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Andy Matuschak

@andy_matuschak

1 Apr

@edelwax

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.

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Andy Matuschak

@andy_matuschak

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…

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Andy Matuschak

@andy_matuschak

5 Mar

https://twitter.com/andy_matuschak/status/1367577034407354368

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)

https://twitter.com/andy_matuschak/status/1367577034407354368

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

Andy Matuschak

@andy_matuschak

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*

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Andy Matuschak

@andy_matuschak

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

Andy Matuschak

@andy_matuschak

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’.

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