Nicola Romanò @nicolaromano@qoto.org

@iergo https://github.com/tsproisl/textcomplexity
Ça marche en anglais et en allemand, mais certaines mesures peuvent aussi être appliquées au français

"All surface-based, sentence-based and dependency-based complexity measures are language independent. Some of the constituency-based measures are also language independent, but most rely on the NEGRA parsing scheme, i.e. can only be applied to German data."

@Guillawme @iris Oh, that looks interesting! Will have a look as well, might be useful thanks

@iris

This looks like a good tutorial

https://www.overleaf.com/learn/latex/Beamer_Presentations%3A_A_Tutorial_for_Beginners_(Part_1)%E2%80%94Getting_Started

I personally don't use Overleaf, but rather VSCode with the Tex extension. This allows to automatically recompile the file when a linked image changes (sorry, I can't remember if it's an option that you need to actively choose or if it does that out of the box)

I personally like to use the Metropolis theme , a lot slicker than the basic one.

You can have a look at the slides in my image analysis course, all done like that. Feel free to take them and modify at will. There's an empty template presentation as well which is a good place to start
https://github.com/nicolaromano/BIA4/tree/main/Lectures

@iris Ok, makes sense. I use LaTeX/Beamer for slides so everything updates automatically when exporting from Inkscape. Not sure about other software sorry!

@iris Not really sure if that's what you are looking for but I generally use Inkscape for that, works perfectly.

@MCDuncanLab Oh, I feel for you! I did that for a few years and It was way more painful than it should have been! Let's not forget faculties complaining their favourite topic was not represented, while not suggesting any relevant speaker...

@WestLawns On the surface, I totally agree with you. There are, however, several more complex issues.

1. Pedagogically, genAI is problematic in the context of traditional assessments (eg because of cognitive offload).
2. There are many ethical issues surrounding genAI usage (copyright, privacy, ecological, modern slavery etc)
3. There is a question of fairness. Our University policy is that genAI use is not allowed in assessment unless specified on a course by course basis. Not every student will want to use it (it's academic misconduct) and given in 90% of cases it is essentially impossible to reliably detect it, students not using it might be at a disadvantage although they are doing the right thing. Even then, richer students who can afford to pay for better models will be more advantaged.

So, yes, it's the same story as with calculators, but at the same time it is not!

@pascal_hingamp I have to say we're very lucky that we were in the middle of a programme review anyway (independently of the whole genAI shenanigans) and our leadership is actually encouraging us to try new methods of teaching and assessment. Some will work and some won't, but at least we're not stuck at writing yet another essay...

Designing a good course is difficult but I believe that if you do students will engage with it, and yes, some people will cheat, that's always happened. 🤷

@Arta I see you teach law. What kind of assessments do you use (sorry it's very much outside my domain)? What learning outcomes are you trying to assess?

In the past couple of years I have been in a lot of meetings centered about the topic of "OMG students are using genAI in assessments what do we doooo?"

After marking a lot of assessments of different type from different courses in different years of study and two different undergraduate programmes, here are my conclusions, some of which I have no way of proving, I know, but that's fine.

1. A lot of students use genAI. In many cases I cannot prove that, it's just a feeling (nobody writes like this, especially not Year 1 non-native speakers), but I had students telling me directly and I do believe them.

2. Looking at marks in the cases above where I have that feeling, I see a wide spread from fails to high A.

3. Following from 2, overall marks have not changed significantly and systematically in any of our courses from the past 5 years. There are of course year-to-year fluctuations, but that's cohort-dependent there seems to be no overall trend.

4. The conclusion you might draw from 2 is that we're rewarding students who are good at using genAI. Possibly, and I have not made up my mind up about this. My answer is that we should change our way of assessing and teaching, rather than trying to "catch" students using genAI. We're currently redesigning our Programmes and that's what we're trying to do. I am in the process of designing a new course and I have tried to do that assuming students will use genAI, but making it so that using it will not be an advantage, and might actually make it more cumbersome to do the assessments.

5. We looked at final year dissertations and plotted marks against %AI writing detected by our submission system. Taking this with a pinch of salt, given that AI detectors are biased and unreliable, there is only a small negative correlation but it's such a small effect size as to be essentially negligible. Again, the distribution of marks is the same as in previous years.

So in conclusion, just like anything else AI related, there's a lot of hype on how this is disruptive and it's terrible or game-changing depending on which side you're on. And yet, in practice...

Has anyone had similar observations? I'd love to hear your thoughts.

#genAI #teaching #highered #undergrad #assessment