|In the Spring term of 1997, the School
of AI tried out a "cascaded learning" scheme. In this, specific students
act as "Cascaders" and give help to other students (usually in lower years).
The help can be of a general nature (e.g. how to find things in the School,
how to get out of awkward computing situations) or specific to areas of
AI covered in the School's courses. This year the Artificial Intelligence
Society has been given the honour of organising the scheme.
You can recognise a student advisor who is available to help people because there will be a bean bag moose sitting on their monitor. If you see such a person in C1 or elsewhere in the School, you are welcome to approach them and ask them about problems they may be able to help with.
Cascaders also have to have time uninterrupted to do their own work. There is a simple rule:
How the system worksIf you don't see any Cascaders in your room or if you're too lazy to get up, type cascade at the Unix prompt. The script will search for Cascaders and return a list (and sometimes photographs) of those closest to you.
The Cascaders (who are volunteers) are not claiming to be experts in all areas of AI, but they are willing to try to help people and to try to point them in a sensible direction if they can't help directly.
Please remember that Cascaders are not there to do your assessed coursework for you. If you are stuck on a piece of work, you can ask them about conceptual problems and things you are having difficulty understanding, but you should not ask them to do part of the exercise. If a student advisor gives you significant help, you should always acknowledge it in your writeup (see the guidelines on plagiarism in your course documents).
If you are a Cascader yourself, just type signin when you are available and type signout when you don't want to be disturbed.
If want to comment about the scheme or require additional information, contact Alexios Chouchoulas (firstname.lastname@example.org) or Lucas Dixon (email@example.com) . All cascaders have been subject to a background check by a member of staff.