It looks interesting to me to know about the variables related to the students performance, so I started to look into the following dataset: https://archive.ics.uci.edu/ml/datasets/Higher+Education+Students+Performance+Evaluation+Dataset
One of the first things I noticed was the nature of the dependent variable.
OUTPUT Grade (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA)
This is obviously an ordinal variable, as the different levels can be easily ranked.
According with the description in the link before, this is a classficiation problem, but the first approach that came to my mind to deal with the ordinallity of the data was to convert it into a regression problem.
Is that approach correct?
In case it is not: How to make my model learn about that sorting/ranking then? How to deal with it in the evaluation phase? For example I see obvious to penalize more an incorrect precition of DD instead of AA, than BA instead of AA.
Any other ideas to deal with ordinallity in a classification problem?