# Weight factors in exam statistics

I recently obtained my exam results at university and while looking at the score distribution I noticed a peculiar remark.

They state that the distribution is obtained using data from the last 5 years, but where the past years are weighted degressively by a factor 0,8.

After looking on the net (and finding for example What is the most scientific way to assign weights to historical data?) I don't really understand why one would apply exponential smoothing in this case. Is there some obvious reason to put more emphasis on the current year that I am missing?

Edit: If the lecturer makes some adjustments to his course/exam every year (for example due to feedback) then it makes sense to suspect that the exam from last year is more relevant than the exam from $$5$$ years ago.
• Something like that would be my interpretation: Since it might not be an easy task to fix a score distribution it makes sense to use data from past years. You want to use data from more than one year to smooth out the differences in difficulty over the years (which might be small but you still want to kind of hit some average). I think it is reasonable to suspect that the difference between this years and last years exam is smaller than the difference between this years exam and the exam from $5$ years ago. Thus exponential smoothing is a viable option here. I added an edit to my post. – araomis Jul 4 at 14:14