I'm having a difficult time imagining how an admissions event in 2021 materializes in the spring semester of 2026 in a class largely taken by first-year students.
In addition to overreliance on AI, Garcia also pointed out that many students are underprepared mathematically, a concern echoed by campus associate teaching professor Gireeja Ranade.
From the article discussed the other week:
Over three years — from fall 2021 to fall 2023 — the letter said, at least 20% of Berkeley first-semester calculus students who took a diagnostic exam showed deficits. “Basic mathematical fluency is analogous to literacy; without it, success in university-level STEM becomes structurally unattainable for students,” faculty wrote.
It's been steadily getting worse. The current article only looks at F's which conveniently hides if there has been a slope down. Additionally, kids entering HS in 2021/2022 would just now be hitting college.
A sudden materialization is what's depicted by the data.
> It's been steadily getting worse.
I don't believe this is accurate. Failing grades are what the observation entails, and the data clearly depict an abrupt change; not a gradual one.
In the section titled "Failing grades in 3 CS classes skyrocket in spring 2026
", there's a clear jump in failing grades for all cited courses between 2025 and 2026. Failing grades for every course jump by multiples of the previous year.
The jump is very likely due to AI usage and lack of skills in mathematics. It seems like prerequisite classes are not being fulfilled.
"Ranade said students are expected to enter the course having taken classes on linear algebra, vector calculus and mathematical proofs. However, she found out in office hours that many students struggled with linear algebra, and was even more shocked when one student told her the linear algebra class they took at UC Berkeley had an “open-internet, open-AI policy” for homework and exams."
Also, this professor doesn't grade on curves? Could be very specific to this teacher. I don't know. Would be great to have more data but it is a big jump and could be very specific to this professor or perhaps this class.
"Also, this professor doesn't grade on curves? Could be very specific to this teacher. I don't know." Someone has to hold standards up -- they seem to be falling down across the board in education.
Actually, when I read they usually graded on a curve, I lost all interest. I don't respect teachers that grade on curves.
You should be graded by how well you know the material - not how well your peers don't know it. I'm always grateful both my undergrad and grad professors didn't curve on a grade.
In my first company, I had 4 different jobs. It was a common adage: Go into a low performing team that does simple work and you'll get promotions much quicker than in a high performing team doing challenging (but fun) work.
It was right. I had 2 "dream" jobs where I did cool, challenging stuff, but where everyone was more than competent. They turned out to be career killers. The promotions I got were all in the other 2 jobs where I did boring business logic coding, and where my peers were barely competent (one had trouble navigating directories using the command line).
That's what happens when you grade on a curve. Smart people begin to work on boring stuff, and not the real challenges.
For failing grades sure, there must be some sort of minimum competence. For sorting out >= B/3.0 grades, a curb can work since you are getting evaluated against your peers to see he is standing out vs just doing acceptable.
If you wanted to grade purely off a curve, you would be stuck with old test problems that were thoroughly vetted and calibrated, an impossible task for smaller classes where the material changes rapidly.
> For sorting out >= B/3.0 grades, a curb can work since you are getting evaluated against your peers to see he is standing out vs just doing acceptable.
I'm still not getting it. For a standard course, the criteria for what is "good" vs "great" should be pretty clear, and it should be independent of your peers. You have a syllabus, and a set of abilities for each grade level. If you hit those targets, you get the grade. If half the class gets an A, then it means they're pretty smart, or you did a great job in teaching. Of course, there's the chance the class was too easy, but you can always fix that.
No, I don't see why you're stuck with old test problems. For standard engineering classes, there's a huge (almost infinite) set of problems one can create.
For smaller classes, grading on a curve is even sillier, as the variance is always higher when the population size is small. For example, a lot of my small classes consisted of highly motivated students (all "A material"), because they're usually obscure electives where the content is challenging. You then pointlessly penalize students who sign up (just like they do at work). In fact, my professors were usually much more lenient on small classes for this very reason (i.e. lowering the standard needed to get an A).
I once took an Intro to Analysis course. It was moderately challenging. I got the highest score in the class, and my grade was A-. Everyone else got B+, B, or lower. A friend of mine (who didn't take the course) got really upset that I didn't get an A (or A+) given that I was the top scoring student.
But I knew my level of understanding/performance. It wasn't that great. I felt even an A- was too high a grade for me. And the teacher did a pretty good job in teaching. Why should I get a higher grade just because the other students were worse?
> For a standard course, the criteria for what is "good" vs "great" should be pretty clear, and it should be independent of your peers.
Do you think upper division college classes are somehow like high school classes with well developed curriculum and teaching professors who teach the same thing every quarter? Now you expect the professor to not only come up with new test material, but also extensively calibrate it before students take it, maybe for a 15-hour per week class (3 hours of teaching + 12 hours of studying), with maybe 15 students? Well, thank God we have AI for these kinds of things now.
Ok, let's exclude upper devision classes and just focus on lower division courses (since you mentioned an Intro to Analysis course). Here you have a relatively better chance of a well understood enough curriculum and testing material to actually not grade on a curve. BUT these are also usually weed out classes, with the idea that they only have N spots for students to proceed on to the upper division course, so curving serves an actual purpose that is aligned with the intended result.
> Do you think upper division college classes are somehow like high school classes with well developed curriculum and teaching professors who teach the same thing every quarter?
I repeatedly said "standard course", which implies it is a commonly taught course (be it upper or lower division). In my undergrad, Analysis I, II and Abstract Algebra I, II were upper division courses. In the engineering departments, stuff like Electromagnetics I, II were upper division.
Anything that is not an elective (and even some popular electives) were standard courses.
Now I'll grant that in CS, some material like machine learning changes rapidly. But in most engineering, very little in the undergrad material changes. Even my semiconductor courses in undergrad haven't changed much in decades.
So yes - for most of those classes (and that means the vast majority of undergrad engineering) classes, the curriculum is relatively standard.
> Now you expect the professor to not only come up with new test material, but also extensively calibrate it before students take it, maybe for a 15-hour per week class (3 hours of teaching + 12 hours of studying), with maybe 15 students?
First: In my very average undergrad university, professors were always careful not to reuse old homeworks/exams. It wasn't a huge burden. Professors who don't do this (e.g. most professors in top universities) signal very clearly their lack of interest in pedagogy.
Second: You want to do a curve on <= 15 students? Are you aware of basic statistics and the problems you get with small N? Are they using a normal distribution or one that is more appropriate for small N?
And as I already said, for a lot of electives where the material isn't standardized, professors lean towards lenient grading. They offer those classes because they want people to take it, and grading via a curve discourages it.
> since you mentioned an Intro to Analysis course
That was an upper division course. Yes, I know some universities have it as a lower division, but many (most in the US?) treat it as upper division.
> BUT these are also usually weed out classes, with the idea that they only have N spots for students to proceed on to the upper division course, so curving serves an actual purpose that is aligned with the intended result.
It was not a weed out course. Neither my undergrad nor grad math departments had weed out classes. I saw that concept only in the engineering departments. My EE department had only Circuits I, Circuits II and digital logic as "lower division". Circuits II was the weed out course, and you were not allowed to take anything else (e.g. E&M, Electronics, etc) unless you got a B or higher.
SAT/ACT math is incredibly simplistic and at worst maybe contributed by not filtering as many out. Math scores have been declining nation wide for decades now, that’s been a big issue for a while.
One big reason is preparation, people start preparing for tests 2 to 3 years in advance. And the method of testing influences exams used in grades before as well.
So assume 4 years of high school and someone that just came in. They are still preparing for SAT like tests in their first year of high school. Someone in final year of high school is well trained in it. So even though the benefits do not carry, enough portion of incoming students are still reaping benefits of standardized tests. The decay only shows later when batches without any benefits of standardized tests are coming through.
> people start preparing for tests 2 to 3 years in advance
Pardon? Is that a normal thing in the USA? I don't think I've ever started preparing for a test more than a week and a half ahead, a month if you count graduation exams. Not sure they ever determined more than a year in advance (more commonly: a bit less than a semester) what tests we'd be given in the first place
> They are still preparing for SAT like tests in their first year of high school.
Literally nobody does that except high achievers whose parents are pushing them for a high SAT score to get into Stanford or whatnot. Those are not likely to be the kids who are now getting Fs.
To be honest, this comment reads to me like LLM output.
You have a history of comments that were clearly written by a human, with character, but this comment stands out to me as an outlier. It has that semi-neutral, slightly pontificating tone of an LLM that just feels off in a way that's difficult to articulate.
I truly mean no offense. There's clearly a human behind this account.
You shouldn't be getting down voted. That comment was definitely generated by an LLM and it was actually disturbing to me that so many people were replying as if it weren't glaringly obvious.
It's not foo, it's bar.
Baz became qux.
That's foobar.
Baar baaz — quux quuz.
It's interesting that even people who are anti-Flock have been convinced to refer to Flock's camera products as automatic license plate readers (ALPRs).
This is similar to how Google has convinced Android users to refer to installing apps from stores other than the Play Store as "side-loading". It's a distraction.
Make no mistake -- Flock cameras are mass-surveillance tools with the ancillary use case of automatic license plate reading. I encourage anyone discussing these products not to refer to them as ALPRs, unless specifically discussing their license plate reading functionality.
I may be imagining this, but I'm nearly certain I was running dillo on a PDA (I want to say Palm Treo) around 2001. I remember it feeling revolutionary to open up a webpage on something other than my linux desktop computer at the time. Over Wifi!
As dumb and useless as I think Grok is, I think you've identified what actually happened, which is significantly more benign than asking a 10 year old for nudes.
Sometimes it's used in the expected way, but (more?) often, "international community" euphemistically refers to whomever is currently one of, or an ally of the above mentioned countries.
Tokens are an implementation detail that have no business being part of product pricing.
It's deliberate obfuscation. First, there's the simple math of converting tokens to dollars. This is easy enough; people are familiar with "credits". Credits can be obfuscation, but at least they're honest. The second and more difficult obfuscation to untangle is how one converts "tokens" to "value".
When the value customers receive from tokens slips, they pay the same price for the service. But generative AI companies are under no obligation to refund anything, because the customer paid for tokens, and they got tokens in return. Customers have to trust that they're being given the highest quality tokens the provider can generate. I don't have that trust.
Additionally, they have to trust that generative AI companies aren't padding results with superfluous tokens to hit revenue targets. We've all seen how much fluff is in default LLM responses.
Pinky promises don't make for healthy business relationships.
Tokens aren't that much more opaque than RAM GB/s for functions or whatever. You'd have to know the entire infra stack to really understand it. I don't really have a suggestion for a better unit for that kind of stuff.
Doesn’t prompt pricing obfuscate token costs by definition? I guess the alternative is everyone pays $500/mo. (And you’d still get more value than that.)
Could you explain?
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