> Do not look at other students’ homework files and do not work with other students. At all.
This is a horrific policy. We work better with other people.
When I studied computer science, other students came to me for help. I helped them. We discussed problems together. I saw their code. They saw mine. No one copied: we shared approaches and style and thinking. I shared how I solved things. They learned my thinking. By explaining, I understood what I knew better. Sometimes I learned from them: an idea I wouldn’t have had alone, a new direction. All of us were better off. This was across multiple people.
It was in no way discouraged and so long as you wrote your work — while asking others for help was fine — you were okay.
Humans learn through teaching. We learn through discussion. We learn through interaction. Solitude — ‘do not work with other students. At all’ — is the exact opposite of a healthy learning environment.
I don’t care what their artificial intelligence policy is. I cannot state strongly enough how much I revolt against their human intelligence policy.
As for the lecturer’s warnings in slides: that reads like a trap.
>> Do not look at other students’ homework files and do not work with other students. At all.
> This is a horrific policy. We work better with other people.
I think that line was in connect with quizzes, which are presumably online. Near the top of the article it says
> You may discuss assignments in a general way with other students, but you may not consult anyone else's work.
I'd be with you if they made everyone work in "clean rooms" but that is appropriate for quizzes and tests.
As someone who's graded lots of undergrads I can tell you that cheating is prolific. It's easy to identify who is copying code, but it is hard to prove. My wife teaches in another department and says the same thing. AI use has grown exponentially and it's really destructive to the education process. Though I have more complex thoughts on this and that it is just exacerbating larger problems that no one wants to actually solve because they'd be very hard to solve in a way you can have clear metrics for the bureaucrats (including industry). Proper evaluation is unfortunately always going to be fuzzy. Metrics are in contention with making a meritocracy, especially if you don't have a deep understanding of said metrics
vintagedave · · focus · HN ↗
This is a horrific policy. We work better with other people.
When I studied computer science, other students came to me for help. I helped them. We discussed problems together. I saw their code. They saw mine. No one copied: we shared approaches and style and thinking. I shared how I solved things. They learned my thinking. By explaining, I understood what I knew better. Sometimes I learned from them: an idea I wouldn’t have had alone, a new direction. All of us were better off. This was across multiple people.
It was in no way discouraged and so long as you wrote your work — while asking others for help was fine — you were okay.
Humans learn through teaching. We learn through discussion. We learn through interaction. Solitude — ‘do not work with other students. At all’ — is the exact opposite of a healthy learning environment.
I don’t care what their artificial intelligence policy is. I cannot state strongly enough how much I revolt against their human intelligence policy.
As for the lecturer’s warnings in slides: that reads like a trap.
godelski · · focus · HN ↗
As someone who's graded lots of undergrads I can tell you that cheating is prolific. It's easy to identify who is copying code, but it is hard to prove. My wife teaches in another department and says the same thing. AI use has grown exponentially and it's really destructive to the education process. Though I have more complex thoughts on this and that it is just exacerbating larger problems that no one wants to actually solve because they'd be very hard to solve in a way you can have clear metrics for the bureaucrats (including industry). Proper evaluation is unfortunately always going to be fuzzy. Metrics are in contention with making a meritocracy, especially if you don't have a deep understanding of said metrics