THESIS
2019
xi, 43 pages : color illustrations ; 30 cm
Abstract
Rankings are a natural and ubiquitous way of making decisions. In fact, the very definition
of making a choice requires you to rank all options and choose the best one. However,
as the number of options grow, we are unable to rank all available options individually,
and so outsource this task to ranking systems. One such outsourced task is the ranking
of universities for studying. As it is infeasible for a student to visit all universities and
rank them individually, they often refer to published rankings such as QS instead. A large
body of work exists that shows how the students can be helped to make a better decision,
but there is another set of users who are affected by such systems: the universities being
ranked. In this case, the universities themselves have a vested intere...[
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Rankings are a natural and ubiquitous way of making decisions. In fact, the very definition
of making a choice requires you to rank all options and choose the best one. However,
as the number of options grow, we are unable to rank all available options individually,
and so outsource this task to ranking systems. One such outsourced task is the ranking
of universities for studying. As it is infeasible for a student to visit all universities and
rank them individually, they often refer to published rankings such as QS instead. A large
body of work exists that shows how the students can be helped to make a better decision,
but there is another set of users who are affected by such systems: the universities being
ranked. In this case, the universities themselves have a vested interest in understanding
how the data they submit and the actions they take will affect their ranking position in the
future. This is a critical issue for a university because any change in its ranking position,
no matter how slight, can affect its access to high quality students and external funding.
This is a challenging problem, as the analysis must allow the universities to visualise the
entire space of scenarios for the following year, depending on how the submitted data is
changed this year. It must also allow the universities to compare their predictions to the
expected movement of rival universities, to see if they are outperforming their rivals in key
areas as well as overall. In this thesis we present RankBooster, a visual analytics system
that explores the set of scenarios for the universities future ranking position and allows
the university to compare itself to its rivals. We use multiple case studies conducted with
university ranking experts to evaluate the effectiveness of our system. We also present a
novel abstraction of our given users tasks, as to the best of our knowledge we are the first
researchers to tackle rankings visualisation from this perspective.
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