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1. EAGER: Student Support in STEM: Developing and Validating a Survey Instrument for Assessing the Magnitude of Institutional Support Provided to Undergraduate Students at a College Level

   Lee, W.C. ; Knight, D.B. ; Godwin, A. ; Moyer, L.A. ; Hasbun, I.M. ( June 2018 , ASEE annual conference & exposition)

   Student-retention theories traditionally focus on institutional retention, even though efforts to support students in science, technology, engineering, and mathematics (STEM) occur at the college level. This study bridges this gap between research and practice by extending and empirically testing the Model of Co-Curricular Support (MCCS), which specifically focuses on supporting and retaining underrepresented groups in STEM. The MCCS is a student-retention model that demonstrates the breadth of assistance currently used to support undergraduate students in STEM, particularly those from underrepresented groups. The aim of this exploratory research is to develop and validate a survey instrument grounded in the MCCS thatmore »can be used by college administrators and student-support practitioners to assess the magnitude of institutional support received by undergraduate students in STEM. To date, such an instrument does not exist. Our poster will present a summary of the instrument development process that has occurred to date. We are developing the survey following best practices outlined in the literature. We are clearly defining the construct of interest and target population; reviewing related tests; developing the prototype of the survey instrument; evaluating the prototype for face and content validity from students and experts; revising and testing based on suggestion; and collecting data to determine test validity and reliability across four institutional contexts. Our institutional sample sites were purposefully selected because of their large size and diversity with respect to undergraduates in STEM. The results from our study will help prioritize the elements of institutional support that should appear somewhere in a college’s suite of support efforts. Our study will provide scientific evidence that STEM researchers, educators, administrators, and policy makers need to make informed decisions to improve STEM learning environments and design effective programs, activities, and services.« less
2. An Integrated Scenario Ensemble-Based Framework for Hurricane Evacuation Modeling: Part 1-Decision Support System: Hurricane Evacuation Decision Support

   https://doi.org/10.1111/risa.12990  

   Davidson, Rachel A. ; Nozick, Linda K. ; Wachtendorf, Tricia ; Blanton, Brian ; Colle, Brian ; Kolar, Randall L. ; DeYoung, Sarah ; Dresback, Kendra M. ; Yi, Wenqi ; Yang, Kun ; et al ( March 2018 , Risk Analysis)
3. Dynamic Inference of Likely Metamorphic Properties to Support Differential Testing

   https://doi.org/10.1109/AST.2015.19  

   Su, Fang-Hsiang ; Bell, Jonathan ; Murphy, Christian ; Kaiser, Gail ( May 2015 , 10th IEEE/ACM International Workshop on Automation of Software Test (AST))

   Metamorphic testing is an advanced technique to test programs without a true test oracle such as machine learning applications. Because these programs have no general oracle to identify their correctness, traditional testing techniques such as unit testing may not be helpful for developers to detect potential bugs. This paper presents a novel system, KABU, which can dynamically infer properties of methods' states in programs that describe the characteristics of a method before and after transforming its input. These Metamorphic Properties (MPs) are pivotal to detecting potential bugs in programs without test oracles, but most previous work relies solely on humanmore »effort to identify them and only considers MPs between input parameters and output result (return value) of a program or method. This paper also proposes a testing concept, Metamorphic Differential Testing (MDT). By detecting different sets of MPs between different versions for the same method, KABU reports potential bugs for human review. We have performed a preliminary evaluation of KABU by comparing the MPs detected by humans with the MPs detected by KABU. Our preliminary results are promising: KABU can find more MPs than human developers, and MDT is effective at detecting function changes in methods.« less
4. A New Approach to Testing Augmented- and Virtual-Reality to Support Tacit Knowledge Generation in Design Assessment

   Hartless, J. ; Borders, J. ; Lam, C. ; Ayer, S.K. ; London, J. ; Wu, W. ( November 2018 , CONVR2018 Evolving Construction: Towards a technological revolution)
5. A new approach to testing augmented- and virtual-reality to support tacit knowledge generation in design assessment.

   Justin Hartless, Jessica Borders ( January 2018 , 18th International Conference on Construction Applications of Virtual Reality)