Late work, extensions, academic integrity, AI use, attendance and accessibility — the rules an assessment brief can point at.
Policies and support#
Every rule here exists because somebody needed it, and each one says what happens rather than what we would prefer. If a situation is not covered, email Adaeze and it will be, next year.
Extensions#
Ask before the deadline and you will get one. Up to five days, no documentation, no explanation required beyond a sentence saying roughly how long you need. Email the convenor. This is not a concession we make reluctantly; a course about the cost of waiting should not pretend that students have uniform amounts of time.
Beyond five days, or after a deadline has passed, the university’s process for extenuating circumstances applies and we cannot go around it.
Late work#
Without an extension, 5% of the available marks per day, weekends included, to a floor of the pass mark for work that is otherwise passing. After ten days a submission can no longer be marked, because feedback returned that late is not feedback.
We would rather have a late piece than no piece, and much rather have an extension request than either.
Academic integrity#
The usual: the work is yours, sources are cited, and you do not submit the same work to two courses.
Two things specific to this course. Field data must be real. A fabricated tally sheet is fabricated research data, which the university treats far more seriously than a badly written essay, and it is easier to detect than people expect — invented counts have the wrong variance. If your observation failed, write up the failure; that is a legitimate submission and has scored full marks.
Collaboration on data collection is encouraged. Counting a busy queue alone is genuinely hard. Share observations, name whoever you worked with, and write your own analysis.
Generative AI#
Permitted, with two conditions: say where you used it, in a short note at the end, and do not use it to produce data.
The reasoning is not moral. It is that the thing this course is trying to teach is the gap between the model and the building — the effect that shows up in the room and not in the simulation, the assumption that quietly stopped being true halfway through your observation. That gap is only visible to someone who was there. A model that was not there will write you a fluent paragraph about a queue that does not exist, and it will be graded as such.
Using AI to write code, check mathematics, restructure an argument or improve prose is fine and is declared in one line.
Attendance#
Labs are not marked for attendance and are also the entire practical component of the course. Weeks 2, 7, 10 and 12 are effectively impossible to reproduce alone — you cannot run a twenty-four-person boarding trial by yourself. If you have to miss one, tell your tutor and we will find you the data.
Accessibility#
If you have an access plan, send it to the convenor in the first fortnight and we will adjust rather than exempt: there is almost always another way to run an exercise.
Some specifics, since students ask every year. The fieldwork can be done sitting
down, and can be done from video if standing in a public space for forty minutes
is not workable. The simulations are keyboard-operable and every one of them
reports its result as text as well as animation; they respect
prefers-reduced-motion and show the converged numbers without moving. The
boarding lab has a non-participating observer-and-timekeeper role that is a
genuine part of the exercise rather than a consolation.
Getting help#
Ask your tutor first — Tomáš for weeks 1–5, Priya for 6–12. Consultation is Wednesdays 14:00–16:00 with Adaeze, no appointment needed, and email works for anything that cannot wait.
For anything not about the course — deadlines colliding, money, health, a week that has gone wrong — the university’s student support services exist and are better at it than we are. Tell us you need an extension; you do not have to tell us why.