Grading Standards for CSC 162

Spring 2010

End-of-semester numeric scores will be weighted roughly as follows: 

class participation	15%
programming projects	25%
labs	20%
workshop attendance	5%
midterm exam	15%
final exam	20%

Letter grades will be assigned by subjectively identifying brackets in the numeric scores.  There is no pre-determined curve.  In particular, I have no objection to giving all high grades (or all low grades) if it seems appropriate. 

Class attendance and active participation are mandatory.  Please arrive on time.  I expect to start at 2:00 sharp, and late arrivals disturb the folks who are already there. 

Assigned reading in the text is also mandatory, and must be done before the associated lecture.  I’ll be posting commentary on the textbook to Blackboard; please peruse this as you do the reading.

If there is anything you don’t understand in class, please stop me and make me explain.  Typically you will find that half the class is similarly confused, and grateful to have you ask.  (Likewise, if I’m belaboring something that everyone understands, please prod me to move on. )

My lecture notes are available on-line, but please note that they come with no guarantee.  In particular, I reserve the right on exams to ask about anything I’ve covered in class, even if it isn’t in the book or lecture notes. 

Homework Projects

Tentative plans call for a programming assignment roughly every two weeks, all (or almost all) in Python.  The workload should be comparable to, or a little heavier than, CSC 161.  The concepts will definitely be more conceptually challenging.  Don’t procrastinate!  Start every assignment well ahead of time, and be sure to read all on-line documentation carefully. 

For some assignments you may have the option of working in pairs.  You can also work alone if you prefer.  In most cases the partners in a pair will receive the same grade, but I reserve the right to assign different grades if I have reason to believe that partners did significantly different amounts of work.  Team membership will be self-selected, and can vary from assignment to assignment. 

Assignments will be turned in via Blackboard.  Late-breaking updates from the TA or professor will be posted there as well.  We will strive to grade assignments within one week of the due date, at which point grades will also be available on Blackboard.  Additional information may be sent to you via email.  If you have any questions about your grades, see the TA or, if she is unable to resolve the issue, the professor. 

You are encouraged to share ideas with fellow students, but only one person or team can get credit for actual code (see the course rules for academic honesty). 

README files

• Code

  • ~60% correctness, completeness, and efficiency:
    Your code should implement everything that was required in the assignment.  It should produce the right output given normal, expected inputs, and some sort of reasonable response to unexpected inputs.  Unless otherwise instructed, and as long as it does not severely compromise programming style, you are expected to use the most efficient data structures and algorithms. 

  • ~20% programming style (including internal documentation and program organization):
    Your code should have appropriate abstractions, data structures, algorithms, and variable names; declarations for all constants; and a judicious number of helpful comments.  To paraphrase Don Knuth, instead of thinking of programming as explaining to the computer what you want it to do, think of programming as explaining to the TA what you want the computer to do. 

• README file

  • ~15% completeness:
    Your write-up should include all the items discussed in the previous sub-section. 

  • ~5% readability:
    Your write-up should be well organized, clear, and concisely presented. 

No Late Assignments

It is my strict policy not to accept late assignments.  Exceptions will be made only under the most dire of circumstances.  Note, however, that I am extremely generous with partial credit, so turn in what you have.

Every semester I have students who let a due date pass.  When I ask them what happened they say “Oh, I didn’t finish, so I didn’t turn anything in.”  Then I have to give them a zero.  If they had turned in even some reasonable preliminary stab at the project they could have received a substantial fraction of the total points.  This can easily make the difference between letter grades at the end of the semester. 

So if it looks like you aren’t going to make a due date, don’t keep debugging down to the wire.  Stop an hour early and take the time to organize what you have and put together a write-up that presents it in the best possible light.  Your transcript will love you for it. 

Extra Credit

There will be opportunities for extra credit on most of the programming projects and exams.  Extra credit will be considered after making the first cut at letter grades for the course.  If you’re near the top of your grade bracket, or the amount of extra work you’ve done is particularly large, you can expect it to push you up a grade. 

Academic Honesty

Student conduct in CSC 254 is governed by the College Academic Honesty Policy, the Undergraduate Laboratory Policies of the Computer Science Department, and the ResNet Acceptable Use Policy.  I helped to write two of these policies myself.  I believe in them strongly, and will enforce them aggressively. 

The following are additional details specific to CSC 162. 

Exams and review questions must be strictly individual work. 

Collaboration on programming assignments among team members is of course expected.  Collaboration on assignments across teams is encouraged at the level of ideas.  Feel free to ask each other questions, brainstorm on algorithms, or work together at a whiteboard.  You may not claim work as your own, however, unless you transform the ideas into substance by yourself.  Among other things, this means that you must leave any brainstorming sessions with no written notes. 

Students are expected to take reasonable precautions to protect their code from accidental or deliberate access by others. 

For purposes of this class, academic dishonesty is defined as

• Any attempt to pass off work on an exam that didn’t come straight out of your own head. 
• Any collaboration on handed-in material in which the collaborating parties don’t clearly and prominently explain exactly who did what, at turn-in time. 
• Any activity that has the effect of significantly impairing the ability of another student to learn.  Examples here might include destroying the work of others, interfering with their access to resources, or deliberately providing them with misleading information.  (Note that grades will be assigned on the basis of individual merit, so there is no benefit—even a dishonest one—to be gained by sabotaging the work of others.)

I work under the assumption that students are honest.  I will not go looking for exceptions.  If I discover one, however, I will come down on it very hard. 

Last Change:  26 April 2010 /