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Table of Contents
The technique that I use to compute grades is often unfamiliar to students but is intended to be as impartial and objective as possible, given the fact that the A,B,C,D,F scale is, by definition, a subjective rating by the instructor.
I do not employ the 100 point-anything-less-than-70-is-a-failure scale familiar to most students from their days in grade school. From a testing and grading standpoint, that scale really has nothing to recommend it except familiarity, and most people have different traditions about what grade constitutes an A, a B etc. This grading scale was evolved in an era before calculators had been invented, when all grade calculations had to be done manually and therefore had to be kept simple.
Working to an arbitrary percentage scale requires an instructor to make almost continual subjective judgements - every assignment, quiz, or other graded item must be designed beforehand to try to yield the desired level of numeric performance. That's an extremely difficult task, which is why so many instructors wind up applying arcane and often arbitrary "curves" afterwards.
Instead, I normalize all scores so that, no matter how easy/hard the assignment or how picky/lax the grading, the class's scores get mapped into a compatable range. This is essentially the same technqiue that is used on the SATs, ACTs, GREs and other national standardized tests.
This is not grading "on a curve". A "curve" is an ad hoc, after-the-fact adjustment made so that the numbers look nice on the traditional 0-100 scale. A "curve", by definition, can't be announced ahead of time. A curve is an admission by the instructor that their original grading policy has failed.
There are different techniques for normalizing scores, and the topic of how to do so properly belongs in a class on statistics. The best-known normalization formula, and the one I use for exams and other situations where the number of scores above and below the average are likely to be equal, is the "z-score":
z = (x - avg)/sd
where x is the student's score,
avg the class average, and sd is the class
standard deviation (a measure of how widely spread the class scores have
been). More information on this score and why it is useful can be found
in any statistics book.
For programming assignments, where experience has shown that the scores tend to be skewed, I have found that the formula
z = 1 - (max - x)/sd
where max is the largest score achieved by the class,
provides a more appropriate normalization.
The normalized scores for the various assignments are averaged together to provide a composite number than can be used to rank students in terms of overall performance. Similar rankings are produced from the normalized scores on the midterm and final exams.
It is the ranking that is the point of this whole process. Over the course of a semester, I will become very familiar with the work of some students, both strong and weak, and will have a pretty good idea of what grade-level they are performing at. By computing a reliable and statistially valid ranking, I can then determine where the students whose work I am less certain of should fall in relation to those others.
There are some teachers who employ this sort of normalization and then give "A"s to a certain percentage of students, "B"s to a certain percentage, and so on. I deplore the use of such quotas - the simple fact is that I have seen classes in which over half of the students did outstanding work and deserved "A"s, and I have seen classes in which very few met the expected level of performance (B).
Therefore, I use the normalized scores as described above simply to place students into a statistically valid ranking. I examine the total work turned in by students at various points within the ranking to determine whether that student has performed overall at an level of meeting my expectations for anyone successfully completing the course (B), exceeding those expectations (A), failing to meet those expectations but demonstrating enough competance to move on to subsequent courses (C), etc. This establishes the bounds for each letter grade within the overall class rank, and the remaining students cane be assigned letter grades accordingly.
Is the final process of assigning letter grades a subjective one? Of course it is, but all letter grading is subjective, and to claim otherwise would be intellectually dishonest. Any instructor is expected to have enough professional expertise to judge what constitutes acceptable, poor, and good work. The only real question is when they exercise that judgement.
Instructors who employ the 100/70 rule are being subjective, first in choosing the 70% threshold and then again in designing their assignments and tests to meet that rule. Instructors who employ quota systems are being subjective in establishing those quotas. I prefer to withhold my subjective judgement until the assignment or exam results are in, when I have the most information available on which to base my decision.
How can you interpret how well you are doing?
Look at your normalized score.
Positive normalized scores generally mean that you are doing well - above my minimal expectations.
Negative normalized scores generally mean you are doing below my minimal expections.
How far away you are from the average is measured in standard deviations. Being a full standard deviation away from the average (+1.0 or -1.0) is significant. As a general rule, 70% of the class will fall within the range -1..+1. 95% of the class will fall in the range -2..+2.
On the Grades page, assignment scores carry an estimated grade. This is a rough calculation based upon how I have interpreted the normalized scores in previous semesters and/or in similar courses.
The estimated grade is expressed on ODU's 4.0 point scale, with 0=F, 1=D, 2=C, 3=B, 4=A.
This estimate is only an approximation. If you are one of the first students to submi an assignment, this estimate may change considerably as more students submit, until the class' standard deviation stabilizes. Also, I reserve the right to re-interpret the normalized scores later in the semester if it becomes apparent that the assignmetns for the semester have proven harder or easier than I anticipated.
Another factor is that, in my official calculation at the end of the semester, I often wind up discarding scores from students who dropped the course, who are caught copying assignments from others, or who try to manipulate the course statistics by deliberately submitting worthless assignments.
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