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...[ Read more ]

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.