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UCSD CSE29 WI26 Syllabus and Logistics

Joe Gibbs Politz (Instructor)

CSE 29 introduces you to the broad field of systems programming, including 1) the basics of how programs execute on a computer, 2) programming in C with direct access to memory and system calls, 3) software tools to manage and interact with code and programs. All very cool stuff that makes every programmer better!

Basics

  • Lecture (attend the one you're enrolled in):
    • A section: Tue/Thu 11:00am, Center Hall 115
    • B section: Tue/Thu 12:30pm, Center Hall 113
  • Discussions:
    • A section: Tue 5:00pm, Pepper Canyon Hall 109
    • B section: Thu 5:00pm, Center Hall 212
  • Labs: Wednesdays (check your schedule!). Either B240 or B250 in the CSE building
  • Exams:
    • Weeks 4 and 8: AP&M Testing Center, flexible scheduling on PrairieTest
    • Week 10: During your regularly-scheduled lab time
    • Finals week: In CSE labs scheduling instructions coming soon!
  • Final exam: AP&M Testing Center, flexible scheduling in final exam week on PrairieTest
  • Professor office hours (these are the standard times but check the calendar below for changes!)
    • Tuesday 2:30-3:30pm, CSE 3206 (advising focused, open to all students)
    • Wednesday 1:30-2:30pm CSE B270/tables outside labs (CSE29 students only)
  • Office Hours: Refer to calendar
  • Q&A forum: Piazza
  • PrairieLearn: https://us.prairielearn.com
  • Textbook/readings: Dive Into Systems, plus additional readings we will assign
  • Reference Sheets: Google Doc

Office Hours Calendar

Schedule

Course Components

There are three main parts of the course: Assignments, Exams, and Social Learning.

Assignments

The course has 5 assignments that involve programming and writing. For each there will be:

  • A problem set that has practice problems related to the assignment
  • A programming project where you create a program that does something interesting and that we automatically test
  • Design questions about the programming project

Grading: There are 6 points available for each assignment.

  • 2 points for the corresponding problem set (2 points for fully correct and complete by the deadline, 1 point for somewhat complete at the deadline, 0 points for incomplete or very little completed at the deadline).
  • 2 points for program correctness (2 points for perfect or nearly-perfect, 1 point for significant errors, 0 for programs that work on very few or no examples)
  • 2 points for design questions (2 points for perfect or nearly-perfect, 1 point for minor errors, 0 points for major errors or incomplete)

After each assignment is graded, you'll have a chance to resubmit all components based on the feedback you received, which will detail what you need to do to increase your score. You can increase your score by up to 1 point in each component on a resubmit.

This is also the only late policy for assignments. Unsubmitted assignments are initially given a 0, and can get a maximum of 3 points on resubmission.

Exams

There will be 3 exams during the quarter and make-up opportunities in finals week.

Exams will use a mix of the testing facility in AP&M B349 (weeks 4 and 8), and our regularly-scheduled lab times (week 10). You will schedule your week 4/8 exam on PrairieTest by logging in with your @ucsd.edu account. You can schedule the exam at a time that's convenient for you in the given exam week, and you will go to that lab and check in for your exam at the time you picked. The exam will be proctored by staff from the Triton Testing Center (not by the course staff from this course). No study aids or devices are allowed to be used in the testing center. You will need only a photo ID and something to write with (scratch paper is available on request). The Triton Testing Center has shared a document of rules and tips for using the testing center.

The exams will be administered through PrairieLearn and PrairieTest. The exams will have a mix of questions; they will typically include some that involve programming and interacting with a terminal.

We'll know the precise scheduling by the end of week 1 for both the exams during the quarter and the final exam.

Exams are (necessarily) cumulative. They typically focus on content that has already had a due date (e.g. PAs, problem sets, design questions, and labs that have their work due before the exam period starts), and typically don't focus on specific details only found in textbook chapters or mentioned conversationally in lecture. However, that's only a statement about the overall design, not a hard constraint: we will never promise that course content from any particular week, lecture, assignment, textbook chapter, or other course component won't appear on an exam. We say this publicly in the syllabus to avoid misunderstandings (“the professor/TA/my friend told me not to study that”) and to make it clear that all the content matters (we wouldn't assign it if it didn't).

Social Learning

Learning (in college) is in part, a social process. Your engagement with it forms part of your grade for the class.

Lecture (Social Learning)

Lectures are designed to introduce you to concepts, show how I (Joe) approach problems, give you a chance to ask questions about content, connect course content to the world outside the classroom, talk to your classmates and the staff, and provide an overarching story for the material.

Most lectures will come with two on-paper activities that are handed in for credit:

  • A review check, near the beginning of class. These serve mainly as a way to check in on content from previous lectures before starting the content for the day, and for us to get some information about how everyone is following along with lecture content. Don't think of them as quizzes, rather as an engagement check. You can discuss these with your classmates, and they are graded on completeness rather than correctness.
  • An exit slip at the end of class (last 5 minutes). These serve as a way to get feedback from you on the lecture; they will often ask you to restate or summarize a learning outcome, or say what the “fuzziest” concept from lecture was for you.

We say these will happen in “most” lectures because sometimes we may skip one or both for various timing or logistics reasons.

Grading: Each lecture has 3 social learning points available:

  • 2 for the review check (only 1 point if handed in but incomplete)
  • 1 for the exit slip

If you miss either or both during class, you can submit the makeup assignment later for 1 point total. This is the only way to make up credit for missing a lecture.

The makeup assignment requires both the review check and exit slip, regardless of whether you turned one in during class. So:

  • If you only hand in one during class, you can submit the full makeup assignment to earn 2/3 points for that day.
  • If you miss lecture entirely, you can submit the full makeup assignment to earn 1 point.

All resubmissions must be before the next lecture to count, with no exceptions.

Labs (Social Learning)

The course's lab component meets for 2 hours. In each lab you'll switch between working on your own, working in pairs, and participating in group discussions about your approach, lessons learned, programming problems, and so on.

The lab sessions and groups will be led by TAs and tutors, who will note your participation in these discussions for credit. Note that you must participate, not merely attend, for credit.

Grading: For each lab there are 4 possible points to earn:

  • 1 for being present
  • 2 for being an active, professional, productive participant while present
  • 1 for submitting and/or getting work checked off that was done in lab

If you miss lab, you can earn the submission/check-off point by submitting the check off work before the next Tuesday at 11am, but cannot earn the 3 points related to participation. There is no way to make up a lab beyond this 1 point, even for illness, travel, or emergencies. Our preference would be to require all 10 labs for an A, and have some kind of excused absences. However, tracking excused absences doesn't really scale, so this policy is how we handle it.

Discussions (Social Learning)

There are discussions on Tuesdays and Thursdays, led by the course staff, for going over problem set problems and the programming assignments.

Grading: Attendance is taken, and attending discussion gives 1 social learning point.

Grading

Each component of the course has a minimum achievement level to get an A, B, or C in the course. You must reach that achievement level in all of the categories to get an A, B, or C.

  • A achievement:
    • ≥85 social learning points (may adjust depending on the total number of available lecture points)
    • ≥27/30 assignment points
    • ≥10/12 exam points
  • B achievement:
    • 70-84 social learning points
    • 23-26 assignment points
    • 8-9 exam points
  • C achievement:
    • 55-69 social learning points
    • 20-23 assignment points
    • 6-7 exam points

Below C achievement (in any category) is an F/No Pass.

Pluses and minuses will be given based on performance on social learning and assignments (exam grades will not be used to determine plus and minus grades; this is to avoid extra load on retake testing). We don't publish the exact cutoffs in advance.

Requests to change this grading policy (for a specific student or class-wide) will be denied with a link to this syllabus section. Consider this: we may, as instructors, decide for academic reasons that the most accurate way of assigning letter grades in the class needs to change (and we tend to only make changes that improve letter grades relative to this starting policy). However, it would be inappropriate for us to do so in response to student requests: that could create an appearance that we give students the grades they ask for rather than the grades they earned.

Policies and Perspective

CSE29, Large Language Models, and You

Evidence suggests that experts can generate (some kinds of) programs more quickly with the assistance of large language models. But, they did not become experts by using them.

We offer the following metaphor as guidance:

Consider physical conditioning, let's take running for fitness in particular. There are many machines in the world that are remarkably effective at moving people around: electric scooters for example. Taking a scooter for a few miles gets you to your destination more quickly and less sweatily than running, but completely misses the point of the run. The goal of running for fitness has little to do with getting to a destination and everything to do with the changes that happen inside your body. Many people can run the same miles on the same road and get the benefits from it, despite them all doing the same work that a machine (the scooter) could have done.

Fitness is just one example like this. Similar comparisons make sense for learning to play an instrument, perform onstage, or upskill at games like billiards, darts, or Fortnite.

The programming in this class is like running for fitness (doing the typing and thinking on your own), not about getting to a destination quickly (handing in a completed program). The answers to many programming problems are known, or close-to-known, and many tools exist that can generate part or all of them, and there are many people who can write them faster than you. We assign the work not because knowing a solution is particularly important, but because the act of creating the solution changes you.

So, you should not use AI to generate code or prose for work in this class.

That said, it's relatively difficult for us to detect AI usage on take-home work. It's also useful to know what AI and code generation can do. So feel free to experiment, and do not read this policy as prohibiting, or assigning formal consequences to LLM use. A good way to go about learning in this course is to do the work yourself first, then go back and see how the LLM would have done it. Then you may be able to notice things, like the LLM generating extra unnecessary code, or doing something in a more or less complex way than you did it, or maybe doing exactly what you did! The key thing is that you developed a sense of what a solution was supposed to look like, rather than trusting the version from the machine.

(Also, the exams require that you program on your own, without the aid of any AI assistants.)

Device Policy

To a large degree, you are responsible for managing your time, attention, and learning in lecture, and I hesitate to make policies that attempt to govern or restrict your choices with respect to note taking or device use. However, lecture is a communal space, and your actions can affect others' learning. In particular, what you have on your screen may be unavoidably in the field of view of other students. Because of this, you are responsible for a fragment of the attention of everyone in a cone of space behind you. With this in mind, the policy for lecture is that if you use a device, you must have lecture-related content onscreen. There is even research that shows that the content of screens in the classroom, even quite far away, can have a detrimental affect on learning. If you cannot resist checking social media, playing a game, or doing other off-topic tasks during lecture, sit in the back 2 rows so that you are only having an affect on your own attention, or the attention of others with a similar mindset.

Assignments and Academic Integrity

You can use code that we provide or that your group develops in lab as part of your assignment. If you use code that you developed with other students (whether in lab or outside it), got from Piazza, or got from the internet, say which students you worked with and a sentence or two about what you did together in CREDITS.txt. All of the writing in assignments (e.g. in open-ended written questions) must be your own.

You can use an AI assistant like ChatGPT or Copilot to help you author assignments in this class. If you do, you are required to include in CREDITS.txt:

  • The prompts you gave to the AI chat, or the context in which you used Copilot autocomplete
  • What its output was and how you changed the output after it was produced (if at all)

This helps us all learn how these new, powerful, and little-understood tools work (and don't).

If you don't include a CREDITS.txt and it's clear you included code from others or from an AI tool, you may lose credit or get a 0 on the assignment, and repeated or severe violations can be escalated to reports of academic integrity violations.

Exams and Academic Integrity

It is a violation of academic integrity to share details of your exam with others until after you receive your grade for it. Keep in mind that the exams are randomized to discourage casual cheating, so your exam may not be the same one that others see. It is a violation of academic integrity to communicate with anyone other than the official proctors during the exam, or to use devices other than the ones you are using to complete the exam.

Lab and Professionalism

Lab work will be highly collaborative, and designed to encourage communication between students. Some of the activities will have you talk to other students, or exchange code, ideas, or commands with other students, and write down what happened.

In all of this communication, remember to be polite, professional, and focus on the work. A huge part of the job of a working software professional or researcher is professional and clear communication.

Some tricks for this: avoid statements that reference the author of the code, frame negative feedback as possible improvements or ways your expectations were violated, and take responsibility for anything you don't understand.

Examples:

  • Don't say “You made a mistake here: ...” Instead say “On line I expect the condition to be true but I think it will be false because XYZ ... we could demonstrate that by doing ABC.”
  • Don't say “It seems like you don't know about...”, instead say “On line 10-12, these 3 lines could be shortened into one line using grep -r instead of find followed by grep: ....” And use some judgment: it's likely that some of this advice is helpful and constructive, and some of it is just showing off.
  • Don't say “This code is (going to be) slow...” Instead wait until you can run it and try an example that demonstrates it “This code took 10 seconds to run for input XYZ and that seems like a lot. When we were writing ... I think I noticed that we did X, if we did Y instead I think it will go faster
  • Don't say “That conditional is ugly:...” Instead say “I find it easier to read these conditions when they are written as .... because ...” If you don't have a good explanation to put after the “because”, how do you know it's a good suggestion? Refer also to the advice about not showing off
  • Don't say “You wrote lines 20-24 very confusingly.” Instead say “I'm having trouble understanding lines 20-24. It would help me to work through an example of how that part is supposed to work”

FAQ/AFQ (Anticipated Frequent Questions)

Q: I am waitlisted for one of the sections at position N. What should I do?

Come to class and do all the work as usual (you will have Canvas access to get all the needed links) so you can be up to speed if you do get a spot. This is aligned with official department policy.

We can't make accurate predictions about chances of getting into the course from a specific waitlist position, or control that process as instructors.

Can I attend a lab section other than the one I'm enrolled in?

No, please do not try to do this. The lab sections have limited seating and are full. We cannot accommodate switching.

How can I switch sections?

You have to drop and re-add (which may involve getting [back on] the waitlist). Sorry.

Can I leave lab early if I'm done or have a conflict?

The labs are designed to not be things you can “finish”. Labs have plenty of extension and exploration activities at the end for you to try out, discuss, and help one another with. Co-located time with other folks learning the same things is precious and what courses are for. Also, if you need an extrinsic motivation, you won't get credit for participation if you don't stay, and participate, the whole time. We can often accommodate one-off exceptions – if you have a particular day where you need to leave early, it's a good idea to be extra-engaged in your participation so your lab leader can give you participation credit before you leave.

Do I have to come to lab?

Yes, see grading above.

I missed lab, what should I do?

The lab page will have instructions on how to submit the make-up, which can get you 1 (of 4 possible) points.

I missed my exam time, what should I do?

Stay tuned for announcements about scheduling make-ups in final exam week.

Where is the financial aid survey?

We do this for you; as long as you submit something in the first two weeks, we will mark you as commencing academic activity.

When are the midterms scheduled?

The midterms will be flexibly scheduled during the quarter using a testing center. More details will come; you will need to set aside some outside-of-class time to do them, but there is not a specific class-wide time you have to put on your calendar.

I have a conflict with the final exam time, what can I do?

The final exam will also be flexibly scheduled during final exam week using the testing center.