This is also happening in K12 schools. Seattle, which has a famously far-left leaning school system, is creating a plan to inject ethnic studies and progressive ideology into math classes: https://www.seattletimes.com/education-lab/new-course-outlin...
>“How is math manipulated to allow inequality and oppression to persist?”
That doesn't seem like injecting ethnic studies into math to me.
In fact, I think this is an excellent type of question. Schools should be teaching more critical thinking exactly like this, in every domain. For example, manipulation of scales, axes, statistical bias, and mathematical context are one of the key tools that people like climate change deniers use to spread their propaganda. Mathematics education should be about more than just handraulically banging out a bunch of long division. Interpretation and context are key underpinnings of mathematics.
Oh course I do, which is why several of my comments in this thread were about the importance of statistics education.
I'm okay with having classes pointing out propaganda so long as it is even handed in pointing out propaganda irrespective of agenda. If a professor only points out the propaganda of those with whom they disagree, that's a bad class.
Part of educational development has to include the realization that your instructors themselves have individual biases. To expect educators to be completely even handed and bias free is unrealistic.
Agree and I think many people here on HN realize that. I can't say the same for many of the people I know that are college educated, which leads me to think that we're largely missing that aspect of educational development.
no you're right. I was able to read the article in full and it doesn't say anything like that. the only thing it said about the math department was that they were going to allow students to apply for "lenient grading" if they felt their studies were negatively impacted by the george floyd killing.
to be fair, there is a subset of leftist thought that advocates placing lived experiences above statistical arguments. you can see this play out on social media where people are scolded for not being fully convinced by an individual's personal story about {insert macro issue}. the argument here is not that math itself is racist (how could it be?), but that the study and application of it can be. in other words, if statistics is disproportionately studied by an already-dominant subset of society, its application will likely lead to conclusions that more or less support the status quo.
If your studies were negatively impacted by <whatever factor outside your control>, why would "lenient grading" be the right response?
"You get a no-penalty 'incomplete' and can complete the course later" seems vastly more appropriate than saying you learned something that you haven't. I'm sure some people were negatively impacted; they should not suffer a poor grade as a result. Nor should they be granted a better grade than the ability they demonstrated.
> If your studies were negatively impacted by <whatever factor outside your control>, why would "lenient grading" be the right response?
I'm not necessarily saying it is, just that the issue covered in the article is only tangentially related to the "math is now racist" strawman. personally, I think no-penalty incomplete would be more fair, although I'm sure someone could find issues with that too.
my guess is "lenient grading" starts to seem more reasonable when you take into account what a lot of schools were already doing in response to covid and remote learning at this time. giving incompletes is a reasonable measure to take when you have a couple students with extenuating circumstances. but what do you do when almost all the students have been impacted by a sudden shift to remote learning? you can't just give the whole class an incomplete and delay everyone by some fraction of a semester.
> Are we giving grades based on effort/attempt or based on knowledge acquired?
it's not really that simple. grades are just a way to measure how well students have met the course expectations. the expectations may or may not be reasonable in the first place. if there was no curving and year after year, 90% of students fail a course, the material would get adjusted. either fewer topics would be covered, or less difficult problems would be selected from each topic. the syllabus itself is an implicit curve, in a way. when someone designs a data structures course, they design it in a way that a target portion of the students will actually pass the class. they don't just say "well, arrays, linked lists, binary trees, rb-trees, BSPs, splay trees, octtrees, hashmaps, bloom filters, skiplists, and all the different types of heaps are essential topics and everyone who can't master every single one in 16 weeks fails". there's no objective standard for exactly what set of topics needs to be covered and learned in a particular course (or major), you just try to teach as much as is reasonable.
Can you elaborate further?