Collection

2024 ASEE PSW Conference

Authors

Monika Neda, University of Nevada - Las Vegas; Jorge Fonseca Cacho, University of Nevada - Las Vegas; Vanessa W. Vongkulluksn Ph.D., University of Nevada - Las Vegas; Jacimaria Ramos Batista, University of Nevada - Las Vegas; Mei Yang, University of Nevada - Las Vegas

Paper ID #44645Enhancing Pre-Calculus Math Proficiency Through Place-Based EngineeringCanvas Applications for Fundamental Mathematics SkillsMonika Neda, University of Nevada - Las Vegas Monika Neda is a Professor in Department of Mathematical Sciences at University of Nevada Las Vegas (UNLV) and the Associate Dean for Research in College of Sciences at UNLV. Monika received her Ph.D. in mathematics at University of Pittsburgh and her expertise is in computational fluid dynamics with recent years involvement in STEM education. In addition to research, she is involved in several programs helping women and underrepresented

Collection

2024 ASEE PSW Conference

Authors

Mehran Andalibi, Embry Riddle Aeronautical University; Heather Marriott, Embry-Riddle Aeronautical University - Prescott; Oyku Eren Ozsoy, Embry-Riddle Aeronautical University - Prescott; Luis Felipe Zapata-Rivera; Sameer Abufardeh, Embry-Riddle Aeronautical University - Prescott

118 Fundamentals of Computer Programming, CS 125Computer Science I, or CS 315 Data Structures and Analysis of Algorithms, during the fallsemester of 2023. The minimum sample size for a population of 466 with a confidence level of90% and a margin error of 5% was calculated as follows:First was calculated the sample size S for an infinite population. Given: Z = 1.650, P = 0.5, M =0.05, where Z is the Z-Score given by the confidence, P is the population proportion (0.5 bydefault) and M is the margin of error. (1 − 0.5) 𝑆 = 1.6502 × 0.5 × = 200 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑛𝑡𝑠 𝐸𝑞(1) 0.0025The adjusted sample

Collection

2024 ASEE PSW Conference

Authors

Matthew Levi Giles, University of Southern California; Joy Uehara, University of Southern California; Haylee Mota, University of Southern California; Emma Katharine Singer, USC Viterbi School of Engineering; Matthew R Gilpin, University of Southern California; Akshay Potnuru; Jessica Aftosmis, University of Southern California

deliver classroom-based engineering curriculum during this student’s Freshman and Sophomore years. However,USC’s dedication to a hands-on engineering pedagogy, which accelerates during student’s Junioryear, presents unique challenges for accessibility. The cornerstone of the active learning ME curriculum during Junior year is the two-semesterlaboratory sequence AME 341a: “Measurement and Instrumentation Laboratory” and AME 341b:“Mechoptronics.” These courses teach students essential laboratory skills, critical assessment ofengineering measurements, and the fundamentals of electronics, automation and integratedsystems. These courses are purposely structured to break students from habits of rote learning andthe expectation that all engineering

Collection

2024 ASEE PSW Conference

Authors

Anahid Behrouzi, California Polytechnic State University, San Luis Obispo; Kennedy Angel Gomez, California Polytechnic State University, San Luis Obispo; Aaron Dewey, California Polytechnic State University, San Luis Obispo

Architectural Engineering curriculum at Cal Poly in San Luis Obispo containsthree computing courses ARCE 352-354 taken in sequence at the junior level. These are pairedwith the structural analysis lecture courses, that together, provide fundamental knowledgenecessary to proceed to the senior-level seismic analysis course. Table 1 shows each of thestructural analysis lecture classes with their paired computing class and its catalog description.The lecture class has a 32-person enrollment that is generally three times per week at 50 minutesa session, while the lab has a 16-person enrollment taking place once a week for nearly threehours. The lab typically includes at least 1-1.5 hours of coding demonstration or discussion bythe faculty followed by in

Collection

2024 ASEE PSW Conference

Authors

Zhen Yu, California State Polytechnic University, Pomona; Kai Noah Arellano, California State Polytechnic University, Pomona; Daniel Keenan Paek, California State Polytechnic University, Pomona; Steven Kent Dobbs, California State Polytechnic University, Pomona

risk. Members aretaught proper battery maintenance, storage procedures, and operation. The bulk of the safetyprotocol is associated with flight testing. Rotating propellors and the possibility of spontaneousin-flight failure necessitate safety rules like setting minimum distances once drones are armed.These rules are detailed in safety documents created by students and enforced in the field.Flight automation software is another important topic taught by the team. To operate in a precisemanner, software is used to write flight plans that the UAVs follow without user input. Thisleaves less room for user error. BANSHEE UAV primarily utilizes ArduPilot’s MissionPlannerbut the skills learned are transferable. The software is used during flight

Collection

2024 ASEE PSW Conference

Authors

Sam B Siewert, California State University, Chico

students who are allrequired to take CSCI 551, Numerical and Parallel Programming, the goal of this paper is todevelop the formal assessment survey questions to be asked in the future and to design twoadditional methods of speeding up algorithms using co-processing. Figure 1 shows the abstractedhardware scaling architecture used by CSCI 551 to enable students to learn shared memoryparallel programming (with OpenMP or Pthreads) and to learn distributed memory parallelprogramming with MPI (Message Passing Interface). Given a network cluster of scaled-up(multi-core) compute nodes, a student can not only write a shared memory program or adistributed memory program that scales and speeds up computation, but can also create a hybridscale-out and scale

Collection

2024 ASEE PSW Conference

Authors

Christine E King, University of California, Irvine; Matthew Lo, University of California, Irvine; Milan Das, University of California, Irvine; Dalton Salvo, University of California, Irvine

Writing Studies from San Diego State Univ., and a MA in English literature from UC Irvine. His current research centers on identifying mental and emotional states generated through human interaction with virtual reality and other virtual artifacts by analyzing physiological data and applying that research to create more effective virtual learning environments. Leveraging this work, he is currently creating a per- sistent and interactive virtual environment for hosting remote learning classes in the Dept. of Biomedical Engineering at UC Irvine. ©American Society for Engineering Education, 2024 Assessment of Student Engagement in Virtual Reality Clinical Immersion Environments

Collection

2024 ASEE PSW Conference

Authors

Matthew Levi Giles, University of Southern California; Bo Jin, University of Southern California; Paul Ronney, University of Southern California; Joy Uehara, University of Southern California

writing hand with a finger tofeel the drawn results, since they cannot otherwise perceive their drawing. This is difficult inpractice, especially for small features and for shapes within other shapes. The student verballycommunicated the differences they identified between their intentions and the actualized sketchand was not penalized for any discrepancies.Assignment for GD&TRelative to the other topics in this course, the topic of geometric dimensioning and tolerancinghas the least dependence on visualization of 3D objects. Consequently, most of the principlespresented in these lectures require little to no adaptation for visually impaired students. However,several key symbols describing dimensioning/tolerancing parameters are introduced in