Grading on a Curve

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Some students may view ‘Grading on the curve’ as a positive thing; the idea being that if the professor grades on a (normal distribution) curve, the highest grade gets an A even if the highest score is a 60 % and then the rest of the student are graded in comparison to that. This is called Norm-Reference assesment. Students are graded in comparison with each other instead of according to a set of predetermined criteria (aka CRA or criteria-reference assessment). So basically, in a class with a high score of 60% nobody should really be getting an A because the standard of the classroom is below what it should be (or the assessments did not reflect what was being taught in class). And if everybody gets 90 % then all students should be getting an A because they’ve achieved the learning outcomes of the course.

Think about a child who is taught by his parent how to ride a bike. Does his mother measure his ability to ride a bike in comparison with other children or does she just check if he could do it or not? Instead of aiming for grades that fall under a curve, professors should aim for a skewed graph (with more A’s than other grades). However, some professors have a thing against giving everybody in a class an A. Maybe it’s because they’re under pressure from administrators who think that too many A’s means that the standards are kept too low, or maybe they don’t want the reputation of their course being an ‘Easy A’ course. But if you really think about it, the aim of a professor should actually be to maximize the number of A’s in class. Because that would mean that all students learned what they were supposed to learn.

So instead of grading on a curve, what about cancelling the curve altogether?

I know this is as random a post as you could get but I thought it was relevant since Final Exams are coming up soon.

Source of information: Teaching for Quality Learning at University, fourth edition by J. Biggs and C. Tang

English: Normal distribution curve that illust...
Normal distribution curve (Photo credit: Wikipedia)

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