to project manage- ment, such as leadership, entrepreneurship, artificial intelligence, process improvement, and burnout. The purpose of this paper, Artificial Intelligence and Machine Learning Applications in Engineering Project Management: Developing A Course Module, is for students to understand the basics of Artificial Intelli- gence and Machine Learning.Dr. Andrew B. Williams, The Citadel Andrew B. Williams, Ph.D. is the Dean of Engineering and the Louis S. LeTellier Chair at The Citadel School of Engineering. Dr. Williams is an alumni of the National Academy of Engineering Frontiers in ©American Society for Engineering Education, 2024
66.84 D 79.12 C Average 91.41 A 73.33 C 82.37 B STDEV 0.00 13.22 6.61Figure 1 compares the students’ grade distribution based on group and individual assignments.The figure also shows the effect of the combination of both assignments in adjusting and assign-ing the final overall grade for each student on the team based on his/her actual contribution to theproject. The team consisted of four students. When the grade was assigned based on the group 2024 ASEE Southeastern Section Conferenceassignments, all students received an “A” grade. However
, Performing, Controlling, Weeks 6-13 and Closing the Project Module 4: Project Management Principles Weeks 14-15 © American Society for Engineering Education, 2024 2024 ASEE Southeastern Section ConferenceModule LectureTwo of the ten previously established course learning outcomes are addressed in this module.The two course learning outcomes are (a) CLO2: Evaluate the concepts related to setting goalsand self-management and (b) CLO 10: Discuss the importance of ethics and professionalresponsibility in project management. The module lecture is a PowerPoint presentation whichincludes the importance of project ethics, a thought exercise, an overview of each of the 12project management
, vol. 56, no. S1, p. 215–242, 2018.[5] A. C. T. Davis K. A., "Exploring differences in perceived innovative thinking skills between first year and upperclassmen engineers.," in IEEE Frontiers in Education Conference (FIE), Erie, PA, 2016.[6] D. &. J. D. &. K. M. J. &. A. C. &. B. R. &. C. J. &. F.-S. T. &. P. M. &. V. N. Wilson, "The Link between Cocurricular Activities and Academic Engagement in Engineering Education," Journal of Engineering Education, vol. 103, no. DOI: 10.1002/jee.20057, 2014.[7] B. J. a. J. B. Main, "Investigating Factors that Inform Engineering Students' Choice of Extracurricular Activities," in ASEE 2022 Annual Conference, Minneapolis, 2022.[8] K. C. H. P. K. G. S. S. S
Selection: Model Cost (per unit) ηT Turbine A $1,000/kW 0.94 Turbine B $800/kW 0.88 Turbine C $500/kW 0.79CondenserThe cycle should have one condenser. You can assume the fluid exits the condenser as a saturatedliquid.The supply of cooling water will enter the condenser as compressed liquid at 20 °C, 100 barflowing at 50 kg/s. The exit state of the cooling water should not exceed saturated liquid.Condenser Models Available for Selection: Model Cost Pmin,allowed (kPa) Condenser A $150/kW 3 Condenser
next section.Results and DiscussionThis section summarizes the experimental results obtained from this study. A comparison wasalso accomplished to verify the effectiveness of the methodologies using the base line data. © American Society for Engineering Education, 2024 2024 ASEE Southeastern Section ConferenceCS405 – “Linux with Application Programming” is a core course in the computer sciencecurriculum at Alabama A&M University. Table 1 includes the student assessment results inCS405 regarding the learning outcomes and the ABC rates (only grades A,B, and C areconsidered as “Pass” according to the computer science curriculum in the universityundergraduate bulletin). The base line
fill this requirement.Most recently, BIOE 2100 has been modified for specific designation as a “writing-intensive”course at the university level. UGA’s Franklin College Writing Intensive Program (WIP)administers the process by which courses acquire the “W” suffix (i.e., BIOE 2100W) andstipulates what is expected of such courses: The W suffix is used for courses taught as writing intensive, which means that the course includes substantial and ongoing writing assignments that a) facilitate learning; b) teach the communication values of a discipline — for example, its practices of argument, evidence, credibility, and format; c) support writing as a process; and d) prepare students for further writing in their
, Engineering, and Mathematics. Report to the President. Executive Office of the President.[3] Kuenzi, J. J., Matthew, C. M., & Mangan, B. F. (2007). STEM Education Issues and Legislative Options. Progress in Education, 14, 161-189.[4] Higher Education Research Institute at UCLA, (2010). Degrees of Success: Bachelor’s Degree Completion Rates among Initial STEM Majors. Accessed May, 2020 from http://www.heri.ucla.edu/nih/downloads.[5] Committee on Underrepresented Groups and the Expansion of the Science and Engineering Workforce Pipeline, (2011). “Expanding Underrepresented Minority Participation: America's Science and Technology Talent at the crossroad”. National Academy of Sciences.[6] Kim, A. S., Choi, S., & Park, S., (2020
. Individual grades are from engineering notebooks.While the two-semester format provides a better design experience, it does create limitations forthe students and the department. a. The rising seniors who fail pre-requisite courses for senior design must pass these courses no later than the summer preceding the fall semester. If such courses are not offered at the university and their equivalent courses are not offered at other universities in the summer, the students will delay their graduation by one year. b. Cooperative Education Program students with two or more work rotations are restricted to starting the first work rotation no later than the spring semester of their junior year. c. One technical elective
pre-flocculation of the cultures were measured using Secchi diskdepth (SDD) in millimeters (mm).Dewatering algae: Based on literature research, we decided to use ferric chloride, zinc chlorideand ferric sulfate comparing two concentrations [9]. Each team measured 2 x 500 ml of algaeculture into two flasks with stir bars then added the corresponding flocculant and stirred. After 30minutes, students transferred the mixtures to graduated cylinders to settle the flocs for 30minutes. They measured the OD for each reaction using a SDD after flocculation. The followingequation was derived to calculate flocculation efficiency: A is the initial OD and B is the ODafter flocculation: Flocculation efficiency % = (1- (A/B)) x 100) [9]. Flocculated algae
Developing Countries Case Study. International Journal for Service Learning in Engineering, Humanitarian Engineering and Social Entrepreneurship 2016, 11 (2), 55–71. https://doi.org/10.24908/ijsle.v11i2.6395.(3) Gordon, A. S.; Plumblee, J. M.; Dancz, C. L. A. Developing Leadership through an Immersive Service- Oriented International Internship; 2017. https://doi.org/10.18260/1-2--28145.(4) Trogden, B. About Crossings. Crossings. https://www.clemson.edu/provost/strategic- plan/initiatives/crossings/about.html (accessed 2021-05-10).(5) ABET. Criteria for Accrediting Engineering Programs; 2022. https://www.abet.org/accreditation/accreditation- criteria/criteria-for-accrediting-engineering-programs-2022-2023/ (accessed 2023-11-25).(6
this learning approach. The students guided by the Foundry follow anoverall strategy to learn and apply concepts related to the modelling of fluid velocity profile; thedetails on how to apply the principles associated with the different aspects involved in the use ofkinematics of fluid flow principles. 4 The end result is a dual level PIT: a)-a “new” model for thefluid velocity profile (microscopic level) and b)-a higher level of skills acquired by the studentresearcher or learner (overall or macroscopic level). Figure 1: The Dual Learning Process Guided by the Foundry (Overall) couple with the Kinematics of Fluid Flow (Micro/Subject Related)Inspiration based on the Kinematics of Flow ApproachPrior to the use of the
various normal and shear stresses at specific locations on the beam. “Named” cells containing the beam & load parameters created for utilization in the Excel formulas used.Fig. 1 Excel Example Spreadsheet for Analysis of a Beam Analysis of Beam Using MATLABFor the beam shown, the expressions for the deflection, slope, shear force, and bending moment areprovided below. In these expressions: E = 29 x 106 psi., I = 1830 in4, L = 20 ft, a = 6 ft, b = 14 ft,and P = 35000 lb. x P
) (b) Figure 1. “Non-ideal” experiment: (a) equipment and (b) a snippet of the lab procedure.The standard low-frequency model of a resistor is a pure resistance whose impedance isindependent of frequency. In reality, however, the leads on the sides of the resistor form acapacitance which provides a non-negligible path for current to be diverted at frequencies above1 kHz [6, 7]. Students begin to “see” this otherwise “hidden” (parasitic) path when, at highfrequencies, measured current increases while the applied voltage remains constant.A similar effect appears when the students perform the same measurements on a 1-mH inductor.The data resembles Figure 2(b). For this experiment, between 5 kHz and 10 kHz, the reactanceof the lead-capacitance
-head motion is 1.36 mm/min. Span Figure 2: Three-point bending test setupData AnalysisStudents record load and deflection data with a data acquisition system until the sample fails.Afterward, they need to calculate stress and strain based on equations provided in the ASTM D790standard. Stress will be calculated as 𝜎 = 3𝑃𝐿/2𝑏𝑑2Here 𝜎 is stress in the outer fibers at the midpoint, P represents load at a given point on the load-deflection curve, L is the support span, b is the width of the sample, and d is the depth of specimen.Strain will be calculated as 𝜀 = 6𝐷𝑑/𝐿2Here 𝜀 is
all the sensors required to gather data for the camera V2 8 MPmetrics 4. Temperature, Barometric pressure, andlisted above. In addition, your design must adhereto the following requirements: humidity module- BME 280 sensor1. Constraints (must-haves) module a. Overall size is limited to 4in x 6in x 5in 5. Luminosity sensor module- TSL 2561 (not including necessary external units, 6. Water quality sensors: i.e. microphones) a. Dissolved oxygen kit b. The enclosure must hold the given solar b. pH kit
7 the interactions between control loops can be mitigated somewhat sothat each loop is slightly underdamped to overdamped in response to a setpoint change. Giventhat the system is nonlinear there still may be quite a lot of tweaking of control parameters for oneor more of the control loops. There are trade offs in performance versus magnitude of disturbancesthat may be caused in the other control loops. It is important for the students to realize that thereis no single set of ideal tuning parameters. Always there will be trade offs between performanceand robustness and how large a magnitude interactions between loops is acceptable. For someprocesses product may still be of good quality even though a setpoint has not been quite reached A. B
to disclose their self-identified races or ethnicities,and the results are shown in Figure 1(b). Among the respondents, a majority self-identified asCaucasian or White, with the second-largest cohort identifying as Hispanic/Latino. While EastAsians and South Asians were also represented in the responses, their numbers werecomparatively smaller than those of the aforementioned ethnicities. The fraction of respondentsidentifying as Native Hawaiian or Pacific Islander was modest, with an even lesser proportionidentifying as American Indians or Alaska Natives. Additionally, a minor percentage ofrespondents refrained from disclosing their racial or ethnic identity. (a
].Only students who met specific criteria were included in the sample population. All datamanipulation and quantitative analysis was completed using the R programming language [9].The final sample includes 33,896 students who: ● completed a first-year engineering program, ● graduated from a degree-granting program in Mechanical, Electrical, Civil, Chemical, Industrial, Computer, or Aerospace Engineering, ● have six years of data available in MIDFIELD, and ● had no discrepancies between the student, term, and degrees tables in MIDFIELD.The median starting year of students who graduated from Institutions A and B was 1995, and forstudents attending Institution C the median starting year was 2003. Given that this data is
offers chances to improve the model, increasing its precision and usefulness in educational settingsworldwide. R EFERENCES[1] A. Jones and B. Smith, “The evolution of speech recognition technology,” Journal of Computer Science and Technology, vol. 33, no. 2, pp. 234–245, 2018.[2] J. Greenwood and H. Lee, “Speech recognition in education: Applications and challenges,” Educational Technology Research and Development, vol. 69, no. 1, pp. 143–160, 2021.[3] X. Liu, S. Zhang, and Y. Wei, “Tensorflow in speech recognition: A review of recent developments,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 3, pp. 865–875, 2020.[4] B. Gold and N. Morgan, Speech
/inputs/outputs used in their project and their utilization within the code), (4) abreakdown of code components (variables, functions, loops, branches, etc.) with accompanyingflowcharts, (5) an explanation of their testing procedures, detailing how they tested their code,and (6) a conclusion encapsulating lessons learned. Your code should be able to: • Take Morse code messages using the push buttons (push button ‘a’ and push button ‘b’), where one is used to input dashes and the other one is used to input dots. • Provide feedback in the form of sound for each input dash and dot. • Once a message has been completely inputted, display the decoded message using the alphabet and send that message via radio. • Provide
engineering disciplines. This expansion aims to capture a broader range ofexperiences and strategies employed by instructors. Additionally, conducting in-depth follow-upstudies with faculty actively using technology during office hours can provide deeper insightsinto the effectiveness and challenges associated with these interventions. Investigating barriersand facilitators to office hour utilization from both faculty and student perspectives is also crucialfor understanding the dynamics influencing engagement.References[1] B.-L. Andrade, K. Pakala, D. Bairaktarova, D. Hagemeier, and H. Subbaraman, “Faculty Perspectives on the Impact of Virtual Office Hours in Engineering Courses,” in 2020 ASEE Virtual Annual Conference Content Access
the program: a) Structural Engineering b) Geotechnical Engineering c) Environmental Engineering d) Water Resources e) Transportation Engineering f) Construction EngineeringFor each of the subdisciplines, the instructional team assigned a case study as an introductoryhomework assignment – addressing learning objective one. This was followed using live pollingtechniques during class to (a) review the content of the case study and (b) emphasize theprofessional and ethical obligations of engineers involved in the case – addressing learningobjective two. As the students were responsible for finding the most valuable information from aprovided resource and then allowed to assess their own performance in class, this provided
-eyes-minds-and-hearts-visual-thinking- strategies-health-care-professionals. [Accessed 10 January 2024].[7] J. Driskell, E. Salas and S. Hughes, "Collective Orientation and Team Performance: Development of an Individual Difference Measure," Human Factors, vol. 52, no. 2, pp. 316-328, 2010.[8] B. Weidmann and D. Deming, "Team Players: How Social Skills Improve Team Performance," Econometrica, vol. 89, no. 6, pp. 2637-2657, 2021.[9] L. Wilson, S. Ho and R. Brookes, "Student perception of teamwork within assessment tasks in undergraduate science degrees," Assess. & Eval. In Higher Ed., vol. 43, no. 5, pp. 786- 799, 2017.[10] S. Naghshineh, J. Hafler and A. e. a. Miller, "Formal art obervation training improves medical
students were asked to describe in a well written paragraph what they liked anddid not like about this course and to provide two substantive recommendations for improving thecourse. In the future, we plan to take the course assessment data and student comments toimprove the course and to develop additional practical application problems which involve theentire process of collecting, organizing, analyzing, and reporting.References1 Greenburg, D. and Davis, J. (2020), “Developing A Probability and Statistics Course For Civil and Construction Engineering Students,” Proceedings of the American Society of Engineering Education, Southeastern Section Annual Conference.2 Rubin, S. J., & Abrams, B. (2015). Teaching Fundamental Skills in Microsoft
success of undergraduate researchers. In the future, we will seek to understand howstudents process the synthesis of the data they have been collecting. It is essential to understandif the benefits of collaboration and organization will translate to that portion of the research. Wewill also hope to assess how the students handled the process of creating the research paper andthe positives and negatives of that experience.References[1] C. Madan and B. Teitge, “The Benefits of Undergraduate Research: The Student’s Perspective,” 2013, doi: 10.26209/MJ1561274.[2] R. M. Narayanan, “Use of Objective‐Based Undergraduate Research Project Experience as a Graduate Student Recruitment Tool,” J. Eng. Educ., vol. 88, no. 3, pp. 361–365, Jul. 1999
oflearner-centered approaches, hands-on activities, and collaborative education to facilitatemeaningful and authentic learning experiences. One limitation of this study is the small samples.It is unclear whether the redesign of this course will still be effective for larger classrooms. Morestudies are needed to investigate the combination of Robert Gagne's Nine Events of Instructionand constructivist principles in senior-level engineering mixed-modality courses.References [1] A. P. Wemhoff, “Restructuring a Pedagogical Course to Benefit Engineering Ph.D. Students and Faculty,” peer.asee.org, Apr. 09, 2021. https://peer.asee.org/restructuring-a-pedagogical-course-to-benefit- engineering-ph-d-students-and-faculty [2] A. B. Keating
Design,” International Journal of Engineering Education, vol. 24, no. 2, 2008.[5] J. S. Linsey, E. F. Clauss, T. Kurtoglu, J. T. Murphy, K. L. Wood, and A. B. Markman, “An experimental study of group idea generation techniques: Understanding the roles of idea representation and viewing methods,” Journal of Mechanical Design, vol. 133, no. 3, 2011, doi: 10.1115/1.4003498.[6] V. Kumar, “Understanding the Role and Importance of Design Problems in Creativity Research,” Clemson University, 2016. [Online]. Available: https://tigerprints.clemson.edu/all_theses[7] N. V. Hernandez, L. C. Schmidt, and G. E. Okudan, “Systematic Ideation Effectiveness Study of TRIZ,” in Proceedings of the ASME Design Engineering Technical
grades ofnineteen (19) students who submitted the study sheet they used for the examination and three (3) students who didnot submit any study sheet. We plan to analyze the data from the other Spring 2023 semester section along with newdata collected from the Fall 2023 semester for a future full paper. B. Data Analysis The data analysis was based on the study sheets the students prepared for the mid-semester examinations. Theycreated a hand-written exam study sheet with notes from the course textbookr and/or class lectures for use during theexamination. The mid-semester exam covered the course terminologies, concepts and applications and consisted oftwo take-home parts: 16 short answer questions accessed via the university's learning
. JS Length = Overall lengthof job search. JS Time = Average time per week spent during job search.Hypothesis TestingFigure 1 illustrates a mediation model with two sequential mediators. Noteworthy results from theanalysis are mapped from Figure 1 to Table 2. After controlling for college GPA, job search length,and average time per week spent on the job search, CSE was positively related to job search self-efficacy (B = .41, SE = .07, p < .01), supporting Hypothesis 1. Job search self-efficacy was alsopositively related to career exploration (B = .45, SE = .16, p < .05), supporting Hypothesis 2. Careerexploration was positively related to the number of job interviews received (B = 1.15, SE = .31, p< .01), but was not related to the