Improvements to finding the best art schools in the world 2019

Improvements to finding the best art schools in the world 2019

We understand the importance of the Rookie Awards and its role in showcasing high quality schools, and it's something we take very seriously. In order to keep improving our system we have some new features coming in 2019 which I know you will appreciate.

Narrowing the Data

We will be narrowing the data set we use for calculating School of the Year. At the moment every student that submits projects from a school is considered. This includes first year students, all the way through to recent graduates (the interesting fact here is that a 1st year student won a Rookie of the Year title recently, so it can have both a positive and negative affect). Either way, we will be narrowing the data set by using a finer date range from the entrants actual graduation date—roughly 6 months either side of submission date.

Increased Minimum Requirement

I feel this one will have the biggest impact. We will be increasing the base amount of required student entries to make sure all schools are required to have a larger dataset to review. We haven't made a final decision, but it will most likely be in the range of 10+ students.

Blind Voting

We are currently developing an improved voting system which will eliminate any potential bias. This means that judges will not be able to see any personal data like school name, gender, age, location, software etc. They will just see the submitted work. It's a small thing, but it's something we feel will really help.

Private Voting

In previous years, when a judge reviews an entry they can see other judges scores and notes. We feel this could possibility influence a judges score positively or negatively. So in 2019, voting will be completely private to remove any external influence.

Individual Judges Weighting

We are also developing a new algorithm to help provide an even more accurate score from each judge. In essence it will weight a judges score across a number of calculations based on their contribution. This means that if Judge A is a consistant high-scorer, their score gets evened out across the full range. Conversely, if Judge A is a low scorer it will be evened out too. The algorithm is pretty involved, but needless to say it’s really going to help standardise our voting system.