Further insights I got from machine learning:

1. There is a mathematically determinable maximum rate of learning. In the real world, this often comes up at small scales, and once you start learning to see the world through a machine learning lens you can see people routinely making enormous leaps off of data that not only doesn't support the leap, it literally is mathematically incapable of supporting it. As the amount of data scales up this starts mattering less, and the dominant factor becomes the fact that as humans, we actually aren't all that great at pulling learned truth from large amounts of data, though some of this is also that the universe is pretty darned noisy and if we were "good" at it, we'd learn an awful lot of untrue stuff. (If that sounds like a description of the current reality... no, I mean orders of magnitude moreso than today's real science problems.)

2. The maximum rate of learning is heavily dependent on two things: The ability of the agent to control input into the environment, and the latency of the feedback. First, it has been well established in both theory (machine learning mathematics) and fact (various psychology experiments with cats in very strange visual environments) that active learning is radically faster than passive learning. Passively sitting in a room and listening to someone attempt to present facts is perhaps not the worst approach to teaching, but it's definitely radically suboptimal.

Second, latency is huge. Trying to learn from a latent signal is intrinsically harder than a less latent one, and it has absolutely nothing to do with willpower or moral fiber... it is intrinsically, mathematically, irreducibly more difficult. The maximum speed of learning continues to increase all the way down into the sub-second response times.

The amount of learning that can even in theory be done under the "listen to a lecture, two days later get quizzed on it, receive marked-up quizzes back a week later" model is shockingly low compared to what is theoretically on the table. If I were in the educational startup field, my near-number-one priority would be speed of feedback. I would literally be happy to hear from one of my programmers that the yes/no feedback on the arithmetic drilling went from .5 to .4 seconds. (If you can get your hands on it, try Big Brain Academy or any of its brethren that have the arithmetic drilling in it, and imagine just how much faster you could have learned basic arithmetic with this incredibly responsive tool around when you were a kid. Reminds me, my oldest is getting near where I ought to be pulling that back out....) Between what's on the table with lower latency and integrated spaced repetition, there ought to be almost unbelievable gains on the table for anyone who can put this all together into the right package. (The aforementioned "Big Brain Academy" line of games being the closest I've seen. Latency for putatively "educational" games is often just awful.)

On that note, if you are in the educational startup space, it behooves you to take a machine learning course. Whether or not you are able to apply the code techniques themselves, the insights about learning itself will more than pay back your time. And, frankly, I'm virtually desperate to see someone get this right soon.

(On a sidenote, I'm open to people's suggestions about such programs that do take latency into account for elementary school age children. Most real-world programs seem to go in exactly the wrong direction and assume that the children are not discriminating, so who cares anyhow, let's not spend money on quality software, and the result is slow, slow, slow... agonizing. My feeling is that if we learn from Big Brain Academy and keep things moving along, the mere act of learning itself can feel good enough to keep most users engaged just fine, and that if you make learning boring, no amount of graphical frippery, loud noises, or encouraging-sounding recorded sound clips of people saying "very good" can make up for it.)