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Annotation 7800480730380

#deep-learning #keras #lstm #python #sequence

a naive method that splits the 1,000-long sequence into 50 sequences (say) each of length 20 and treats each sequence of length 20 as a separate training case. This is a sensible approach that can work well in practice, but it is blind to temporal dependencies that span more than 20 time steps.

— Training Recurrent Neural Networks, 2013
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... a naive method that splits the 1,000-long sequence into 50 sequences (say) each of length 20 and treats each sequence of length 20 as a separate training case. This is a sensible approach that can work well in practice, but it is blind to temporal dependencies that span more than 20 time steps. — Training Recurrent Neural Networks, 2013 This means as part of framing your problem you must split long sequences into subsequences that are both long enough to capture relevant context for making predictions, but short enough

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Annotation 7800482565388

#abm #agent-based #machine-learning #model #priority #synergistic-integration
Modeling skills can be applied to systems with different complexities, ranging from a simple system, like a circuit with a resistor (Ohm’s Law), to complex systems, such as global economic system
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Modeling skills can be applied to systems with different complexities, ranging from a simple system, like a circuit with a resistor (Ohm’s Law), to complex systems, such as global economic system. Most real-life systems involve an extended range of spatial or temporal scales, as well as interactions between the built environment and natural system, which can be very complex </sp

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Annotation 7800523984140

#AI
displacement
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I’ve started to call this displacement wave the Fuckening because that feels more visceral.

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&lt;title&gt;<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-heart"><path d="M19 14c1.49-1.46 3-3.21 3-5.5A5.5 5.5 0 0 0 16.5 3c-1.76 0-3 .5-4.5 2-1.5-1.5-2.74-2-4.5-2A5.5 5.5 0 0 0 2 8.5c0 2.3 1.5 4.05 3 5.5l7 7Z"></path></svg>&lt;g&gt;&lt;svg&gt;<div class="label">738</div>&lt;button&gt;&lt;div&gt;<button tabindex="0" type="button" aria-label="View comments (8)" data-href="https://blog.andrewyang.com/p/the-end-of-the-office/comments" class="pencraft pc-reset pencraft post-ufi-button style-button post-ufi-comment-button has-label with-border"><svg role="img" style="height:20px;width:20px;" width="20" height="20" viewBox="0 0 24 24" fill="#000000" stroke-width="2" stroke="#000" xmlns="http://www.w3.org/2000/svg" class="icon"><g><title></title><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg></g></svg><div class="label">8</div></button><button tabindex="0" type="button" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft post-ufi-button style-button has-label with-border"><svg role="img" style="height:20px;width:20px;" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke-width="2" stroke="#000" xmlns="http://www.w3.org/2000/svg" class="icon"><g><title></title><path d="M21 3V8M21 8H16M21 8L18 5.29962C16.7056 4.14183 15.1038 3.38328 13.3879 3.11547C11.6719 2.84766 9.9152 3.08203 8.32951 3.79031C6.74382 4.49858 5.39691 5.65051 4.45125 7.10715C3.5056 8.5638 3.00158 10.2629 3 11.9996M3 21V16M3 16H8M3 16L6 18.7C7.29445 19.8578 8.89623 20.6163 10.6121 20.8841C12.3281 21.152 14.0848 20.9176 15.6705 20.2093C17.2562 19.501 18.6031 18.3491 19.5487 16.8925C20.4944 15.4358 20.9984 13.7367 21 12" stroke-linecap="round" stroke-linejoin="round"></path></g></svg><div class="label">193</div></button>&lt;div&gt;<div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft post-ufi-button style-button has-label with-border"><div class="label">Share</div></button></div>&lt;div&gt;&lt;div&gt;&lt;div&gt;<div class="visibility-check"></div><div><div class="available-content"><div dir="auto" class="body markup"><p>I write this filled with sadness.</p><p><span>Last week, I </span><a href="/bubo5/browser?url=https%3A//blog.andrewyang.com/p/the-ai-meltdown" rel="nofollow">wrote</a><span> about how AI is now replacing white-collar workers in earnest as Claude’s Co-work released plug-ins for legal, financial and marketing functions. Social media is filled with accounts from long-time software developers declaring themselves obsolete based on AI’s capabilities right now. Physicists report the same.</span></p><p>Someone in my family had AI program a website for them this week. It completed in minutes what used to take a designer or a firm days of work.</p><p>How many roles essentially consist of processing information and then presenting it to someone to make a decision? Now not only the process and report will be automated, but perhaps the decision as well.</p><p>This will result in the great disemboweling of white-collar jobs.</p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21sHky%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!sHky!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg 424w, https://substackcdn.com/image/fetch/$s_!sHky!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg 848w, https://substackcdn.com/image/fetch/$s_!sHky!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg 1272w, https://substackcdn.com/image/fetch/$s_!sHky!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!sHky!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg" width="1456" height="1092" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/f59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:1092,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:594733,&quot;alt&quot;:&quot;&quot;,&quot;title&quot;:null,&quot;type&quot;:&quot;image/jpeg&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://blog.andrewyang.com/i/188089435?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" title="" srcset="https://substackcdn.com/image/fetch/$s_!sHky!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg 424w, https://substackcdn.com/image/fetch/$s_!sHky!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg 848w, https://substackcdn.com/image/fetch/$s_!sHky!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg 1272w, https://substackcdn.com/image/fetch/$s_!sHky!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59397f0-fdc3-4a85-b3f5-08a364451bad_4000x3000.jpeg 1456w" sizes="100vw" fetchpriority="high" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p>This automation wave will kick millions of white-collar workers to the curb in the next 12 - 18 months. As one company starts to streamline, all of their competitors will follow suit. It will become a competition because the stock market will reward you if you cut headcount and punish you if you don’t. As one investor put it, “Sell anything that consists of people sitting at a desk looking at a computer.”</p><p>I’ve started to call this displacement wave the Fuckening because that feels more visceral.</p><p>Do you sit at a desk and look at a computer much of the day? Take this very seriously.</p><p>I want to forecast a few of the immediate social impacts of the Fuckening:</p><p>1. Mid-career office workers will be fired in droves. Right now, there are about 70 million white-collar workers in the United States. Expect that number to be reduced substantially, by 20 – 50% in the next several years. Even a reduction of several million would be tectonic, and I fully expect it to go well beyond that level. Major firms are firing people right now. I spoke to the CEO of a publicly traded tech company. He said, “We’re firing 15% of workers right now. We’ll probably do another 20% 2 years from now. And then another 20% 2 years later. After that, who knows?”</p><p>Millions of workers are about to be given their pink slips.</p><p>The most vulnerable are middle managers who have become more expensive. What will they do next? They’ll look for another job. Good luck with that as job search times have already been climbing and corporations are going to be loathe to hire. Many will find something part-time to pass the time. One friend of mine who lost his job began volunteering with a local non-profit that now might want to pay him to manage their IT infrastructure. The pay would be 80% less than what he used to make, but he might wind up taking it. There will be a ton of similar downgrades ahead.</p><p>Expect the Starbucks in your local suburb to become occupied with middle-aged former office workers who want to get out of the house. That’s a benign portrait, but a lot of these families still owe mortgages on their houses that they won’t be able to maintain. If I were a homeowner in Silicon Valley or Westchester County, I might consider putting my house up for sale to see what I could get because there’s going to be downward pressure on these communities. It might not feel great being first, but you don’t want to be last.</p><p>It won’t just be the managers. It will be the call-center workers, the marketers, the coders, the financial forecasting teams, and on and on; business functions are going to get compressed into a handful of key employees supplemented by AI.</p><p>In essence, the number of people necessary to make large corporations function is going to be dramatically reduced. What do you say if your child asks you about becoming a newspaper journalist? You say, “Well, that field hasn’t really been growing, you might want to consider something else.”</p><p>Substitute “programmer/designer/accountant/lawyer/finance/marketing/consulting/analyst/researcher/anything-that-involves-looking-at-a-computer-and-going-to-an-office” and you’ve got the right idea.</p><p>Basically, brainpower is now a commodity that is going cheap.</p><p>If you are one of the professionals who is likely to be affected, I’m sorry. But this is real. Cut your expenses and take precautionary measures, because there will be many people losing their jobs and you could easily be one of them. This has left the lab and is now at a desk beside you. Only it doesn’t even need a desk – too expensive.</p><p>2. Personal bankruptcies will surge. Americans are in general already on unsteady ground financially, but that’s going to get much much worse. Millions of Americans of every walk of life are about to see their way of life threatened. If you lose your job, you typically can get by for a little while on savings, but then it gets rocky pretty quickly. Here, you can see mortgage delinquency rates are already on the rise.</p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21GNxO%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!GNxO!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg 424w, https://substackcdn.com/image/fetch/$s_!GNxO!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg 848w, https://substackcdn.com/image/fetch/$s_!GNxO!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg 1272w, https://substackcdn.com/image/fetch/$s_!GNxO!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!GNxO!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg" width="399" height="501" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:501,&quot;width&quot;:399,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:30201,&quot;alt&quot;:&quot;&quot;,&quot;title&quot;:null,&quot;type&quot;:&quot;image/jpeg&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://blog.andrewyang.com/i/188089435?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" title="" srcset="https://substackcdn.com/image/fetch/$s_!GNxO!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg 424w, https://substackcdn.com/image/fetch/$s_!GNxO!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg 848w, https://substackcdn.com/image/fetch/$s_!GNxO!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg 1272w, https://substackcdn.com/image/fetch/$s_!GNxO!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7adbd6cb-6824-455f-970a-46e3bd8ad7a0_399x501.jpeg 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p>Even if you’re not an office worker, you may be affected. Let’s say you’re a drycleaner or a dog walker or a hairstylist. If people in your community stop going to the office, your business is going to suffer because there are fewer business shirts to launder, people will walk their dogs themselves and cut back on trips to the salon, etc.</p><p>The amount of money getting paid to human labor is about to go down. The value is going to get soaked up into the cloud. The K-shaped economy, where the rich enjoy the growth and keep spending while the bottom 80% or so are trying to tread water will become all the more dramatic.</p><p>People and families are going to be left behind in large numbers.</p><p>Of course, where financial ills and stresses increase, social ills follow in terms of broken relationships, addictive behavior and despair.</p><p>I’m sorry I don’t have better news here. But it’s going to get very rough out there for a lot of people. Make a plan. Be adaptable.</p><p>3. College grads won’t be able to find jobs. Already, the proportion of seniors who find a job in their field has dropped to 30% and underemployment of recent grads is up to 52%. Enterprises that used to need an annual flow of whippersnappers are now firing some of last year’s whippersnappers. A ton of people will be moving home with Mom and Dad or trying to hide out in graduate school.</p><p>School loans will become all the more burdensome without the ability to get a high-paying job. Many young people will be faced with getting a job that doesn’t require a degree while paying for the one that they got at great expense.</p><p>Basically, the education premium is going to be sharply reduced with AI’s arrival; many degrees will become devalued. Dozens, perhaps hundreds, of marginal colleges are going to close over a number of years. The ones that remain will be asked to prove their value in various ways, with the elite doing fine and the others struggling or evolving.</p><p>If you’re weighing going back to school or sending your child in this direction, question the cost of the degree heavily, as the cost-benefit is changing – for the worse – in real time. If it’s low-cost, vocational or top-tier, you’re fine. If it’s expensive and doesn’t lead to a concrete opportunity . . . maybe re-examine it.</p><p>4. Downtowns and office parks will empty out. What to do with all of this office space? Many commercial districts struggled through COVID and work-from-home. This will get considerably worse. Commercial buildings are generally very difficult to convert to some other purpose (i.e. residential) because of the floor plans, plumbing and the like.</p><p>This will become an overhang in New York, Chicago, Los Angeles, San Francisco and other cities with significant office districts. Not that long ago, these offices were a source of significant tax revenue. Their devaluation will hurt municipal balance sheets for years and make cities less able to provide services in other neighborhoods. Washington D.C. should be on the list too but mass layoffs in agencies are more of a political choice and most of those workers are unionized. Major urban centers will become less central and vital with AI, and empty office blocks will become the norm. Think urban wasteland and eerie downtowns leading to municipal tax hikes and declining services.</p><p>5. Pessimism and anger will rise up. I saw a note on social media that said, “Do you really think politicians will let millions lose their jobs? They’ll ban AI first.”</p><p>Has this person been paying attention? Go talk to the manufacturing workers or the journalists and see how it worked out. Hundreds of billions of dollars have not been spent on this technology for corporate juggernauts to stop now, and most officials have been cheerleading what they see as progress. The genie is out of the bottle.</p><p>But yes, people are going to be pissed.</p><p>The social contract of ‘study hard, go to school, get a good job, live a decent life’ is about to be vaporized to smithereens. Upward mobility for most will be a thing of the past.</p><p>People are not going to take it well. Particularly educated people who think that they deserve better. That’s an ingredient for revolt.</p><p>There’s been a lot of talk this past year about how young people are lurching to the left, the growing popularity of socialism in the Democratic Party, and Zohran Mamdani as emblematic of the next wave of leaders.</p><p>Imagine what people are going to think when we all feel like serfs to AI overlords that have soaked up the white-collar work?</p><p>Everything that has preceded this will be tame compared to what’s coming in terms of anger, despair and unrest. It won’t be very clear or organized, but people increasingly will feel like they have less to lose.</p><p><span>I argued in </span><a href="/bubo5/browser?url=https%3A//shop.andrewyang.com/products/the-ware-on-normal-people-hardcover" rel="nofollow">The War on Normal People</a><span> that capitalism has done a lot to bring people out of poverty, but capitalism plus AI will be no one’s idea of a desired result, and that we have to advance the economy to revolve around people. That would start with Universal Basic Income.</span></p><p>I’ll have more to say about this as time goes on and the impact becomes clearer. But I’m deeply saddened. As the guy who jumped up and down for the better part of 3 years saying “AI is coming, it’s going to wipe out millions of jobs!” I know that we could and should be doing much more for the people and families of this country. In the absence of that kind of coming together, we are left with every-person-for-yourself in the face of the greatest technological transformation of our lifetimes.</p><p>Expect it to get incredibly, intergenerationally rough out there. Batten down the hatches, and do what you can for yourself and those around you.</p><p>- Andrew</p><p><span>I’ll be on the book tour in New Jersey and DC this week and then out West next week - check dates </span><a href="/bubo5/browser?url=https%3A//www.andrewyang.com/events" rel="nofollow">here</a><span>. </span><a href="/bubo5/browser?url=https%3A//www.offlineparty.com/" rel="nofollow">Offline</a><span> is coming to DC this week and SF next week. To start saving money on your data, check out </span><a href="/bubo5/browser?url=http%3A//noblemobile.com/yang" rel="nofollow">noblemobile.com/yang</a><span> for 3 free months off your wireless bill. Make good decisions.</span></p></div></div><div class="visibility-check"></div><div class="post-footer"><div class="pencraft pc-display-flex pc-gap-16 pc-paddingTop-16 pc-paddingBottom-16 pc-justifyContent-space-between pc-alignItems-center pc-reset flex-grow-rzmknG border-top-detail-themed-k9TZAY border-bottom-detail-themed-Ua9186 post-ufi"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><div class="like-button-container post-ufi-button style-button"><button tabindex="0" type="button" aria-label="Like (738)" aria-pressed="false" class="pencraft pc-reset pencraft post-ufi-button style-button has-label with-border"><svg role="img" style="height:20px;width:20px;" width="20" height="20" viewBox="0 0 24 24" fill="#000000" stroke-width="2" stroke="#000" xmlns="http://www.w3.org/2000/svg" class="icon"><g><title></title><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide 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47.1778 64.9205M82.7358 64.8765C85.2556 66.0834 87.6515 67.7209 89.7667 69.8938C97.9573 77.8669 98.5421 89.8306 97.1218 97.3712C89.5811 98.7916 77.6175 98.2068 69.6443 90.0162C67.6781 88.1022 66.1502 85.9583 64.9832 83.7008M64.9832 83.7008C63.8145 85.9674 62.2826 88.12 60.3092 90.041C52.3361 98.2316 40.3724 98.8164 32.8317 97.3961C31.4114 89.8554 31.9962 77.8918 40.1868 69.9186C42.2908 67.7572 44.6726 66.1255 47.1778 64.9205" stroke="var(--color-fg)" stroke-width="8.125" stroke-linejoin="round"></path></svg></div></div></span></div><a data-native="true" href="/bubo5/browser?url=https%3A//blog.andrewyang.com/p/the-end-of-the-office/comment/215352142" rel="nofollow" title="Feb 16, 2026, 5:12 PM" class="pencraft pc-reset color-secondary-ls1g8s decoration-hover-underline-ClDVRM reset-IxiVJZ"><span class="pencraft pc-reset color-secondary-ls1g8s line-height-20-t4M0El font-text-qe4AeH size-13-hZTUKr weight-regular-mUq6Gb reset-IxiVJZ">4d</span></a></div></div></div><div class="pencraft pc-display-flex pc-flexDirection-column pc-reset flex-grow-rzmknG"></div><div class="pencraft pc-display-flex pc-reset triggerContainer-eX588u"><button tabindex="0" type="button" aria-label="Ellipsis" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft trigger-j08Uop iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_quaternary-kpMibu"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-ellipsis"><circle cx="12" cy="12" r="1"></circle><circle cx="19" cy="12" r="1"></circle><circle cx="5" cy="12" r="1"></circle></svg></button></div></div><div class="comment-body expanded"><p><span>At some point I would love for someone to explain how large corporations and high level execs/leaders who are supposed to be thriving due to AI are going to be so profitable when as many people as you estimate will be unemployed, have no medical insurance, etc., will be paying for the products that all the AI companies excitingly dumping their employees will be offering? Use simple examples like iphones or streaming services etc? Will unemployed people surviving on growing their own vegetables be buying $1500 smart phones or buying expensive Meta Glasses? Will all these unemployed people be paying for Netflix and the myriad of streaming services they pay for today? Someone recent said on CNBC that &quot;with people having so much free time after AI takes their jobs, they will be able to do what they love like go to sporting events and concerts, etc. which is why we are investing so much money in the sport industry.&quot; Really? All these unemployed people will be buying tickets that cost hundreds of dollars, to go watch an NFL game? Or unemployed people will be buying 5 streaming services so they can watching their favorite sports everyday?? I feel like no one ever talks about corporations who leverage AI, which if course they will, when half of the people/customers/consumers don't have disposable income. </span></p></div><div class="pencraft pc-display-flex pc-gap-16 pc-paddingTop-8 pc-justifyContent-flex-start pc-alignItems-center pc-reset comment-actions withShareButton-hQzuEn"><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a class="pencraft pc-reset link-_X6et2 link-LIBpto"><div class="pencraft pc-display-flex pc-gap-6 pc-alignItems-center pc-reset"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="var(--color-fg-secondary-themed)" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg><div class="pencraft pc-reset color-pub-secondary-text-hGQ02T line-height-20-t4M0El font-meta-MWBumP size-11-NuY2Zx weight-medium-fw81nC 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overflow-hidden-WdpwT6"></div></div></div></div><div class="comment"><div class="comment-anchor"></div><div class="comment-anchor"></div><div role="article" aria-label="Comment by David Goldsmith" class="pencraft pc-display-flex pc-gap-12 pc-paddingBottom-12 pc-reset comment-content"><div class="pencraft pc-display-flex pc-flexDirection-column pc-reset"><a href="/bubo5/browser?url=https%3A//substack.com/profile/5217065-david-goldsmith%3Futm_source%3Dcomment" aria-label="View David Goldsmith's profile" class="pencraft pc-display-contents pc-reset" rel="nofollow"><div style="--scale:32px;" tabindex="0" class="pencraft pc-display-flex pc-width-32 pc-height-32 pc-justifyContent-center pc-alignItems-center pc-position-relative pc-reset bg-secondary-UUD3_J flex-auto-j3S2WA animate-XFJxE4 outline-detail-vcQLyr pc-borderRadius-full overflow-hidden-WdpwT6 sizing-border-box-DggLA4 pressable-sm-YIJFKJ showFocus-sk_vEm container-TAtrWj interactive-UkK0V6"><div style="--scale:32px;" title="David Goldsmith" class="pencraft pc-display-flex pc-width-32 pc-height-32 pc-justifyContent-center pc-alignItems-center pc-position-relative pc-reset bg-secondary-UUD3_J flex-auto-j3S2WA outline-detail-vcQLyr pc-borderRadius-full overflow-hidden-WdpwT6 sizing-border-box-DggLA4 container-TAtrWj"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!5Dg1!,w_32,h_32,c_fill,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcf3c935a-180e-4f64-a05d-ff322733eec6_144x144.png 32w, https://substackcdn.com/image/fetch/$s_!5Dg1!,w_64,h_64,c_fill,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fcf3c935a-180e-4f64-a05d-ff322733eec6_144x144.png 64w, 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weight-regular-mUq6Gb reset-IxiVJZ"><span class="pencraft pc-reset line-height-20-t4M0El font-text-qe4AeH size-15-Psle70 weight-medium-fw81nC reset-IxiVJZ"><span style="min-width:0;" data-state="closed"><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a href="/bubo5/browser?url=https%3A//substack.com/profile/5217065-david-goldsmith%3Futm_source%3Dsubstack-feed-item" showBack="" data-native="true" class="link-LIBpto" rel="nofollow">David Goldsmith</a></span></span> <div data-testid="user-badge" class="pencraft pc-reset cursor-pointer-LYORKw inlineContainer-FbhgTi"><div class="pencraft pc-display-flex pc-reset"><svg width="12" height="12" viewBox="0 0 130 130" fill="none" xmlns="http://www.w3.org/2000/svg" class="subscriberBadge-Lpjxjx"><circle cx="65" cy="65" r="65" fill="var(--color-bg)"></circle><path d="M67.5321 67.2143C62.0162 68.2532 54.1338 68.2193 47.1778 64.9205M62.2892 67.2143C61.2169 61.5215 61.2875 53.3079 64.9106 46.1935M62.2892 62.5387C67.8295 61.4951 75.7573 61.534 82.7358 64.8765M67.6643 62.5635C68.7424 68.2874 68.6652 76.5598 64.9832 83.7008M64.9106 46.1935C66.0867 43.8841 67.6371 41.6906 69.6443 39.7368C77.6175 31.5462 89.5811 30.9614 97.1218 32.3817C98.5421 39.9224 97.9573 51.886 89.7667 59.8592C87.6515 62.032 85.2556 63.6696 82.7358 64.8765M64.9106 46.1935C63.7346 43.8841 62.1841 41.6906 60.177 39.7368C52.2038 31.5462 40.2401 30.9614 32.6995 32.3817C31.2791 39.9224 31.8639 51.886 40.0546 59.8592C42.1955 62.0585 44.6242 63.7095 47.1778 64.9205M82.7358 64.8765C85.2556 66.0834 87.6515 67.7209 89.7667 69.8938C97.9573 77.8669 98.5421 89.8306 97.1218 97.3712C89.5811 98.7916 77.6175 98.2068 69.6443 90.0162C67.6781 88.1022 66.1502 85.9583 64.9832 83.7008M64.9832 83.7008C63.8145 85.9674 62.2826 88.12 60.3092 90.041C52.3361 98.2316 40.3724 98.8164 32.8317 97.3961C31.4114 89.8554 31.9962 77.8918 40.1868 69.9186C42.2908 67.7572 44.6726 66.1255 47.1778 64.9205" stroke="var(--color-fg)" stroke-width="8.125" stroke-linejoin="round"></path></svg></div></div></span></div><a data-native="true" href="/bubo5/browser?url=https%3A//blog.andrewyang.com/p/the-end-of-the-office/comment/215269034" rel="nofollow" title="Feb 16, 2026, 2:14 PM" class="pencraft pc-reset color-secondary-ls1g8s decoration-hover-underline-ClDVRM reset-IxiVJZ"><span class="pencraft pc-reset color-secondary-ls1g8s line-height-20-t4M0El font-text-qe4AeH size-13-hZTUKr weight-regular-mUq6Gb reset-IxiVJZ">4d</span></a></div></div></div><div class="pencraft pc-display-flex pc-flexDirection-column pc-reset flex-grow-rzmknG"></div><div class="pencraft pc-display-flex pc-reset triggerContainer-eX588u"><button tabindex="0" type="button" aria-label="Ellipsis" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft trigger-j08Uop iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_quaternary-kpMibu"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-ellipsis"><circle cx="12" cy="12" r="1"></circle><circle cx="19" cy="12" r="1"></circle><circle cx="5" cy="12" r="1"></circle></svg></button></div></div><div class="comment-body expanded"><p><span>Andrew, I would love to hear you talk about the need for a Small Business First coalition and an economy that's based on prioritizing small business over big business. After all, it is big business, major corporations that are driving AI. </span></p><p><span>Don't you agree that we need to have an economy that is local as opposed to one that is driven by major corporations? The whole paradigm that we've grown up with is that we want big businesses in our state because it looks good And employs lots of people. </span></p><p><span>Well that's the past. Wouldn't you agree?</span></p><p><span> Local economies based on small business have higher degrees of diversity and therefore strength. They have more stratification, more opportunities for employment and creativity, job satisfaction, and life satisfaction. </span></p><p><span>I'd love to hear you talk about this. What other thought leaders are talking about this? What states are changing their laws to create a Small Business First set of policies?</span></p></div><div class="pencraft pc-display-flex pc-gap-16 pc-paddingTop-8 pc-justifyContent-flex-start pc-alignItems-center pc-reset comment-actions withShareButton-hQzuEn"><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a class="pencraft pc-reset link-_X6et2 link-LIBpto"><div class="pencraft pc-display-flex pc-gap-6 pc-alignItems-center pc-reset"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="var(--color-fg-secondary-themed)" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg><div class="pencraft pc-reset color-pub-secondary-text-hGQ02T line-height-20-t4M0El font-meta-MWBumP size-11-NuY2Zx weight-medium-fw81nC transform-uppercase-yKDgcq reset-IxiVJZ meta-EgzBVA">Reply</div></div></a></span><span 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cause the stock market will reward you if you cut headcount and punish you if you don’t. As one investor put it, “Sell anything that consists of people sitting at a desk looking at a computer.” <span>I’ve started to call this displacement wave the Fuckening because that feels more visceral. Do you sit at a desk and look at a computer much of the day? Take this very seriously. I want to forecast a few of the immediate social impacts of the Fuckening: 1. Mid-career office wo



Annotation 7800542334220

#Learning
If there is a central conundrum in instructional design, it is this: firstly, how to disassemble a domain of knowledge into teachable parts without destroying what made it a domain in the first place. And then secondly, how to reassemble those parts into flexible understanding without mistaking performance on individual components for comprehension of the whole.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

&lt;title&gt;<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-heart"><path d="M19 14c1.49-1.46 3-3.21 3-5.5A5.5 5.5 0 0 0 16.5 3c-1.76 0-3 .5-4.5 2-1.5-1.5-2.74-2-4.5-2A5.5 5.5 0 0 0 2 8.5c0 2.3 1.5 4.05 3 5.5l7 7Z"></path></svg>&lt;g&gt;&lt;svg&gt;<div class="label">131</div>&lt;button&gt;&lt;div&gt;<button tabindex="0" type="button" aria-label="View comments (35)" data-href="https://carlhendrick.substack.com/p/the-embarrassingly-parallel-problem/comments" class="pencraft pc-reset pencraft post-ufi-button style-button post-ufi-comment-button has-label with-border"><svg role="img" style="height:20px;width:20px;" width="20" height="20" viewBox="0 0 24 24" fill="#000000" stroke-width="2" stroke="#000" xmlns="http://www.w3.org/2000/svg" class="icon"><g><title></title><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg></g></svg><div class="label">35</div></button><button tabindex="0" type="button" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft post-ufi-button style-button has-label with-border"><svg role="img" style="height:20px;width:20px;" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke-width="2" stroke="#000" xmlns="http://www.w3.org/2000/svg" class="icon"><g><title></title><path d="M21 3V8M21 8H16M21 8L18 5.29962C16.7056 4.14183 15.1038 3.38328 13.3879 3.11547C11.6719 2.84766 9.9152 3.08203 8.32951 3.79031C6.74382 4.49858 5.39691 5.65051 4.45125 7.10715C3.5056 8.5638 3.00158 10.2629 3 11.9996M3 21V16M3 16H8M3 16L6 18.7C7.29445 19.8578 8.89623 20.6163 10.6121 20.8841C12.3281 21.152 14.0848 20.9176 15.6705 20.2093C17.2562 19.501 18.6031 18.3491 19.5487 16.8925C20.4944 15.4358 20.9984 13.7367 21 12" stroke-linecap="round" stroke-linejoin="round"></path></g></svg><div class="label">36</div></button>&lt;div&gt;<div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft post-ufi-button style-button has-label with-border"><div class="label">Share</div></button></div>&lt;div&gt;&lt;div&gt;&lt;div&gt;<div class="visibility-check"></div><div><div class="available-content"><div dir="auto" class="body markup"><p><em>If there is a central conundrum in instructional design, it is this: firstly, how to disassemble a domain of knowledge into teachable parts without destroying what made it a domain in the first place. And then secondly, how to reassemble those parts into flexible understanding without mistaking performance on individual components for comprehension of the whole. </em></p><p><em>For example, a student can learn the dates of every major battle in the First World War, define &quot;alliance&quot; and &quot;imperialism,&quot; and correctly identify the assassination of Archduke Franz Ferdinand as the triggering event, yet still have no real understanding of why the war happened. Or more commonly, the student who can accurately define a set of vocabulary words and still be unable to use them appropriately in speech or writing.</em></p><p><em>What makes this conundrum so persistent is that the two problems; decomposition and reassembly, look superficially similar but are structurally different. The first lends itself to analysis, optimisation, and scale. The second depends on sequencing, connection, and judgement, and resists being reduced to a set of independent operations. One of the reasons education keeps getting this wrong, I suspect, is that we lack a precise language for describing the difference.</em></p><div><hr></div><p><span>One of the more unexpected parts of my work lately has been collaborating with </span><a href="/bubo5/browser?url=https%3A//www.superbuilders.dev/" rel="nofollow">software engineers and developers </a><span>on learning apps. It’s been fascinating working with people from a different field, not because they “do education differently” in some vague way, but because they force you to make your assumptions explicit. Engineers think in constraints: what can be decomposed, what can be measured, what can be scaled. And when those constraints meet the science of learning, you get clarity, albeit sometimes of the uncomfortable kind.</span></p><p><span>Yesterday I was working with a developer on a vocabulary app and he used a phrase I hadn't encountered before. We were discussing vocabulary as knowledge components and how the system could deliver individualised practice to thousands of students simultaneously, and he said, </span><em>&quot;Well, that's what we call an embarrassingly parallel problem.</em><span>&quot; </span></p><p>I’d never heard that before and asked him to explain. In computer science, an “embarrassingly parallel” problem is one that can be broken into completely independent sub-tasks, each solvable at the same time with no communication or coordination between them. To “parallelise” a problem then, means to break it into multiple parts that can be worked on at the same time, rather than in sequence, without one part needing the results of another. The opposite is &quot;serial&quot; or &quot;sequential&quot; processing, where task B cannot begin until task A has finished, task C cannot begin until task B has finished, and so on.</p><p><span>The word “embarrassingly” carries a sort of self-deprecation: some problems are almost </span><em>too </em><span>easy to parallelise because there are no dependencies, no sequencing constraints, no need for one process to wait on another. Rendering millions of pixels in an image, running the same simulation with different parameters, sorting independent records: each unit of work proceeds in blissful ignorance of its neighbours.</span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%212jiW%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!2jiW!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 424w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 848w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1272w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!2jiW!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png" width="1240" height="526" 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srcset="https://substackcdn.com/image/fetch/$s_!2jiW!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 424w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 848w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1272w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1456w" sizes="100vw" fetchpriority="high" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p></p><p>The phrase stuck with me because it seemed to illuminate something important about both the promise and the peril of trying to apply what we know about learning to educational technology, and about a distinction that cognitive psychology has been circling for decades without quite naming it in these terms.</p><h3 class="header-anchor-post">Breaking Knowledge Without Breaking Meaning<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/breaking-knowledge-without-breaking-meaning" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h3><p>If there was a central conundrum in instructional design, I would say it is the question of how you a) decompose knowledge domains into components small enough to teach to novices and b) reassemble those components into something that resembles the integrated, flexible understanding that made the domain worth teaching in the first place. </p><p><span>In education we would call this curriculum design and a core part of the ‘knowledge-rich’ approach. And as </span><a href="/bubo5/browser?url=https%3A//www.civitas.org.uk/content/files/The-value-of-a-knowledge-rich-curriculum-web.pdf" rel="nofollow">Christine Counsell notes in a recent essay,</a><span> a knowledge-rich curriculum must do more than list disconnected items, it must “ask not only ‘What is the ambitious expertise we want our students to leave us with?’ but also ‘What are the most effective steps to help our students to develop this expertise?’”, emphasising careful sequencing and practice if understanding is to emerge rather than mere component mastery.</span></p><p>The decomposition of knowledge is necessary because novices cannot apprehend the whole all at once; working memory will not permit it. But the reassembly is where instruction most reliably fails, because the connections between components are not themselves components. They are relationships, contingencies, contextual judgements; the mortar, not the bricks. And no amount of optimising the bricks will produce a wall.</p><h2 class="header-anchor-post">The Parallel Layer: What Scales Easily in Learning<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-parallel-layer-what-scales-easily-in-learning" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p>There is a sense in which much of what we know about effective learning exploits genuinely parallel processes. Retrieval practice strengthens individual memory traces. Spaced repetition exploits consolidation mechanisms that operate on discrete items. Immediate feedback corrects specific misconceptions at the point of error. Vocabulary can be drilled word by word. Multiplication tables can be practised fact by fact. These processes are, in a meaningful sense, embarrassingly parallel: they can be run simultaneously across thousands of learners, each operating independently, with no degradation in quality.</p><p>This is precisely why intelligent tutoring systems scale so well, with no degradation in quality. Because each learner’s feedback loop operates as an independent process, personalised practice and feedback can be delivered to tens of thousands of students simultaneously. Large-scale randomised controlled trials have found modest but reliable effects on standardised tests, with substantially larger gains for lower-attaining students, often equivalent to close to a year of additional progress. And the costs are strikingly low. This is the economics of embarrassingly parallel processing applied to education: marginal costs approaching zero, with impact distributed across vast numbers of learners at once.</p><p><span>Spaced repetition systems like </span><a href="/bubo5/browser?url=https%3A//apps.ankiweb.net/" rel="nofollow">Anki </a><span>work on the same principle. Each flashcard is an independent thread. The algorithm optimises the spacing for each item without reference to any other. The system does not need to know that the student is also learning French irregular verbs while reviewing anatomy terms; each item is its own self-contained process. And the evidence for spaced practice is robust: a recent meta-analysis of classroom studies found effect sizes of 0.54 SD, with seven-day intervals proving consistently effective.</span></p><p><span>What’s interesting to me is that the parallel layer is not standing still; it is getting substantially more sophisticated. There is a lot going on in the spaced repetition scheduling field which illustrates this perfectly. Until recently, most systems used an algorithm called </span><a href="/bubo5/browser?url=https%3A//super-memory.com/english/ol/sm2.htm" rel="nofollow">SuperMemo-2, </a><span>developed by a college student in 1987 based on his personal experiments. It was somewhat arbitrary: see a card after one day, then six days, then apply an exponential backoff. Get it wrong and you reset to day one. The implicit assumption was that the forgetting curve for every piece of knowledge is roughly the same, which is not true at all. </span></p><p><span>However, a new algorithm called </span><a href="/bubo5/browser?url=https%3A//github.com/open-spaced-repetition/fsrs4anki/wiki/ABC-of-FSRS" rel="nofollow">FSRS (Free Spaced Repetition Scheduler),</a><span> developed by Jarrett Ye, has replaced this blunt instrument with something far more precise. FSRS is built on a </span><a href="/bubo5/browser?url=https%3A//supermemo.guru/wiki/Three-component_model_of_memory" rel="nofollow">“Three Component Model of Memory”</a><span> which holds that three variables are sufficient to describe the status of a unitary memory: retrievability (the probability that you can recall an item at a given moment), stability (how long, in days, it takes for that probability to drop from 100% to 90%), and difficulty (how hard it is to increase a memory’s stability after each review). Each card has its own values for these three components, collectively known as its “memory state.”</span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21g9lX%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!g9lX!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 424w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 848w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1272w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!g9lX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png" width="1438" height="618" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:618,&quot;width&quot;:1438,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:358505,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!g9lX!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 424w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 848w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1272w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a><figcaption class="image-caption">FSRS uses machine learning to predict exactly when each individual memory trace will fade, and schedules the review accordingly. Each card is an independent prediction problem</figcaption></figure></div><p>Using machine learning trained on several hundred million reviews from around 10,000 users, FSRS calculates 21 parameters that model how these three variables interact, and it can be further optimised against a learner’s own review history to fit their personal forgetting curves. The result is a system that can predict with considerable accuracy when a learner’s recall probability will drop to a specified threshold, and schedule the review accordingly. Users can set their desired retention rate (say 90%, or 70% to reduce workload), and FSRS will calculate the optimal intervals to achieve it. Compared to Anki’s legacy algorithm, FSRS requires 20 to 30% fewer reviews to achieve the same level of retention. Even with default parameters, before any personalisation, it outperforms the old approach.</p><p>This is seriously impressive, and it represents the parallel layer operating at its most refined. Each card is basically an independent prediction problem, optimised without reference to any other card, and machine learning makes that optimisation far more efficient than any hand-crafted formula could. The parallel infrastructure of learning is getting better, and that is worth celebrating.</p><p><span>But it also sharpens the question at the heart of designing effective learning. For exampole, FSRS can tell you exactly</span><em> when </em><span>to review the Japanese word 眼科医 (ophthalmologist), but it cannot tell you </span><em>how </em><span>that word functions differently in a medical report versus a casual conversation, how it relates to the broader semantic field of Japanese medical terminology, or how it interacts with the grammatical structures required to use it in a coherent sentence. </span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21_eOO%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!_eOO!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 424w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 848w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1272w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!_eOO!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png" width="1456" height="758" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:758,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:2266606,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!_eOO!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 424w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 848w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1272w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p>The algorithm optimises the retention of isolated items with extraordinary precision. What it does not, and cannot, address is the integration of those items into the kind of flexible, context-sensitive knowledge that constitutes actual competence. </p><p>In other words, you can memorise 3,650 words a year in twenty minutes a day. But knowing 3,650 isolated words is not the same as reading a novel, following a conversation, or writing a paragraph. Those require precisely the serial, integrative processes that no scheduling algorithm touches.</p><p>Ok. So far, so good, and so….well, limited. The parallel layer of learning is real, it is well-evidenced, and technology serves it brilliantly. The question is what happens next.</p><h2 class="header-anchor-post">The Serial Layer: What Cannot Be Parallelised<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-serial-layer-what-cannot-be-parallelised" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p><span>But here is where the whole thing becomes truly interesting and problematic, because it forces us to confront what learning is </span><em>not</em><span>. In computer science, the problems that are not embarrassingly parallel are those where sub-tasks are interdependent: where one process must wait for another, where outputs become inputs, where the order of operations matters and cannot be rearranged without corrupting the result. The overhead required to manage these dependencies is called </span><a href="/bubo5/browser?url=https%3A//itrevolution.com/articles/on-coordination-costs-moving-a-couch-and-painting-a-room/" rel="nofollow">coordination cost,</a><span> and it is often the binding constraint on performance.</span></p><p><span>Learning, at its most meaningful, is emphatically </span><em>not</em><span> embarrassingly parallel. To understand the causes of the First World War is not to hold a collection of independent facts, each stored and retrievable in isolation, but to grasp a web of interdependencies: alliances, economic pressures, nationalist ideologies, contingent decisions, structural forces. Each node in this web derives its meaning from its connections to the others. You cannot parallelise the construction of such understanding any more than you can build the tenth floor of a building before the ninth.</span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%218MRL%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f5e2c715c-1653-4e23-a115-54920a664227_1222x626.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!8MRL!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 424w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 848w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1272w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!8MRL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f5e2c715c-1653-4e23-a115-54920a664227_1222x626.png" width="728" height="372.93617021276594" 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srcset="https://substackcdn.com/image/fetch/$s_!8MRL!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 424w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 848w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1272w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a><figcaption class="image-caption">Knowledge decomposes into independent items. Understanding does not.</figcaption></figure></div><p><span>Again, </span><a href="/bubo5/browser?url=https%3A//carlhendrick.substack.com/p/in-praise-of-artificial-learning" rel="nofollow">Herbert Simon’s concept of “near decomposability” </a><span>is helpful here. Complex systems, Simon argued, can be partially broken down into sub-systems, but those sub-systems still interact at their boundaries. A curriculum is near-decomposable: you can teach phonics and handwriting in relative independence, but reading comprehension depends on vocabulary, which depends on background knowledge, which depends on prior reading, which depends on the very comprehension you are trying to build. These are serial dependencies. They have an order and they resist parallelisation.</span></p><h2 class="header-anchor-post">The Measurement Signal Problem<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-measurement-signal-problem" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p><span>The evidence base for educational technology contains a signal that, viewed through this lens, becomes remarkably clear. Across multiple meta-analyses and large-scale evaluations, effect sizes on curriculum-aligned tests are consistently two to three times larger than effect sizes on standardised tests. </span><a href="/bubo5/browser?url=https%3A//www.jstor.org/stable/1170612" rel="nofollow">Kulik’s meta-analysis of mastery learning found 0.50 SD</a><span> on experimenter-made tests but only 0.08 SD on standardised measures. </span><a href="/bubo5/browser?url=https%3A//journals.sagepub.com/doi/10.3102/00028312019002237" rel="nofollow">A tutoring meta-analysis </a><span>found 0.84 SD on narrow tests versus 0.27 SD on standardised assessments. This pattern is ubiquitous.</span></p><p>The conventional explanation is test-curriculum alignment: students perform better on tests that closely match what they were taught. This is obviously true, but it is not the whole story. What curriculum-aligned tests measure is whether the parallel processes worked; whether students acquired the targeted skills, memorised the targeted facts, mastered the targeted procedures. </p><p>What standardised tests measure, however imperfectly, is whether those acquisitions have been integrated into something more flexible; whether the student can apply knowledge in contexts not explicitly rehearsed, make inferences across domains, transfer understanding to novel problems. In other words, the gap between aligned and standardised test scores is not merely a measurement artefact. It is a signal. The parallel layer is working. The serial layer is not being adequately served.</p><h3 class="header-anchor-post">The Atomisation Trap: When the Whole Never Reappears<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-atomisation-trap-when-the-whole-never-reappears" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h3><p>The great temptation of educational technology, (and it is a temptation born of genuine success), is to optimise for what is measurable and “parallelisable” at the expense of what is neither. When a platform can demonstrate that students have mastered 47 discrete skills with 85% accuracy, it is natural to conclude that learning has occurred. And in a sense it has, but mastering 47 discrete skills is not the same as understanding the domain those skills inhabit. The parts have been acquired; the whole has not been assembled.</p><p>This is what we might call the atomisation trap: the reduction of a curriculum to its “embarrassingly parallel” components, optimised independently, measured independently, and never reconstituted into any kind of coherent understanding. It produces students who can pass skill checks but cannot write a sustained argument, who can solve practiced problem types but freeze when the problem is reframed, who know the dates but cannot explain why they matter.</p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%216d6S%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!6d6S!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 424w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 848w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1272w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!6d6S!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png" width="1402" height="600" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:600,&quot;width&quot;:1402,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:508452,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!6d6S!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 424w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 848w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1272w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a><figcaption class="image-caption">The atomisation trap: optimising each component independently does not ensure they reconstitute into understanding.</figcaption></figure></div><p><span>An example of this I am fond of using is from a study by </span><a href="/bubo5/browser?url=https%3A//eric.ed.gov/%3Fid%3DEJ358469" rel="nofollow">George Miller and Patricia Gildea, published in </a><em><a href="/bubo5/browser?url=https%3A//eric.ed.gov/%3Fid%3DEJ358469" rel="nofollow">Scientific American</a></em><a href="/bubo5/browser?url=https%3A//eric.ed.gov/%3Fid%3DEJ358469" rel="nofollow"> in 1987</a><span> in which children were given dictionary definitions of words and asked to use them in sentences. The results were technically correct at the component level but semantically absurd when used in a sentence. One child, having learned that &quot;correlate&quot; means &quot;be related,&quot; wrote: &quot;Me and my parents correlate, because without them I wouldn't be here.&quot; Another, told that &quot;meticulous&quot; means &quot;very careful,&quot; produced: &quot;I was meticulous about falling off the cliff.&quot; </span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21mMwp%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!mMwp!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 424w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 848w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1272w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!mMwp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png" width="1192" height="680" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:680,&quot;width&quot;:1192,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:150375,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!mMwp!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 424w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 848w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1272w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p>Each child had successfully completed the parallel operation: definition retrieved, synonym matched, sentence generated. But the serial layer, the contextual understanding of how a word actually functions in living language, was entirely absent. The parts had been acquired. The whole had not been assembled. This is the atomisation trap in miniature: every component mastered, but no guarantee they recombine into understanding.</p><p><span>The evidence from</span><a href="/bubo5/browser?url=https%3A//web.archive.org/web/20150402210147/http%3A//www.highbeam.com/doc/1P3-73736679.html" rel="nofollow"> ALEKS, an adaptive learning platform, </a><span>is instructive here. When ALEKS replaces traditional instruction entirely, the effect is essentially zero (Hedge’s g = 0.05). When it supplements teacher-led instruction, the effect is a meaningful 0.43. The parallel processing of the platform, its adaptive diagnostics, its individualised practice, its immediate feedback, all of this works. But it works only when there is a human being providing the serial layer: the sequencing, the connecting, the contextualising, the building of one idea upon another in a way that the platform cannot do because it was not designed to do it.</span></p><p><span>As </span><a href="/bubo5/browser?url=https%3A//carlhendrick.substack.com/p/the-algorithmic-turn-the-emerging" rel="nofollow">I’ve written about</a><span> before, </span><a href="/bubo5/browser?url=https%3A//www.carnegielearning.com/solutions/math/mathia" rel="nofollow">Carnegie Learning’s MATHia </a><span>is one of the more sophisticated attempts to address this. Its cognitive models track dependencies </span><em>between </em><span>skills, not just mastery of isolated components, using production rules that represent the relational structure of mathematical knowledge. Its blended approach, combining the platform with teacher-led instruction, nearly doubled growth on standardised tests compared to traditional curricula alone. The system is effective precisely because it does not treat mathematics as embarrassingly parallel; it respects the serial dependencies between concepts while exploiting parallel infrastructure for practice and feedback.</span></p><h2 class="header-anchor-post">What Scales and What Doesn’t<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/what-scales-and-what-doesnt" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p><span>There is a deeper lesson here about why educational interventions so reliably lose potency at scale. </span><a href="/bubo5/browser?url=https%3A//static1.squarespace.com/static/6297c2b5c8bc35721cc7a65c/t/685ecf2600e9642f25e4dbfd/1751043879093/Kraft%2Bet%2Bal%2B2024%2BTutoring%2Bat%2Bscale.pdf" rel="nofollow">Kraft and colleagues found that tutoring studies with fewer than 100 students average d = 0.38</a><span>, while programmes with over 2,000 students average d = 0.11: a 66% reduction. </span><a href="/bubo5/browser?url=https%3A//www.nature.com/articles/s41598-025-97652-6" rel="nofollow">The Harvard study of GPT-4 </a><span>based tutoring achieved extraordinary effects of 0.73 to 1.3 SD, but with only 194 students. No one has replicated anything close to that at scale.</span></p><p>The embarrassingly parallel framing explains why. Scaling the parallel layer is trivial: if the software works for 200 students, it works for 200,000. The marginal cost is negligible. But scaling the serial layer, the human infrastructure of professional development, school culture, teacher knowledge, instructional leadership, adaptive response to context, is not embarrassingly parallel at all. Each school has its own dependencies. Each teacher requires individual support. Each classroom context demands coordination that cannot be automated away.</p><p><a href="/bubo5/browser?url=https%3A//www.achievementnetwork.org/" rel="nofollow">Achievement Network, an assessment platform,</a><span> provides a decent example. In high-readiness schools, those with supportive culture and strong leadership, the intervention produced 0.18 SD gains. In low-readiness schools, it produced nothing. The technology was identical. The parallel layer was the same. What differed was the serial layer: the human capacity to take data and weave it into coherent instructional response. </span></p><h2 class="header-anchor-post">Parallel Delivery, Serial Understanding<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/parallel-delivery-serial-understanding" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p>This whole concept of “embarrassingly parallel” problems crystallised something I had been thinking about for a long time. The science of learning has identified powerful mechanisms; retrieval practice, spacing, feedback, interleaving, worked examples etc. that genuinely operate with a degree of independence and can be delivered in parallel at scale via technology which serves this layer extraordinarily well, and the evidence is clear that it works.</p><p>But deep understanding is not embarrassingly parallel. It is painstakingly serial: built connection by connection, layer by layer, in a process that requires time, sequence, and the kind of integrative thinking that no algorithm yet replicates. The mistake is not in building platforms that exploit the parallel layer; the mistake is in believing that the parallel layer is all there is.</p><p>The best educational technology respects this distinction. It parallelises what can be parallelised: the delivery, the feedback, the scheduling, the data collection, the routine practice. And it preserves, protects, and supports what cannot: the teacher’s capacity to sequence ideas, to connect the dots between isolated skills, to build the kind of coherent understanding that transfers beyond the platform and into the world.</p><p><span>The developers I am working with are part of </span><a href="/bubo5/browser?url=https%3A//alpha.school/" rel="nofollow">Alpha School </a><span>and their model is one that is seriously working on these problems. The idea is deceptively simple: if technology is exceptionally good at delivering the parallel layer (diagnosis, retrieval practice, spacing, interleaving, feedback, scheduling) then its role should be to make that layer as efficient and reliable as possible, while deliberately preserving the “serial” work of integration support for a human being. This support comes in the form of guides who provide the human scaffolding that technology cannot: mentoring, motivation, tutoring when needed, and the steady support required to keep students engaged in the longer, serial work of understanding.</span></p><p><span>What is interesting about this model is that it does not attempt to automate the hardest part of instruction. Instead, it </span><em>acknowledges </em><span>it. Alpha’s guides exist precisely because understanding does not scale in the same way that practice does. The system scales the parallel layer aggressively, while concentrating scarce human attention where it matters most. I have seen these guides in action and the care and attention they provide students is phenomenal, and on a level that is virtually impossible for most teachers. </span></p><p><span>There’s been a gradual acceptance for some time now that what we are expecting classroom teachers to do is beyond what can be reasonably expected, and this is reflected in the current </span><a href="/bubo5/browser?url=https%3A//www.nfer.ac.uk/press-releases/teacher-recruitment-and-retention-crisis-shows-no-signs-of-abating-new-report-reveals/" rel="nofollow">recruitment and retention crisis.</a><span> The expectation of managing thirty or more students simultaneously, each at a different point of understanding, each with different misconceptions, each requiring the kind of sustained, responsive attention that is, by its very nature, serial: one conversation at a time, one connection at a time, one moment of recognition at a time.</span></p><div><hr></div><p>The vocabulary app and others I’m working on have really made me think hard about this problem of parallel vs serial. The drilling of individual words is genuinely embarrassingly parallel, and the scheduling of those drills is now being refined by machine learning algorithms like FSRS that can predict with remarkable accuracy when a learner is about to forget a specific item. That precision is a gift; it means we can build the parallel layer with a confidence and efficiency that would have been unimaginable even five years ago.</p><p><span>But vocabulary acquisition in the </span><em>deeper </em><span>sense; understanding how words relate to one another, how they shift in register and connotation, how they unlock comprehension of increasingly complex texts, that is a serial process. It requires context, connection, and the slow, patient work of building a mind that knows not just the parts, but how the parts compose a whole. No scheduling algorithm, however sophisticated, will provide that. It has to be designed into the instruction itself: the sequencing of words within semantic fields, the embedding of new vocabulary in meaningful texts, the explicit teaching of morphological relationships and contextual variation. This is the work of instructional design, where the goal is not to make learning embarrassingly parallel. It is to know which parts already are, exploit them brilliantly, and never mistake them for the whole.</span></p><div><hr></div><div class="subscription-widget-wrap"><div class="subscription-widget show-subscribe"><div class="preamble"><p>The Learning Dispatch is a reader-supported publication. 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6.53394C3.63888 6.01025 3.92086 5.55819 4.27893 5.17773C4.64148 4.79728 5.05774 4.50635 5.52771 4.30493C6.00216 4.09904 6.51241 3.99609 7.05847 3.99609C7.73433 3.99609 8.31844 4.16618 8.81079 4.50635C9.30762 4.84652 9.70374 5.28963 9.99915 5.83569C10.299 5.28516 10.6951 4.84204 11.1875 4.50635C11.6843 4.16618 12.2707 3.99609 12.9465 3.99609C13.4836 3.99609 13.9894 4.09904 14.4639 4.30493C14.9428 4.50635 15.3613 4.79728 15.7194 5.17773C16.0774 5.55819 16.3572 6.01025 16.5586 6.53394C16.7645 7.05762 16.8674 7.63725 16.8674 8.27283C16.8674 9.23067 16.6145 10.1818 16.1088 11.1262C15.603 12.0662 14.8891 12.9815 13.967 13.8722C13.0495 14.7584 11.9708 15.6044 10.731 16.41C10.6056 16.4906 10.4758 16.5622 10.3416 16.6249C10.2118 16.692 10.0976 16.7256 9.99915 16.7256Z"></path></g></svg></div>Liked by Carl Hendrick</button></div></div><div class="pencraft pc-display-flex pc-flexDirection-column pc-reset flex-grow-rzmknG"></div><div class="pencraft pc-display-flex pc-reset triggerContainer-eX588u"><button tabindex="0" type="button" aria-label="Ellipsis" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft trigger-j08Uop iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_quaternary-kpMibu"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-ellipsis"><circle cx="12" cy="12" r="1"></circle><circle cx="19" cy="12" r="1"></circle><circle cx="5" cy="12" r="1"></circle></svg></button></div></div><div class="comment-body expanded"><p><span>Super interesting, I like the framing. </span></p><p><span>This reminded me that when I’ve taken exams (and I’ve taken many over the years, from school, undergrad and grad school, to actuarial exams) I actually never relied on flash cards preferentially, precisely because I disliked that that approach meant that related facts became disconnected from each other. </span></p><p><span>Rather I would first compile a giant document, my first pass of “notes” that included every bit of knowledge that I thought relevant to the various parts of the syllabus, whether it be classroom notes, notes on the textbook or videos, problems I thought especially illustrative etc. This would all be laid out in syllabus order. Then my task would be to condense the information, using several passes. As I condensed, it would become clearer what parts of the information were genuinely tricky or hard to retain and I’d make sure to include those. But more importantly, I’d start to get a sense of how all these parts fit together, how x was really a special case of y, and you did z except in these circumstances, where you did w and so on. I could feel the mental map coming together and a large measure of whether I felt I’d be successful in an upcoming exam was how robust I felt that map to be. I would usually describe this as clicking. Once that happened I could often condense my notes to just two or three pages, even for a semester end exam, as facts hung easily on the structure and new facts absorbed with little effort. Everything just made sense together. </span></p><p><span>Coming from that perspective (having never really preferred flash cards as a study aid), I’m finding it fascinating both reading about recent developments in improving them, as well as their limitations. </span></p></div><div class="pencraft pc-display-flex pc-gap-16 pc-paddingTop-8 pc-justifyContent-flex-start pc-alignItems-center pc-reset comment-actions withShareButton-hQzuEn"><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a class="pencraft pc-reset link-_X6et2 link-LIBpto"><div class="pencraft pc-display-flex pc-gap-6 pc-alignItems-center pc-reset"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="var(--color-fg-secondary-themed)" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg><div class="pencraft pc-reset 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83.7008C63.8145 85.9674 62.2826 88.12 60.3092 90.041C52.3361 98.2316 40.3724 98.8164 32.8317 97.3961C31.4114 89.8554 31.9962 77.8918 40.1868 69.9186C42.2908 67.7572 44.6726 66.1255 47.1778 64.9205" stroke="var(--color-fg)" stroke-width="8.125" stroke-linejoin="round"></path></svg></div></div></span></div><span style="min-width:0;" data-state="closed"></span><a data-native="true" href="/bubo5/browser?url=https%3A//carlhendrick.substack.com/p/the-embarrassingly-parallel-problem/comment/211702724" rel="nofollow" title="Feb 8, 2026, 3:36 PM" class="pencraft pc-reset color-secondary-ls1g8s decoration-hover-underline-ClDVRM reset-IxiVJZ"><span class="pencraft pc-reset color-secondary-ls1g8s line-height-20-t4M0El font-text-qe4AeH size-13-hZTUKr weight-regular-mUq6Gb reset-IxiVJZ">Feb 8</span></a></div></div><div class="pencraft pc-display-flex pc-gap-4 pc-reset"><button tabindex="-1" type="button" class="pencraft pc-display-flex pc-gap-4 pc-height-20 pc-paddingLeft-6 pc-paddingRight-6 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3.13086 8.27283C3.13086 7.63725 3.23157 7.05762 3.43298 6.53394C3.63888 6.01025 3.92086 5.55819 4.27893 5.17773C4.64148 4.79728 5.05774 4.50635 5.52771 4.30493C6.00216 4.09904 6.51241 3.99609 7.05847 3.99609C7.73433 3.99609 8.31844 4.16618 8.81079 4.50635C9.30762 4.84652 9.70374 5.28963 9.99915 5.83569C10.299 5.28516 10.6951 4.84204 11.1875 4.50635C11.6843 4.16618 12.2707 3.99609 12.9465 3.99609C13.4836 3.99609 13.9894 4.09904 14.4639 4.30493C14.9428 4.50635 15.3613 4.79728 15.7194 5.17773C16.0774 5.55819 16.3572 6.01025 16.5586 6.53394C16.7645 7.05762 16.8674 7.63725 16.8674 8.27283C16.8674 9.23067 16.6145 10.1818 16.1088 11.1262C15.603 12.0662 14.8891 12.9815 13.967 13.8722C13.0495 14.7584 11.9708 15.6044 10.731 16.41C10.6056 16.4906 10.4758 16.5622 10.3416 16.6249C10.2118 16.692 10.0976 16.7256 9.99915 16.7256Z"></path></g></svg></div>Liked by Carl Hendrick</button></div></div><div class="pencraft pc-display-flex pc-flexDirection-column pc-reset flex-grow-rzmknG"></div><div class="pencraft pc-display-flex pc-reset triggerContainer-eX588u"><button tabindex="0" type="button" aria-label="Ellipsis" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft trigger-j08Uop iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_quaternary-kpMibu"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-ellipsis"><circle cx="12" cy="12" r="1"></circle><circle cx="19" cy="12" r="1"></circle><circle cx="5" cy="12" r="1"></circle></svg></button></div></div><div class="comment-body expanded"><p><span>Brilliantly-said. </span></p><p><span>Also, this piece captures important details that get recognition in some corners of the field, but fail to achieve broad embrace. For example, in the literacy space, we know that words are best learned/taught in context. Your example of the children writing awkward sentences because they learned surface-level definitions (kind-of accurate, but lacking context, so they missed the mark) illustrates the point. But much of the field is still game to drill vocabulary. </span></p><p><span>And in US schools, we have far too many book-lite classrooms and science/history-starved school schedules. The memo about the role of contextual knowledge has been slow to spread.</span></p><p><span>Thank you for writing. Please keep writing.</span></p></div><div class="pencraft pc-display-flex pc-gap-16 pc-paddingTop-8 pc-justifyContent-flex-start pc-alignItems-center pc-reset comment-actions withShareButton-hQzuEn"><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a class="pencraft pc-reset link-_X6et2 link-LIBpto"><div class="pencraft pc-display-flex pc-gap-6 pc-alignItems-center pc-reset"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="var(--color-fg-secondary-themed)" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg><div class="pencraft pc-reset color-pub-secondary-text-hGQ02T line-height-20-t4M0El font-meta-MWBumP size-11-NuY2Zx weight-medium-fw81nC transform-uppercase-yKDgcq reset-IxiVJZ meta-EgzBVA">Reply</div></div></a></span><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a 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learning that doesn't, and what a concept from computer science can teach us about where educational technology succeeds and where it reliably fails. Carl Hendrick Feb 07, 2026 131 35 36 Share <span>If there is a central conundrum in instructional design, it is this: firstly, how to disassemble a domain of knowledge into teachable parts without destroying what made it a domain in the first place. And then secondly, how to reassemble those parts into flexible understanding without mistaking performance on individual components for comprehension of the whole. For example, a student can learn the dates of every major battle in the First World War, define "alliance" and "imperialism," and correctly identify the assassination of Archduke Franz



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Yesterday I was working with a developer on a vocabulary app and he used a phrase I hadn't encountered before. We were discussing vocabulary as knowledge components and how the system could deliver individualised practice to thousands of students simultaneously, and he said, "Well, that's what we call an embarrassingly parallel problem. "
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&lt;title&gt;<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-heart"><path d="M19 14c1.49-1.46 3-3.21 3-5.5A5.5 5.5 0 0 0 16.5 3c-1.76 0-3 .5-4.5 2-1.5-1.5-2.74-2-4.5-2A5.5 5.5 0 0 0 2 8.5c0 2.3 1.5 4.05 3 5.5l7 7Z"></path></svg>&lt;g&gt;&lt;svg&gt;<div class="label">131</div>&lt;button&gt;&lt;div&gt;<button tabindex="0" type="button" aria-label="View comments (35)" data-href="https://carlhendrick.substack.com/p/the-embarrassingly-parallel-problem/comments" class="pencraft pc-reset pencraft post-ufi-button style-button post-ufi-comment-button has-label with-border"><svg role="img" style="height:20px;width:20px;" width="20" height="20" viewBox="0 0 24 24" fill="#000000" stroke-width="2" stroke="#000" xmlns="http://www.w3.org/2000/svg" class="icon"><g><title></title><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg></g></svg><div class="label">35</div></button><button tabindex="0" type="button" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft post-ufi-button style-button has-label with-border"><svg role="img" style="height:20px;width:20px;" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke-width="2" stroke="#000" xmlns="http://www.w3.org/2000/svg" class="icon"><g><title></title><path d="M21 3V8M21 8H16M21 8L18 5.29962C16.7056 4.14183 15.1038 3.38328 13.3879 3.11547C11.6719 2.84766 9.9152 3.08203 8.32951 3.79031C6.74382 4.49858 5.39691 5.65051 4.45125 7.10715C3.5056 8.5638 3.00158 10.2629 3 11.9996M3 21V16M3 16H8M3 16L6 18.7C7.29445 19.8578 8.89623 20.6163 10.6121 20.8841C12.3281 21.152 14.0848 20.9176 15.6705 20.2093C17.2562 19.501 18.6031 18.3491 19.5487 16.8925C20.4944 15.4358 20.9984 13.7367 21 12" stroke-linecap="round" stroke-linejoin="round"></path></g></svg><div class="label">36</div></button>&lt;div&gt;<div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft post-ufi-button style-button has-label with-border"><div class="label">Share</div></button></div>&lt;div&gt;&lt;div&gt;&lt;div&gt;<div class="visibility-check"></div><div><div class="available-content"><div dir="auto" class="body markup"><p><em>If there is a central conundrum in instructional design, it is this: firstly, how to disassemble a domain of knowledge into teachable parts without destroying what made it a domain in the first place. And then secondly, how to reassemble those parts into flexible understanding without mistaking performance on individual components for comprehension of the whole. </em></p><p><em>For example, a student can learn the dates of every major battle in the First World War, define &quot;alliance&quot; and &quot;imperialism,&quot; and correctly identify the assassination of Archduke Franz Ferdinand as the triggering event, yet still have no real understanding of why the war happened. Or more commonly, the student who can accurately define a set of vocabulary words and still be unable to use them appropriately in speech or writing.</em></p><p><em>What makes this conundrum so persistent is that the two problems; decomposition and reassembly, look superficially similar but are structurally different. The first lends itself to analysis, optimisation, and scale. The second depends on sequencing, connection, and judgement, and resists being reduced to a set of independent operations. One of the reasons education keeps getting this wrong, I suspect, is that we lack a precise language for describing the difference.</em></p><div><hr></div><p><span>One of the more unexpected parts of my work lately has been collaborating with </span><a href="/bubo5/browser?url=https%3A//www.superbuilders.dev/" rel="nofollow">software engineers and developers </a><span>on learning apps. It’s been fascinating working with people from a different field, not because they “do education differently” in some vague way, but because they force you to make your assumptions explicit. Engineers think in constraints: what can be decomposed, what can be measured, what can be scaled. And when those constraints meet the science of learning, you get clarity, albeit sometimes of the uncomfortable kind.</span></p><p><span>Yesterday I was working with a developer on a vocabulary app and he used a phrase I hadn't encountered before. We were discussing vocabulary as knowledge components and how the system could deliver individualised practice to thousands of students simultaneously, and he said, </span><em>&quot;Well, that's what we call an embarrassingly parallel problem.</em><span>&quot; </span></p><p>I’d never heard that before and asked him to explain. In computer science, an “embarrassingly parallel” problem is one that can be broken into completely independent sub-tasks, each solvable at the same time with no communication or coordination between them. To “parallelise” a problem then, means to break it into multiple parts that can be worked on at the same time, rather than in sequence, without one part needing the results of another. The opposite is &quot;serial&quot; or &quot;sequential&quot; processing, where task B cannot begin until task A has finished, task C cannot begin until task B has finished, and so on.</p><p><span>The word “embarrassingly” carries a sort of self-deprecation: some problems are almost </span><em>too </em><span>easy to parallelise because there are no dependencies, no sequencing constraints, no need for one process to wait on another. Rendering millions of pixels in an image, running the same simulation with different parameters, sorting independent records: each unit of work proceeds in blissful ignorance of its neighbours.</span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%212jiW%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!2jiW!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 424w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 848w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1272w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!2jiW!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png" width="1240" height="526" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:526,&quot;width&quot;:1240,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:177499,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:false,&quot;topImage&quot;:true,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!2jiW!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 424w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 848w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1272w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1456w" sizes="100vw" fetchpriority="high" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p></p><p>The phrase stuck with me because it seemed to illuminate something important about both the promise and the peril of trying to apply what we know about learning to educational technology, and about a distinction that cognitive psychology has been circling for decades without quite naming it in these terms.</p><h3 class="header-anchor-post">Breaking Knowledge Without Breaking Meaning<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/breaking-knowledge-without-breaking-meaning" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h3><p>If there was a central conundrum in instructional design, I would say it is the question of how you a) decompose knowledge domains into components small enough to teach to novices and b) reassemble those components into something that resembles the integrated, flexible understanding that made the domain worth teaching in the first place. </p><p><span>In education we would call this curriculum design and a core part of the ‘knowledge-rich’ approach. And as </span><a href="/bubo5/browser?url=https%3A//www.civitas.org.uk/content/files/The-value-of-a-knowledge-rich-curriculum-web.pdf" rel="nofollow">Christine Counsell notes in a recent essay,</a><span> a knowledge-rich curriculum must do more than list disconnected items, it must “ask not only ‘What is the ambitious expertise we want our students to leave us with?’ but also ‘What are the most effective steps to help our students to develop this expertise?’”, emphasising careful sequencing and practice if understanding is to emerge rather than mere component mastery.</span></p><p>The decomposition of knowledge is necessary because novices cannot apprehend the whole all at once; working memory will not permit it. But the reassembly is where instruction most reliably fails, because the connections between components are not themselves components. They are relationships, contingencies, contextual judgements; the mortar, not the bricks. And no amount of optimising the bricks will produce a wall.</p><h2 class="header-anchor-post">The Parallel Layer: What Scales Easily in Learning<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-parallel-layer-what-scales-easily-in-learning" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p>There is a sense in which much of what we know about effective learning exploits genuinely parallel processes. Retrieval practice strengthens individual memory traces. Spaced repetition exploits consolidation mechanisms that operate on discrete items. Immediate feedback corrects specific misconceptions at the point of error. Vocabulary can be drilled word by word. Multiplication tables can be practised fact by fact. These processes are, in a meaningful sense, embarrassingly parallel: they can be run simultaneously across thousands of learners, each operating independently, with no degradation in quality.</p><p>This is precisely why intelligent tutoring systems scale so well, with no degradation in quality. Because each learner’s feedback loop operates as an independent process, personalised practice and feedback can be delivered to tens of thousands of students simultaneously. Large-scale randomised controlled trials have found modest but reliable effects on standardised tests, with substantially larger gains for lower-attaining students, often equivalent to close to a year of additional progress. And the costs are strikingly low. This is the economics of embarrassingly parallel processing applied to education: marginal costs approaching zero, with impact distributed across vast numbers of learners at once.</p><p><span>Spaced repetition systems like </span><a href="/bubo5/browser?url=https%3A//apps.ankiweb.net/" rel="nofollow">Anki </a><span>work on the same principle. Each flashcard is an independent thread. The algorithm optimises the spacing for each item without reference to any other. The system does not need to know that the student is also learning French irregular verbs while reviewing anatomy terms; each item is its own self-contained process. And the evidence for spaced practice is robust: a recent meta-analysis of classroom studies found effect sizes of 0.54 SD, with seven-day intervals proving consistently effective.</span></p><p><span>What’s interesting to me is that the parallel layer is not standing still; it is getting substantially more sophisticated. There is a lot going on in the spaced repetition scheduling field which illustrates this perfectly. Until recently, most systems used an algorithm called </span><a href="/bubo5/browser?url=https%3A//super-memory.com/english/ol/sm2.htm" rel="nofollow">SuperMemo-2, </a><span>developed by a college student in 1987 based on his personal experiments. It was somewhat arbitrary: see a card after one day, then six days, then apply an exponential backoff. Get it wrong and you reset to day one. The implicit assumption was that the forgetting curve for every piece of knowledge is roughly the same, which is not true at all. </span></p><p><span>However, a new algorithm called </span><a href="/bubo5/browser?url=https%3A//github.com/open-spaced-repetition/fsrs4anki/wiki/ABC-of-FSRS" rel="nofollow">FSRS (Free Spaced Repetition Scheduler),</a><span> developed by Jarrett Ye, has replaced this blunt instrument with something far more precise. FSRS is built on a </span><a href="/bubo5/browser?url=https%3A//supermemo.guru/wiki/Three-component_model_of_memory" rel="nofollow">“Three Component Model of Memory”</a><span> which holds that three variables are sufficient to describe the status of a unitary memory: retrievability (the probability that you can recall an item at a given moment), stability (how long, in days, it takes for that probability to drop from 100% to 90%), and difficulty (how hard it is to increase a memory’s stability after each review). Each card has its own values for these three components, collectively known as its “memory state.”</span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21g9lX%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!g9lX!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 424w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 848w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1272w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!g9lX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png" width="1438" height="618" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:618,&quot;width&quot;:1438,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:358505,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!g9lX!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 424w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 848w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1272w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a><figcaption class="image-caption">FSRS uses machine learning to predict exactly when each individual memory trace will fade, and schedules the review accordingly. Each card is an independent prediction problem</figcaption></figure></div><p>Using machine learning trained on several hundred million reviews from around 10,000 users, FSRS calculates 21 parameters that model how these three variables interact, and it can be further optimised against a learner’s own review history to fit their personal forgetting curves. The result is a system that can predict with considerable accuracy when a learner’s recall probability will drop to a specified threshold, and schedule the review accordingly. Users can set their desired retention rate (say 90%, or 70% to reduce workload), and FSRS will calculate the optimal intervals to achieve it. Compared to Anki’s legacy algorithm, FSRS requires 20 to 30% fewer reviews to achieve the same level of retention. Even with default parameters, before any personalisation, it outperforms the old approach.</p><p>This is seriously impressive, and it represents the parallel layer operating at its most refined. Each card is basically an independent prediction problem, optimised without reference to any other card, and machine learning makes that optimisation far more efficient than any hand-crafted formula could. The parallel infrastructure of learning is getting better, and that is worth celebrating.</p><p><span>But it also sharpens the question at the heart of designing effective learning. For exampole, FSRS can tell you exactly</span><em> when </em><span>to review the Japanese word 眼科医 (ophthalmologist), but it cannot tell you </span><em>how </em><span>that word functions differently in a medical report versus a casual conversation, how it relates to the broader semantic field of Japanese medical terminology, or how it interacts with the grammatical structures required to use it in a coherent sentence. </span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21_eOO%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!_eOO!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 424w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 848w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1272w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!_eOO!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png" width="1456" height="758" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:758,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:2266606,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!_eOO!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 424w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 848w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1272w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p>The algorithm optimises the retention of isolated items with extraordinary precision. What it does not, and cannot, address is the integration of those items into the kind of flexible, context-sensitive knowledge that constitutes actual competence. </p><p>In other words, you can memorise 3,650 words a year in twenty minutes a day. But knowing 3,650 isolated words is not the same as reading a novel, following a conversation, or writing a paragraph. Those require precisely the serial, integrative processes that no scheduling algorithm touches.</p><p>Ok. So far, so good, and so….well, limited. The parallel layer of learning is real, it is well-evidenced, and technology serves it brilliantly. The question is what happens next.</p><h2 class="header-anchor-post">The Serial Layer: What Cannot Be Parallelised<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-serial-layer-what-cannot-be-parallelised" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p><span>But here is where the whole thing becomes truly interesting and problematic, because it forces us to confront what learning is </span><em>not</em><span>. In computer science, the problems that are not embarrassingly parallel are those where sub-tasks are interdependent: where one process must wait for another, where outputs become inputs, where the order of operations matters and cannot be rearranged without corrupting the result. The overhead required to manage these dependencies is called </span><a href="/bubo5/browser?url=https%3A//itrevolution.com/articles/on-coordination-costs-moving-a-couch-and-painting-a-room/" rel="nofollow">coordination cost,</a><span> and it is often the binding constraint on performance.</span></p><p><span>Learning, at its most meaningful, is emphatically </span><em>not</em><span> embarrassingly parallel. To understand the causes of the First World War is not to hold a collection of independent facts, each stored and retrievable in isolation, but to grasp a web of interdependencies: alliances, economic pressures, nationalist ideologies, contingent decisions, structural forces. Each node in this web derives its meaning from its connections to the others. You cannot parallelise the construction of such understanding any more than you can build the tenth floor of a building before the ninth.</span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%218MRL%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f5e2c715c-1653-4e23-a115-54920a664227_1222x626.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!8MRL!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 424w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 848w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1272w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!8MRL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f5e2c715c-1653-4e23-a115-54920a664227_1222x626.png" width="728" height="372.93617021276594" 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srcset="https://substackcdn.com/image/fetch/$s_!8MRL!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 424w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 848w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1272w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a><figcaption class="image-caption">Knowledge decomposes into independent items. Understanding does not.</figcaption></figure></div><p><span>Again, </span><a href="/bubo5/browser?url=https%3A//carlhendrick.substack.com/p/in-praise-of-artificial-learning" rel="nofollow">Herbert Simon’s concept of “near decomposability” </a><span>is helpful here. Complex systems, Simon argued, can be partially broken down into sub-systems, but those sub-systems still interact at their boundaries. A curriculum is near-decomposable: you can teach phonics and handwriting in relative independence, but reading comprehension depends on vocabulary, which depends on background knowledge, which depends on prior reading, which depends on the very comprehension you are trying to build. These are serial dependencies. They have an order and they resist parallelisation.</span></p><h2 class="header-anchor-post">The Measurement Signal Problem<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-measurement-signal-problem" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p><span>The evidence base for educational technology contains a signal that, viewed through this lens, becomes remarkably clear. Across multiple meta-analyses and large-scale evaluations, effect sizes on curriculum-aligned tests are consistently two to three times larger than effect sizes on standardised tests. </span><a href="/bubo5/browser?url=https%3A//www.jstor.org/stable/1170612" rel="nofollow">Kulik’s meta-analysis of mastery learning found 0.50 SD</a><span> on experimenter-made tests but only 0.08 SD on standardised measures. </span><a href="/bubo5/browser?url=https%3A//journals.sagepub.com/doi/10.3102/00028312019002237" rel="nofollow">A tutoring meta-analysis </a><span>found 0.84 SD on narrow tests versus 0.27 SD on standardised assessments. This pattern is ubiquitous.</span></p><p>The conventional explanation is test-curriculum alignment: students perform better on tests that closely match what they were taught. This is obviously true, but it is not the whole story. What curriculum-aligned tests measure is whether the parallel processes worked; whether students acquired the targeted skills, memorised the targeted facts, mastered the targeted procedures. </p><p>What standardised tests measure, however imperfectly, is whether those acquisitions have been integrated into something more flexible; whether the student can apply knowledge in contexts not explicitly rehearsed, make inferences across domains, transfer understanding to novel problems. In other words, the gap between aligned and standardised test scores is not merely a measurement artefact. It is a signal. The parallel layer is working. The serial layer is not being adequately served.</p><h3 class="header-anchor-post">The Atomisation Trap: When the Whole Never Reappears<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-atomisation-trap-when-the-whole-never-reappears" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h3><p>The great temptation of educational technology, (and it is a temptation born of genuine success), is to optimise for what is measurable and “parallelisable” at the expense of what is neither. When a platform can demonstrate that students have mastered 47 discrete skills with 85% accuracy, it is natural to conclude that learning has occurred. And in a sense it has, but mastering 47 discrete skills is not the same as understanding the domain those skills inhabit. The parts have been acquired; the whole has not been assembled.</p><p>This is what we might call the atomisation trap: the reduction of a curriculum to its “embarrassingly parallel” components, optimised independently, measured independently, and never reconstituted into any kind of coherent understanding. It produces students who can pass skill checks but cannot write a sustained argument, who can solve practiced problem types but freeze when the problem is reframed, who know the dates but cannot explain why they matter.</p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%216d6S%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!6d6S!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 424w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 848w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1272w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!6d6S!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png" width="1402" height="600" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:600,&quot;width&quot;:1402,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:508452,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!6d6S!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 424w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 848w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1272w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a><figcaption class="image-caption">The atomisation trap: optimising each component independently does not ensure they reconstitute into understanding.</figcaption></figure></div><p><span>An example of this I am fond of using is from a study by </span><a href="/bubo5/browser?url=https%3A//eric.ed.gov/%3Fid%3DEJ358469" rel="nofollow">George Miller and Patricia Gildea, published in </a><em><a href="/bubo5/browser?url=https%3A//eric.ed.gov/%3Fid%3DEJ358469" rel="nofollow">Scientific American</a></em><a href="/bubo5/browser?url=https%3A//eric.ed.gov/%3Fid%3DEJ358469" rel="nofollow"> in 1987</a><span> in which children were given dictionary definitions of words and asked to use them in sentences. The results were technically correct at the component level but semantically absurd when used in a sentence. One child, having learned that &quot;correlate&quot; means &quot;be related,&quot; wrote: &quot;Me and my parents correlate, because without them I wouldn't be here.&quot; Another, told that &quot;meticulous&quot; means &quot;very careful,&quot; produced: &quot;I was meticulous about falling off the cliff.&quot; </span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21mMwp%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!mMwp!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 424w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 848w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1272w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!mMwp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png" width="1192" height="680" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:680,&quot;width&quot;:1192,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:150375,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!mMwp!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 424w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 848w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1272w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p>Each child had successfully completed the parallel operation: definition retrieved, synonym matched, sentence generated. But the serial layer, the contextual understanding of how a word actually functions in living language, was entirely absent. The parts had been acquired. The whole had not been assembled. This is the atomisation trap in miniature: every component mastered, but no guarantee they recombine into understanding.</p><p><span>The evidence from</span><a href="/bubo5/browser?url=https%3A//web.archive.org/web/20150402210147/http%3A//www.highbeam.com/doc/1P3-73736679.html" rel="nofollow"> ALEKS, an adaptive learning platform, </a><span>is instructive here. When ALEKS replaces traditional instruction entirely, the effect is essentially zero (Hedge’s g = 0.05). When it supplements teacher-led instruction, the effect is a meaningful 0.43. The parallel processing of the platform, its adaptive diagnostics, its individualised practice, its immediate feedback, all of this works. But it works only when there is a human being providing the serial layer: the sequencing, the connecting, the contextualising, the building of one idea upon another in a way that the platform cannot do because it was not designed to do it.</span></p><p><span>As </span><a href="/bubo5/browser?url=https%3A//carlhendrick.substack.com/p/the-algorithmic-turn-the-emerging" rel="nofollow">I’ve written about</a><span> before, </span><a href="/bubo5/browser?url=https%3A//www.carnegielearning.com/solutions/math/mathia" rel="nofollow">Carnegie Learning’s MATHia </a><span>is one of the more sophisticated attempts to address this. Its cognitive models track dependencies </span><em>between </em><span>skills, not just mastery of isolated components, using production rules that represent the relational structure of mathematical knowledge. Its blended approach, combining the platform with teacher-led instruction, nearly doubled growth on standardised tests compared to traditional curricula alone. The system is effective precisely because it does not treat mathematics as embarrassingly parallel; it respects the serial dependencies between concepts while exploiting parallel infrastructure for practice and feedback.</span></p><h2 class="header-anchor-post">What Scales and What Doesn’t<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/what-scales-and-what-doesnt" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p><span>There is a deeper lesson here about why educational interventions so reliably lose potency at scale. </span><a href="/bubo5/browser?url=https%3A//static1.squarespace.com/static/6297c2b5c8bc35721cc7a65c/t/685ecf2600e9642f25e4dbfd/1751043879093/Kraft%2Bet%2Bal%2B2024%2BTutoring%2Bat%2Bscale.pdf" rel="nofollow">Kraft and colleagues found that tutoring studies with fewer than 100 students average d = 0.38</a><span>, while programmes with over 2,000 students average d = 0.11: a 66% reduction. </span><a href="/bubo5/browser?url=https%3A//www.nature.com/articles/s41598-025-97652-6" rel="nofollow">The Harvard study of GPT-4 </a><span>based tutoring achieved extraordinary effects of 0.73 to 1.3 SD, but with only 194 students. No one has replicated anything close to that at scale.</span></p><p>The embarrassingly parallel framing explains why. Scaling the parallel layer is trivial: if the software works for 200 students, it works for 200,000. The marginal cost is negligible. But scaling the serial layer, the human infrastructure of professional development, school culture, teacher knowledge, instructional leadership, adaptive response to context, is not embarrassingly parallel at all. Each school has its own dependencies. Each teacher requires individual support. Each classroom context demands coordination that cannot be automated away.</p><p><a href="/bubo5/browser?url=https%3A//www.achievementnetwork.org/" rel="nofollow">Achievement Network, an assessment platform,</a><span> provides a decent example. In high-readiness schools, those with supportive culture and strong leadership, the intervention produced 0.18 SD gains. In low-readiness schools, it produced nothing. The technology was identical. The parallel layer was the same. What differed was the serial layer: the human capacity to take data and weave it into coherent instructional response. </span></p><h2 class="header-anchor-post">Parallel Delivery, Serial Understanding<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/parallel-delivery-serial-understanding" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p>This whole concept of “embarrassingly parallel” problems crystallised something I had been thinking about for a long time. The science of learning has identified powerful mechanisms; retrieval practice, spacing, feedback, interleaving, worked examples etc. that genuinely operate with a degree of independence and can be delivered in parallel at scale via technology which serves this layer extraordinarily well, and the evidence is clear that it works.</p><p>But deep understanding is not embarrassingly parallel. It is painstakingly serial: built connection by connection, layer by layer, in a process that requires time, sequence, and the kind of integrative thinking that no algorithm yet replicates. The mistake is not in building platforms that exploit the parallel layer; the mistake is in believing that the parallel layer is all there is.</p><p>The best educational technology respects this distinction. It parallelises what can be parallelised: the delivery, the feedback, the scheduling, the data collection, the routine practice. And it preserves, protects, and supports what cannot: the teacher’s capacity to sequence ideas, to connect the dots between isolated skills, to build the kind of coherent understanding that transfers beyond the platform and into the world.</p><p><span>The developers I am working with are part of </span><a href="/bubo5/browser?url=https%3A//alpha.school/" rel="nofollow">Alpha School </a><span>and their model is one that is seriously working on these problems. The idea is deceptively simple: if technology is exceptionally good at delivering the parallel layer (diagnosis, retrieval practice, spacing, interleaving, feedback, scheduling) then its role should be to make that layer as efficient and reliable as possible, while deliberately preserving the “serial” work of integration support for a human being. This support comes in the form of guides who provide the human scaffolding that technology cannot: mentoring, motivation, tutoring when needed, and the steady support required to keep students engaged in the longer, serial work of understanding.</span></p><p><span>What is interesting about this model is that it does not attempt to automate the hardest part of instruction. Instead, it </span><em>acknowledges </em><span>it. Alpha’s guides exist precisely because understanding does not scale in the same way that practice does. The system scales the parallel layer aggressively, while concentrating scarce human attention where it matters most. I have seen these guides in action and the care and attention they provide students is phenomenal, and on a level that is virtually impossible for most teachers. </span></p><p><span>There’s been a gradual acceptance for some time now that what we are expecting classroom teachers to do is beyond what can be reasonably expected, and this is reflected in the current </span><a href="/bubo5/browser?url=https%3A//www.nfer.ac.uk/press-releases/teacher-recruitment-and-retention-crisis-shows-no-signs-of-abating-new-report-reveals/" rel="nofollow">recruitment and retention crisis.</a><span> The expectation of managing thirty or more students simultaneously, each at a different point of understanding, each with different misconceptions, each requiring the kind of sustained, responsive attention that is, by its very nature, serial: one conversation at a time, one connection at a time, one moment of recognition at a time.</span></p><div><hr></div><p>The vocabulary app and others I’m working on have really made me think hard about this problem of parallel vs serial. The drilling of individual words is genuinely embarrassingly parallel, and the scheduling of those drills is now being refined by machine learning algorithms like FSRS that can predict with remarkable accuracy when a learner is about to forget a specific item. That precision is a gift; it means we can build the parallel layer with a confidence and efficiency that would have been unimaginable even five years ago.</p><p><span>But vocabulary acquisition in the </span><em>deeper </em><span>sense; understanding how words relate to one another, how they shift in register and connotation, how they unlock comprehension of increasingly complex texts, that is a serial process. It requires context, connection, and the slow, patient work of building a mind that knows not just the parts, but how the parts compose a whole. No scheduling algorithm, however sophisticated, will provide that. It has to be designed into the instruction itself: the sequencing of words within semantic fields, the embedding of new vocabulary in meaningful texts, the explicit teaching of morphological relationships and contextual variation. This is the work of instructional design, where the goal is not to make learning embarrassingly parallel. It is to know which parts already are, exploit them brilliantly, and never mistake them for the whole.</span></p><div><hr></div><div class="subscription-widget-wrap"><div class="subscription-widget show-subscribe"><div class="preamble"><p>The Learning Dispatch is a reader-supported publication. 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6.53394C3.63888 6.01025 3.92086 5.55819 4.27893 5.17773C4.64148 4.79728 5.05774 4.50635 5.52771 4.30493C6.00216 4.09904 6.51241 3.99609 7.05847 3.99609C7.73433 3.99609 8.31844 4.16618 8.81079 4.50635C9.30762 4.84652 9.70374 5.28963 9.99915 5.83569C10.299 5.28516 10.6951 4.84204 11.1875 4.50635C11.6843 4.16618 12.2707 3.99609 12.9465 3.99609C13.4836 3.99609 13.9894 4.09904 14.4639 4.30493C14.9428 4.50635 15.3613 4.79728 15.7194 5.17773C16.0774 5.55819 16.3572 6.01025 16.5586 6.53394C16.7645 7.05762 16.8674 7.63725 16.8674 8.27283C16.8674 9.23067 16.6145 10.1818 16.1088 11.1262C15.603 12.0662 14.8891 12.9815 13.967 13.8722C13.0495 14.7584 11.9708 15.6044 10.731 16.41C10.6056 16.4906 10.4758 16.5622 10.3416 16.6249C10.2118 16.692 10.0976 16.7256 9.99915 16.7256Z"></path></g></svg></div>Liked by Carl Hendrick</button></div></div><div class="pencraft pc-display-flex pc-flexDirection-column pc-reset flex-grow-rzmknG"></div><div class="pencraft pc-display-flex pc-reset triggerContainer-eX588u"><button tabindex="0" type="button" aria-label="Ellipsis" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft trigger-j08Uop iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_quaternary-kpMibu"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-ellipsis"><circle cx="12" cy="12" r="1"></circle><circle cx="19" cy="12" r="1"></circle><circle cx="5" cy="12" r="1"></circle></svg></button></div></div><div class="comment-body expanded"><p><span>Super interesting, I like the framing. </span></p><p><span>This reminded me that when I’ve taken exams (and I’ve taken many over the years, from school, undergrad and grad school, to actuarial exams) I actually never relied on flash cards preferentially, precisely because I disliked that that approach meant that related facts became disconnected from each other. </span></p><p><span>Rather I would first compile a giant document, my first pass of “notes” that included every bit of knowledge that I thought relevant to the various parts of the syllabus, whether it be classroom notes, notes on the textbook or videos, problems I thought especially illustrative etc. This would all be laid out in syllabus order. Then my task would be to condense the information, using several passes. As I condensed, it would become clearer what parts of the information were genuinely tricky or hard to retain and I’d make sure to include those. But more importantly, I’d start to get a sense of how all these parts fit together, how x was really a special case of y, and you did z except in these circumstances, where you did w and so on. I could feel the mental map coming together and a large measure of whether I felt I’d be successful in an upcoming exam was how robust I felt that map to be. I would usually describe this as clicking. Once that happened I could often condense my notes to just two or three pages, even for a semester end exam, as facts hung easily on the structure and new facts absorbed with little effort. Everything just made sense together. </span></p><p><span>Coming from that perspective (having never really preferred flash cards as a study aid), I’m finding it fascinating both reading about recent developments in improving them, as well as their limitations. </span></p></div><div class="pencraft pc-display-flex pc-gap-16 pc-paddingTop-8 pc-justifyContent-flex-start pc-alignItems-center pc-reset comment-actions withShareButton-hQzuEn"><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a class="pencraft pc-reset link-_X6et2 link-LIBpto"><div class="pencraft pc-display-flex pc-gap-6 pc-alignItems-center pc-reset"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="var(--color-fg-secondary-themed)" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg><div class="pencraft pc-reset 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3.13086 8.27283C3.13086 7.63725 3.23157 7.05762 3.43298 6.53394C3.63888 6.01025 3.92086 5.55819 4.27893 5.17773C4.64148 4.79728 5.05774 4.50635 5.52771 4.30493C6.00216 4.09904 6.51241 3.99609 7.05847 3.99609C7.73433 3.99609 8.31844 4.16618 8.81079 4.50635C9.30762 4.84652 9.70374 5.28963 9.99915 5.83569C10.299 5.28516 10.6951 4.84204 11.1875 4.50635C11.6843 4.16618 12.2707 3.99609 12.9465 3.99609C13.4836 3.99609 13.9894 4.09904 14.4639 4.30493C14.9428 4.50635 15.3613 4.79728 15.7194 5.17773C16.0774 5.55819 16.3572 6.01025 16.5586 6.53394C16.7645 7.05762 16.8674 7.63725 16.8674 8.27283C16.8674 9.23067 16.6145 10.1818 16.1088 11.1262C15.603 12.0662 14.8891 12.9815 13.967 13.8722C13.0495 14.7584 11.9708 15.6044 10.731 16.41C10.6056 16.4906 10.4758 16.5622 10.3416 16.6249C10.2118 16.692 10.0976 16.7256 9.99915 16.7256Z"></path></g></svg></div>Liked by Carl Hendrick</button></div></div><div class="pencraft pc-display-flex pc-flexDirection-column pc-reset flex-grow-rzmknG"></div><div class="pencraft pc-display-flex pc-reset triggerContainer-eX588u"><button tabindex="0" type="button" aria-label="Ellipsis" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft trigger-j08Uop iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_quaternary-kpMibu"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-ellipsis"><circle cx="12" cy="12" r="1"></circle><circle cx="19" cy="12" r="1"></circle><circle cx="5" cy="12" r="1"></circle></svg></button></div></div><div class="comment-body expanded"><p><span>Brilliantly-said. </span></p><p><span>Also, this piece captures important details that get recognition in some corners of the field, but fail to achieve broad embrace. For example, in the literacy space, we know that words are best learned/taught in context. Your example of the children writing awkward sentences because they learned surface-level definitions (kind-of accurate, but lacking context, so they missed the mark) illustrates the point. But much of the field is still game to drill vocabulary. </span></p><p><span>And in US schools, we have far too many book-lite classrooms and science/history-starved school schedules. The memo about the role of contextual knowledge has been slow to spread.</span></p><p><span>Thank you for writing. Please keep writing.</span></p></div><div class="pencraft pc-display-flex pc-gap-16 pc-paddingTop-8 pc-justifyContent-flex-start pc-alignItems-center pc-reset comment-actions withShareButton-hQzuEn"><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a class="pencraft pc-reset link-_X6et2 link-LIBpto"><div class="pencraft pc-display-flex pc-gap-6 pc-alignItems-center pc-reset"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="var(--color-fg-secondary-themed)" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg><div class="pencraft pc-reset color-pub-secondary-text-hGQ02T line-height-20-t4M0El font-meta-MWBumP size-11-NuY2Zx weight-medium-fw81nC transform-uppercase-yKDgcq reset-IxiVJZ meta-EgzBVA">Reply</div></div></a></span><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a 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straints: what can be decomposed, what can be measured, what can be scaled. And when those constraints meet the science of learning, you get clarity, albeit sometimes of the uncomfortable kind. <span>Yesterday I was working with a developer on a vocabulary app and he used a phrase I hadn't encountered before. We were discussing vocabulary as knowledge components and how the system could deliver individualised practice to thousands of students simultaneously, and he said, "Well, that's what we call an embarrassingly parallel problem." I’d never heard that before and asked him to explain. In computer science, an “embarrassingly parallel” problem is one that can be broken into completely independent sub-tasks, each sol



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In computer science, an “embarrassingly parallel” problem is one that can be broken into completely independent sub-tasks, each solvable at the same time with no communication or coordination between them.
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&lt;title&gt;<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-heart"><path d="M19 14c1.49-1.46 3-3.21 3-5.5A5.5 5.5 0 0 0 16.5 3c-1.76 0-3 .5-4.5 2-1.5-1.5-2.74-2-4.5-2A5.5 5.5 0 0 0 2 8.5c0 2.3 1.5 4.05 3 5.5l7 7Z"></path></svg>&lt;g&gt;&lt;svg&gt;<div class="label">131</div>&lt;button&gt;&lt;div&gt;<button tabindex="0" type="button" aria-label="View comments (35)" data-href="https://carlhendrick.substack.com/p/the-embarrassingly-parallel-problem/comments" class="pencraft pc-reset pencraft post-ufi-button style-button post-ufi-comment-button has-label with-border"><svg role="img" style="height:20px;width:20px;" width="20" height="20" viewBox="0 0 24 24" fill="#000000" stroke-width="2" stroke="#000" xmlns="http://www.w3.org/2000/svg" class="icon"><g><title></title><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg></g></svg><div class="label">35</div></button><button tabindex="0" type="button" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft post-ufi-button style-button has-label with-border"><svg role="img" style="height:20px;width:20px;" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke-width="2" stroke="#000" xmlns="http://www.w3.org/2000/svg" class="icon"><g><title></title><path d="M21 3V8M21 8H16M21 8L18 5.29962C16.7056 4.14183 15.1038 3.38328 13.3879 3.11547C11.6719 2.84766 9.9152 3.08203 8.32951 3.79031C6.74382 4.49858 5.39691 5.65051 4.45125 7.10715C3.5056 8.5638 3.00158 10.2629 3 11.9996M3 21V16M3 16H8M3 16L6 18.7C7.29445 19.8578 8.89623 20.6163 10.6121 20.8841C12.3281 21.152 14.0848 20.9176 15.6705 20.2093C17.2562 19.501 18.6031 18.3491 19.5487 16.8925C20.4944 15.4358 20.9984 13.7367 21 12" stroke-linecap="round" stroke-linejoin="round"></path></g></svg><div class="label">36</div></button>&lt;div&gt;<div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft post-ufi-button style-button has-label with-border"><div class="label">Share</div></button></div>&lt;div&gt;&lt;div&gt;&lt;div&gt;<div class="visibility-check"></div><div><div class="available-content"><div dir="auto" class="body markup"><p><em>If there is a central conundrum in instructional design, it is this: firstly, how to disassemble a domain of knowledge into teachable parts without destroying what made it a domain in the first place. And then secondly, how to reassemble those parts into flexible understanding without mistaking performance on individual components for comprehension of the whole. </em></p><p><em>For example, a student can learn the dates of every major battle in the First World War, define &quot;alliance&quot; and &quot;imperialism,&quot; and correctly identify the assassination of Archduke Franz Ferdinand as the triggering event, yet still have no real understanding of why the war happened. Or more commonly, the student who can accurately define a set of vocabulary words and still be unable to use them appropriately in speech or writing.</em></p><p><em>What makes this conundrum so persistent is that the two problems; decomposition and reassembly, look superficially similar but are structurally different. The first lends itself to analysis, optimisation, and scale. The second depends on sequencing, connection, and judgement, and resists being reduced to a set of independent operations. One of the reasons education keeps getting this wrong, I suspect, is that we lack a precise language for describing the difference.</em></p><div><hr></div><p><span>One of the more unexpected parts of my work lately has been collaborating with </span><a href="/bubo5/browser?url=https%3A//www.superbuilders.dev/" rel="nofollow">software engineers and developers </a><span>on learning apps. It’s been fascinating working with people from a different field, not because they “do education differently” in some vague way, but because they force you to make your assumptions explicit. Engineers think in constraints: what can be decomposed, what can be measured, what can be scaled. And when those constraints meet the science of learning, you get clarity, albeit sometimes of the uncomfortable kind.</span></p><p><span>Yesterday I was working with a developer on a vocabulary app and he used a phrase I hadn't encountered before. We were discussing vocabulary as knowledge components and how the system could deliver individualised practice to thousands of students simultaneously, and he said, </span><em>&quot;Well, that's what we call an embarrassingly parallel problem.</em><span>&quot; </span></p><p>I’d never heard that before and asked him to explain. In computer science, an “embarrassingly parallel” problem is one that can be broken into completely independent sub-tasks, each solvable at the same time with no communication or coordination between them. To “parallelise” a problem then, means to break it into multiple parts that can be worked on at the same time, rather than in sequence, without one part needing the results of another. The opposite is &quot;serial&quot; or &quot;sequential&quot; processing, where task B cannot begin until task A has finished, task C cannot begin until task B has finished, and so on.</p><p><span>The word “embarrassingly” carries a sort of self-deprecation: some problems are almost </span><em>too </em><span>easy to parallelise because there are no dependencies, no sequencing constraints, no need for one process to wait on another. Rendering millions of pixels in an image, running the same simulation with different parameters, sorting independent records: each unit of work proceeds in blissful ignorance of its neighbours.</span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%212jiW%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!2jiW!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 424w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 848w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1272w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!2jiW!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png" width="1240" height="526" 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srcset="https://substackcdn.com/image/fetch/$s_!2jiW!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 424w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 848w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1272w, https://substackcdn.com/image/fetch/$s_!2jiW!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F51d87cfc-e8e7-4892-bed4-f688029d6250_1240x526.png 1456w" sizes="100vw" fetchpriority="high" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p></p><p>The phrase stuck with me because it seemed to illuminate something important about both the promise and the peril of trying to apply what we know about learning to educational technology, and about a distinction that cognitive psychology has been circling for decades without quite naming it in these terms.</p><h3 class="header-anchor-post">Breaking Knowledge Without Breaking Meaning<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/breaking-knowledge-without-breaking-meaning" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h3><p>If there was a central conundrum in instructional design, I would say it is the question of how you a) decompose knowledge domains into components small enough to teach to novices and b) reassemble those components into something that resembles the integrated, flexible understanding that made the domain worth teaching in the first place. </p><p><span>In education we would call this curriculum design and a core part of the ‘knowledge-rich’ approach. And as </span><a href="/bubo5/browser?url=https%3A//www.civitas.org.uk/content/files/The-value-of-a-knowledge-rich-curriculum-web.pdf" rel="nofollow">Christine Counsell notes in a recent essay,</a><span> a knowledge-rich curriculum must do more than list disconnected items, it must “ask not only ‘What is the ambitious expertise we want our students to leave us with?’ but also ‘What are the most effective steps to help our students to develop this expertise?’”, emphasising careful sequencing and practice if understanding is to emerge rather than mere component mastery.</span></p><p>The decomposition of knowledge is necessary because novices cannot apprehend the whole all at once; working memory will not permit it. But the reassembly is where instruction most reliably fails, because the connections between components are not themselves components. They are relationships, contingencies, contextual judgements; the mortar, not the bricks. And no amount of optimising the bricks will produce a wall.</p><h2 class="header-anchor-post">The Parallel Layer: What Scales Easily in Learning<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-parallel-layer-what-scales-easily-in-learning" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p>There is a sense in which much of what we know about effective learning exploits genuinely parallel processes. Retrieval practice strengthens individual memory traces. Spaced repetition exploits consolidation mechanisms that operate on discrete items. Immediate feedback corrects specific misconceptions at the point of error. Vocabulary can be drilled word by word. Multiplication tables can be practised fact by fact. These processes are, in a meaningful sense, embarrassingly parallel: they can be run simultaneously across thousands of learners, each operating independently, with no degradation in quality.</p><p>This is precisely why intelligent tutoring systems scale so well, with no degradation in quality. Because each learner’s feedback loop operates as an independent process, personalised practice and feedback can be delivered to tens of thousands of students simultaneously. Large-scale randomised controlled trials have found modest but reliable effects on standardised tests, with substantially larger gains for lower-attaining students, often equivalent to close to a year of additional progress. And the costs are strikingly low. This is the economics of embarrassingly parallel processing applied to education: marginal costs approaching zero, with impact distributed across vast numbers of learners at once.</p><p><span>Spaced repetition systems like </span><a href="/bubo5/browser?url=https%3A//apps.ankiweb.net/" rel="nofollow">Anki </a><span>work on the same principle. Each flashcard is an independent thread. The algorithm optimises the spacing for each item without reference to any other. The system does not need to know that the student is also learning French irregular verbs while reviewing anatomy terms; each item is its own self-contained process. And the evidence for spaced practice is robust: a recent meta-analysis of classroom studies found effect sizes of 0.54 SD, with seven-day intervals proving consistently effective.</span></p><p><span>What’s interesting to me is that the parallel layer is not standing still; it is getting substantially more sophisticated. There is a lot going on in the spaced repetition scheduling field which illustrates this perfectly. Until recently, most systems used an algorithm called </span><a href="/bubo5/browser?url=https%3A//super-memory.com/english/ol/sm2.htm" rel="nofollow">SuperMemo-2, </a><span>developed by a college student in 1987 based on his personal experiments. It was somewhat arbitrary: see a card after one day, then six days, then apply an exponential backoff. Get it wrong and you reset to day one. The implicit assumption was that the forgetting curve for every piece of knowledge is roughly the same, which is not true at all. </span></p><p><span>However, a new algorithm called </span><a href="/bubo5/browser?url=https%3A//github.com/open-spaced-repetition/fsrs4anki/wiki/ABC-of-FSRS" rel="nofollow">FSRS (Free Spaced Repetition Scheduler),</a><span> developed by Jarrett Ye, has replaced this blunt instrument with something far more precise. FSRS is built on a </span><a href="/bubo5/browser?url=https%3A//supermemo.guru/wiki/Three-component_model_of_memory" rel="nofollow">“Three Component Model of Memory”</a><span> which holds that three variables are sufficient to describe the status of a unitary memory: retrievability (the probability that you can recall an item at a given moment), stability (how long, in days, it takes for that probability to drop from 100% to 90%), and difficulty (how hard it is to increase a memory’s stability after each review). Each card has its own values for these three components, collectively known as its “memory state.”</span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21g9lX%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!g9lX!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 424w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 848w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1272w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!g9lX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png" width="1438" height="618" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:618,&quot;width&quot;:1438,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:358505,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!g9lX!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 424w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 848w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1272w, https://substackcdn.com/image/fetch/$s_!g9lX!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F1b8ee07c-8975-4b99-8070-630ff0ee9b3e_1438x618.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a><figcaption class="image-caption">FSRS uses machine learning to predict exactly when each individual memory trace will fade, and schedules the review accordingly. Each card is an independent prediction problem</figcaption></figure></div><p>Using machine learning trained on several hundred million reviews from around 10,000 users, FSRS calculates 21 parameters that model how these three variables interact, and it can be further optimised against a learner’s own review history to fit their personal forgetting curves. The result is a system that can predict with considerable accuracy when a learner’s recall probability will drop to a specified threshold, and schedule the review accordingly. Users can set their desired retention rate (say 90%, or 70% to reduce workload), and FSRS will calculate the optimal intervals to achieve it. Compared to Anki’s legacy algorithm, FSRS requires 20 to 30% fewer reviews to achieve the same level of retention. Even with default parameters, before any personalisation, it outperforms the old approach.</p><p>This is seriously impressive, and it represents the parallel layer operating at its most refined. Each card is basically an independent prediction problem, optimised without reference to any other card, and machine learning makes that optimisation far more efficient than any hand-crafted formula could. The parallel infrastructure of learning is getting better, and that is worth celebrating.</p><p><span>But it also sharpens the question at the heart of designing effective learning. For exampole, FSRS can tell you exactly</span><em> when </em><span>to review the Japanese word 眼科医 (ophthalmologist), but it cannot tell you </span><em>how </em><span>that word functions differently in a medical report versus a casual conversation, how it relates to the broader semantic field of Japanese medical terminology, or how it interacts with the grammatical structures required to use it in a coherent sentence. </span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21_eOO%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!_eOO!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 424w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 848w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1272w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!_eOO!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png" width="1456" height="758" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:758,&quot;width&quot;:1456,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:2266606,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!_eOO!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 424w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 848w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1272w, https://substackcdn.com/image/fetch/$s_!_eOO!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5adccd9d-a6f4-45d6-a7cc-a63c5fd6eaec_1936x1008.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p>The algorithm optimises the retention of isolated items with extraordinary precision. What it does not, and cannot, address is the integration of those items into the kind of flexible, context-sensitive knowledge that constitutes actual competence. </p><p>In other words, you can memorise 3,650 words a year in twenty minutes a day. But knowing 3,650 isolated words is not the same as reading a novel, following a conversation, or writing a paragraph. Those require precisely the serial, integrative processes that no scheduling algorithm touches.</p><p>Ok. So far, so good, and so….well, limited. The parallel layer of learning is real, it is well-evidenced, and technology serves it brilliantly. The question is what happens next.</p><h2 class="header-anchor-post">The Serial Layer: What Cannot Be Parallelised<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-serial-layer-what-cannot-be-parallelised" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p><span>But here is where the whole thing becomes truly interesting and problematic, because it forces us to confront what learning is </span><em>not</em><span>. In computer science, the problems that are not embarrassingly parallel are those where sub-tasks are interdependent: where one process must wait for another, where outputs become inputs, where the order of operations matters and cannot be rearranged without corrupting the result. The overhead required to manage these dependencies is called </span><a href="/bubo5/browser?url=https%3A//itrevolution.com/articles/on-coordination-costs-moving-a-couch-and-painting-a-room/" rel="nofollow">coordination cost,</a><span> and it is often the binding constraint on performance.</span></p><p><span>Learning, at its most meaningful, is emphatically </span><em>not</em><span> embarrassingly parallel. To understand the causes of the First World War is not to hold a collection of independent facts, each stored and retrievable in isolation, but to grasp a web of interdependencies: alliances, economic pressures, nationalist ideologies, contingent decisions, structural forces. Each node in this web derives its meaning from its connections to the others. You cannot parallelise the construction of such understanding any more than you can build the tenth floor of a building before the ninth.</span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%218MRL%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f5e2c715c-1653-4e23-a115-54920a664227_1222x626.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!8MRL!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 424w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 848w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1272w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!8MRL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f5e2c715c-1653-4e23-a115-54920a664227_1222x626.png" width="728" height="372.93617021276594" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/5e2c715c-1653-4e23-a115-54920a664227_1222x626.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:626,&quot;width&quot;:1222,&quot;resizeWidth&quot;:728,&quot;bytes&quot;:313982,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!8MRL!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 424w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 848w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1272w, https://substackcdn.com/image/fetch/$s_!8MRL!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F5e2c715c-1653-4e23-a115-54920a664227_1222x626.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a><figcaption class="image-caption">Knowledge decomposes into independent items. Understanding does not.</figcaption></figure></div><p><span>Again, </span><a href="/bubo5/browser?url=https%3A//carlhendrick.substack.com/p/in-praise-of-artificial-learning" rel="nofollow">Herbert Simon’s concept of “near decomposability” </a><span>is helpful here. Complex systems, Simon argued, can be partially broken down into sub-systems, but those sub-systems still interact at their boundaries. A curriculum is near-decomposable: you can teach phonics and handwriting in relative independence, but reading comprehension depends on vocabulary, which depends on background knowledge, which depends on prior reading, which depends on the very comprehension you are trying to build. These are serial dependencies. They have an order and they resist parallelisation.</span></p><h2 class="header-anchor-post">The Measurement Signal Problem<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-measurement-signal-problem" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p><span>The evidence base for educational technology contains a signal that, viewed through this lens, becomes remarkably clear. Across multiple meta-analyses and large-scale evaluations, effect sizes on curriculum-aligned tests are consistently two to three times larger than effect sizes on standardised tests. </span><a href="/bubo5/browser?url=https%3A//www.jstor.org/stable/1170612" rel="nofollow">Kulik’s meta-analysis of mastery learning found 0.50 SD</a><span> on experimenter-made tests but only 0.08 SD on standardised measures. </span><a href="/bubo5/browser?url=https%3A//journals.sagepub.com/doi/10.3102/00028312019002237" rel="nofollow">A tutoring meta-analysis </a><span>found 0.84 SD on narrow tests versus 0.27 SD on standardised assessments. This pattern is ubiquitous.</span></p><p>The conventional explanation is test-curriculum alignment: students perform better on tests that closely match what they were taught. This is obviously true, but it is not the whole story. What curriculum-aligned tests measure is whether the parallel processes worked; whether students acquired the targeted skills, memorised the targeted facts, mastered the targeted procedures. </p><p>What standardised tests measure, however imperfectly, is whether those acquisitions have been integrated into something more flexible; whether the student can apply knowledge in contexts not explicitly rehearsed, make inferences across domains, transfer understanding to novel problems. In other words, the gap between aligned and standardised test scores is not merely a measurement artefact. It is a signal. The parallel layer is working. The serial layer is not being adequately served.</p><h3 class="header-anchor-post">The Atomisation Trap: When the Whole Never Reappears<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/the-atomisation-trap-when-the-whole-never-reappears" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h3><p>The great temptation of educational technology, (and it is a temptation born of genuine success), is to optimise for what is measurable and “parallelisable” at the expense of what is neither. When a platform can demonstrate that students have mastered 47 discrete skills with 85% accuracy, it is natural to conclude that learning has occurred. And in a sense it has, but mastering 47 discrete skills is not the same as understanding the domain those skills inhabit. The parts have been acquired; the whole has not been assembled.</p><p>This is what we might call the atomisation trap: the reduction of a curriculum to its “embarrassingly parallel” components, optimised independently, measured independently, and never reconstituted into any kind of coherent understanding. It produces students who can pass skill checks but cannot write a sustained argument, who can solve practiced problem types but freeze when the problem is reframed, who know the dates but cannot explain why they matter.</p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%216d6S%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!6d6S!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 424w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 848w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1272w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!6d6S!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png" width="1402" height="600" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:600,&quot;width&quot;:1402,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:508452,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!6d6S!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 424w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 848w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1272w, https://substackcdn.com/image/fetch/$s_!6d6S!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F35d50fb6-998b-47d7-a3e4-dec674822233_1402x600.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a><figcaption class="image-caption">The atomisation trap: optimising each component independently does not ensure they reconstitute into understanding.</figcaption></figure></div><p><span>An example of this I am fond of using is from a study by </span><a href="/bubo5/browser?url=https%3A//eric.ed.gov/%3Fid%3DEJ358469" rel="nofollow">George Miller and Patricia Gildea, published in </a><em><a href="/bubo5/browser?url=https%3A//eric.ed.gov/%3Fid%3DEJ358469" rel="nofollow">Scientific American</a></em><a href="/bubo5/browser?url=https%3A//eric.ed.gov/%3Fid%3DEJ358469" rel="nofollow"> in 1987</a><span> in which children were given dictionary definitions of words and asked to use them in sentences. The results were technically correct at the component level but semantically absurd when used in a sentence. One child, having learned that &quot;correlate&quot; means &quot;be related,&quot; wrote: &quot;Me and my parents correlate, because without them I wouldn't be here.&quot; Another, told that &quot;meticulous&quot; means &quot;very careful,&quot; produced: &quot;I was meticulous about falling off the cliff.&quot; </span></p><div class="captioned-image-container"><figure><a href="/bubo5/browser?url=https%3A//substackcdn.com/image/fetch/%24s_%21mMwp%21,f_auto,q_auto%3Agood,fl_progressive%3Asteep/https%3A%252f%252fsubstack-post-media.s3.amazonaws.com%252fpublic%252fimages%252f409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png" data-component-name="Image2ToDOM" rel="nofollow" class="image-link image2 is-viewable-img can-restack"><div class="image2-inset can-restack"><picture><source type="image/webp" srcset="https://substackcdn.com/image/fetch/$s_!mMwp!,w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 424w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 848w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1272w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1456w" sizes="100vw"><img src="https://substackcdn.com/image/fetch/$s_!mMwp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https:%2f%2fsubstack-post-media.s3.amazonaws.com%2fpublic%2fimages%2f409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png" width="1192" height="680" data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:680,&quot;width&quot;:1192,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:150375,&quot;alt&quot;:null,&quot;title&quot;:null,&quot;type&quot;:&quot;image/png&quot;,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:&quot;https://carlhendrick.substack.com/i/187184454?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png&quot;,&quot;isProcessing&quot;:false,&quot;align&quot;:null,&quot;offset&quot;:false}" alt="" srcset="https://substackcdn.com/image/fetch/$s_!mMwp!,w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 424w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 848w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1272w, https://substackcdn.com/image/fetch/$s_!mMwp!,w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F409ee1a1-f0be-49c1-bf58-25ecd85a2edd_1192x680.png 1456w" sizes="100vw" loading="lazy" class="sizing-normal"></picture><div class="image-link-expand"><div class="pencraft pc-display-flex pc-gap-8 pc-reset"><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container restack-image"><svg role="img" style="height:20px;width:20px" width="20" height="20" viewBox="0 0 20 20" fill="none" stroke-width="1.5" stroke="var(--color-fg-primary)" stroke-linecap="round" stroke-linejoin="round" xmlns="http://www.w3.org/2000/svg"><g><title></title><path d="M2.53001 7.81595C3.49179 4.73911 6.43281 2.5 9.91173 2.5C13.1684 2.5 15.9537 4.46214 17.0852 7.23684L17.6179 8.67647M17.6179 8.67647L18.5002 4.26471M17.6179 8.67647L13.6473 6.91176M17.4995 12.1841C16.5378 15.2609 13.5967 17.5 10.1178 17.5C6.86118 17.5 4.07589 15.5379 2.94432 12.7632L2.41165 11.3235M2.41165 11.3235L1.5293 15.7353M2.41165 11.3235L6.38224 13.0882"></path></g></svg></button><button tabindex="0" type="button" class="pencraft pc-reset pencraft icon-container view-image"><svg xmlns="http://www.w3.org/2000/svg" width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-maximize2 lucide-maximize-2"><polyline points="15 3 21 3 21 9"></polyline><polyline points="9 21 3 21 3 15"></polyline><line x1="21" x2="14" y1="3" y2="10"></line><line x1="3" x2="10" y1="21" y2="14"></line></svg></button></div></div></div></a></figure></div><p>Each child had successfully completed the parallel operation: definition retrieved, synonym matched, sentence generated. But the serial layer, the contextual understanding of how a word actually functions in living language, was entirely absent. The parts had been acquired. The whole had not been assembled. This is the atomisation trap in miniature: every component mastered, but no guarantee they recombine into understanding.</p><p><span>The evidence from</span><a href="/bubo5/browser?url=https%3A//web.archive.org/web/20150402210147/http%3A//www.highbeam.com/doc/1P3-73736679.html" rel="nofollow"> ALEKS, an adaptive learning platform, </a><span>is instructive here. When ALEKS replaces traditional instruction entirely, the effect is essentially zero (Hedge’s g = 0.05). When it supplements teacher-led instruction, the effect is a meaningful 0.43. The parallel processing of the platform, its adaptive diagnostics, its individualised practice, its immediate feedback, all of this works. But it works only when there is a human being providing the serial layer: the sequencing, the connecting, the contextualising, the building of one idea upon another in a way that the platform cannot do because it was not designed to do it.</span></p><p><span>As </span><a href="/bubo5/browser?url=https%3A//carlhendrick.substack.com/p/the-algorithmic-turn-the-emerging" rel="nofollow">I’ve written about</a><span> before, </span><a href="/bubo5/browser?url=https%3A//www.carnegielearning.com/solutions/math/mathia" rel="nofollow">Carnegie Learning’s MATHia </a><span>is one of the more sophisticated attempts to address this. Its cognitive models track dependencies </span><em>between </em><span>skills, not just mastery of isolated components, using production rules that represent the relational structure of mathematical knowledge. Its blended approach, combining the platform with teacher-led instruction, nearly doubled growth on standardised tests compared to traditional curricula alone. The system is effective precisely because it does not treat mathematics as embarrassingly parallel; it respects the serial dependencies between concepts while exploiting parallel infrastructure for practice and feedback.</span></p><h2 class="header-anchor-post">What Scales and What Doesn’t<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/what-scales-and-what-doesnt" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p><span>There is a deeper lesson here about why educational interventions so reliably lose potency at scale. </span><a href="/bubo5/browser?url=https%3A//static1.squarespace.com/static/6297c2b5c8bc35721cc7a65c/t/685ecf2600e9642f25e4dbfd/1751043879093/Kraft%2Bet%2Bal%2B2024%2BTutoring%2Bat%2Bscale.pdf" rel="nofollow">Kraft and colleagues found that tutoring studies with fewer than 100 students average d = 0.38</a><span>, while programmes with over 2,000 students average d = 0.11: a 66% reduction. </span><a href="/bubo5/browser?url=https%3A//www.nature.com/articles/s41598-025-97652-6" rel="nofollow">The Harvard study of GPT-4 </a><span>based tutoring achieved extraordinary effects of 0.73 to 1.3 SD, but with only 194 students. No one has replicated anything close to that at scale.</span></p><p>The embarrassingly parallel framing explains why. Scaling the parallel layer is trivial: if the software works for 200 students, it works for 200,000. The marginal cost is negligible. But scaling the serial layer, the human infrastructure of professional development, school culture, teacher knowledge, instructional leadership, adaptive response to context, is not embarrassingly parallel at all. Each school has its own dependencies. Each teacher requires individual support. Each classroom context demands coordination that cannot be automated away.</p><p><a href="/bubo5/browser?url=https%3A//www.achievementnetwork.org/" rel="nofollow">Achievement Network, an assessment platform,</a><span> provides a decent example. In high-readiness schools, those with supportive culture and strong leadership, the intervention produced 0.18 SD gains. In low-readiness schools, it produced nothing. The technology was identical. The parallel layer was the same. What differed was the serial layer: the human capacity to take data and weave it into coherent instructional response. </span></p><h2 class="header-anchor-post">Parallel Delivery, Serial Understanding<div class="pencraft pc-display-flex pc-alignItems-center pc-position-absolute pc-reset header-anchor-parent"><div class="pencraft pc-display-contents pc-reset pubTheme-yiXxQA"><div class="pencraft pc-reset header-anchor offset-top"></div><button tabindex="0" type="button" aria-label="Link" data-href="https://carlhendrick.substack.com/i/187184454/parallel-delivery-serial-understanding" class="pencraft pc-reset pencraft iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_secondary-S63h9o"><svg xmlns="http://www.w3.org/2000/svg" width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></button></div></div></h2><p>This whole concept of “embarrassingly parallel” problems crystallised something I had been thinking about for a long time. The science of learning has identified powerful mechanisms; retrieval practice, spacing, feedback, interleaving, worked examples etc. that genuinely operate with a degree of independence and can be delivered in parallel at scale via technology which serves this layer extraordinarily well, and the evidence is clear that it works.</p><p>But deep understanding is not embarrassingly parallel. It is painstakingly serial: built connection by connection, layer by layer, in a process that requires time, sequence, and the kind of integrative thinking that no algorithm yet replicates. The mistake is not in building platforms that exploit the parallel layer; the mistake is in believing that the parallel layer is all there is.</p><p>The best educational technology respects this distinction. It parallelises what can be parallelised: the delivery, the feedback, the scheduling, the data collection, the routine practice. And it preserves, protects, and supports what cannot: the teacher’s capacity to sequence ideas, to connect the dots between isolated skills, to build the kind of coherent understanding that transfers beyond the platform and into the world.</p><p><span>The developers I am working with are part of </span><a href="/bubo5/browser?url=https%3A//alpha.school/" rel="nofollow">Alpha School </a><span>and their model is one that is seriously working on these problems. The idea is deceptively simple: if technology is exceptionally good at delivering the parallel layer (diagnosis, retrieval practice, spacing, interleaving, feedback, scheduling) then its role should be to make that layer as efficient and reliable as possible, while deliberately preserving the “serial” work of integration support for a human being. This support comes in the form of guides who provide the human scaffolding that technology cannot: mentoring, motivation, tutoring when needed, and the steady support required to keep students engaged in the longer, serial work of understanding.</span></p><p><span>What is interesting about this model is that it does not attempt to automate the hardest part of instruction. Instead, it </span><em>acknowledges </em><span>it. Alpha’s guides exist precisely because understanding does not scale in the same way that practice does. The system scales the parallel layer aggressively, while concentrating scarce human attention where it matters most. I have seen these guides in action and the care and attention they provide students is phenomenal, and on a level that is virtually impossible for most teachers. </span></p><p><span>There’s been a gradual acceptance for some time now that what we are expecting classroom teachers to do is beyond what can be reasonably expected, and this is reflected in the current </span><a href="/bubo5/browser?url=https%3A//www.nfer.ac.uk/press-releases/teacher-recruitment-and-retention-crisis-shows-no-signs-of-abating-new-report-reveals/" rel="nofollow">recruitment and retention crisis.</a><span> The expectation of managing thirty or more students simultaneously, each at a different point of understanding, each with different misconceptions, each requiring the kind of sustained, responsive attention that is, by its very nature, serial: one conversation at a time, one connection at a time, one moment of recognition at a time.</span></p><div><hr></div><p>The vocabulary app and others I’m working on have really made me think hard about this problem of parallel vs serial. The drilling of individual words is genuinely embarrassingly parallel, and the scheduling of those drills is now being refined by machine learning algorithms like FSRS that can predict with remarkable accuracy when a learner is about to forget a specific item. That precision is a gift; it means we can build the parallel layer with a confidence and efficiency that would have been unimaginable even five years ago.</p><p><span>But vocabulary acquisition in the </span><em>deeper </em><span>sense; understanding how words relate to one another, how they shift in register and connotation, how they unlock comprehension of increasingly complex texts, that is a serial process. It requires context, connection, and the slow, patient work of building a mind that knows not just the parts, but how the parts compose a whole. No scheduling algorithm, however sophisticated, will provide that. It has to be designed into the instruction itself: the sequencing of words within semantic fields, the embedding of new vocabulary in meaningful texts, the explicit teaching of morphological relationships and contextual variation. This is the work of instructional design, where the goal is not to make learning embarrassingly parallel. It is to know which parts already are, exploit them brilliantly, and never mistake them for the whole.</span></p><div><hr></div><div class="subscription-widget-wrap"><div class="subscription-widget show-subscribe"><div class="preamble"><p>The Learning Dispatch is a reader-supported publication. 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6.53394C3.63888 6.01025 3.92086 5.55819 4.27893 5.17773C4.64148 4.79728 5.05774 4.50635 5.52771 4.30493C6.00216 4.09904 6.51241 3.99609 7.05847 3.99609C7.73433 3.99609 8.31844 4.16618 8.81079 4.50635C9.30762 4.84652 9.70374 5.28963 9.99915 5.83569C10.299 5.28516 10.6951 4.84204 11.1875 4.50635C11.6843 4.16618 12.2707 3.99609 12.9465 3.99609C13.4836 3.99609 13.9894 4.09904 14.4639 4.30493C14.9428 4.50635 15.3613 4.79728 15.7194 5.17773C16.0774 5.55819 16.3572 6.01025 16.5586 6.53394C16.7645 7.05762 16.8674 7.63725 16.8674 8.27283C16.8674 9.23067 16.6145 10.1818 16.1088 11.1262C15.603 12.0662 14.8891 12.9815 13.967 13.8722C13.0495 14.7584 11.9708 15.6044 10.731 16.41C10.6056 16.4906 10.4758 16.5622 10.3416 16.6249C10.2118 16.692 10.0976 16.7256 9.99915 16.7256Z"></path></g></svg></div>Liked by Carl Hendrick</button></div></div><div class="pencraft pc-display-flex pc-flexDirection-column pc-reset flex-grow-rzmknG"></div><div class="pencraft pc-display-flex pc-reset triggerContainer-eX588u"><button tabindex="0" type="button" aria-label="Ellipsis" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft trigger-j08Uop iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_quaternary-kpMibu"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-ellipsis"><circle cx="12" cy="12" r="1"></circle><circle cx="19" cy="12" r="1"></circle><circle cx="5" cy="12" r="1"></circle></svg></button></div></div><div class="comment-body expanded"><p><span>Super interesting, I like the framing. </span></p><p><span>This reminded me that when I’ve taken exams (and I’ve taken many over the years, from school, undergrad and grad school, to actuarial exams) I actually never relied on flash cards preferentially, precisely because I disliked that that approach meant that related facts became disconnected from each other. </span></p><p><span>Rather I would first compile a giant document, my first pass of “notes” that included every bit of knowledge that I thought relevant to the various parts of the syllabus, whether it be classroom notes, notes on the textbook or videos, problems I thought especially illustrative etc. This would all be laid out in syllabus order. Then my task would be to condense the information, using several passes. As I condensed, it would become clearer what parts of the information were genuinely tricky or hard to retain and I’d make sure to include those. But more importantly, I’d start to get a sense of how all these parts fit together, how x was really a special case of y, and you did z except in these circumstances, where you did w and so on. I could feel the mental map coming together and a large measure of whether I felt I’d be successful in an upcoming exam was how robust I felt that map to be. I would usually describe this as clicking. Once that happened I could often condense my notes to just two or three pages, even for a semester end exam, as facts hung easily on the structure and new facts absorbed with little effort. Everything just made sense together. </span></p><p><span>Coming from that perspective (having never really preferred flash cards as a study aid), I’m finding it fascinating both reading about recent developments in improving them, as well as their limitations. </span></p></div><div class="pencraft pc-display-flex pc-gap-16 pc-paddingTop-8 pc-justifyContent-flex-start pc-alignItems-center pc-reset comment-actions withShareButton-hQzuEn"><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a class="pencraft pc-reset link-_X6et2 link-LIBpto"><div class="pencraft pc-display-flex pc-gap-6 pc-alignItems-center pc-reset"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="var(--color-fg-secondary-themed)" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg><div class="pencraft pc-reset 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3.13086 8.27283C3.13086 7.63725 3.23157 7.05762 3.43298 6.53394C3.63888 6.01025 3.92086 5.55819 4.27893 5.17773C4.64148 4.79728 5.05774 4.50635 5.52771 4.30493C6.00216 4.09904 6.51241 3.99609 7.05847 3.99609C7.73433 3.99609 8.31844 4.16618 8.81079 4.50635C9.30762 4.84652 9.70374 5.28963 9.99915 5.83569C10.299 5.28516 10.6951 4.84204 11.1875 4.50635C11.6843 4.16618 12.2707 3.99609 12.9465 3.99609C13.4836 3.99609 13.9894 4.09904 14.4639 4.30493C14.9428 4.50635 15.3613 4.79728 15.7194 5.17773C16.0774 5.55819 16.3572 6.01025 16.5586 6.53394C16.7645 7.05762 16.8674 7.63725 16.8674 8.27283C16.8674 9.23067 16.6145 10.1818 16.1088 11.1262C15.603 12.0662 14.8891 12.9815 13.967 13.8722C13.0495 14.7584 11.9708 15.6044 10.731 16.41C10.6056 16.4906 10.4758 16.5622 10.3416 16.6249C10.2118 16.692 10.0976 16.7256 9.99915 16.7256Z"></path></g></svg></div>Liked by Carl Hendrick</button></div></div><div class="pencraft pc-display-flex pc-flexDirection-column pc-reset flex-grow-rzmknG"></div><div class="pencraft pc-display-flex pc-reset triggerContainer-eX588u"><button tabindex="0" type="button" aria-label="Ellipsis" aria-haspopup="menu" aria-expanded="false" data-state="closed" class="pencraft pc-reset pencraft trigger-j08Uop iconButton-mq_Et5 iconButtonBase-dJGHgN buttonBase-GK1x3M buttonStyle-r7yGCK size_sm-G3LciD priority_quaternary-kpMibu"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-ellipsis"><circle cx="12" cy="12" r="1"></circle><circle cx="19" cy="12" r="1"></circle><circle cx="5" cy="12" r="1"></circle></svg></button></div></div><div class="comment-body expanded"><p><span>Brilliantly-said. </span></p><p><span>Also, this piece captures important details that get recognition in some corners of the field, but fail to achieve broad embrace. For example, in the literacy space, we know that words are best learned/taught in context. Your example of the children writing awkward sentences because they learned surface-level definitions (kind-of accurate, but lacking context, so they missed the mark) illustrates the point. But much of the field is still game to drill vocabulary. </span></p><p><span>And in US schools, we have far too many book-lite classrooms and science/history-starved school schedules. The memo about the role of contextual knowledge has been slow to spread.</span></p><p><span>Thank you for writing. Please keep writing.</span></p></div><div class="pencraft pc-display-flex pc-gap-16 pc-paddingTop-8 pc-justifyContent-flex-start pc-alignItems-center pc-reset comment-actions withShareButton-hQzuEn"><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a class="pencraft pc-reset link-_X6et2 link-LIBpto"><div class="pencraft pc-display-flex pc-gap-6 pc-alignItems-center pc-reset"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="var(--color-fg-secondary-themed)" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-message-circle"><path d="M7.9 20A9 9 0 1 0 4 16.1L2 22Z"></path></svg><div class="pencraft pc-reset color-pub-secondary-text-hGQ02T line-height-20-t4M0El font-meta-MWBumP size-11-NuY2Zx weight-medium-fw81nC transform-uppercase-yKDgcq reset-IxiVJZ meta-EgzBVA">Reply</div></div></a></span><span class="pencraft pc-reset decoration-hover-underline-ClDVRM reset-IxiVJZ"><a 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dividualised practice to thousands of students simultaneously, and he said, "Well, that's what we call an embarrassingly parallel problem." I’d never heard that before and asked him to explain. <span>In computer science, an “embarrassingly parallel” problem is one that can be broken into completely independent sub-tasks, each solvable at the same time with no communication or coordination between them. To “parallelise” a problem then, means to break it into multiple parts that can be worked on at the same time, rather than in sequence, without one part needing the results of another.