Frequently Asked Questions

Q: Who can rate a school or course?

A: Everyone, who has actually attended the school he wants to rate, can write a review and rate. The course end date must be within the last year.

Q: How to avoid fake reviews?

A: Flag and report a review from someone who seems not to be a real student of your school. We will contact the student, asking for a screenshot of the school confirmation or other proof to validate the course or study.

Q: How does EducationRating verify the authenticity of the reviews and ratings?

A: The quality of the ratings is a very important issue for EducationRating. There are two possibilities to verify a rating. Former students of schools, who join our quality programme, get a code, with which they can rate the school. Former students can also verify their rating with an uploaded evidence, which can be a student identity card, a course confirmation, an invoice or an other document.

Q: What can be rated?

A: On you can share your experiences with others. Help future students to find the perfect school and course for them. For example: University, College, Private School, Elementary School, International Language School, National Language School, General Further Education, Driving School, Dance School, Combat Sport School, Diving School and Riding School

Q: What is the bayesion average?

A: A Bayesian average is a method of estimating the mean of a population where instead of estimating the mean strictly from the available data set, other information - especially a pre-existing belief with regard to the population which is a central feature of Bayesian interpretation - is incorporated into the calculation which is especially relevant when the available data set is small.

Bayesian Average is in widespread use ranging from marketing to genetics.

Calculating the Bayesian average uses the prior mean m and a constant C. C is assigned a value that is proportional to the typical data set size. The value is larger when the expected variation between data sets (within the larger population) is small. It is smaller when the data sets are expected to vary substantially from one another.

(Source: Wikipedia)

Q: Why does EducationRating show a lower or higher rating than I expect?

A: EducationRating uses a lot of algorithms.
How recent are the reviews? How many reviews were written in the past? What is the quality of the review. What is the value of the rating (5 Minutes, 10 Minutes or 15 Minutes Review).

EducationRating is also using a Bayesion average. If you have only a few reviews and they are old, EducationRating is using its large dataset of reviews to model and estimate what the real average would be if you had more and more recent reviews.

Q: What is the impact of more reviews?

A: Michael Luca from the Harvard University writes in his working paper that reviews have 50% more impact when the organisation has at least 50 reviews.

Q: Why is my school not ranked?

A: A school needs a minimum of 9 ratings and reviews to be ranked.

Q: What happens, if a school rates itself?

A: Through various algorithms our system can detect "fake" ratings. After three selfmade ratings, we allow us to admonish a school.
Does the school again rate itself, the school entry gets a "yellow card", which will be visible for anyone.
In an extreme case we reserve the right to give the "red card" and block the possibility to rate this school.