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by remus 2118 days ago
It seems like a classic political no-win situation: if you go with an algorithm then anyone who gets a lower than predicted grade is going to complain (and of course has every right to, as any algorithm is likely to unfairly disadvantage some section of the population). If you give everyone their predicted grades then you're accepting massive grade inflation which makes the grades far less useful as a predictor of a student's ability which then means lots of problems further down the line.
2 comments

However, the unfairnesses of the two options are very different. On the one hand, you leave it to educators and employers to select candidates based on unreliable grades, versus excluding swathes of the population from opportunity. Leave it to decision-makers that seem to fetishise exam grades and you come to what I'm sure the vast majority of people regard as the wrong conclusion. Yes, there would have been fallout, but so much less toxic had they chosen otherwise.
> On the one hand, you leave it to educators and employers to select candidates based on unreliable grades, versus excluding swathes of the population from opportunity.

That "versus" is unwarranted - unless everyone is given top marks, exclusion happens in either case. In fact it would happen in that case as well, as top colleges (practically by definition of 'top') cannot accept everyone. All you've done is changed who gets excluded, by not adjusting for differing school grading.

> decision-makers that seem to fetishise exam grades

Calling it "fetishizing" is a fine way to suggest there's something wrong with it, without stating what, or how to improve it. Would it be better if, instead of on the basis of grades, students were judged based on who they know, or how much they can donate to the college?

Ofqual Chairman Says It Was A "Fundamental Mistake" To Believe Algorithm Grades "Would Ever Be Acceptable" https://www.politicshome.com/news/article/ofqual-algorithm-m...
"Students will now receive grades based on their teacher’s estimate of what their grade would have been"

But if those estimates are improved using statistics, there's a political fallout. There's some valid criticism of the algorithm used (far less than that BBC article tries to imply), but there's no question the algorithm's estimates were more accurate.

So much ink was spilled calling the algorithm biased for its 4% increase in A-grades for independent schools, yet teacher's 40% increase of grades above the expected average is... what? Unbiased?

On any other topic, such a position would be called "anti-science".

> there's no question the algorithm's estimates were more accurate.

Some people were predicted A's and given U's by the algorithm. It might have been less biased as an average. But it's results were nevertheless completely unacceptable.

There’s a fundamental asymmetry here. Fail a student unfairly, and the harm to them is potentially irreparable. Pass more than usual, and you increase competition for places and while there’s certainly some unfairness there, the system will ultimately compensate through interviewing, delayed starts, etc.
> the system will ultimately compensate through interviewing, delayed starts, etc.

A roundabout way of saying that some students that would have been accepted to their chosen college, won't be, because their grades weren't as inflated as their competitions. Isn't that also potentially irreparable harm, not just "some unfairness"?

Well, yes. By a curious coincidence he is also chair of the Centre for Data Ethics and Innovation which is publishing a paper on bias in algorithmic decision making.
There is another alternative though: do the damn test. Then everyone gets the grade that they earned.
That assumes that all pupils have had an equal opportunity to study through lock down. I'd speculate that pupils from poorer backgrounds will not have had the same opportunities to study as those from better off backgrounds, so pupils from poorer backgrounds would be disproportionately disadvantaged.
Which is probably true normally to a lesser extent. If you don't have a good place at home to do your homework.