Observe without judgment

The highest form of human intelligence is to observe yourself without judgment.

This quote of Jiddu Krishnamurti, which I got from the book Nonviolent Communication, seems to directly contradict my post Defining “smart”, where I argue smartness is a process of judging. Is this a paradox?

Perhaps a better definition of intelligence is a two-step process. The first is to observe without judgment, and the next is to apply judgment among possible responses to the observation, invoking a quote from Hadarmard’s The Psychology of Invention in the Mathematical Field:

To invent is to choose.

Some examples:

  • From Nonviolent Communication, the context is that an intelligent communicator is able to non-judgmentally observe the feelings of oneself and others and then choose an empathetic response.
  • A typical design process is to brainstorm while deferring judgment, followed by a critical synthesizing.
  • A good way to learn to draw is being able to observe without invoking iconography (a form of judgment). As you develop as a component drawer, you become an artist by choosing what to observe and draw (perhaps “observing” from your mind’s eye).
  • A mathematician may observe a mathematical object before attempting to judge the correctness of a property.
  • The scientific method is first to observe without bias, then to judge the validity of hypotheses.

From my previous “smart” post, it’s clear I find intelligence in the act of analyzing and choosing. I believe observation is not a trivial step and can be at least as challenging.

I have experience with observing to draw. Techniques (which you can learn about in Drawing on the Right Side of the Brain) like drawing upside-down, blind contour drawing, and observing negative space require a great deal of focus and mental energy. Likewise meditation is a focus on observing your breath or body and is very challenging–one is constantly fighting off distracting and judgmental thoughts. In fact with meditation the act of observation itself can lead to healing of physical discomfort, as described from a skeptic’s perspective in Teach Us to Sit Still.

Finally, what about another possible step to intelligence: generating ideas? Isn’t the design process example about generation and creativity rather than observation? It’s subtle but I’d argue that you observe what comes to mind rather than doing generation yourself. Going back to Hadamard, he notes that mathematicians generally make breakthroughs after taking their mind away from the problem. The answer comes in a flash, and the mathematician merely observes it.

Designing learning systems with spaced repetition

Spaced repetition is a valuable technique for learning. The typical design of a spaced repetition system (SRS) presents users with a queue of all items that are due according to its scheduling algorithm1. The motivation behind this post is that the queue can quickly become overwhelming, and endless item review is frankly boring. Can we do better?


The SRS design is based several assumptions:

  1. The user wants to retain everything, all the time. SRS queues get big because they contain everything the user has thought to add to the system, whether or not they still want to know it. Sure, the user can delete things, but leaving this kind of maintenance work up to the user isn’t ideal.
  2. The user doesn’t review any item outside of your system. In a previous post, I talked about how spaced repetition occurs naturally when we attempt to learn something in a natural way. In other words, if you are actively engaged in learning Portguese in Portugal, your exposure to many words will be spaced and repeated. By assuming that users only learn in the system, you either drive them away from reviewing with more natural processes or your algorithm is based on poor assumptions. (When I was studying Chinese only in Skritter, it was spacing too far, so I’m guessing their parameters were adjusted for people who studied outside the system.)
  3. There’s only one way to review an item. In a typical SRS, all items are independent. But think about basic addition skills. Do you need to review them constantly? No, they come up all the time when you learn multiplication, division, and then any other topic involving math. In Learnstream Atomic, we attempted to break down physics questions into components and mark everything as reviewed. I think that was only the tip of the iceberg.

This sounds obvious, but one way to reconsider spaced repetition systems is to realize that they provide two values: spacing and repeating. The overwhelming queue is a design that favors repeating items more than spacing them, at least when you consider item review outside of a closed system. Imagine another system that take the opposite approach, favoring spacing: perhaps a website that has links to different items but warns you not to look at something that you’ve looked at recently.

If you’re considering implementing a spaced repetition system for a learning tool, consider carefully the assumptions made by existing systems and the two values provided by spaced repetition. What would you do differently?

[1] Every SRS I’m familiar with uses the SuperMemo algorithm, based on the idea of an exponential memory decay.

Defining “smart”

I don’t like to use the label “smart” because there are many positive ways of being human, all of which involve using your brain. Here I’ll play with a definition of it anyway:

Smartness is the ability to sort statements by their knowledge value.

A mathematician writes a proof, which is a sequence of statements, each giving an essential piece of knowledge to decide a previously-undecided truth. It isn’t about brevity: the particular writeup may contain many other statements that explain things to the audience. A compentant mathematician would be able to point out which statements are essential to the proof, the statements with the most knowledge value.

As a counterexample, imagine someone uttering, “Education needs to be disrupted.” (I’m not pointing fingers, many of us have.) The issue isn’t about correctness. We have a mutual understanding that the term “needs” is probably too strong. And the statement may be encapsulating many other thoughts about why education needs to be disrupted. But a smart person should recognize that identifying a significant component of education that can be changed within a broader context, or a method of disrupting education that works in the long run, would both have far more knowledge value.

Actually I’ll revise the definition to be more abstract: smartness is the ability to sort objects by some property value. Smart photographers can sort pictures by their emotional value. Smart comedians can sort jokes by their comic value. In an Esquire interivew, Woody Allen says:

I don’t think of the joke and then say it. I say it and then realize what I’ve said. And I laugh at it, because I’m hearing it for the first time myself.

Taken to the extreme, this defintion suggests that creative endeavors are more about perceiving value than producing it. My belief, which I won’t justify here, is that this is true, though production also requires well-practiced techniques. For example a basketball player needs to have well-practiced shooting techniques but also (more “smartly”) needs to be able to perceive the value of taking a shot in the current situation.

Getting beyond massively lousy online courses

Sebastian Thrun on Udacity:

We have a lousy product.

In the article, Thrun says that MOOCs, massive open online courses that gained popularity a couple years ago when introduced by professors from Stanford University, didn’t live up to their hype in democratizing education for the whole world.

Personally I’d been anticipating the start of a particular MOOC for several months–there isn’t very much educationally-oriented material on the topic in existence. Recently, on the week it finally came out, I finished Portal 2 instead of the first assignment, which involved installing, troubleshooting, and navigating a complex program and hunting down the dataset within the MOOC software–all before the deadline.

Ain’t nobody got time for that.

What can MOOCs learn from Portal 2 about making a compelling product? Let’s take a look.

Why am I playing this game at all? Plot. I’m stuck in a dystopian science facility being avenged by the evil computer system GLaDOS. The startling setting and crazy characters immediately draw me in.

Each level in Portal 2 has a clear goal: open the door. Generally I need to learn one new thing to complete the level while integrating what I’ve learned before, providing incremental difficulty. Furthermore, the environment that you interact with has many affordances, guiding you to play with tools like blocks, buttons, and magical scientific bouncy goo.


Even if I’ve discovered the tools to use, it takes some trial to succeed in the level. The game provides feedback when something isn’t working right: I fall into a pit and drown in toxic water instead of reaching the other ledge when I haven’t figured out how to jump far enough.

Progress is concrete: I finish a level in about 10 minutes. Further, I receive a reward at the end in the form of taunting from GLaDOS that’s genuinely funny as I ride the elevator to the next level.

Compelling plots

The “why?” of a MOOC is usually confined to the professor droning on a few minutes during the first lecture giving a list of ways the subject has been applied. There’s lots to say about storytelling, but there’s a reason that “vague list” isn’t a story archetype. Plots are, partly, about fantasy–we can put the learner in the applications and make it big and dramatic. Language learning? Take me to a foreign land. Applied math? Let me be that guy from Numb3rs. At least in college, I was a student on a four-year quest for a degree with my classmates. In a MOOC, I’m just a registered user who gets a lot of annoying emails.

Online learning has yet to go very far with this idea. One example is Codecademy, where you at least have a larger objective of completing a project.

{<2>}Codecademy's final JavaScript lesson is framed as replacing a broken cash register

Clear goals

MOOCs often ask you to complete a complex task in a complex environment. You need to switch back and forth between the software and slides for step-by-step instructions, and you don’t even understand what you’ve achieved at the end.

DragonBox teaches algebra using the principle of clear goals. Each level has the same goal of isolating the spiral, but they incrementally teach all aspects of solving algebraic equations.

{<9>}DragonBox has a clear goal: isolate the spiral (grounding the idea of 'solve for x')

Incremental difficulty

Professors seem to love to jump into applied knowledge. Before making sure you get the definition of something, they’re asking you to transform and apply it.

{<11>}DuoLingo highlights the one new word introduced in this problem

In contrast, DuoLingo succeeds in incremental difficulty: it typically presents one new word at a time.


Check out Quill: it presents a textbox claiming “There are nine errors in this passage. To edit a word, click on it and re-type it.” I have no desire to learn anything more about grammar, yet I corrected several errors during my first visit to the page. The textbox, the existence of errors, and even the typography and the way individual words are selected when clicking, all afford me to play with it.

{<3>}Quill's interface affords testing your knowledge of correct writing

While it’s true that multiple choice prompts common on MOOCs are an affordance for providing an answer, these are generally removed from the environment and tools you’d actually be working with.


One of my major takeaways from interviewing many users of online learning systems is that the loop of instruction, practice, and feedback is way too long. Imagine that I watch several hours of video lecture over the course of a couple days, then I come back another day to do the assignment. Of course there are key ideas in the lecture I didn’t understand or remember, so I have to go hunt them down within those hours of video. Of the dozens of concepts covered in the videos, I get about 10 questions worth of practice on the quiz. Finally, I might not even receive immediate feedback on that quiz–I have to wait until after the quiz deadline to see what I missed anything and understand why. If I even come back to look it.

Based on Bret Victor’s principle that creators should immediately see the effects of their changes, Khan Academy’s computer programming environment allows you to adjust variables in the code and see the results on screen.

{<5>}Khan Academy CS lets you adjust numerical input values and instantly see the result

In other words, you get feedback as you adjust the code. However, this feature is only responding on one very minor aspect of programming. Imagine an environment that gives feedback about a misunderstanding of conditionals or recursive, and then we’re getting somewhere. Indeed Victor responded with an article about how they got it all wrong. You should read it.

Meaningful rewards

In the Power of Habits, Charles Duhigg explains that concluding an interaction with a reward is a powerful way to instill habits. The trend of gamification has driven this effect through badges and points. But as Portal 2 shows, rewards are an opportunity to entertain and drive the plot forward, not just pad pockets with a fake currency.

CodeCombat (disclosure: friends with one of the founders) is a new effort to teach programming that uses this idea well. Once you’ve successfully programmed your soldier, you get to watch him execute his program and kill the ogre. You also get to see the “spells” that you learned in that level. It’s like collecting badges but also uses the opportunity to allow you to reflect on what you’ve just learned.

{<4>}CodeCombat displays your code execution as your character defeating the ogre

Final thoughts

Some of these principles apply to developing better tools for us to do our work. If a tool is already well designed, learning it is easier. However, it is still important to understand the learner’s state, that is differences between what different users already know and understand. Considering the learner’s state implies we should set goals of incremental difficulty and indicate and reward when those goals are achieved, just as good games put sequence levels with clear goals in incremental difficulty for the player.

There’s plenty more to consider for an ideal learning environment. I’ve written before about spaced repetition, mnemonics, and multimedia. But I believe that solid execution on these principles gets us 80% of the way there. As Sebastian Thrun’s resignation demonstrates, we have a very difficult job ahead in that.

The biased versus the heartless

Decision making is hard. For instance, we seem to be awful at making hiring decisions. Daniel Willingham explains a study that accurate answers to interviewers’ questions did not gain any advantage over random information. Google has examined the data in practice and found that structured interviews with a rubric are more effective than brainteasers. A particular example from marketing professor Adam Alter is particularly offensive: people with easier names are more readily promoted.

We like easy names. That’s a clear picture of how we are biased, emotional, and have limited processing capacity.

But on the other side of the coin, trusting decisions to computers, has its own subtle set of problems, as examined in two recent articles.

Nicholas Carr tells us that “All Can Be Lost” when we put knowledge in the hands of a machine. While computers automation in, for example, flying planes may initially seem safer and more effective, human operators meanwhile begin to lose their skills.

Sooner or later, even the most advanced technology will break down, misfire, or, in the case of a computerized system, encounter circumstances that its designers never anticipated.

And at this point the human operator is no longer capable of taking over. It’s a race against pilots losing their knowledge and technology advancing its knowledge and robustness. In this case, technology seems to be winning: air travel is already much safer than car travel and been getting increasingly safe.

What about computers as actors in complex systems? In The Real Privacy Problem Evgeny Morozov makes several points about the inadequancy of technological solutions to protecting our privacy. One in particular is we may not be able to interpret the decisions or predictions of machines. This undermines our legal and political systems that are rooted in deliberation through natural language.1

My understanding of politics is limited, but there’s an analogue in educational technology. In adaptive learning systems, computer models are used to make predictions and assist learners based on their performance. Similarly, there is backlash, such as Dan Meyer’s, that machines may be able to determine that an answer is incorrect, but they aren’t able to connect to the human mind making that mistake the way a teacher can. There are tools such as teacher dashboards, as the blogger from Pearson proposes to Meyer, or open learner models that expose the computer’s knowledge of the student such that the student can scrutinize their own model. As Meyer correctly notes, however, designing a tool that’s actually useful entails its own difficulties.

What can we conclude?

  1. Inevitably, more and more of our knowledge will be in the hands of computers. We can’t just hope this won’t happen.
  2. We must understand and codify how humans learn, that we are biased, emotional, and have limited processing capacity, but can learn complex patterns when given accurate feedback. With that knowledge, computers will be able to teach their results to humans. Morozov links to the concept of adversial design, where technology can “provoke and engage the political.”
  3. Whether decisions are made by humans or machines or some system of both, the impact of single decisions should be kept as small as possible. Mistakes can thus minimize damage and even support growth. One failing flight (out of 30,000 per day in the US)–as tragic as it may be–can teach us a great deal. (This is Taleb’s idea of antifragility.)
  4. None of this will be easy. That’s why you should hire me.

[1] As contrasted to computation. See again The Mother of All Disruptions.

Individuals as knowledge producers

eBay founder Pierre Omidyar is forming a general news site “with a core mission around supporting and empowering independent journalists.”

Academia.edu is similarly supports academics in promoting their work and providing new metrics for scientific impact.

With these efforts, along with more ubiquitous social network services like blogs and Twitter, we see individual brands moving into the space occupied by organizations like the New York Times or Science. I call these brands because there are sometimes several or many people working behind the individual name put forward–from large law firms named for their partners to ghostwriters on Twitter.

Why does there seem to be a shift to individual brands? One reason may be the power of tools and outsourcing to do what would have previously required larger organizations.

What are the advantages of individuals becoming their own knowledge producers? Will we be able to figure out who to follow and how best to disperse knowledge from the long tail of individuals?

More reading:

Knowledge science = data science + learning science

If a computer proves a theorem and no human understands it, is it math?

We are experiencing a new era of data abundance rising from machines transmitting human data and, increasingly, producing data of their own. How do we understand and make use of it all?

Data transfers among humans and machines. It is communicated by one end and learned by the other1. We tend to call such data knowledge when it is learned and understood by a human.

We all have countless experiences with human-to-human knowledge–conversation, lecture, writing, dance–but it is changing both in scale and process. Online courses can reach hundreds of thousands of people, and they are often taught by professors who have invented some of what is taught. But is this the best way to learn? Are the right things being taught?

Machine-to-machine knowledge is something we increasingly need to understand. Venkatash Rao calls computing the Mother of All Disruptions and anticipates machines increasingly treated as economic agents. How does such knowledge evolve when it is outside of human intervention, and what scope of tasks will we afford machines to apply this knowledge?

Your FitBit sits on your wrist and silently records data throughout the day, and at the end of the day conveys an insight about your walking habits: “I don’t walk as much as I thought.” This is a machine-to-human example. Data visualization is one of many ways that machines will be able to communicate (how about music?).

Codifying human learning processes will be a major task in adapting to data abundence. Given new knowledge generated by a computer–say, a novel mathematical theorem–how can the computer use a model of human learning to best communicate that result? Can the computer teach what it creates, or will there be knowledge that we abandon forever to the machine-to-machine realm?

Finally, human-to-machine knowledge has quickly evolved from punch cards to high-level programming languages to Siri. But what will the divide in digital literacies–such as programming ability–mean for economic inequality, or humanity in general, when machines are given increasing power?

[1] Data may sometimes be merely experienced by the receiving end, but I’m not qualifying that as data transfer. My working definition of learning is a change in long-term memory. (The definition works for computers too: something is stored.)

Spaced repetition in natural and artificial learning

In the last installment, I argued that sometimes we need to learn (gotta start easy). In a couple of Quora answers, I’ve been building up how we do that: an overview of effective learning techniques and a deep look at perceptual chunks and deliberate practice. What I’m slowly working toward is a philosophy and framework for designing learning tools. Today I want to talk more about a core tenant of this philosophy (hinted at in the last paragraph of my deliberate practice answer as well as a weekly review about Shuff’s philosophy): we are naturally good at learning within our local environment. However, we are increasingly living in a global, disconnected, artificial world that requires artificial environments to act and learn effectively.

Consider the example from Design vs. learning: my concern about my friend’s choices about bottled water was not about perceivable effects in the local environment, but rather the effects from its manufacturing halfway around the world. Rather than naturally learning within a responsive external representation, we require building up a mental model and evaluating decisions by running the effects of an action on that model. As my friend illustrates, this takes more work than most people will do, even with good intentions.

Let me revisit my favorite learning topic, spaced repetition, with this philosophy in mind. Scott H. Young and Khatzumoto are two of my favorite bloggers that write about learning. Several years ago Khatz introduced me to spaced repetition. To say it left an impact is an understatement. Since then I’ve made a series of projects that use spaced repetition in online learning systems. But I’ve always questioned when exactly spaced repetition is a good design choice for a learning tool.

Scott’s MIT Challenge is his attempt to study a four-year MIT curriculum in one year using online resources. He recently posted a great article questioning the value of spaced repetition for his own learning. He argues that aggressively pressing forward in learning new and more advanced material will naturally re-expose him to material from before, making a spaced repetition system unnecessary.

Such a result has been found for an elementary math curriculum. In Why Students Don’t Like School, Daniel Willingham (another of my favorite learning bloggers) summarizes the results of a longitudinal study: “A student who gets a C in his first algebra course but goes on to take several more math courses will remember his algebra, whereas a student who gets an A in his algebra course but doesn’t take more math will forget it. That’s because taking more math courses guarantees that you will continue to think about and practice basic algebra.”

Enter Khatzumoto. In his Unified Reading Process, he uses decks in a spaced repetition system as a collection system for everything interesting that he encounters. He lists thirteen examples of decks that he uses!

Some things that humans learn fold out naturally. Our brains co-evolved with the environment to let that happen. When we are born into the world, that world has, for hundreds of thousands of years, been one where we are expected to be in a society of people who talk to us as we naturally learn language and how to attract and care for other people and in an environment where learn how to navigate and hunt.

A good curriculum teaches you an artificial subject in a similar manner. It’s like a game that’s carefully set up to advance in difficulty as you use the skills and equipment you’ve gathered on the way. Subjects like math and science may be artificial, but curricula have undergone thousands of years of refinement to be somewhat learnable. This is what Scott Young is relying on when he presses forward with the MIT challenge.

But Khatzumoto’s method is setting up an artificial world where one doesn’t exist, where not even a good curriculum exists. He’s building his curriculum in place. Topics like foreign languages have some options for curricula, but why not make them contemporary and interesting by using real media? There’s no curriculum for being up to date with the latest trends in business or software engineering, but it’s important: you need to be able to converse with others in that vocabulary, and you may pick up some wisdom along the way.

Spaced repetition specifically replicates some of the advantages of a natural environment. Memory works like this: when we encounter important things a number of times in different contexts, we begin to learn them in the abstract. Otherwise, we’d be totally overwhelmed by the number of abstract concepts we could apply in any circumstance. By artificially spacing repetition, we allow the context to vary via the passing of time. Not only do our physical surroundings change, but the knowledge we have that can be related to what we learn changes.

So I don’t agree with the extent to which Scott thinks aggressive learning makes spaced repetition unnecessary. Moreover, he’s overlooking the “spaced” part of the concept: that not repeating, and allowing the context to vary, is an equally important part of the equation. But there is an art to keeping that environment spruce: Khatzumoto says, “I choose decks in order of fun/priority and I delete extensively. If I’m avoiding a deck, then I go on a deletion spree, and I keep deleting until the deck feels good again.” More on that to come!

Design vs. learning

The angriest I’ve gotten in recent memory is when arguing with a friend about her bottled water drinking habits. It wasn’t that she drank the water that made me angry but that she didn’t want to consider any information that might suggest why she should or shouldn’t drink (as much) bottled water (if you’re scratching your head about why this may even be an issue, see for example http://science.howstuffworks.com/environmental/green-science/bottled-water4.htm). Her argument was along the lines of “I know how behavior change works. Information won’t lead to change in my behavior.” This is fascinating because it’s a failure in rationality that results from a misunderstanding of theories about failures in rationality, which have come to the attention of someone like her only recently due to the proliferation of behavioral economics.

Her reasoning is rooted in empirical research. One example of many from Nudge is where Minnesota taxpayers were given different types of information about complying with tax law. Only one group had a significant change in behavior. It was the one given a not-so-informative social cue, simply that “90 percent of Minnesotans already compiled, in full, with their obligations under tax law.” The generalization abstracted from this and many similar studies is that various social and perceptual cues are far more effective than information in producing a desired change in outcome. As a generality, I think it makes sense.

Consider this analogical experiment, which sounds like a horrible word problem come to life. A group of fifth graders were taken to a store that was having a sale. Two identical shirts were for sale, but one was 60% off while the other was 30% off with an additional 40% off the sale price. One group of children was given information about how to multiply percents. Another group of children were not given the information but rather the sign for the cheaper shirt was shown in bright colors. The result (OK, not a real experiment, but a reasonable guess): many more children in the second group got the cheaper shirt! So information sucks, right??

There are two problems with this conclusion that are much easier to recognize than the tax compliance one. First, the information is not properly presented. Fifth graders are not able to understand and apply math with percents after the presumably brief intervention provided by the experiment. The second problem is that the second group of children happened to be nudged to the cheaper shirt[1]. Generalizing this idea of “let us be nudged” relies on some unnamed party to have the good intentions toward the nudgee, the right idea about how to nudge, and so on.

When we can understand and apply information, we become more powerful and free. If we know some math, we can calculate that the first shirt is 40% of the original cost, while the second is 42%, so the first is cheaper.

There is a limit to what we can know. We may be able to, by ourselves, calculate the better deal in a store. We cannot calculate the aerodynamics of the wings of an aircraft before hoping on board. We trust our lives to the engineers, the pilots, and the air traffic controllers when we fly on a plane. Even what we learn is trusted to the planning of curriculum designers, school board members, and teachers.

Back to the bottled water. My friend, who drinks the bottled water at work, was in some sense nudged into this habit by the free and readily available water. She is even nudged into some sense of environmental responsibility due to the recycling efforts at her workplace. But assuming that she has access to tap water and not an overly biased perception of the taste of tap water[2], there is little barrier to amending this habit.

Imagine my friend was a thoughtful and steadfast environmentalist and, somehow, wasn’t aware of any possible negative consequences of consuming bottled water. I imagine that she would have quickly devoured the information I presented and took action to change her behavior. But if she doesn’t have that disposition, she will not simply act on the information alone. Like the fifth graders at the store, she must learn. If she is to be convinced to change her behavior on this issue, she must learn facts about the effects of bottled water, and, more importantly, beliefs about the importance of the issue[3]. But learning is difficult and time-consuming, and most people devote little if any conscious time to learning.

At one point she pointed to the company. “If I shouldn’t be drinking bottled water, they should do something about it.” But who is “the company”? Practically speaking, it’s probably the office manager who’s in charge of what is made available in the office kitchen. But even the office manager may get passed down instructions on what to offer based on central planning for all office locations.

My friend, empowered with knowledge, could try moving up the chain, talking to the office manager and then to the manager’s manager. In a company like hers, it’s likely that the office manager has a bit of flexibility. In the most positive front, there seems to be a movement in design towards allowing decisions to be made locally by end-users[4]. But even if my friend is designing the office kitchen herself, before (and maybe even after) her argument with me, she would choose the bottled water. First she must learn.

EDIT 7/27 – changed the wording and added footnote about what she “must learn” since multiple people were confused.

[1] There is a middle ground between knowing math and nudging someone directly to the answer, which is better tools to do (“truthful”) math without a full understanding of the underlying concepts. I’ve discussed this in past weekly reviews in relation with Bret Victor’s Kill Math project. It didn’t fit into this post, but maybe I’ll bring it up again.

[2] “But a couple of very non-scientific, blind taste tests have found that most people — or most people in New York City, to be more accurate — can’t actually tell the difference between tap water and bottled water once they’re all placed in identical containers.” http://science.howstuffworks.com/environmental/green-science/bottled-water3.htm

[3] I think what was finally a bit convincing for her was the fact about how much water and fuel are used in manufacturing of the bottle, which is not offset by recycling it. I estimated that her consumption over a year could perhaps be enough to offset what someone needs to live, say in India.

[4] Co-design and metadesign are some terms for this. I plan to talk about this a lot more in the future.

Are mnemonics a waste of time for language learning?

Suppose you want to learn to write the (simplified) Chinese character 汉 (meaning: Chinese or Han). It’s made of two components, a 水 (meaning: water), represented as the three dots on the left, and a 又 (meaning: again), on the right. To remember this character I might remember a story using the two components and the meaning of the character: “Like a tide of water, dynasties like the Han have, again and again, risen and fell.”

Such mnemonic systems are popular. I have been following the “Heisig system” of the Remembering Simplified Hanzi books for a couple years. The question for this post is, is it worth it? I assume either way you are using a spaced repetition system in a standard way.

Scenario 1: You’re taking a class on Chinese, you might have tests where you are trying to remember a fixed set of characters, and you have a good amount of time to do so. You see “Han” on the page, think about it, and get a flicker of an image of a dynasty receding like a tide. “The tide–water… again and again… yes, that’s it!” The mnemonic certainly seems useful here, assuming you spent a shorter amount of time studying than without it (probably true).

Scenario 2: You’re learning Chinese over a span of several years. You want to be totally fluent in writing. Meaning that if you’re writing out an article, you don’t want to be conjuring up a story for every character and then translating that to components. You aren’t worried about knowing specific characters for quizzes in the intermediate stages. Which of the following is most accurate:

  1. A mnemonic is worth it in terms of how quickly you can remember a character. You can get fast enough with them that you don’t need to transition from “story translation” to automaticity.
  2. A mnemonic is worth it. As you become more and more fluent, you will transition to automaticity, and the mnemonic serves as a useful scaffold.
  3. A mnemonic is not worthwhile. Because you will eventually need to learn the character writing to automaticity, the mnemonic is simply an extraneous step.

I would doubt the first explanation because I no longer need to consciously recall the story for many characters. Although it could be that the story is being somehow used unconsciously. An even more extreme position would be that any memorization, whether you use a deliberate mnemonic or not, ties itself to stories and images in your unconscious!

The usefulness could be determined if you had many participants and several years for an experiment. The best I could find in a quick literature search was from Lawson & Hogben, 1997,

There is also support for the value of using deliberate
mnemonic strategies, particularly in the early stages of foreign
language learning (e.g., Carter, 1987; Carter & McCarthy, 1988;
Nation, 1990; Oxford, 1990).

“Early stages”, so already not what I’m looking for.

Finally, the concept of desirable difficulties (Bjork & Bjork, 2011) could lend support for the third theory, but I’m not clear on what conditions that is actually applicable.