My friend @edelwax has been working on this school for social systems design for years: human-systems.org/school. The material itself is rich and important—but I wanna nerd out about the form! The goal is to teach the material *through your pre-existing work / activities*. (cont)
The idea (as I understand!) is that a guide works with you to understand your current design projects, life activities, etc—then selects/adapts and sequences "quests" created to let you enact the textbook's contents in the context of your actual work/life.
This is an intriguing path for instructional design!

It's like project-based learning, but those curricula usually supply/scaffold the projects, rather than working with ones you already have. It's like unschooling (framed around your projects), but with an explicit curriculum.
I'm excited about this because I think building (or going beyond) the Young Lady's Illustrated Primer really means developing a) a mass communications media form which b) communicates through "enacted experiences"; c) which themselves serve some intrinsically meaningful purpose.
By "enacted experiences" I mean: activities structured (sometimes secretly) so that the act of participating in them tends to make you experience certain emotions / appreciate certain values / acquire understandings. Game designers are the masters of this. notes.andymatuschak.org/Enacted_experi…
A great dinner party host often pulls this off: they invite people and structure the environment so that certain experiences will be had.

Y Combinator's batch activities cause founders to intensely experience certain values and understandings.

But these aren't mass mediums!
Games *are* a mass medium. Journey is designed to produce a series of powerful emotional experiences, made all the more powerful because you feel like you bring them about with your actions. And those enacted experiences serve an intrinsically meaningful purpose: aesthetics.
It's that last point that "educational game" designers miss. They see that mass media for enacted experiences are powerful, but they (basically always) fail to make those activities serve either aesthetic joy or some other meaningful purpose for the player.
The form of Joe's class is awfully close to checking all three boxes. Learning primarily happens not through implicit instruction, but as a carefully-crafted side effect of participating in activities woven into meaningful parts of your life. And it's *almost* a mass medium!
The guides in Joe's class are, I suppose, a little like the ractors in the Primer. Maybe they'll experience the same fate! Hm hm.

(This thread's a bit ranty—I hope you'll forgive me. I've been collecting notes on this problem for a couple years, and I'm still lost in the forest)

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More from @andy_matuschak

31 Mar
Thrilled to discover nbdev from @jeremyphoward & @GuggerSylvain. It's an attempt to solve a big problem with computational notebooks like Jupyter: you explore problems with a notebook, but usually need to "switch" to a more powerful tool for "real" impls: fast.ai/2019/12/02/nbd…
nbdev tries to solve this problem by giving you
- automatically turning notebooks into publishable Python modules
- bidirectional sync with plaintext .py for IDE usage
- fixes for other "real" project needs: tests, continuous integration, documentation export, conflict resolution
While nbdev seems focused on helping individual developers avoid the "switch" when implementing their own projects, I think layers like this could help solve a big problem with "executable books": the huge barrier for *readers* to build on embedded code to do anything real.
Read 8 tweets
11 Mar
In SRS design, Anki and Quantum Country ask you to think of the answer; Duolingo and Execute Program ask you to input an answer.

I’d thought: latter’s likely more effective, but annoying & slow. Surprised to see these studies found little diff in recall: andymatuschak.org/files/papers/L…
(See chapter 6, which describes the three experiments. Some limitations: targets were Swahili–English word associations; performed on smallish sample of undergrads; maximum retrieval interval of a week. This thesis is intensely interesting throughout!)
More accumulated notes on self-grading vs. machine-grading in SRS design: notes.andymatuschak.org/z7gWUD4AnndX5C…
Read 5 tweets
5 Mar
One recent way this helped me: I think we can make inboxes (email, tasks, tabs, reading lists) less burdensome by replacing high-stakes mechanics (“close tab”) w/ low-stakes ones (“not right now”, decay). The insight comes from understanding these as queueing systems! (cont)
Inboxes only “work” if you trust how they’re drained. From a queue-processing perspective: the departure rate must be below some threshold. Inbox Zero “works” by aggressively increasing that rate (via defer, delegate, drop)—blunt, but ensures that departure rate > arrival rate.
This tactic requires you to make a decision about every item in the inbox. Maybe fine when queue is small, but explicitly deferring a task imposes an emotional cost, possibly unnecessarily: “inbox zero” is only necessary if the arrival rate *always* exceeds the departure rate.
Read 6 tweets
4 Mar
In cogsci, Marr suggests 3 levels at which a system (eg vision) can be analyzed: computational (the fundamental problem being solved), algorithmic (how it’s solved, abstractly), and implementation (hardware details).

It’s an interesting taxonomy for analyzing tools for thought!
e.g. for memory systems, three kinds of analysis:

* computational: the dynamics of human memory
* algorithmic: schedules which optimize learning relative to those dynamics
* implementation: details of software implementing those schedules

All important and intertwined!
One thing I like about this approach (same motivation for Marr in cogsci): it pushes you to characterize the computational task your system is performing.

e.g. if you’re designing creativity support systems, you’ll benefit from insights about what creative problem-solving *is*
Read 7 tweets
27 Feb
In practice, now with ~5 substantive texts written in the medium, it's pretty consistent that ~2-5% of readers engage with the prompts; 25-50% answer ~all (very length dependent); around half of those do any reviews.

What are the implications for authors and their incentives?
If you have thousands of readers, only a few tens might actually review your material over time. Writing those prompts takes a lot of effort—is it "worth it"?

It's an easier case to make for "platform knowledge" like Quantum Country, which can draw 100k's of readers.
But of course "visitor" numbers are misleading. For every 100 unique visitors an article's analytics count, it wouldn't surprise me if 80 bounce without reading much and 10+ read shallowly. So maybe this is actually reaching most of the serious readers.
Read 8 tweets
26 Feb
I’ve been studying dynamics of reader memory with the mnemonic medium, running experiments on interventions, etc. A big challenge has been that I'm roughly trying to understand changes in a continuous value (depth of encoding) through discrete measurements (remembered / didn’t).
I can approximate a continuous measure by looking at populations: “X% of users in situation Y remembered.” Compare that % for situations Y and Y’ to sorta measure an effect. This works reasonably well when many users are “just on the edge” of remembering, and poorly otherwise…
It’s a threshold function on the underlying distribution. Imagine that a person will remember something iff their depth-of-encoding (a hidden variable)—plus some random noise (situation)—is greater than some threshold. Our population measure can distinguish A vs A’, not B vs B’.
Read 9 tweets

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