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Growing use of network technology in Higher Education means that there hasbeen increasing demand to adapt technology platforms and tools that transformstudent learning strategies, faculty teaching, research modalities, as well asgeneral operations. Many of the new modalities are necessary for IHE business.In August 2019, we began collecting and analyzing data from the campus WIFInetwork. A goal of the research was to answer question like what passivesensing of the IHE WIFI might tell us about the dynamics of the WIFI weather inthe IHE ecosystem and what does anonymized data tell us about the IHEecosystem. The analogy with weather prediction seemed appropriate and a viableapproach. Starting Fall 2019, data were collected in the observational phase.In the analysis phase, we applied Singular Spectrum Analysis decomposition, todeconstruct WIFI data from dorms, the central campus dining cafeteria, therecreation center, and other buildings on campus. That analysis led to theidentification of clusters of buildings that behaved similarly. Just as in thecase of models of the weather, a final component of this research wasforecasting. We found that weekly forecast of WIFI behavior in the Fall 2019,were straight forward using SSA and seemed to present behavior of a lowdimensional dynamical system. However, in Spring 2020, and the COVIDperturbation, the campus ecosystem received a shock and data show that thecampus changed very quickly. We found that as the campus moved to conductremote learning, teaching, the closure of research labs, and the edict to workremotely, SSA forecasting techniques not trained on the Spring 2020, data afterthe shock, performed poorly. While SSA forecasting trained on a portion of thedata did better.

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