All the things I wish I knew about studying at school

My niece reached out to me a few days back asking about tips for studying at school. She was specifically interested in any ideas I had about how to excel in her maths studies. I wrote up my thoughts for her and it occurred to me yesterday that there might be some benefit from putting these notes online as well.

Without further ado, therefore…

Study Skills Advice

As I understand it, your question takes two parts:

1. how do I learn / study better
2. how do I specifically learn / study for my upcoming exams (in particular for mathematics)

I've split my response up into two parts since they're somehow related but also different. Part one ('how do I learn new things') is sort of required for part two ('what do I have to do to get good grades on my exams'), since it's pretty hard to try to cram ideas and concepts into your head if you don't first understand them.

1. Learning to Understand

Something you just have to 'learn'

First off, you'll want to learn the small tiny 'fact-like' things first. Some of this you just have to put in some work and learn them. It'll help you a LOT when it comes to getting a grasp on the bigger-picture concepts. To give an example, if there are certain equations or definitions of concepts, it can help to take a bit of time and just learn those by heart (in whatever way usually works for you for that). Note that this is limited to just small fact-like things. For sciences, maybe there are some facts or properties of something derived from the periodic table, etc, that are important. In the specific case of quadratics that you asked about, it might be simple things like:

• the usual form of a quadratic equation
• the quadratic formula
• 'what is a discriminant'
• what does the completed square form look like, and how do you solve it?
• what does this or that quadratic look like when you graph it out?

The key thing is to isolated and master those tiny morsels of information / facts early on, since a) it needs to be done anyway and b) it'll help you with the big-picture stuff. Sometimes textbooks help out by highlighting or bolding those key concepts. Just make sure you're not just trying to learn EVERYTHING by rote, since that advice is as good as no advice in my opinion!

Learning for understanding: foundations

Something else I'll say early on is that there are some core foundations that will serve you well:

• start with a / the problem — it will help to try to wrestle with the kinds of things you're expected to do with this particular topic you're studying. It can actually help you to do this at the very beginning of starting a new topic: before you even study things, take a look at the kinds of questions that you'll be tested on and expected to answer. Maybe even try to solve them. You'll learn a lot about the subject area just by rolling around in the mud and struggling a bit.
• curiosity — this almost goes without saying, but you should try to find a way to be curious about whatever you're meant to be studying. It'll help a LOT with your motivation and interest, which will in turn help you keep moving forward and give you energy at moments when things get hard. Sometimes the trick to this is just trying to ask questions about whatever it is you're studying: why is this important? how does it connect to topic X or Y we already studied? what is most surprising about this topic? Sometimes it can also help to know a tiny bit about the history of a topic. Calculus is a bit down the road in terms of your studies, but back in the day (late 19th century, early 20th century) there were really epic debates between the various people who were developing this new area of maths. Lots of drama and falling out between people. So sometimes just knowing a bit about the personalities behind things can help.
• growth mindset — I wrote a blog on this a while back but the core thing is just to believe that you have it in you to master this thing. Once you have that, and if you start from that place, then you'll have much of what you need to keep moving forward. And as one teacher once told me, as long as you never give up then you'll eventually master it. Sometimes you just have to be a bit patient. Most things come if you give them enough time :)

Retrieval Practice

This is sort of the core of most learning, in my opinion, and I have a lot of thoughts and practice around this. (Have read a LOT about what research recommends in this area, but I figure you're interested mostly in whatever is practical so I'll keep it mostly at that level.)

The core: if you want to learn something / master it, you have to retrieve it from your memory somehow. (Retrieve can mean lost of things: for German vocabulary it might mean knowing the gender or translation of a particular word. For Maths it might mean a particular formula, or even a high level understanding of how one concept relates to another.)

The important thing about this retrieval is that it will be and should be hard to do. (This is one reason why people don't necessarily enjoy doing this, and even fewer actually do it.) It's hard to struggle to put together a coherent explanation of topic x or y, but that struggle is what helps create neural pathways that cement the understanding going forward.

One possible way you could do this is to write short summaries of what you understood of a particular topic, then check your notes to see if you were correct. IMPORTANT: the key thing is to do this from memory / without notes. Otherwise you're not actually reinforcing materials in your head.

Another way of testing things (e.g. in your specific case of quadratics) is to do practice examples. Your school or textbook probably gives you some practice examples, but you shouldn't confine yourself just to doing those. Make up your own examples, or go online and find more examples (use Khan Academy or whatever).

It can also help to make distilled summary sheets at some point during your studies which gather together your understanding on a single piece of paper for the entire topic. Here actually you can see the one I made digitally for my own study of quadratics a few months ago:

The key thing with retrieval practice is to get a lot of it, and to try to make it the 'hard' / 'difficult' kind of retrieval. Mostly this starts with a blank sheet of paper and then you try to write down what you know about a topic, or a concept, or whatever specific thing you're trying to understand. Writing things down will help you realise (quickly! painfully!) which parts you don't actually understand. So it's as much to reveal to you which parts you need to work on as it is for anything else.

(There's a much-praised technique named after a well-known American scientist, Richard Feynman, and you can do something like this, too:

1. Write the title of a topic that you want to study / test yourself on
2. Write or map out an explanation of that subject intelligible / appropriate to a non-specialist. Do this from memory.
3. Identify any gaps in your explanation / understanding.
4. Relearn / restudy / interrogate to fill in the gaps.

You can use narrative / diagrams to condense and clarify your explanation. It's basically the same idea. And yes, bullet points or spider diagrams are all possible ways of doing this.)

Developing mental models

There's this idea that the whole thing you're doing when you learn something is developing 'mental models', which I personally find a bit hard to wrap my head around, but it is a thing… It's maybe the next layer up in what's happening when you try to learn something.

Mental models are, for me, about making a topic your own somehow. They're also about making the concepts of that topic manipulable somehow.

The 'making it your own' part has a lot to do with confidence, somehow, but it's also just feeling familiar and effective with the concepts that you feel comfortable solving problems in that area. If you see a problem, e.g., you know which techniques (or which subset of techniques) are needed to solve it. If there are multiple possible ways to solve something, you'll have a good feel for the tradeoffs: i.e. why this way is better than that way etc. In the case of quadratics, for example, we know that there is this amazing thing which is the quadratic formula, but you probably don't want to use that formula the whole time because it's easy to make a mistake with it and it's a bit cumbersome. Instead, we often use other simpler techniques that work for many (if not totally 100%) of the problems that you'll be exposed to.

One way to help develop mental models is to try to explain the topic to someone else. You already did a bit of this in the retrieval practice above: trying to explain it on paper is already some of this. But trying to explain a topic to people at different levels of understanding can be really clarifying. I.e. if you had to explain quadratics to a 5-year old it's probably different to how you'd explain it to a 40-year old. (Along with this, you can test out this approach by chatting with a chatbot about the topic. I'm sure you've heard of ChatGPT, but Claude is also another good option, esp for things like maths. The key thing is to start the conversation by saying something like "I would like to have a conversation about quadratics. I've been studying it and I'd like to test out my explanations of some core concepts with you. I would like you to tell me if things feel unclear about what I'm saying, or if you notice that there are some areas where I could improve my understanding.")

(While we're here, using things like ChatGPT to develop mental models can be useful. I will often have conversations that begin with something like "What is a good way to think about the discriminant in relation to quadratic equations? Please make your explanation simple to follow and use some concrete items in your reply, like only items that you'd find in a kitchen.")

Making mental models is hard! But the work you do to solidify things and make them your own is really worth it!

Anyway, the big point here is to reflect on what you're studying. Make sure to also give some time to connecting it to other things either in your maths studies or outside, or even life in general. It's not the best to just view everything completely isolated and disconnected from the other topics, so try to take a step back from time to time! (Unfortunately, most schools aren't built to encourage that process much, but it's important!)

Specific contexts

There are some other specific contexts that require different / more targeted advice, but you didn't mention them so I'll ignore them a bit. But language learning is one of them, and learning some kind of 'motor skill' is another (i.e. that requires coordination or physical movement like playing golf or the yoyo or whatever).

In Practice: Understanding Quadratics

To summarise the practical points listed above:

• learn the small fact-sized pieces early on
• get lots of retrieval practice (a mixture of examples of doing whatever the skill requires of you, and/or writing or explaining the topic at various levels)
• develop mental models where you can.

2. Studying for Exams

I'll take it for granted that you agree that you can't study for something without properly understanding it, so somehow the things in part 1 are sort of a prerequisite for this section; you can't get ready for an exam if you don't understand what's going on.

That said, there are some tactical things you can do to help your chances of success once you do have an understanding of a particular topic. Note that for all of this, it's a bit of a question of picking which parts seem doable / manageable. It's probably unwise / counterproductive to necessarily try to do EVERYTHING :)

Ground rules

You should understand the requirements of the exam. Take a bit of time to read through some previous exam papers. I'm sure your teachers have also given you clear guidelines on what kinds of things to expect. That will give you a map for how to prepare, so be sure to do this.

Foundations: Exam Study

There are some basic foundations here which for various reasons get forgotten when you're under exam pressure, but it's good to remind yourself of these, since if you neglect these it'll negatively affect your ability to study etc.

• sleep
• eating things that nourish your body instead of just feeding cravings
• taking breaks (every hour, ideally, get up and walk around for a minute or two)
• minimise distractions (put your phone in airplane mode or in a lock box while studying)
• movement in general / going out of the house for walks a few times a day is a good minimum.
• 'managing your energy' — this one's a bit hard to quantify / explain, but I'd say it's worth trying to embody the principle that you should only study as much today as allows you to keep studying tomorrow. I.e. if you overdo it and you study a lot today, but it's a bit too much and tomorrow then you can't do any study etc, then that was counterproductive. (Hope that was clear!)

Mnemonic / memory tricks

There are a TON of memory tricks out in the world. All of them are useful, but not all of them are equally useful for every situation :)

Things like the major system, the link system and the peg system are all useful, but they require a bit of time and probably also someone who knows how they work to explain them to you.

If you already have a bit of experience with these things, then I'd encourage you to use them in your studies, but if you don't have much experience then I'd say probably that it's not going to be the difference between an A and a B grade so probably it's a waste of your time to try to get into that in the run-up to exams.

That said, it would TOTALLY be a really useful investment to learn a few of these during summer holidays in a non-stress / fun way. You can play around with learning the order of decks of cards etc — I can explain all this if you're interested — and then you'll have that skill available to you if you need it next year or throughout your life.

There are some general memory principles that you can rely on in general terms:

• when trying to remember something, make it memorable in your mind! so maybe try to imagine the concepts as characters in some kind of image in your mind, and use all your senses and bring in some shock or drama etc etc. (LMK if you want more of these kinds of advice. I have a lot, but not sure how useful it is for you right now.)

Spaced Repetition

This is a really useful tool, but it requires a bit of upfront (time) investment and unless you're feeling super comfortable / not stressed at all, I might suggest to add it to the list of 'things to learn about over summer / winter (?) holidays'.

Basically this means testing yourself with (digital) flashcards, but the twist is that you only get shown the flashcard at exactly the optimum time / day when you need to be tested on it. (There's a whole science to this which I won't go into, but there's a TON of backing to the fact that this is the way to make things get into your memory.)

The best option for this is a piece of software called Anki. It runs on your laptop and phone etc, but it has a bit of a steep learning curve mainly because the defaults it comes with aren't great. So if you were interested I could help you set that up, but the key thing to know is that using this requires a bit of extra work.

The main idea is that you create (digital) flashcards for all the things you need to know, and then every day you check in with Anki to review whatever cards it says you need to know. There's an algorithm that calculates which cards you should review. It should mesh well with your intuitive sense of how memory works: i.e. over time you slowly forget things, so Anki will prompt you to recall a particular concept just at the point before you forget it, since that exact moment is the best time to review it. When you review it at that moment and you get it right, it'll really strengthen your memory for that thing. If you don't remember it, then it'll reset the status of that card and it'll know to show it more often for a few days etc.

There are more manual ways to get the same effect, but (for a lot of reasons) they're not as effective since humans don't work / behave like computers so really using a digital tool is the only way to go.

For quadratics, to use my experience, I have a bunch of cards relating to that that I get tested on. Writing a 'good flashcard' is a bit of an art, and we can get into that if you're interested, but I'll just lay it here as an option for now.

Interleaving

This is a fancy word for saying: 'don't study just one topic on its own'. When you're testing yourself on things that you'll need to know for exams, make sure to switch things up a lot. This means doing one problem from quadratics, then another relating to trigonometry, and another relating to topic z etc etc.

There is again quite a bit of evidence that this makes you much stronger in your understanding / learning, even though (or maybe because!) it's a bit harder to do.

If at least part of your review of topics / facts are handled by Anki it'll take care of giving you random flashcards anyway, so this more relates to things like solving maths problems by hand.

So don't just do 50 iterations of the same maths problem, in other words. Make sure you're switching topics etc.

3. Next Steps

• Check the practical suggestions above
• let me know if anything's unclear / or you want to know more about how to do thing x or y
• gather some problems to solve so you can make sure you're practicing the things you need to study
• get into some good habits around retrieval practice (i.e. writing things down to test whether you know them or not)

Automating social media posting for my new blogposts

I love blogging and I've benefitted a lot from what it's done for me ever since I started my first Geocities page in the mid 1990s. I maintain a technical blog at mlops.systems and a somewhat less technical blog at alexstrick.com/blog, though hope at some point to merge these together.

In the past I would have been content with ensuring that my blog published an RSS feed and known that anyone wanting to follow what I was writing could do so simply by connecting their feed reader and subscribing. I've become more conscious in recent years of a healthy brew of ambivalence, ignorance or even outright hostility to even the idea of RSS feeds and readers. It seems many people don't have RSS as an essential part of their informational hygiene any more. (I'll put my sadness / confusion about this to one side for now.)

And if I love blogging, I really dislike having to post my new blog posts to social media one by one, coming up with some catchy yet not overtly breathless summary of what I wrote, since this is apparently what many people use instead of RSS.

I've been grumbling under my breath about this situation for this for a few years now, but when ChatGPT came out it seemed like an obvious use: summarise my blogpost and repost to all my social media accounts taking into account their particular needs. (Mastodon uses hashtags more than the others, whereas LinkedIn posts can be a bit longer, vs Twitter which needs to be a bit shorter and so on.)

I held off, thinking I'd want to set up some system fully under my control involving serverless function calls and so on, but then I was reminded that I already use Zapier for some other administrative tasks. So this afternoon I set up and turned on some automation for social media posting to my Mastodon, Twitter and LinkedIn accounts. Posting happens at one step removed since I queue my posts in Buffer so that they go out at a time when people are more likely to see them. I apologise / don't apologise for this. My blog writings remain wholly un-automated; it would completely remove the point of 'learning through writing' if I were to automate the things that I blog about. My social media postings (just one post per blogpost so as not to spam you all) are from now on automated. As an additional courtesy / discourtesy, I've tweaked the prompt such that the social media posts should always read just slightly 'off' and will be labelled with an #automated hashtag.

FastAI Lesson Zero: video notes

[These are mainly notes for myself, based off Jeremy Howard’s ‘Lesson 0’ video that was recently posted. It doesn’t capture the entirety of what was said during the course, but it includes pieces that I felt are relevant to me now and that might be relevant to me in the future.]

• decide when you’re studying
  • be precise about how much time you’re going to spend
  • think about how it’s going to fit into your day or your life
  • give yourself deadlines and goals, perhaps, but also don’t worry if disruptions happen.
  • Mainly make sure that if something does come up, make sure you get back on the horse and keep going. (Tenacity counts for a lot)
• Finish. The. Course.
  • make a commitment to complete the course, and make sure you actually do that.
  • If you’re attending the course and working through it, you should follow through on your original commitment and actually work through the course.
• Finish a Project
  • build a project and make it really great.
  • You’ll probably have several projects here and there that you work on during the course of the fastai course, but at a minimum make sure you pick one of those and make it really great.
  • (It doesn’t have to be unique or world-changing. Even replicating something that’s already in existence can still be worth it).
• Find good role models
  • Jeremy raises up the example of Radek Osmulski (who recently published an ebook called Meta Learning).
  • (Jeremy himself is a good role model too).
• Learn by doing. Use what you learn and figure out the rest as you go. (Don’t get paralyzed by trying to learn ‘pre-requisites’ like complex mathematics topics, esp since most of them aren’t actually needed to become a practitioner).
• Share and communicate your work
  • (Jeremy doesn’t mention the book, but I’ll insert here that the book “Show Your Work” by Austin Kleon is a great starter on this point).
  • If you consistently blog during your studies, at the end of it you’ll likely have a huge collection of artefacts of that study, showing what you’ve learned and accomplished.
  • Alongside that, being a good citizen and contributing in the forums etc is also a really solid way to extend whatever knowledge you have to others, and quite possibly cement things in your own mind as you reply.
• How to do a lesson
  • watch the video / read the chapter
  • Run the notebook & experiment — play around with things + make sure you actually understand what’s happening
  • Reproduce the notebook from scratch — (and really start with nothing here, and try to reimplement whatever was done during the lesson. From previous experience, this work will be hard, but it’s super worth it. Recall learning is the best kind of learning)
  • Repeat with a different dataset — use the techniques you learned in the course on a dataset of your own / or solve some related problem using these techniques
• Using a notebook server vs a full linux server
  • the notebook server allows you to get going much faster
  • A full linux server is more ‘your own’ and you get to also practice a bunch of other not-specifically-deep-learning skills along the way
  • With the fastsetup library, Jeremy has made getting going with an EC2 instance pretty easy.
  • the video spends a fair amount of time showing how to do this with Colab Noteboks and a AWS EC2 instance. Refer to the FastAI website and the full video for more details.
• Get better as a developer
  • just doing the course, you’ll also work on your development skills along the way
  • Two important things to do to help with this:
    • Read a lot of code
    • Write a lot of code
• Start with a simple baseline & get a basic end-to-end solution up and running
  • When you’re working on a project, it’s a really good idea to start with a naive / super-basic baseline so that you know whether you’re making progress or whether you’re achieving anything with the work you’re doing.
  • Successful ML projects that Jeremy has seen start with the simplest possible end-to-end solution and then incrementally grow from there.
  • The work of getting your pipeline working / your data imported etc will take a bit of time, and if you get that all sorted upfront it’ll help you focus on the actual work you want to be focused on.
• (At some point during the course) join a Kaggle competition and make a serious attempt to do your best
  • just getting a model on the leaderboard tests your knowledge and your skills
  • just work regularly on things, show up every day, try to make your model a little better each day
  • Do these things:

• For getting a job in the space
  • having a public-facing portfolio of writings and projects will take you a really long way
  • Some companies are more interested in people having the right credentials etc and will never choose you.
  • Startups are a great place where this matters less.
• Try to take the second course
  • The first course gets you going as a practitioner of deep learning, but the second course allows you to implement algorithms and models from scratch and digs far more into the depths of the subject.
  • Jeremy wishes more people would take part two + encourages them to do so.
• The fastsetup library is great for installing everything on a Ubuntu machine (like an AWS EC2 instance)
• Experiment tracking software
  • The two big players are TensorBoard and Weights & Biases.
  • Jeremy doesn’t use these. Finds it too tempting to spend your time watching your models train instead of doing something else that is probably more valuable.
  • There are some cases where it might help to use this software.
  • Weights & Biases seems like a good company to work for & they’ve hired FastAI grads in the past.

Solid Study Habits for Coders

I’ve been working to diversify my skills over the past three years, in particular to grow as a software developer / computer programmer. To that end, I started working my way through the Launch School syllabus a few months back. I’m at an inflection point in the course right now so I thought it worth reflecting on what study habits have served me well as well as which ones I still haven’t quite managed to adopt.

Before I started, I exhaustively read through their forum comments and in-house blog posts to see how I might best approach my study time. They recommend you spend 20+ hours per week working on their course materials. With such a large investment of my time, I wanted to make sure I was getting the best return.

Core principles / Mental Models

• ‘Get Good at the Fundamentals’

This is a bit of a mantra at Launch School and I find it greatly appealing as a principle to drive how I study. It’s also useful as a heuristic to decide whether to dive deep on something, to read an article, to attend a meetup and so on. (See more here.)

• ‘Process, not goals’

I’m not sure I saw this explicitly enunciated somewhere, but given the fact that it can take around two years to work your way through the syllabus it seemed useful to keep in mind. I already have adopted this thanks to Beeminder and a really excellent piece by Scott Adams from 2013. With a somewhat long-term goal, arbitrary goal-focused time deadlines (‘finish the 202 course by May’) are less useful than something where you specify the amount of time you’d like to regularly invest into the study process.

• ‘Finishing is not the goal, mastery is.’

There’s a little bit of fetishising the idea of ‘mastery’ at Launch School, but this principle reminds me not to be in so much of a rush while working through the course. As Chris Lee mentioned in the forums, “if you’re in a rush, my question is: “where are you going?””

• (Find ways to) enjoy what is hard

This one I find quite difficult, lazy and averse to struggling with difficult things as I am. The whole skill/career of coding and wrestling with these abstract concepts in your head is one where you’re constantly upgrading your baseline of difficulty. This is rewarding in that there are always challenging problems to chew down on. This is also frustrating, though, because there’s always somewhere harder / out of reach to work towards. A number of students and TAs on the forums mentioned that having a mindset where you actively seek out those moments as opportunities for growth was a game-changer for them. (Note: strong intersections with Dweck’s growth mindset here).

Mindset

• Abandon artificial timelines

Don’t work to get through a course in x or y number of months ‘just because’. Rather, only advance if you’ve actually mastered the things in that course. This is a freeing perspective to take if you can find a way to adopt it. Society / culture encourages always keeping an eye on the clock, so this is a hard one (for me at least).

• Comparisons are odious

It doesn’t help to compare timelines. Everyone is coming at things with a different background and set of experience behind them. It also doesn’t help to compare competency / progress through the course for the same reason. It adds very little to my ability to grasp a particular topic to know where I lie on the bell curve. The only slight exception to this is knowing that the full core curriculum will take somewhere between 6-24 months to complete; this way if I am still studying it 20 years from now I can maybe recalibrate.

• Lean in to hard problems

I covered this earlier. If I have it as a sort of rubric which reminds me to lean into the difficulty whenever I encounter such a patch, then that’s useful.

• Master the current problem in front of you, right at this moment, without anxiety

Books are written one word at a time. Similarly with logic problems, or broken code or whatever, taking a calm look at something that looks knotted and gnarly is the way to approach it. There’s no need for anxious sudden movements, or huge massive changes. Work your way through things one step at a time.

• Be clear about the ‘why’

For something that isn’t completed quickly (i.e. within a few days), it pays to remember why you’re doing it. This helps with motivation, it helps reorient you to what kinds of choices you have to make, and it (often) helps remind you of some best practices around working towards that mastery.

• (Be specific)

This is sort of a sub-point relating to specifying the reason why you’re doing something. Try to specifically connect with some kind of emotion, visualise the outcome in some kind of image etc; this will help get the most of your reasons why in terms of it translating into motivation and clarity of purpose.

Practice

• Study ‘to depth’

Make sure to study the exercises or problems to their full depth rather than just completing them for the sake of moving on. A useful image to have in your mind is that of squeezing an orange. You don’t want to abandon the orange (i.e. the opportunity to study a particular problem) until you’ve squeezed every last drop out of it. There’s a point at which you’ve spent too much time, of course, but for many / most people you’re likely erring on the side of ‘making progress’ vs getting lost going too deep.

• Do code exercises

There’s lots of options for this. You probably don’t want to start out doing this when you’re still figuring out what a variable is, but fairly soon it pays to get into the habit of doing an exercise or three each day. Think of it almost like your daily workout. You’re building your muscle memory for solving problems, as well as cementing various syntactic and idiomatic phrasings. Of course, Launch School has you doing a mountain of exercises as well, so don’t worry too much about supplementing from outside the course, at least not initially.

• Reinforce concepts / theories using active recall

This is learning meta-methods 101, but always worth reminding oneself to actively test yourself. Don’t just read through notes, but keep a list of higher-level concepts or method names and then take 30 minutes or an hour once a week to type out an example or two using each of those concepts and methods. (More on this later).

• Deliberate practice

This builds on the previous concept of active recall. For me, this often takes the following shape: I’ll complete an exercise or a challenge. Perhaps I’ve solved the problem fine using my own code / approach, but when I finish and look at the ‘official’ solution it differs from mine in some (big or small) way. I’ll take a look, make sure I understand the principles around which it’s organised, then I’ll shut that page / window and try to replicate that approach myself. Sometimes, if a problem was hard enough to solve, I’ll even leave it a few days and just try to work the problem from scratch in any possible way that I can think of. I have enough of a goldfish brain that sometimes this will feel like I’ve never solved the problem in the past.

• Compare solution tradeoffs

Alongside just trying to code it from scratch yourself, it sometimes helps to compare the various approaches (perhaps adding in solutions given by other students) and seeing which might be optimum. Asking ‘why’ at this point is often a useful place to stretch towards, even though the answer to that ‘why’ is often a few modules ahead of where you are. I keep a log of these bigger picture ‘why’ questions.

• Build things

Perhaps slightly controversial amongst those taking Launch School. They don’t encourage a ‘hack and slash’ mentality but rather direct towards a more methodical and deliberate approach. That said, once you’re at a certain point, it can be useful, motivating and just practically beneficial in terms of the reps it gives you, to build something that has meaning for you or for others. At the moment I’m just working on somewhat more pure Ruby code in the terminal / via the command line, but later on I imagine this will become more relevant. (To that end, perhaps keep a list of project ideas).

Taking Notes / ‘Studying’

This is the one that people seem to have the most thoughts about on the Launch School forums. That’s possibly explained by the fact that people will know what works for them — or at least will have some patterns that they feel have worked for them in the past, in somewhat analogous situations.

• Type out all code examples manually

This one is easy to forget. When you’re following along with a video or textbook, make sure to type out the examples, however easy or seemingly comprehensible they seem. Typing not only engages your muscles, giving you an additional hook for memory, but it forces you to slow down and gives an opportunity to question what each character of a code snippet is doing.

• Take notes with pen and paper first time round

This is especially true for things that I’m not fully comfortable with. For something where I have thousands of hours of experience under my belt — the modern political history of Afghanistan, let’s say — I have more of a structure in my head and so can get away with typing notes. For the rest, like all these new things I’m learning about Ruby, pen and paper is pretty unbeatable. There’s a bunch of good science confirming that that’s the case, and it aligns with my experiential sense as well.

Some people in the forums noted how one of their preparatory tasks prior to an assessment was to type up their handwritten notes into a more formal digital reference of the course materials. I’m in two minds as to whether I think that’s going to be useful, mainly because the online documentation exists to serve that purpose. But I’m at the point where I have to decide about that so watch this space…

• Anki for things you need to remember

If I think a particular method / fact / morsel of knowledge is something I’m going to want to actively recall from memory in the future, I’ll add it to Anki. I try to follow the principles outlined in Piotr Wozniak’s essential piece entitled “Twenty rules of formulating knowledge”. (Seriously if you use Anki and you haven’t read it, stop right now and go read it).

I use a mix of cloze-deletion cards and custom templates for coding.

This card is a simple cloze deletion card. I wanted to make sure I was learning the options for file permissions as part of the command line portion of the prep course.

This card tests my ability to produce an example where I’m using the .reduce method. I try to include a bunch of these production cards since they mimic the kinds of situations / circumstances where I’d need to recall this piece of information in real life.

[Even though comparisons are odious (see above), in case it’s of interest to others, I’m just through RB101 and about to begin RB109 and I have 536 cards that I generated during the course of my studies.]

• Ask questions

Ask lots of questions. The advice given by Launch School instructors is to focus on the why questions when asking others. (The how questions are usually a matter of looking something up). I found this was broadly true. I have a reserved set of pages at the end of my notebook where I write down these questions that seem a bit outside my comfort zone.

Some of these get answered in the course of the programme. For example, I see that one of my questions was about why symbols are often preferable to non-symbols when used as hash keys. This was answered (sort of / enough to give me a sense of the answer) a few lessons later. Others are bigger issues for which there probably is no final answer. For example: “doesn’t dynamic typing force us to spend a lot of time validating input? isn’t static typing safer?”

Asking ‘why’ something behaves they way it does allows you to better develop your mental models and can be an effective way to grasp what’s going on.

• Feynman technique

I use a slight variant of this much-lauded technique. It consists of something approximate to the following steps:

1. Write the title of a topic that you want to study / test yourself on
2. Write or map out an explanation of that subject intelligible / appropriate to a non-specialist. Do this from memory.
3. Identify any gaps in your explanation / understanding.
4. Relearn / restudy / interrogate to fill in the gaps.

Part of one of my Feynman reviews that I do on Saturday afternoons

You can use narrative / diagrams to condense and clarify your explanation. For my Launch School studies, I do this once a week on Saturdays. I keep a list of new methods that I’m learning about during the week. Particularly towards the end of RB101 these started to mount up. Then on the weekend, I’d take the list of methods and test myself by writing out (by hand, with pen and paper) code that illustrated how to use each method. If needed, I’d write or say out loud an explanation relating to that method. This is a humbling exercise; you realise that you don’t know the things that you thought you knew.

• Write blog posts to explain anything that feels unclear

My last blog post is an illustration of this. I was having trouble getting what the .zip method could be used for and how it transformed things to which it was applied. So I wrote up some notes to myself in the form of a blog post.

I sometimes worry that too many of these posts — written purely for me to understand something — are alienating for those who subscribe to the blog via something intrusive like the email newsletter. I considered putting my ‘writing for the purposes of understanding’ posts in a separate location. In the end, though, I’m probably overthinking it. Like it or lump it!

• Make mental models of how things work

A lot is made of this, both at Launch School and in the wider world of study techniques. I’m not sure I have too many examples of where I’ve formally had to think through something using a mental model of how it works. I imagine this will start to be more applicable in later courses. Or perhaps it’s just that I already have some mental models for how the code behaves owing to my previous studies in coding. At any rate, I’m keen to get the most out of this but so far haven’t found it to apply too much.

• Make a cheat sheet at the end of a topic

I haven’t done this yet, though I can see how it’d be useful. For my current course and assessment, I think it’s probably hard to do — i.e. condensing the Ruby language to a single sheet of paper. But I’m reserving the right to do this at a later stage.

• Spend time reading the code of others

I tried to do this a little bit while going through the exercises. My hesitation was initially that the code hadn’t been reviewed or formally assessed and I didn’t want to absorb bad patterns from others who — like me — were likely at the beginning of their journey and who couldn’t be expected to know if they were writing something that should be emulated or not.

In the end, I think it’s still useful to read the code of others. It’ll usually be somewhat clear or not whether an answer is overly complicated. And answering why you think that is in itself a revealing process.

Review

The Launch School syllabus takes a year or two to get through. You start off with Ruby, but later on you’re working on Javascript or whatever. The concepts and the methods and the details could easily start to get forgotten if you’re not regularly reviewing and practicing things you studied in the past.

• Anki as the cornerstone for spaced repetition

As evidenced by the large number of cards in my Anki deck, Anki occupies a central place in my strategy to outsource the need to worry about reviews and recall over the long-term. If there’s something I want to be able to recall a year from now and it’s not something I’m using literally every day or two, then it’s going to end up in Anki.

An important note, though: learn the material FIRST, before adding it to Anki. I’ve learned this the hard way in the past; if you don’t learn it before adding to Anki, you’ll find Anki becomes slow and filled with a sense of drudgery. It’s also inefficient to group those two very separate tasks into a single moment at the point where you’re reviewing a card.

The great thing about Anki is that as a particular fact passes from your short term memory to your long-term memory, the need for reviews becomes less frequent, so you’ll see it less often. Trust the spaced repetition algorithm. It knows what it’s doing.

• Explain a concept out loud

This overlaps with the Feynman technique (see above). You can do this on your own, or to a friend / captive audience. Doing this with people who are much older or younger than you can be instructive as to whether you actually understand a particular topic.

I sometimes build this in as part of my Anki studies. I’ll have a card where the front says “What is [concept x or whatever]? Try explaining it out loud.” Then I’ll either find someone to do this with or I’ll just do it myself. Then on the back of the card it’ll either have a small list of key sub-concepts to make sure I got it right or it’ll say “Go look at your notes to confirm that you covered everything important”. This way I’m getting prompts to review old material, but they’re reoccurring at regular enough intervals that I don’t have to worry about having to do this too often.

• Keep Ruby / other fundamentals sharp

People in the forums mentioned that when they moved on to Javascript or other parts of the course, they found their Ruby skills starting to atrophy. Many said they wished they’d been more formal about reviewing old materials and keeping those skills fresh.

I think this is best solved by doing regular code challenge exercises as provided by sites like those listed above. I’ll use Beeminder to keep me honest and doing at least one or two every few days.

• Review old materials

People suggested that roughly 20% of your weekly study time should be devoted to reviewing old materials. I wonder if that’s a little high, but for me that might translate to keeping a solid hour or two on Saturdays for review of those old materials. And by review I mainly mean active recall using some of the techniques mentioned above, and then reading through notes to confirm whether I still knew something or not.

Getting Stuck

This is where I struggle the most with coding — the mental game of failure. But having a process to deal with those moments — because they will come — addresses a good chunk of the issue when it comes to getting stuck. The following are possible options for working through it, as suggested by fellow students / instructors:

• Read the error messages thoroughly
• First try it with pseudocode
• Solve problems on your own before looking at solutions or asking others for help. (If you can’t solve it, and depending on the size of the problem, still don’t look at the solution until a day or two has passed).
• Use the rubber ducky method of talking through a problem. (This can be translated to writing as well, where just the act of writing up where you’re stuck can often be enough to get you unstuck)
• Don’t cheat yourself of the opportunity to learn if the problem is difficult. Work through it and take your time.
• Use the PEDAC system / process

Community

Everyone studying at Launch School is doing so remotely. We’re all scattered around the world, but doing thing with others can be really helpful with motivation. I don’t always find it the most time-efficient way of studying, but working with others in a structured manner seems to be strongly recommended by posters in the forums. For me I think this will take two forms: attending the group study sessions appropriate to my particular level, and interacting with discussions on the forums and Slack channel.

I might supplement this with ad hoc study groups depending on my need for that during assessment preparation. At any rate, there seems to be a good deal of opportunity for that kind of thing among fellow students.

Habits / Meta-Structure

A lot of this comes under the rough category of ‘life fundamentals’. Most shouldn’t need too much explanation.

• Stop using / viewing social media or meaningless input — for me that’s Twitter and YouTube. I have turned this off completely outside specific windows.
• No email — I also have email turned off everywhere except outside certain windows. On my phone, I’ve set it up that I don’t have access to IMAP mail via apps or the standard ‘Mail’ app, but I am able to send mails through SMTP email.
• Be careful with caffeine / tea — I don’t drink coffee, but some varieties of tea cause me to get a little too edgy and scatter-brained. So this is just a reminder to be conscious of that and always err on the side of caution. Also, periodically taking 1-3 weeks completely without caffeine (as with salt) allows me to reset my baseline, needing less to cause the stimulating effect it has.
• Keep the house / room clean
• Go for a walk every day
• Exercise — do it.
• Sleep — this is maybe the most important item in this entire post for me.

Scheduling Study / Core Routine

• Study Every Day

I try to study a minimum of two hours every day on average, moving up to a stretch goal of three or four if I am able. This allows me to make meaningful progress in my studies. Ideally, I try to have at least one day / week which is a 3-4 hour uninterrupted slot. But probably four hours is my maximum when it comes to focused, productive work.

• Pick a sustainable pace

I could probably do a few days of full-on / flat-out work, but eventually I’d burn out. The trick here is to do enough that you can always work the next day. This means not working from a place of exuberance or excitement. Learning and coding doesn’t have to be high-octane. That doesn’t mean it can’t be enjoyable or satisfying, needless to say.

This means starting with reasonable weekly hours and slowly increasing them rather than jumping straight into an intense pace of 5 hours a day, let’s say.

For me, this initially looked like starting with 15 hours per week and I’m currently at around 18. Reaching somewhere around 25-30 would be my ideal, but I have to listen to how my mind and body responds, and have to balance other work to support (read: pay for) this course of study.

• Create a routine / habit around Launch School study

For me, this means having my studies as the first thing I do in the day. I want to give it the time when I’m at my best during the day, so that means first thing in my morning. I potter around for 30-60 minutes after getting up, and then I immediately start work on my studies.

• Block things

I covered this above a little, but basically for me it means using tools like Freedom and Focus apps to keep me honest and not distracted. I’m fairly good about using these tools, but not always and it’s easy to notice the difference.

• Take breaks

I usually segment my studies with inbuilt short breaks. These can be anywhere from 5-10 minutes and I try to get up, stretch or do some kind of physical exercise / movement. For Macs, the Move! app is a great utility. I like to take somewhere between one and two breaks per hour on average.

• Think about posture early on

This is something which I haven’t thought too much about, but people on the Launch School slack channel and elsewhere have suggested it’s important. If you’re going to spend hours behind some sort of laptop, getting posture and ergonomics right seems like a good thing to aim for. I’ve had good experiences with the Ninja Standing Desk (sadly not being produced any more, it seems!) though I don’t currently have it with me. This is something to return to in a few months.

• Take a day off

I take one day off each week which is for non-tech things, or family or friends. This is usually Saturdays for me but not always. I turn off phones and laptops and don’t let myself use them for the whole twenty-four hour period. I sometimes lose this habit when I have too many things going on and am overcommitted — witness that I’m currently writing this blogpost on my supposedly sacred Saturday ‘day off’ — but in any given year I’ll stick to it perhaps 80% of the time. The more I work on Launch School the more I’m reminded that it’s a good thing to disconnect from digital input and reconnect with the world around me.

• A routine for the end of the day

Currently the two key parts to this involve writing down what’s coming up tomorrow and where to start with them. It also includes making my environment conducive to just starting work the next day — i.e. cleaning my desk.

Using Regex with Python to Find Strings

The next step in my data processing project is to find strings matching certain patterns in the PDF data. Today I worked my way through the relevant chapter (#7) of Al Sweigart's excellent / useful Automate the Boring Stuff with Python.

I've left some sample code above as a reminder (mainly for myself) of the basic pattern / syntax that you can use. I saw a slightly more concise pattern for running the search in Data Wrangling with Python; I may experiment with that in the future. That has you running something like:

search_result = re.search(word, fulltext)

I guess one of them will have a speed advantage, especially when multiplied over hundreds of thousands of pieces of text.

The next step with this project will be to connect this regex function with the splitting file. That way when I split the file, I can rename the file at the same time with a string that I've extracted using a regex search.

If you've reached this far and you don't know what I'm talking about, there's an interesting article by Cory Doctorow where he argues that regular expressions should probably be taught as a foundational skill to children:

Knowing regexp can mean the difference between solving a problem in three steps and solving it in 3,000 steps. When you're a nerd, you forget that the problems you solve with a couple keystrokes can take other people days of tedious, error-prone work to slog through.

My new book: The Taliban Reader

My new book is out (finally). The Taliban Reader is somehow the culmination of years of work to drive studies of the Taliban back to primary sources. Some of this work was accidental; more recently it was more purposeful. The book I produced (together with Felix Kuehn) is long and detailed.

Comments and feedback prior to publication were extremely positive. It'll presumably take readers a while to start getting some real independent reviews in, but I look forward to feedback and whatever conversation is generated off the back of it all.

You can pick up a copy at any good bookshop or from Amazon here.

Tweeting to the Void

I've previously written about how I turned off Facebook's news feed. I keep an account with Facebook because people occasionally contact me there. It is also an unfortunate truth that many companies in Jordan (where I live) or in the wider Middle East only have representation on Facebook instead of their own website. (Why they insist on doing this baffles me and is perhaps a topic for a future post).

I have long preferred Twitter as a medium for filtering through or touching -- however obliquely -- things going on at any particular moment. I have no pretensions to actively follow every single tweet to pass through my feed. Rather, it's something I dip into every now and then.

Increasingly in recent months, I found myself growing dissatisfied with the pull it often has on me. It has become something of a truism to state that 'twitter isn't what it once was', but there's less and less long-term benefit in following discussions as and when they happen.

RescueTime tells me that I spent 86 hours and 16 minutes on Twitter in 2017 -- just under quarter of an hour each day. That feels like a lot to me.

Enter 'Tweet to the Void'. This is a Chrome extension. (For Firefox and other browsers, I have to imagine things like this exist.) When I visit twitter.com, the feed is not visible. All I see is somewhere to post a tweet if that's what I want to do. (There is still some value in posting blogposts and articles there, since I know some people don't use RSS). Of course, I can always turn off the extension with ease, but adding this extra step has effectively neutralised Twitter for me. 

Try it; see how you feel about having something standing in the way of your social media fix. Let me know how you get on.

Tabula for extracting table data from PDFs

Have you ever come across a PDF filled with useful data, but wanted to play around with that data yourself? In the past if I had that problem, I'd type the table out manually. This has some disadvantages:

• it is extremely boring
• it's likely that mistakes will get made, especially if the table is long and extends over several pages
• it takes a long time

I recently discovered a tool that solves this problem: Tabula. It works on Windows and Mac and is very easy and intuitive to use. Simply take your page of data:

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A page listing Kandahar's provincial council election polling stations from a few years back. Note the use of English and Dari scripts. Tabula handles all this without problems.

Then import the file into Tabula's web interface. It's surprisingly good at autodetecting where tables and table borders are, but you can do it manually if need be:

Then check that the data has been correctly scraped, select formats for export (from CSV to JSON etc):

And there you have it, all your data in a CSV file ready for use in R or Python or just a simple Excel spreadsheet:

Note that even though the interface runs through a browser, none of your data touches external servers. All the processing and stripping of data from PDFs is done on your computer, and isn't sent for processing to cloud servers. This is a really nice feature and I'm glad they wrote the software this way.

I haven't had any problems using Tabula so far. It's a great time saver. Highly recommended.

Walk Around the Block

People increasingly live in cities. There's a good chance that if you're reading this, you live in a city. That's good for exchanging ideas and stimulating opportunities, but it often means we don't get as much movement as we'd like or as would probably benefit us.

So here's a little trick I've used for the past three or four years. First off, you shouldn't be working uninterrupted for much longer than 45-60 minutes at a time. When you take a break from work, get up and move your body. Ideally, leave the house or place where you're working, walk round the block or the building. That shouldn't take you much longer than 5 or 10 minutes. If you're working for 45-minute segments, it's an ideal amount of time and it'll refresh you physically so that your next session isn't hampered by waning energy levels.

Try it out and let me know how you find it...

Variations on a Pomodoro

I've recently started work on a large translation project (about which more in due course). This sees me grappling with the Arabic text for several hours each day. Early on, I tried replicating my '4 perfect hours' approach that had worked so well when writing my PhD dissertation.

Translation is hard work, and tougher on the brain in ways I hadn't first anticipated, not having done it full time before. So I've found myself adjusting my 45-minutes-work, 15-minutes-rest cycle to something closer to 25-minutes-work, 5-minutes-rest (with a longer 15-20 minute gap every 4 cycles).

For writing and anything where you're trying to hold lots of pieces of information in your head, it makes sense to go for longer stretches of time if you can. 45 minutes was ideal for writing my PhD, since any shorter and I felt like I was loosing too much time rebooting all the information at the beginning of each new session.

Translation is far more piecemeal, in a way. I can work on one sentence at a time, at least in this stage of the process, so shorter sessions work just fine.

This is all just an extended way of saying that customisation is important, and whatever you read online (or hear on a podcast or whatever) needs to fit into your own life, needs and patterns rather than the other way round.

Resetting my base line: caffeine edition

I've blogged enthusiastically about tea in the past. I enjoy discovering new varieties and the winter weather in particular makes an ever-ready cuppa almost obligatory.

I've been busy with work projects in the past month or two, so I was surprised to realise how much my caffeine intake has increased, almost imperceptibly. Prior to my intervention, I was drinking perhaps two or three cups of caffeinated tea per day. That may seem like nothing to those of you with a six-coffees-a-day habit, but my body (and mind) is more sensitive than most to caffeine. Three cups per day left me feeling rattled and edgy.

Caffeine, for me, can be good in certain very specific circumstances. It's great when I'm generating ideas -- either for a particular work project or just in general -- but particularly bad when I need to sit down and write (and focus). Caffeine seems to stimulate the randomly associative part of my brain, the one that has me connecting chocolate to fish sentience or making some other strange link.

I've found that when I need to focus, I tend to prefer drinks that are completely caffeine-free. Even my newly-discovered favourite, kukicha or twig tea, comes with a caffeine punch, albeit one that is greatly reduced.

So a few weeks back I instituted a caffeine-only-very-seldom policy. It's working well so far. I feel much better, more focused, less jittery, and when I do choose to drink a cup of chocolate puerh, for example, as I did this morning, one is more than enough. (I've done similar things in the past with salt and sugar and had equally positive experiences.) As always, Beeminder is holding my hand all the way.

Introducing CoachBot: Your Personal Language Taskmaster

For languages that aren’t new, I often feel like I’m stagnating and get bored when I reach the intermediate levels. This can reflect a lack of materials from which to study (as was the case with Pashto when I first started studying it) or — more commonly — a surfeit of materials. This creates a kind of choice paralysis where the number of options means I’m far less likely to sit down and pick one of them. (In a similar way, I'll sometimes choose not to watch any of the in-flight entertainment because there are too many choices to pick from.)

Studying a brand new language is (almost) always fun: you’re making quick progress, everything is new so you still have that nice-and-shiny feeling, and you feel like you’re really on your way to success. Continuing that study after two to four years of effort is a little harder. Like with any longer-term project, you start having to find ways to remind yourself of why you’re even working on it in the first place. It can often feel like you’ve lost that original magic somehow, even to the extent that you question whether you actually want to learn the language.

It is useful to address some of these issues ahead of time. That way, when you hit a period of less energy or motivation, you have a pre-formulated plan of action (made by you when you weren’t consumed by whatever mood is dominant). For me, this takes the form of making lists of suggestions to my future-self. I have pre-made task lists for:

• When I’m travelling
• When I’m feeling sick
• When I have no time to study
• When I have oodles of time to study
• When I have lots of energy and enthusiasm for learning
• When I have no enthusiasm for learning

Try to have at least 10 or 15 tasks in whatever lists you do end up creating. Maybe save a few pages at the back of your language notebook to list these tasks. This way, you always have them handy. It helps to have a good amount of variety in the tasks you pre-assign to yourself.

I keep lists as described above, but they weren’t as effective as I'd hoped. I’d glance at the tasks, feel only a limited enthusiasm for the options available and then put the list to one side. I needed a different solution.

I happened to be teaching myself to program/code at around the same time, so I thought this might make an interesting practice problem to try to solve. (I was studying Python and so I found a way to make a web app that uses that to connect to Flask.)

CoachBot is the free tool I designed to solve the problem of study choice paralysis for language-learners. It’s still only a prototype, but I'm soft-launching it here now since I imagine it might help those reading who are in similar situations.

CoachBot gives you a task that you can complete within a specific time-frame. If you have only 5 minutes, it'll pick a random task from the database that I curated and wrote myself. Have an hour? It'll suggest a different kind of task. If you don't want to do a particular task that it suggests, just click a button to get a new one.

These are the kinds of tasks I suggest when working with students one-on-one. They’re also the kinds of tasks I had written down in my lists. As of writing, there are 386 unique tasks in the database, which means that the suggestions are far more varied and creative than anything I was previously using.

I’d suggest you use it as follows: if you ever feel like you don’t know what to do to keep going with your language studies, open up CoachBot, pick a time corresponding to your needs and do whatever it tells you to do. When you’re done, make a note of what you did and how long it took in your learning log. Consider doing another session.

I’ve been using this for a few weeks already and can attest to its value. One of the key benefits I’ve found is just in getting started. Sometimes I’ll only need to do a five-minute task before I realise that there was something else that I wanted to read or study and then I’ll get busy working on that.

There are lots of features that I hope to build in for future versions. I want to include user accounts and tracking of how much time you spend on the different tasks. I want to sub-divide by language skill (i.e. which skill is being trained) and eventually to build in some kind of guidance and interactivity to how the tool functions. But for now, use it as it is: get some studying done by outsourcing the choice of what you’ll be studying.

There are more details on the website itself. You can click through to the project’s roadmap where you can see an updated version of features coming soon. You can also make suggestions for tasks that you’d like included in the Bot and/or specific features you’d like me to build as part of the project.

[Special thanks to Alex, Ian, Kevin and Peter for patiently answering my questions while I was building this initial prototype].

Things We Control: On Internal vs External Goals

When it comes to goal setting, I’ve found it helps to distinguish between the kinds of goals you envision and create. I first came across this concept in a book on Stoic philosophy: A Guide to the Good Life, by William Irvine. (If you’re interested in the full details/writeup, read chapter five.)

The basic premise Irvine lays out goes as follows:

Instead of seeking contentment by trying to change the external world, rather we should think about how to change ourselves (i.e. internally).

He splits up the internal / external distinction a bit further:

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Here you can see that there are some things over which we have complete control. There are also things which we don't completely control. This can be split into things we have no control over at all, and things we have some control over but not total. (This is actually a trichotomy derived from the philosopher Epictetus.)

An example of something we have no control over at all is when the sun rises. Things that we have some control over but not total are generally impulses, desires and aversions, so you can control your attitude in a certain situation or what kind of effort you put into a language-learning exercise. Things we have complete control over include our opinions, the goals we set, the values we consider important and our character.

This is a really useful way of splitting things up, and you can maybe see why it might be useful. We should spend no time at all thinking about or worrying about the things that we have no control over at all. In terms of language learning, this might relate to any putative 'innate' or genetically-based learning ability or aptitude. You're stuck with who you are, so there's no point being upset, particularly when it comes to comparing yourself to others. (Plus, a lot of this discussion of 'innate talent' for learning languages misses the point: there are many things that one can do to speed up one's progress.)

The other two categories are really what we should be concerning ourselves with, since this is where we have the ability to influence the outcome. We can set our goals in a useful manner (as described above) and we can change our impulses and perspectives on particular tasks.

The practical realisation from all of this, for a language learner, is that it really makes sense to find ways to set internal goals as opposed to external goals.

Think of a game of tennis, for example: you might have a goal to 'win the match'. This would be an externally facing goal. It is one over which you don't have complete control. Your opponent may have advantages over you that you can't anticipate or prepare for.

Alternatively, an internal goal might be to 'play my best'. This has the advantage of being something you have full control over (i.e. how your effort is directed) and it is not something that can be refuted or quashed by your opponent on the court. Even if you lose the match, by taking this perspective of 'doing one's best' you'll be able to take solace in the fact that there was actually nothing you could have done to change this outcome. Plus, you met your goal (presuming you did actually try your best).

In terms of language-learning, this translates to formulating your goals in terms of process rather than fixed targets. So: rather 'study for 10 minutes every day' than 'reach a B2 level by September'. This way, you're setting yourself up for success and you can focus on what matters in your studies without the feeling of pressure. Same thing with something like writing a PhD. (I've written elsewhere about how you can think of writing a PhD in terms of time rather than wordcounts or sections completed.)

I hope I've convinced you that goal-setting can be an important lever in how you tackle the meta-goals of your language-learning journey. It's certainly been an important realisation for me in how I setup goals and intentions. It also works really well with commitment tools like Beeminder, though that's a topic for another day!

[This is an edited excerpt from my new book, Master Arabic, now available for discounted pre-order here. Use the offer code 'preorderthankyou' at checkout to get 10% off.]