Weather of the Dorm WIFI Ecosystem at the University of Colorado Boulder for Fall Semester 2019 to Spring Semester 2020 a Case Study of WIFI and a Campus Response to the COVID-19 Perturbation
Growing use of network technology in Higher Education means that there has been increasing demand to adapt technology platforms and tools that transform student learning strategies, faculty teaching, research modalities, as well as general operations. Many of the new modalities are necessary for IHE...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
24.09.2021
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Subjects | |
Online Access | Get full text |
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Summary: | Growing use of network technology in Higher Education means that there has
been increasing demand to adapt technology platforms and tools that transform
student learning strategies, faculty teaching, research modalities, as well as
general operations. Many of the new modalities are necessary for IHE business.
In August 2019, we began collecting and analyzing data from the campus WIFI
network. A goal of the research was to answer question like what passive
sensing of the IHE WIFI might tell us about the dynamics of the WIFI weather in
the IHE ecosystem and what does anonymized data tell us about the IHE
ecosystem. The analogy with weather prediction seemed appropriate and a viable
approach. Starting Fall 2019, data were collected in the observational phase.
In the analysis phase, we applied Singular Spectrum Analysis decomposition, to
deconstruct WIFI data from dorms, the central campus dining cafeteria, the
recreation center, and other buildings on campus. That analysis led to the
identification of clusters of buildings that behaved similarly. Just as in the
case of models of the weather, a final component of this research was
forecasting. We found that weekly forecast of WIFI behavior in the Fall 2019,
were straight forward using SSA and seemed to present behavior of a low
dimensional dynamical system. However, in Spring 2020, and the COVID
perturbation, the campus ecosystem received a shock and data show that the
campus changed very quickly. We found that as the campus moved to conduct
remote learning, teaching, the closure of research labs, and the edict to work
remotely, SSA forecasting techniques not trained on the Spring 2020, data after
the shock, performed poorly. While SSA forecasting trained on a portion of the
data did better. |
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DOI: | 10.48550/arxiv.2109.12143 |