If you have any questions or concerns about these policies, please don't hesitate to ask in lecture, during office hours, on the course newsgroup, or by email.
Graded homeworks and exams
- You can pick up your graded homeworks and exams during regular office hours. Under normal circumstances, your graded work should be ready to pick up at most two weeks after you submit it.
- We will post homework and exam grades on Compass Gradebook ('My Grades' under 'My Tools').
- We will post homework solutions a few days after each submission deadline; we will post exam solutions immediately after the exam ends. Posted solutions will include suggested rubrics for grading each problem. If the graders modify the suggested rubrics for any reason, we will post final rubrics when grading is complete.
Regrade requests
- Please check that your grades are tabulated and recorded correctly. If you notice a mistake, please bring your graded work to Jeff or Alina; we will correct it immediately.
- If you believe that your homework or exam has been graded unfairly, please request a regrade. Alina will regrade homework; Jeff will regrade exams. To request a regrade, resubmit the work in question along with a brief written explanation why you think you were graded unfairly. (For example, "My answer to problem 2 is correct; see the posted solutions." or "My grade does not match the posted rubric.") Don't revise or explain your answer; we can only grade what you submitted the first time.
- Regrade requests must be submitted at most two weeks after the homework or exam is returned. Except for arithmetic mistakes, late regrade requests will be ignored.
- If you submit a regrade request, your entire homework or exam will be regraded from scratch. Your grade may go down.
- We will readily admit, apologize for, and correct our mistake if you have been graded unfairly. However, please remember that "unfairly" means your grade is inconsistent with the published grading standard, or that you were graded more harshly than other people in the class, not just that you think the grading standard is too harsh. Please also keep in mind that each homework point is worth about 0.1% of your final course grade. Frivolous regrade requests will be met with the scorn they deserve.
Final course grades
We will determine final course grades as follows. (What do you expect from an algorithms course?)
- Compute everyone's raw total: 30% homework + 70% exams, excluding extra credit points.
- There will be six graded homeworks (HW0 through HW5), each with five problems. We will drop your five lowest homework problem scores (except for zeros from cheating offenses); this is equivalent to dropping one entire homework, or one problem from each assignment after HW0. Each of the 25 remaining homework problems is worth just over 1% of your raw total.
- There will be two midterm exams, each with five questions, and a final exam with seven questions. We will drop your three lowest exam problem scores; this is equivalent to dropping one problem from each exam. Each of the 14 remaining exam problems is worth 5% of your raw total.
- Compute everyone's adjusted total, by adding extra credit points to the raw totals. Extra credit points are not necessarily worth the same as regular points.
- Anyone with an adjusted total over 95% automatically gets an A+. In a typical semester, this rule applies only to the top 1–3 students.
- Anyone with an adjusted total below 50%, or who otherwise does not appear to be making a good-faith effort, automatically gets an F. In a typical semester, this rule applies only to the bottom 1–3 students. (This is not the only way to fail!)
- Determine letter grade cutoffs from raw totals, excluding outliers at both ends of the curve. The mean is a borderline A-/B+, and each standard deviation is worth half a letter grade. For example, the B+/B cutoff is 2/3 standard deviations below the mean. The lowest passing grade is B-.
- Compute final letter grades from adjusted averages, except for the outliers from steps 4 and 5.
- Adjust grades (only upwards!) at the instructor's whim.
This algorithm ensures that extra credit can only increase your grade, that other people's extra credit does not affect your grade, and that the curve isn't skewed by the handful of outliers in every class.
Here are approximate statistics from the last five semesters Jeff taught 473 or 573, using the same grading algorithm. Scores in 573 tend to be higher and more tightly clustered, despite the more difficult course material.
Class Semester Mean ± stdev Min pass 473 Fall 2006 64% ± 12% 40% 573 Spring 2007 77% ± 10% 57% 573 Fall 2008 70% ± 10% 50% 473 Spring 2009 69% ± 13% 43% 473 Spring 2010 72% ± 12% 47%