CS 473: Grading Policies

If you have any questions or concerns, please ask in lecture, during office hours, or on Piazza.


Graded work


Regrade requests


Final course grades under COVID-19

The campus's new Academic Policy Modifications for Spring 2020 allows instructors to petition for their classes to switch from standard letter grades to Pass/No Pass "in cases where the modification in course assessment makes it extremely difficult to fairly follow our standard grading system". I intend to submit such a petition for CS 473.

I do not believe it is possible to fairly assess students under the current circumstances. While many students have made the transition to online instruction smoothly, others have (or will have) extenuating circumstances that make focusing on this class impossible, such as time zone differences, sick family members, loss of child care, loss of family income, an unsafe home environment, unreliable or unavilable broadband, and the mental health effects of social isolation.

That said, I also believe it is important to offer and grade homework assignments normally, to provide useful pracice and feedback, for the students' own benefit. We're still trying to figure out the right format for exams, but at least for the moment, we are still planning to offer them as announced at the start of the semester. Even if the course moves to pass/no-pass, we will continue to grade both homeworks and exams as usual. I assume that you are here primarily to learn, and that it is our job to help you learn to the best of our ability.

Pass/No Pass is distinct from the Credit/No-Credit option, which students request on an individual basis for classes that normally offer letter grades. Offering a class Pass/No Pass means that those are the only two grades available; there is no option for students to individually request letter grades.


Final course grades (usually)

Under normal circumstances, we would determine final course grades as follows. (What do you expect from an algorithms course?) If I am forced to give letter grades, no student will receive a worse letter grade than described by the following algorithm.
  1. Compute raw totals from homework and exam scores, excluding extra credit.

     HwCount  = min(24, max(actual number of homework submissions, 16))
     HwAve    = (sum of HWcount highest homework scores) / (HWcount * 10) 
     ExAve    = (sum of exam scores) / (max possible sum of exam scores)
     HwWeight = HWcount * 0.0125
     ExWeight = 1.0 - HwWeight
     RawTotal = HwAve * HwWeight + ExAve * ExWeight
    

  2. Compute adjusted totals, which include extra credit points. Extra credit points are not necessarily worth the same as regular points.

  3. Remove outliers and exceptional cases.

  4. Determine letter-grade cutoffs from the undergraduate raw totals. For example, the B+/A– cutoff is 2/3 standard deviations above the mean, and the B–/C+ cutoff is 2/3 standard deviations below the mean. Outliers and graduate students are excluded from the cutoff computation to avoid unfairly skewing the curve for undergraduates.

  5. Compute final letter grades (for non-outliers) from adjusted totals.

  6. Adjust grades upwards at the instructor's whim.

Past grade distributions

Here are the grade distributions for all Jeff's previous offerings of CS 473. This isn't really enough for the “typical” distribution to make sense, but there it is anyway. (Spring 2015 was a pilot offering, which did not use the current flexible homework percentage.)

Semester Mean ± stdev Min pass #As #Bs #Cs #Ds #Fs
Spring 2015* 65% ± 12% 42% ugrads: 7 12 5 0 0
grads: 13 6 0 0 0
Spring 2016 74% ± 11% 42% ugrads: 27 29 21 3 0
grads: 11 11 0 0 0
Spring 2017 73% ± 13% 41% ugrads: 28 30 22 3 4
grads: 6 7 3 0 0
Typical 72% ± 12% 42% ugrads: 32% 37% 25% 3% 2%
grads: 52% 42% 5% 0% 0%

For comparison, here are the grade distributions for all of Jeff's previous offerings of CS 374. Like this semester, the mean was at the C+/B– boundary, and each standard deviation was a full letter grade. Spring 2014 and Fall 2014 were pilot offerings, with significantly smaller enrollments, unsettled curricula, and no flexible homework percentage, so I don't regard those grade distributions as "typical".

I don't have a good explanation for the sharp improvement starting Spring 2018. (We did start distributing a large collection of practice/study problems before each exam in Fall 2016, but I don't think that's a sufficient explanation.) I also don't have a good explanation for the differences in grade distributions between fall and spring semesters.

You can compare my grade distributions with others here.

Semester Mean ± stdev Min pass #As #Bs #Cs #Ds #Fs
Spring 2014* 59% ± 11% 38% 8 11 8 8 1
Fall 2014* 62% ± 12% 38% 16 22 22 12 0
Fall 2016 64% ± 12% 39% 87 113 124 60 14
Spring 2018 71% ± 14% 44% 70 87 74 36 5
Fall 2019 72% ± 12% 47% 68 99 89 54 11
Typical 68% ± 13% 43% 23% 30% 29% 15% 3%