Collection

2018 ASEE Zone IV Conference

Authors

Tyler Jay Ashby, Utah State University; Wade H Goodridge, Utah State University; Sarah E Lopez, Utah State University; Natalie L Shaheen, National Federation of the Blind; Benjamin James Call, Utah State University - Engineering Education

Tagged Topics

Diversity

ability with Dr. Goodridge at Utah State University. He focuses work in this area towards the adaptation of spatial ability assessment instruments for accessible use with blind and visually impaired populations. Additionally, he is focused on developing engineering educational teaching experiences that aim to deliver engineering content to this population as well as refining existing engineering curriculum to reflect a focus on spatial connections.Dr. Wade H Goodridge, Utah State University Wade Goodridge is an Assistant Professor in the Department of Engineering Education at Utah State University. He holds dual B.S. degrees in Industrial Technology Education and Civil and Environmental Engineering. His M.S. and Ph.D

Collection

2018 ASEE Zone IV Conference

Authors

Zsuzsa Balogh, Metropolitan State University of Denver; Akbarali Thobhani, Metropolitan State University of Denver

Tagged Topics

Diversity

courses formore than one semester term is recommended, as it appears to be beneficial to the program.Expanding the cooperation with possible new academic program developments such as a dualdegree [8] between the Environmental Engineering and Architecture Engineering is planned andrecommended. In order to seek student feedback, the design of a survey tool to be distributed to all students,declared or non-declared architecture minors, has been initiated. The short survey will reflect onthe six ARCH courses offered and will provide the basis for recommendations for futurerefinements of the minor program.References[1] Balogh, Z. E. (2012). Structural Engineering Masters Level Education Framework ofKnowledge for the Needs of Initial Professional

Collection

2018 ASEE Zone IV Conference

Authors

Bridget Benson, California Polytechnic State University, San Luis Obispo; Matt Jamison Burnett, State University of New York at Canton

Tagged Topics

Diversity

ofConvergence in Vermont, New York, and Berlin in 2018.We conclude with four quotes from faculty and students involved in the making of Convergencethat illustrate the project’s impact.Matt Burnett, the faculty project lead and artistic author, remarked that by combining the skillsets, working methods and perspectives of several people, the resulting production vastlyexceeded the capabilities of the involved individuals. “It can be difficult for an artist to give up creative control; but this is perhaps more reflective of arts in the 21st century; a switch from the model of artist as “isolated genius” to “project manager.” The whole is greater than the sum of its parts - which is the thematic spirit of Convergence in the first place

Collection

2018 ASEE Zone IV Conference

Authors

Cheng Chen, San Francisco State University; Amelito G Enriquez, Canada College; Wenshen Pong P.E., San Francisco State University; Zhaoshuo Jiang P.E., San Francisco State University; Hamid Mahmoodi, San Francisco State University; Hao Jiang, San Francisco State University; Kwok Siong Teh, San Francisco State University; Hamid Shahnasser, San Francisco State University; Jun Jian Liang, San Francisco State University; Christopher Alexander Amaro, Cañada College; Adam Albert Davies, ASPIRES ; Priscila Joy Silva Chaix, Cañada College; Jesus Caballero, Canada College; Juvenal Marin Sanchez, San Jose State University; Xiaorong Zhang, San Francisco State University

Tagged Topics

Diversity

set D. The Posterior probability could be calculated as 𝑃𝑃(𝜃𝜃)𝑃𝑃�𝐷𝐷 �𝜃𝜃 �𝑃𝑃(𝜃𝜃|𝐷𝐷) = 𝑃𝑃(𝐷𝐷) , where the Likelihood 𝑃𝑃 (𝐷𝐷|𝜃𝜃) is the probability of realizing anexperimental data D given a set of parameters θ ; the denominator 𝑃𝑃(𝐷𝐷) is the probability of theevidence and could be considered as a normalizing factor; 𝑃𝑃(𝜃𝜃) is the reflected known value ofthe considered parameters, also called as Prior. The MH algorithm is an improved algorithmbased on Markov chain Monte Carlo (MCMC) simulation. The MH algorithm externallimitations and targets are set or approximated before the simulation for better efficiency. ThePosterior probability is then generated using the MH