloria Kim, is at the forefront of educational innovation and aims to transform the landscape of learning in these technologically advanced fields. Chibuzor
summary to a hypothetical manager. Thisassignment was not factored into their grade for the course, but it did serve as a baseline for eachstudent to compare their progress over the term. The average grade was 65% (D) using thepredefined rubric (Table 5 in the Appendix B). As shown in Figure 2, by the end of the term,students had mastered each of the elements represented in the rubric demonstrating their progressand retention of the material taught in the class. Graph 2. Pre. vs. Post course Technical Summary Class Avearge Grades 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Pre-course Post-courseFigure 2. Results of evaluation of writing assignment at the start
.[6] E.L. Deci and R.M. Ryan. 2012. Self-determination theory. In Handbook of theories of socialpsychology, P.A.M. van Lange, A.W. Kruglanski, and E.T. Higgins (Eds.). Sage PublicationsLtd., 416–436.[7] C.S. Dweck. 2006. Mindset: The new psychology of success. New York: Random House.[8] Chang, Y., Voils, C. I., Sandelowski, M., Hasselblad, V., & Crandell, J. L. (2009).Transforming verbal counts in reports of qualitative descriptive studies into numbers. Westernjournal of nursing research, 31(7), 837-852.[9] Zimmerman, B. J., & Pons, M. M. (1986). Development of a structured interview forassessing student use of self-regulated learning strategies. American Educational ResearchJournal, 23(4), 614-628.[10] Greene, B. A. (2015). Measuring
Orleans, Louisiana. 10.18260/p.27347[12] Andrawis, M. (2011, June), Using Active Learning in TeachingElectromagnetics Paper presented at 2011 ASEE Annual Conference & Exposition,Vancouver, BC. 10.18260/1-2—18538[13] Cheville, A., & Derr, B. H. (2016, June), Using Videos to Elicit Self-Explanations ofEmergent Electromagnetic Concepts Paper presented at 2016 ASEE Annual Conference &Exposition, New Orleans, Louisiana. 10.18260/p.27172[14] Shao, Y. V., & Cheng, Z. (2021, July), Work in Progress: Synergy of Visualization andExperiment in Undergraduate Engineering Electromagnetics Course Paper presented at2021 ASEE Virtual Annual Conference Content Access, Virtual Conference.https://peer.asee.org/38199[15] González-Carvajal, E. J., &
to build this version of the circuitwere successful.Figure 3: LED calculator circuit using (a) discrete components and (b) an input/output PCB [31]Intellectually challenging PBL projects that maintain a high success rate are vital for building self-efficacy among students. In the summer 2019, a PCB version of the LED calculator activity wasdeveloped that uses surface-mounted components for the 5V regulator, switches, LEDs, andresistors. See Figure 3b. By abstracting away the complex input and output circuitry, campers wereable to focus on the wiring connections between the switches, logic gates, and LED outputs,thereby increasing the success rate of building the LED calculator to 100% for the 36 students whoparticipated when the camp was
; Education, Feb. 2020[6] P. Abichandani, V. Sivakumar, D. Lobo, C. Iaboni, and P. Shekhar, “Internet-of-thingscurriculum, pedagogy, and assessment for stem education: A review of literature,” IEEE Access,vol. 10, pp. 351–369, 2022[7] L. Hong, L. Keel and C. McCurry, “Work-in-Progress: Enhance Undergraduate ElectricalEngineering Education with CPS/IoT Infusion,” in ASEE Annual Conference, July 2021[8] B. Burd, L. Barker, M. Divitini, J. Guerra, F. Perez, I. Russell, B. Siever, L. Tudor, M.McCarthy and I. Pollock “The Internet of Things in CS Education: Updating Curricula andExploring Pedagogy.” In Proceedings of the 23rd Annual ACM Conference on Innovation andTechnology in Computer Science Education, ITiCSE 2018, Association for ComputingMachinery
. Gent, B. Johnston, and P. Prosser, “Thinking on Your Feet in Undergraduate Computer Science: a constructivist approach to developing and assessing critical thinking,” Teach. High. Educ., vol. 4, no. 4, pp. 511–522, Oct. 1999, doi: 10.1080/1356251990040407.[9] D. Kang et al., “Providing an Oral Examination as an Authentic Assessment in a Large Section, Undergraduate Diversity Class,” Int. J. Scholarsh. Teach. Learn., vol. 13, no. 2, 2019, Accessed: Jan. 05, 2024. [Online]. Available: https://eric.ed.gov/?id=EJ1218283[10] Boedigheimer, M. Ghrist, Peterson, and Kallemyn, “Individual Oral Exams in Mathematics Courses: 10 Years of Experience at the Air Force Academy,” PRIMUS, vol. 25, Feb. 2015, doi: 10.1080
members from the ECE department. This expansion aims to increase the number ofparticipants as well as to understand faculty’s perspectives, ultimately contributing to thedevelopment of comprehensive guidelines for mentoring meetings. These guidelines will beparticularly beneficial for new faculty members who are leading these sessions for the first time,enhancing the overall effectiveness of the mentoring process.References[1] M. S. Jaradat and M. B. Mustafa, “Academic advising and maintaining major: Is there a relation?” Social Sciences, vol. 6, no. 4, p. 151, 2017.[2] A. M. Lucietto, E. Dell, E. M. Cooney, L. A. Russell, and E. Schott, “Engineering technology undergraduate students: A survey of demographics and mentoring,” 2019.[3] J. K
intelligence in healthcare: past, present and future," Stroke and vascular neurology, vol. 2, no. 4, 2017.[21] T. M. Mitchell, Machine learning. McGraw-hill New York, 2007.[22] A. Pathak. "Top 14 In-Demand Skills Required for AI Professionals." Geekflare. https://geekflare.com/skills-required-for-ai-professionals/ (accessed Feb 28, 2023).[23] B. Marr. "9 Soft Skills Every Employee Will Need In The Age Of Artificial Intelligence (AI)." Forbes. https://www.forbes.com/sites/bernardmarr/2020/09/28/9-soft-skills-every- employee-will-need-in-the-age-of-artificial-intelligence-ai/?sh=66e4a4b354b8 (accessed February 28, 2023).[24] F. Ertam and G. Aydın, "Data classification with deep learning using Tensorflow," in
. Lester, an IEEE and SPIE Fellow, received the B.S. in Engineering Physics in 1984 and the Ph.D. in Electrical Engineering in 1992, both from Cornell University. He joined Virginia Tech in 2013 as the Head of the Bradley Department of Electrical a ©American Society for Engineering Education, 2024 Paper ID #41151Dr. Kenneth Reid, University of Indianapolis Kenneth Reid is the Associate Dean and Director of Engineering at the R. B. Annis School of Engineering at the University of Indianapolis. He and his coauthors were awarded the Wickenden award (Journal of Engineering Education, 2014) and Best Paper
), interventionscan be divided into three different groups. The first group is called motivational interventionwith three categories: (a) task value interventions, (b) framing interventions, and (c) personalvalue interventions [4]. According to Pressley et al. (1989) [5], the second group is calledlearning strategies interventions defined as identifying and implementing the right processes tohelp facilitate students’ performance on a given task. This intervention has three categories aswell: (a) cognitive strategies, (b) metacognitive strategies, and (c) management strategies [6].The third group is practice-based and/or research-based instructional strategy (PBRBIS)interventions which include but are not limited to conceptual change strategies, cooperative
Paper ID #37131Board 87: Work in Progress WIP Comparing the most demanded skills forElectrical and Computer Engineers (ECE) Graduates in the United Statesfrom the Perspective of ECE Academic Department Heads and ECEProfessional EngineersDr. Mohammad Al Mestiraihi, University of Texas Rio Grande Valley Mohammad Al Mestiraihi got his Ph.D. degree from the Engineering Education Department at Utah State University (USU) in July 2022 under Professor Kurt Becker’s supervision. Before getting his Ph.D. from USU, Mohammad was a student at Oklahoma State University where he received a Master of Science (M.Sc.) degree from the Electrical
Program”, Proceedings of the2020 American Society for Engineering Education conference and exposition, 2020.[3] Hawkins, N., Lewis, J., Robinson, B., and Foreman, J., “Computational Instruction through PLCs in a Multi-Disciplinary Introduction to Engineering Course”, Proceedings of the 2019 American Society for EngineeringEducation conference and exposition, 2019.[4] Otieno, A., and Mirman, C., “A Laboratory Based Programming Logic Controller (PLC) Course for aManufacturing Curriculum”, Proceedings of the 2003 American Society for Engineering Education conference andexposition, 2003.[5] Jack, H., and Rowe, S., “Teaching Industrial Control with Open-Source Software”, Proceedings of the 2023American Society for Engineering Education conference and
supply for their boards. Figure 4 Analog Discovery 2 100MS/s USB Oscilloscope, Logic Analyzer and Variable Power Supply4. Logic indicator section with 8 LED pairs LD1 to LD8: This section consists of eight pairs of red-blue LEDs. The LEDs are used for reading the logic value of the signal at the tie-point (i.e. we can read the logic at any point in the circuits by connecting them to the input tie-point of one of the LED pairs). a. Parts – For building this partition 8 blue LEDs, 8 red LEDs, 3 × 8-position DIP switches, and 16 resistors were used. b. Function – The Red-Blue LED pairs show the voltage values at the connector. The Blue LED is lit when the input is logic ‘0’ and the Red LED is lit when the input
The simulation and analysis have utilized TMY 2020 data,detailed PV option consists of 7 tabs that are used to define the and the summary of the results is provided in the table below:system: a) Location and Resource, b) Module, c) Inverter, d)System Design, e) Shading and Layout, f) Losses, and g) Grid TABLE V: Results SummaryLimits. Output Summery The typical meteorological year (TMY) data has been Annual AC Energy in Year 1 (MWh) 4,387.93downloaded for the project location coordinate (42.51, -71.20) DC
, especially the project, indicatedthat they had absorbed all the learning objectives. They used knowledge of coding, PCBdesign and MCU hardware to design systems based on the MCU. And used it forapplications like gaming, smart home systems etc. Upon completing the course, they havethe skills to design any other system and use it for engineering or non-engineeringapplications. They can also integrate several VLSI chips into a system using theknowledge gained in this course.All the students in this course performed well - 75% secured A grade and 25% secured B.None secured C or lower. In a comparable digital design course, with similar content andsame instructor, 60% of the students secured A, 30% B and 10% secured C or lowergrades. The difference could
examining differenceswithin each department, to identify differences in course characteristics or topics that haveunbalanced student enrollment.References[1] T. Ross, G. Kena, A. Rathbun, A. KewalRamani, J. Zhang, P. Kristapovich, and E. Manning.“Higher Education: Gaps in Access and Persistence Study (NCES 2012-046)”. U.S. Departmentof Education, National Center for Education Statistics. Washington, DC: Government PrintingOffice, 2012.[2] R. Fry, Kennedy, B. and C. Funk, “STEM jobs see uneven progress in increasing gender,racial and ethnic diversity”. Pew Research Center, 2021, pp.1-28.[3] S. James, S. Singer. “From the NSF: The National Science Foundation's Investments inBroadening Participation in Science, Technology, Engineering, and Mathematics
the overall efficiency of the educational management process. Figure 2 depicts the outline of the interfaces for IntelliGroups. a. Model of Landing Page b. Model of Group Pagec. Model of Group Parameter Page d. M odel for Custom Questions Input Page e. Model for Creating Initial Survey Figure 2. The outline of the interfaces for IntelliGroups he initial version of IntelliGroup incorporated three grouping criteria: gender of Tstudents, students’ grades in the class, and project preference. The application
Towards Seeing Themselves as Scholars," The Review of Higher Education, vol. 42, pp. 1527-1547, 01/01 2019, doi: 10.1353/rhe.2019.0074.[13] J. P. Azevedo, M. Gutierrez, R. de Hoyos, and J. Saavedra, "The unequal impacts of COVID-19 on student learning," Primary and secondary education during Covid-19: Disruptions to educational opportunity during a pandemic, pp. 421-459, 2022.[14] E. CAMP. "Students lost one-third of a school year of learning during the pandemic." https://reason.com/2023/02/03/children-lost-one-third-of-a-year-of-learning-during-the- pandemic-analysis-finds/ (accessed Feb 3, 2023.[15] K. A. Bird, B. L. Castleman, and G. Lohner, "Negative impacts from the shift to online learning
discussions enhance learning outcomes? b. A thorough assessment of these modules is necessary to gauge their effectiveness comprehensively. c. We should consider transitioning our examples to focus exclusively on topics relevant to electrical engineering.These steps will help us refine our approach and better meet the educational needs of ourstudents in the realm of ethics and professional responsibility within electrical engineering.Table 1. Assessment of student outcome 4 Outcome (4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global