The data herein, our-workshops.csv, lists general (i.e.,
non-course-affiliated) Library workshops, primarily
RDS and DREAM
Lab workshops, dating back to
2019, for which scheduling and instruction modality were decisions on
our part and not dictated by externalities. The information for a
workshop includes modality (in-person vs. online), number of
students/attendees, and scheduling characteristics (single session
vs. multiple sessions, week within quarter, day(s) within week, and
time of day). See data-description.txt for a fuller description of
the dataset.
The goal is to experiment with models against this data, to determine which factors most significantly affect (or at least, are correlated with) higher attendance.
The primary source for the data was the instruction stats
page
maintained by Teaching & Learning. This was processed by prep.R and
then hand-edited (and hand-corrected in a few places) to add
additional column values by referring back to old Google Calendar
events and the DREAM Lab's log of historical
workshops.
decision-tree.qmd presents a decision tree analysis of the data.