Multilingual authoring for the win

I have been working on a French translation for the math book and in the process I stumbled upon some really powerful “authoring hacks” that I would like to describe here in case they might be useful for other bilingual authors and educators.

Let’s see les maths!

Before we begin with the “How it’s made” episode, let me show you some examples of the final product. I have selected the best four “backports” — explanations that now exist in the English version thanks to the additions in the French version.

  1. Reader feedback was consistent at pointing out the algebra sections as boring and TL;DR. Readers are willing to learn algebra (the rules for manipulating math expressions), but then when it comes to algebra “techniques” they are not sold on the concept. One solution to this problem would be to drop the “boring stuff” (lower the expectations of the reader), but I was having none of this. Instead I decided to just improve the explanations and add pictures: Completing the square en Français et in English.
  2. Functions (modelling superpowers) are the best thing ever, and probably the most powerful tool readers will develop in the book. This is why proper definitions and examples of functions are essential.
  3. Polar coordinates are super important—for both practical reasons and for the “aha” moment (knowledge buzz) that occur when readers understand $(x,y)$ is just one example of the many possible representations of the points in the Cartesian plane and $r\angle \theta$ is an equivalent representation (instructions that specify the position of a particular point int he Cartesian plane based on the distance $r$ and direction $\theta$).
  4. Speaking of knowledge buzz through representation theory, the book now finally has a proper motivation why readers need to think about the concept of a basis (a set of direction vectors that is used as the coordinate system for a vector space). On this one I go back to the basics—explain through an example.

Contuinuez à lire si ça a l’air intéressant. Read on if you’re interested.

Context

The No Bullshit Guide to Mathematics (a.k.a. the green book) is a short summary of all the essential topics from high school math intended for adult learners. Last year, by sheer luck and good fortune, I was introduced to Gerard Barbanson who offered to translate the book to French. Gerard is a professional mathematician, a native French speaker, and has also taught math in English for many years, which makes him the perfect translator. Gerard is leading the translation project and provides lots of useful feedback and improvements for the text.

Look out for a followup blog post and announcement about the release of the French translation (in a few months). This blog post is not about that, but about the benefits of the translation efforts brought to the original English version.

Translation as a way to highlight problems

While reviewing Gerard’s “first pass” of translation, I kept noticing spots where the explanations didn’t work well. My initial reaction was that this was a bug in the translation, but every time I looked into a passage, I realized the problem existed in the original English text, and the translation only magnified the problem and made it more noticeable. Examples of “weak spots” include paragraphs that are too conversational (i.e. no content), missing definitions, and explanations that are unclear or confusing.

I found this process to be extremely useful. Even though I’ve read and reread the English version many times, I never noticed these weak spots until now. The translation process highlighted the lack of clarity in certain specific parts and forced me to think of ways to fix these explanations. Essentially, if an explanation is good, it will “survive” the process of translation, but if it’s not 100% solid and clear, then it turns into “noise” at the end of the process.

An analogy between translation process and a communication scenario with a sender and a receiver.
The process of translation adds “communication noise.”

We can think of translating explanations as a communication scenario, where the source language (English) is the transmitter, and the target language (French) is the receiver. The process of translation adds “noise” in the form of ambiguities, so the received signal is a degraded version of the original signal. The French translation will be good only if the original English explanation is really solid and clear. This puts additional pressure on the original English version to be extra clear and precise.

The language of mathematics

Another benefit that came out of the translation work has been the focus on the consistent use of terminology and notation. For the most part, mathematical concepts translate well between English and French, but sometimes French has more precise terminology available. For example I’ve adopted the precise terminology of source set and target set to refer to the sets that appear in the function “type signature,” which are distinct concepts from the function’s domain and image.

One of the core responsibilities of any math teacher is to use precise and consistent language to describe mathematics, including choosing the simplest terminology when the complicated terminology is unnecessary, but not shying away from the “real math” terms when they help illuminate the concepts. Working with Gerard to explicitly establish our conventions for the French version forced me to also be consistent in the English version as well.

An illustration of two parallel narratives (English and French) with the definitions of math terminology serving as common anchor points.
Using explicit conventions about math notation, terminology, and definitions serve as anchor points that ensure concepts are defined in the right order.

I guess that’s not too surprising—using consistent terminology and notation is just best practices.

Bilingual writing for better explanations

Perhaps the most surprising thing I noticed from the translation project is the amazing efficiency of developing English and French explanations in parallel, sometimes aided by Google Translate. This was most apparent in writing the new sections on polar coordinates and vectors. Normally writing a new section would take me days, going through several mediocre versions, rereading on paper, and slowly converging to a decent narrative. I noticed the new sections I added over the holidays converged to a “quality product” much faster. Here is the process I followed:

  1. Explain the concept in English.
  2. Translate explanation to French improving and simplifying it in the process.
  3. Take the best parts of the French explanation and incorporate them back into the English version. Go to step 2.
Box diagram of an English and a French explanation of the same concept being co-developed in parallel though iterative process.
Crafting better explanations by continuously “borrowing” the better way to do things from the other language.

After a few cycles of going between the English and French version, I saw clear improvements from the initial English version and of course the French version was improving in tandem.

Kaizen for textbooks

I guess the thing that makes me excited about these “authoring hacks” is the fact that they allow me to go one step deeper in the process of continuous improvement of the books. I’ve read and reread the text at least a dozen times, worked closely with my editor Sandy Gordon to iron out all the major flaws and acted on feedback from readers to fix confusing passages, but at some point I get tired and start to let things go. I say to myself things like “yeah this is not the clearest explanation, but it’s kind of OK as is.” This is partially out of laziness, but also because of the law of diminishing returns: sometimes rewriting makes things worse!

There is a famous quote that says:

“There is no great writing, only great rewriting.”

― Louis D. Brandeis

I know this is good advice, but it’s hard to adhere to it. After five editions of the math book, I find it difficult to motivate myself to rewrite things, even if I know there is still room for improvement. That’s why I’m always looking for hacks that can help with the process (see for example the text-to-speech proofreading hack). The translation work of the past few months gave me the impetus to do more productive rewriting without it feeling like a chore. Look out for the updated No Bullshit Guide to Mathematics v5.4 coming soon in both English and French. Sign up for the mailing list if you want to be notified.

January Optimism about OER

Last year in March I did a lot of soul searching about my mission in the EdTech space. At the time, figuring out the incentives for authors and teachers to produce open educational resources (OER) seemed like an insurmountable mountain to climb. I didn’t see a clear path for interoperability between content sources. OER yes, but OER how?

Since then I’ve learned a lot more about the open content landscape and I’m starting to feel more optimistic about the prospects for OER. Could year 2018 be when the switch-over happens? I think so.

 

Tech Strategy for 2018

Below is a list of technology building blocks that will be half-built by Mar 2018 and fully built by Mar 2019. Together they represent the foundation for OER becoming a mainstream phenomenon.

Content library

One year from now, accessing all the CC-licensed educational material from the web will be a solved problem. When a school principal wants to setup a local OER server, they’ll be able to import educational content from a choice of multiple free content libraries. (Technical details such as the data format wget+zip, web archive, OpenZIM, cartridge, kchannel, etc. and the browsing and search interfaces can be solved by something like pandoc for OER content and listing syndication and indexing service integration.)

Editing tools

Given a global content channel like Khan Academy, a teacher might want to edit the content structure to: change folder titles and descriptions; reorder items within folders; cut, copy, and paste items; and add new items by adding files or creating them from scratch with the authoring tools.

Authoring tools

A lot of the OER content out there isn’t that good. If learning will happen on a new medium like a tablet, then it might be easier to start from scratch and produce new content adapted for tablets. We need more tools for educators, teachers, and students to produce learning activities.

Make some sushi and feed the kids for a day, or teach them how to make sushi on their own and feed them for life.

Diff tools

Since content channels are not static, we need to make an easy-to-use system for distributing and applying updates. The source channel that you imported has changed, do you want to update your local copy from the source channel? Click Yes or review changes by looking at diff between old content and new content: nodes added, nodes removed, nodes moved, nodes whose content was edited.

When looking at individual content items that consist mostly of text, there could be copyediting and typo correction tools. Any contributor could fix a typo in the description of a content item and submit a typo fix “pull request” upstream to the original content node owner (use word-level diffs git diff --color-words ... for showing text changes).

Standards alignment tool

Imagine an editing interface for manipulating educational standards and setting parallels. For example, let MATH.US and MATH.MX be the math education standards for the US and Mexico. We can setup links that say “Every content item tagged with MATH.US.tagP should be automatically tagged with MATH.MX.tagQ” and this rule can be applied whenever importing content. (i.e. do the standards alignment once up front, instead of doing it for every content item).

Student backpack (like in RPG games)

I’m generally against gamification techniques because I don’t see what’s the point of introducing metaphorical rewards like points and badges. However, if achievement rewards are related to the content matter then things could be very interesting.

What if badges were awarded when students pass some exam or “validation test” that confirms they know the concept inside out. You get the badge for X when you’ve completed all the tests required for X. For example the QEQN badge can be awarded whenever students have proved they know the formula x = (-b +/- sqrt(b^2-4ac))/(2*a) for obtaining the solution set to the equation  ax^2 + bx +c = 0.

When you unlock the QEQN badge, the “quadratic equation tool” will be available for all the problems you will solve in the future. By earning the QEQN badge you proved you know the rule x = (-b +/- sqrt(b^2-4ac))/(2*a) by doing all the exercises, therefore from now we’ll stop forcing you to do this calculation by hand and let you use the quadratic equation tool when solving problems (click view source to see how it works).

I’m not sure every skill can be turned into an applet, so we could instead give them a “knowledge scroll” as the achievement—a short document that summarizes the concept that students can use whenever they need to use an X-related formula. Another option would be to earn “effort” badges for investing a lot of time to read/practice.

 

Perhaps I’m being too optimistic and the OER revolution is still far away, but I don’t think so. Now that I know the good folks at LE are thinking about these problems, I feel free software for universal education is coming up soon! It will take a combination of vision, technical expertise, implementations experience, and partnerships to make this work. As we say in Bulgaria, “Сговорна дружина, планина повдига,” which roughly translates to “An organized group can lift a mountain.” If you’re interested in lifting some mountains with us, check out the LE jobs page.

Impression from NYC and the RC

Two months ago I was on a train going from Montreal to New York City. It’s a long ride, but I used the time on the train to triage all the coding project ideas I could work on while at the Recurse Center (RC). So many projects; so many ideas.

Today I’m on the same train heading back to Montreal and have another 10 hours to triage the thoughts, experiences, and observations about the big city and the social experiment that is RC. Here is my best shot at it—stream-of-consciousness-style—before I forget it all.

New York City

The past two months have all been a blur. From the day of arrival when I tried to enter the wrong apartment, to the first contact with NYC street noise insanity outside of my window, to the snow storm, and all sorts of good foods. I’m very impressed with the city, but I’m not in love. Here are my observations.

New York is a big city. Compared to Montreal, it’s as if someone copy-pasted 10x the city. Or perhaps the better computer analogy is someone filling a map with the “bucket-fill” tool and completely forgetting to stop. Seriously, it seems like there is waaaay too much shopping areas with high-fashion and luxury brands. Do people really need to do so much shopping? I don’t mean to be judgmental, but as someone who is ideologically against consumerism, I felt like I was totally in the wrong place.

The energy of the city is amazing. It seems everyone is getting things done, shipping products, or otherwise being creatively on top of their game. Now I realize it is impossible for everyone to be successful, but people certainly carry themselves as if they’re crushing it. Most people I talked to made a good impression on me. They’re proud of what they do, confident, but not overly full of themselves. New Yorkers are actually interested in hearing you out, and seeing what you have to say. I felt very little closed-mindedness and little-mindedness from the locals, which is great.

I really like the demographics of the city. Everywhere I went, there are young people: from school kids who talk like adults, to the well-represented university crowd, through the numerous artists, to the middle-aged professionals contingent, and also older people who still keep in shape. Everyone is well dressed and good looking. At times I felt as if there is some sort of giant “face control” department at NYC ports and train stations that does not allow non-good-looking people to come to the city (How did I get in?).

I’m used to “measuring my words” when meeting new people in order not to alienate my interlocutors by mentioning math, quantum physics, or computer topics. When touching on such topics, I use an interactive approach to “feel out” the level of comfort of the person I’m talking with and judge how far I can go with this topic of conversation. The last thing you want is to jabber endlessly about a technical topic to someone not interested in tech, or to talk about math with a person who has a math phobia. Talking with people in NYC, I was pleasantly surprised to realize I don’t need to measure my words all that much. I would hit people with the full geekfest, computer jargon, and even quantum topics and they would handle it just fine. People are more knowledgeable than you think, and those that don’t know anything about the subject matter are willing to go into it and still had interesting things to say. That’s really nice. It’s great to be around people who can handle the tech talk and the science talk. All the New Yorkers I spoke with are smart, open minded, and generally well aware of the world.

The New York lifestyle is definitely something I could get used to. Two months of living there is not enough to get used to it or see enough of it to be a judge, but I met enough locals and saw enough to get a general feeling for what it’s like. First you need a NYC job. That’s a key thing if you are to enjoy the \$8 pints, \$15 cocktails, and \$30+ entrees. I mean anyone can afford to eat out once in a while, but if you’re not making 100K+ in this city, you’ll have a lot less opportunity to enjoy all the restaurants and bars. All these places of socialization are waiting for you after work. Unlike the dwellers of most North American cities, New Yorkers tend to go out after work. Whether it’s for a fancy dinner, or a quick dinner, or for straight-up alcoholism activities, people don’t want to go home. The city is very European in that way. You get to talk to people, see your friends, and generally hang out rather than go home. I like that very much.

The noise though. Oh. My. Fuckin’. God. The noise is terrible. It seems like every person who brings a car into Manhattan wants to exercise their right to use the klaxon as much as possible. Seriously, if the street is jam-packed for hundreds of meters ahead of you, will honking really make a difference? It’s not just the honking though, the constant presence of people at all hours with their need to scream, the large open spaces that carry sound, the delivery trucks, the construction work, the garbage collection trucks, and the highways. I am generally not affected by noise, but I never thought it can be this intense. In fact, I read that some people can get used to the noise level and need it for creative stimulation.

But it’s not just New Yorkers in cars that like to signal. Everyone in NYC is big on signalling. There are a lot of expensive bars and restaurants where the locals can feel special because of the hefty bills they will pay, but I guess it’s like that everywhere around the world. The specific signals New Yorkers use to show their higher status relate to the most difficult things to have in the city: cars and dogs. Owning a car in Manhattan must be an absolute nightmare with all the traffic and how difficult it must be to find parking. For this reason, if you’re seen rolling down the street in a fancy convertible, then you really must be something special. It’s a bit like high-heels—they’re attractive because they’re totally impractical. It seems it’s the same with owning a dog in NYC. We’re talking about a stone and concrete jungle with very little green areas. Why do you have a dog? Where are you going to take it out for a walk? Still, if you own a dog in these difficult conditions, then you must represent. Anyway, human nature is weird.

The Recurse Center

My six weeks at the Recurse Center were very much what I expected them to be, but also very surprising and inspirational at the same time. I kind of knew what to expect when going into RC from talking with people who’ve attended: RC is a shared office space with a focus on coding projects and exploration. This description is accurate, but completely fails to account for the amazing people that are present in that shared office space. Imagine co-workers that you actually want to talk to? How cool is that?

I’m in total awe with the people that I met. There were extremely knowledgeable people with very interesting background and achievements, but also inspiring “junior” people with insane ability to learn quickly. We had people who know how to use nearly every imaginable programming language: Agda, Clojure, Coq, C, C++, Erlang, Go, Java, JavaScript, PROLOG, Python, and even COBOL. How often would you have access to  such variety of people and be able to ask questions of them, or just “Hello Joe, can you give me an intro to PROLOG?”

I definitely felt some impostor syndrome after meeting all the people and hearing about the projects they were working on, but then I stopped comparing myself to everyone and it was OK after that.

The main idea for the Recurse Center is to provide the learning environment but not impose any structure. The first day has a lot of “official” events organized with the goal of getting people to know each other, but apart from that we’re left pretty much on our own to figure out what we want to do. People organize events, form reading groups, and work on projects in pairs or larger groups.

The first week was a bit strange and anti-social. I would show up in the early morning and work on some coding projects, try to socialize over lunch, then back to more coding in the afternoon, then go home. No beer or after-work pub activity. I think the people in the Spring 1 batch were realizing they are halfway done with their batch and trying to be more productive and the new Spring 2 people were not going to suddenly start talking to each other and making friends. Everyone felt distant, busy, and not wanting to be disturbed until the first Thursday game night. This evening was a marked change, at least for me, since after a few beers I felt much more comfortable talking with people. Beer is the universal ice breaker. After this first social event, I felt much more comfortable talking to people, joining conversations, and generally interacting with people. As if somehow drinking a few beers in the same room had turned us from coworkers into buddies.

Throughout my time at RC there was a constant tension between working on projects and socializing. My original plan was to push forward on one project, or another, until it is in a shape where it can be shown and shared with others. Needless to say this never happened, since “cleaning up” the projects and getting them into “not ashamed of the code” state was too much work, and would have required much more time than a half-batch. Once I realized this, I said “fuck my projects” and decided to focus more on talking to people, sometimes helping with whatever they’re working on, sometimes teaching math, doing Django demos, explaining pointers in C, and generally trying to be useful as a teacher. At the same time I played the student role in several other contexts where people more knowledgeable than me introduced me to PROLOG, Neural Networks, and Formal Type systems.

Something that I realized near the end of the batch is that RC puts a lot of emphasis on not imposing any formal structure to the learning process. There are no instructors. There are no classes. There is no set curriculum for people to follow.

Not adhering to any strict procedure, formula, method, or curriculum is a very powerful idea. It makes me think of Alfred N. Whitehead’s essay titled The Aims of Education in which he warns about the danger of inert ideas. Every now and then powerful new ideas and ways of thinking erupt on the intellectual scene and overturn old and established ideas. Slowly over time the new ideas become codified and structured until they become the new dogma. Whitehead warns us that the seemingly “efficient” approaches where students are taught to “skip ahead” and directly memorize useful facts might not be the best way forward if we want truly developed learners. Instead of memorizing thousands of facts, Whitehead advocates exposing students to a few important ideas and letting them apply these ideas in various practical scenarios.

I find RC’s structure-less approach very interesting because it is diametrically opposite to my thinking about education. The power of this unstructured approach is that it will never degenerate into dogma, rote learning, or formulaic explanations. By sticking to meta-learning techniques like study groups, reading groups, pair programming, weekly meetings, and activities, RCs learning will stay evergreen. I imagine the the 2014 web study group were about Backbone.js and the 2017 it’s all about React.js. RC never becomes outdated.

At the same time I believe that some structure could be very useful for beginners. How much time was wasted by each person to setup their basic coding environment? How much time is wasted learning from the wrong source? How can beginners know which tutorial is good and which tutorial is bad? The whole assumption of putting learners in charge of their learning process assumes that learners are adults and have well-developed meta-cognitive skills. It just so happens that all the people at RC are very smart and capable of “taking ownership” of their learning process so it works out, but I’m still left thinking that some structure could be introduced into RC without losing the flexibility.

 

Possible improvements

I thought of a number of ideas which would have made my experience better at RC and which could benefit people who are learning to code in general.

  • More short-term, throwaway code projects. I think I wasted a lot of time thinking of the projects that are good, worthwhile, or of general interest. I felt I need to come up with a really good idea for a coding project if I’m going to invite others to collaborate with me. From what I heard around me, many people were working on quite serious, long-term projects (think three week-long projects, or six-week long reading group). Those are cool and you’ll learn a lot in the end, but I think short, throwaway projects are much more valuable for learning. I had a lot of fun working on half-day projects in collaboration with people. If I come back to RC, I would focus 70% on such projects: get technology A to work, install B on top of C, wrap D inside and E environment, write a hello world in F then package your code and make it run in the G cloud. All of these activities would take an expert 30 minutes to do. A solo beginner would take 5-12 hours, and a team of beginners 3-5 hours. All the boring scary parts of getting things off the ground are so much better when you’re working in a team.
  • At the same time I think it’s good for everyone at RC to have one long term “show off” project to work on, dig in, and become sort of expert at. This could also be a collaborative project, it doesn’t need to be a “my portfolio” project, but I think it’s important for everyone to learn how to polish the code beyond the “school project” level. It would be great to have the full end-to-end experience from idea, design, implementation, coding enough to make the app useful to end users, deploy the code in a production environment, writing tests, etc. It doesn’t make sense to do all these extra steps for each of the throwaway projects, but it totally makes sense to go through the motions at least once for one project.
  • Code reviews. We had a great conversation over lunch with A., B., and N. where we all agreed that we somehow need to introduce the notion of code reviews and collaboration best practices (releases, feature branches, pull requests). It doesn’t make sense to do code review on the throwaway projects, but I think everyone would benefit from code review on their “show off” project. Imagine pairing a Python beginner and a Python expert on a simple web app project. Using only one hour per week of productive time, the Python expert could transfer all the best practices about conventions, code organization, idiomatic Python, testing tools, and deployment to the beginner.
  • Starter tutorials and curated learning resources. This is basically what I was working on throughout most of my batch. Imagine a collection of links to learning resources about every imaginable computer-related topic and each topic comes with a short tutorial prepared by a recurser that gives you the “hello world” introduction to the topic. I think this sort of curated list of links to resources, if continuously updated, can really improve the life of beginners. Instead of trusting “uncle google” to find the most relevant tutorial on topic X, you can go to a trusted source and get started learning topic X without the fear you’re missing out on some better tutorial somewhere else. Perhaps the page on topic X could use the Socratic method and just ask questions? For example, the page on JavaScript could ask: What is this? What is a prototype? What is the difference between == and ===? And other questions that will lead learners to seek answers on their own.

I think the above four ideas would really improve the RC experience for future attendees. I personally feel I wasted a lot of time in the first weeks thinking of a grandiose project to work on, and in retrospective I wish I had worked on smaller half-day, or one-day projects to learn as much as possible. I think mini-projects lend themselves better to collaboration. Imagine someone asks you to collaborate on a week-long project to do some big thing, versus someone asks you to collaborate on a small toy project for half a day?

Conclusion

Overall my stay at RC and in NYC was a good experience. I learned a lot. I got out of my Montreal routine/bubble. Above all, I met some amazing people. Fuck computers and coding resources… people are the best resource of them all! Many thanks to the RC Staff, Spring 1 and 2 batchmates, and all the alumni. Hope to see y’all again.

No bullshit linear algebra v2 release

The NO BULLSHIT guide to LINEAR ALGEBRA is finally ready. After two years of writing and two years of editing, the book is now complete! Thanks to all the feedback from readers and the amazing attention to detail of my editor Sandy Gordon, the first print release is very polished.

No bullshit guide to linear algebra product shot

Can I see a preview?

I’ve posted an extended preview of the book here (160 pages, PDF) so you can get a feel of what’s included. I tried to make the preview useful on its own: rather than showing only a few pages from the introduction, I’ve included the definitions from all the sections, which are the most important part of the book.

If you’re not interested in reading a whole book, but just want to see the graphical representation of all the linear algebra topics, I encourage you to check the concept maps here. If you’re looking for a quick refresher on linear algebra concepts, you can check the four-page linear algebra summary here.

 

Why should I learn linear algebra?

Linear algebra is the foundation of science and engineering. Knowledge of linear algebra is a prerequisite for studying statistics, machine learning, computer graphics, signal processing, chemistry, economics, and quantum mechanics. Indeed, linear algebra offers a powerful toolbox for modelling the real world. All areas of advanced science and engineering make use of linear algebra models in one way or another. So essentially, you need to learn linear algebra if you want to do science.

 

Why do I need this book?

There are many great books about linear algebra that exist out there[1,2,3]. The NO BULLSHIT guide to LINEAR ALGEBRA is special because of the concise, conversational tone it is written in, the prerequisites material it includes, and the numerous exciting applications of linear algebra it discusses. This book is the result of  years of private tutoring, which makes the narrative feel much more like a conversation with a friend rather than a stuffy lecture. I know from experience that many adults don’t remember basic math topics like algebra, functions, and equations, so the book includes a comprehensive review chapter (Chapter 1) to make sure everyone is on board with the fundamentals.

The “main course” of the book (Chapters 2 through 6) consists of all the standard material covered in linear algebra courses with lots of examples, exercises, and practice problems with solutions.

The book concludes with three “dessert” chapters that discuss applications of linear algebra. We start with applications to chemistry, economics, electrical engineering, graph theory, numerical optimization, cryptography, and signal processing (Chapter 7). Next we followup with a chapter on probability theory, Markov chains, and an exploration of Google’s PageRank algorithm (Chapter 8). The book concludes with a chapter that introduces the fundamental ideas of quantum mechanics and quantum computing (Chapter 9). Many of the topics covered in chapters 7, 8, and 9 are considered “advanced” or “graduate level,” but readers of the book who’ve gained a solid grasp of linear algebra concepts will be able to learn about these exciting applications with no problem at all.

 

Where can I buy this book?

It depends on what you want. The eBook version costs $29 and is a pretty sweet deal since it comes with all future updates. But if you really want to learn the material, you should get the softcover print version, which is much better for focused learning, and costs about the same: \$39 – 20% off = \$31. If money isn’t a constraint for you right now, you should get the hardcover, which is printed on larger paper and has a construction that will last for decades.

Regardless of your choice of medium, you’ll be getting the same high quality book that will introduce you to the wonderful subject of linear algebra in the least intimidating way possible.

The textbook business

This is a followup on my previous post about the challenges of open educational resources (OER) production and adoption. I’ve come to the conclusion that the key aspect holding back the “OER dream” is not the lack of collaboration tools or the ability for teachers to discover material, but the quality of the content. You can’t write a textbook by committee. It’s as simple as that!

I wish collectives of qualified authors/teachers could come together to create the free textbooks and other educational material, but it seems human nature doesn’t work this way, and it’s very difficult for multiple authors to “sync” their thoughts together and come up with a coherent narrative for book-length projects.

The key to the production of quality educational content is to set the right incentives for knowledgeable authors to write. I’m thinking graduate students writing tutorials, blog posts, HOWTOs, books, and generally living from the proceeds of their work. Rather than wishful thinking about “collaborative projects” where multiple authors come together to write a book, why don’t we focus on single-author textbooks? Rather than a “commons” approach where nobody owns the project, let’s have a single author “own” the project and make them personally involved with its success. I believe the monetary incentives will be enough to make the author invest the time needed to make the book/project/resource a success.

It’s possible to replicate, and generalize the business model I used with the MATH&PHYS, and LA books with experts in other fields: chemistry, biology, economics, psychology, history, etc. If a young person (think an M.Sc. or a Ph.D. student) with practical and teaching experience in a particular subject writes books, I can guarantee the book will be a success.

 

Business model

The beautify of the matter is we don’t need to invent anything new. We’ll use a very traditional business model, where we sell products to make cash. The authors will make a significant profit from each copy of the book sold, and will thus be incentivized to produce quality content that sells.

The publisher (Minireference Co.) will also make profits from the book sales and be able to fund software development, marketing efforts, author advances, and generally take care of the business overhead for running operations. The main innovation of this business model is that, rather than the publisher being in an exploitative relationship with the author, they are equal partners. Rather than authors making 10% royalties, at Minireference Co. authors earn 50% royalties. The publishers takes care of the boring stuff and allows the authors to focus on the hard task—writing, polishing, and curating the content. Equal partners, with parts of the proceeds.

The market(s)

The main audience for the textbooks will be students, but students in the broadest sense of the term. We’re talking advanced-level high school students,  university students, parents, and adult learners. By setting a price point for the products around $29 to $39, we’ll make sure the books are affordable for everyone, but also make enough money to sustain the authors so they can keep producing more books.

 

Okay, so where is the OER in all of this?

By now, my dear readers, you might be wondering if there is no case of “bate and switch” going on here. We started with the promise/mission to make open educational resources more accessible to students and adult learners around the world, and somehow we ended-up with a reaffirmation of a business plan to make money from selling books. Perhaps there is some of this going on, but you must agree that building stable organizations with individuals who earn a living by teaching is a step in the right direction.

The approach that I imagine for getting achieving the “OER dream” is to encourage authors to sell their university-level books, but contribute primary and high school material as OER. I think the “university for money, but high schools stuff for free” approach will work for two reasons. Some authors might start from an altruistic point of view, and want to do something good for society by releasing some introductory lessons for free. Other authors might be motivated by purely capitalistic incentives, since releasing the high school material for free is an excellent way to promote their work.

 

Focus, focus, focus

There’s only a limited things one person can do in their lifetime so it’s important to focus on the things that make sense, and which have potential for growth and high impact. I’ve invested the past 5+ years of my life in the math textbook business so I think it’s important to continue that project instead of changing priorities or working on other projects.

The beauty of this idea is that it doesn’t require any miracles, breakthroughs, or external funding. All it takes is an evolution of the project I have currently going on, so I can work with more authors. Life always tends to make things more complicated over time, so starting with a simple plan, and keeping the focus is generally a good way forward. Vamolos; ándale!