My Bayesian Case Studies students had to hand in their final report last week; as I expected, some handed it in after the deadline.
I know several colleagues whose official position is that if the report is handed in late, the student fails the course. This is fine if the report is very late, but it seems a bit drastic for a small delay (e.g. a few minutes!). The problem of course is to define very and small in the previous sentence. And even if you decide that the course is failed for students who are more than (say) 24 hours late, it seems a bit unfair to have such a big difference between being 23 hours 59 minutes late and 24 hours 1 minute…
This time, I am using a continuous decreasing function as penalty: I told the students that
“Your report should be sent by e-mail by 2 April 2013 at noon. In case of a late submission, your grade will be multiplied by exp(-0.001 ⋅ t³ /86400³ ) where t is the number of seconds between the deadline and your submission.”
This basically means that they can be 2 days late for free. If a student has a legitimate reason for being late, they will usually manage to fit in that window, so they do not need to explain their lateness. Being 4 days late knocks off 6% of the grade; being a week late knocks off 30%. A student more than 8.8 days late loses 50% of their grade, meaning the course is necessarily failed, even if the report is perfect.
In this case, the last student to hand in his work was about 3.5 days late, meaning the mark will be multiplied by 0.953.
I quite like this system, although I think it was a bit too generous with this exact implementation. I cannot claim to have invented it; I read about it somewhere else, with a different penalty function, but cannot find where.
Edit: Thanks to Mahendra for the original post: here, on the Messy Matters blog.