I have a fairly long experience of both teaching and grading, even though I stopped a couple of years ago. After a couple of years of trial-and-error, I think I found the "right" way:
* Alternate between theory and practice in the course of a single 2-hours slot. Theory is necessary, but the attention span of people is very limited.
* Grade either a homework assignment in the form of a real project with specifications. You give the assignment half-semester, so that students who start early can ask questions. Otherwise, grade a in-session exam, with every resource available, including the course and the whole Internet. However, you don't assess the knowledge, but if the student is able to apply their knowledge to the different tasks at hand.
These approaches are now completely moot in the age of AI.
I was surprised the first time a colleague asked me to do an oral exam. I thought it was a burden on people with poor social interaction skills. It went surprisingly well: you could see in a couple of minutes if the student had integrated the concepts of the course, even if they were shy. Now I wonder if it's the way to go. There's one caveat, though: it doesn't scale.
I was thinking that one of the great ironies of artificial intelligence is that it will make good teaching even more labour intensive. Those kids that can afford the one to one tuition required to push past our natural inclination to be lazy will learn things, while everyone else will succumb to outsourcing all their thinking.
There’s no way I’d have learnt everything I know now about software in today’s environment. So much of software development - debugging techniques, architectural decisions, structuring data etc is learned through trial and error. As the models get better is becoming easier and easier to just not look too closely at their output. I suspect it’s human nature.
Maybe the Germans were right? One in-person test, preferably an interview, at the end of the semester should be enough to motivate the smarter part of the bunch to make sure they understand the issues at hand. The rest is chaff anyway. Don't optimize for chaff.
This makes me think of exams — I always did well on open-book exams, but I didn't do well on exams where I couldn't look at the book. Whether it's because of ADHD or just a lack of memorization ability.
Similarly, I did well in interviews where I could use the internet, but I didn't do well in interviews where I couldn't use the internet.
Everyone has a testing format they're good at. I'm curious about how this kind of thing is evaluated, and whether there are any papers related to this.
I have a fairly long experience of both teaching and grading, even though I stopped a couple of years ago. After a couple of years of trial-and-error, I think I found the "right" way:
* Alternate between theory and practice in the course of a single 2-hours slot. Theory is necessary, but the attention span of people is very limited. * Grade either a homework assignment in the form of a real project with specifications. You give the assignment half-semester, so that students who start early can ask questions. Otherwise, grade a in-session exam, with every resource available, including the course and the whole Internet. However, you don't assess the knowledge, but if the student is able to apply their knowledge to the different tasks at hand.
These approaches are now completely moot in the age of AI.
I was surprised the first time a colleague asked me to do an oral exam. I thought it was a burden on people with poor social interaction skills. It went surprisingly well: you could see in a couple of minutes if the student had integrated the concepts of the course, even if they were shy. Now I wonder if it's the way to go. There's one caveat, though: it doesn't scale.
I was thinking that one of the great ironies of artificial intelligence is that it will make good teaching even more labour intensive. Those kids that can afford the one to one tuition required to push past our natural inclination to be lazy will learn things, while everyone else will succumb to outsourcing all their thinking.
There’s no way I’d have learnt everything I know now about software in today’s environment. So much of software development - debugging techniques, architectural decisions, structuring data etc is learned through trial and error. As the models get better is becoming easier and easier to just not look too closely at their output. I suspect it’s human nature.
Maybe the Germans were right? One in-person test, preferably an interview, at the end of the semester should be enough to motivate the smarter part of the bunch to make sure they understand the issues at hand. The rest is chaff anyway. Don't optimize for chaff.
This makes me think of exams — I always did well on open-book exams, but I didn't do well on exams where I couldn't look at the book. Whether it's because of ADHD or just a lack of memorization ability.
Similarly, I did well in interviews where I could use the internet, but I didn't do well in interviews where I couldn't use the internet.
Everyone has a testing format they're good at. I'm curious about how this kind of thing is evaluated, and whether there are any papers related to this.
Thanks for sharing. Interesting to see how teachers are adapting to LLMs. I agree that trying to ban AI use is futile.
Just grade the process, not the result. It's literally that simple.
AI can talk the talk, but it can't walk the walk.
A simple "explain why this is the best solution" will make it shit the bed 10 out 10 times.