I was talking with friends recently about an interesting phenomenon that all self-taught programmers have observed, which we ended up calling “learning loops.” A learning loop is a process in which learners are motivated to advance their knowledge thanks to the positive feedback on their performance.

In this blog post, I want to look at the mechanics that make learning loops work and think about ways they could be used by teachers, private tutors, and publishers to build learning experiences in which learners have more agency and control over their learning. We’ll also look at the related phenomenon of game mechanics that exists in certain “addictive” computer games. Figure 1 contains a visual summary of the ideas we’ll discuss in this blog post. The two main questions we’re interested in are: “What can teachers within the formal educational system learn from autodidacts?” and “What can autodidacts learn from the gaming industry about staying motivated?”

In the second part of the blog post we’ll think about the role of teachers and educational resources in supporting and reinforcing learning loops. I’m writing this mostly as a self-reflection and welcome comments by other educators, content creators, and learning experience designers interested in this phenomenon.

Figure 1: The main question I’m interested in thinking about is how to introduce aspects of self-directed learning into the formal educational system, in order to give students more agency over their learning process.

Examples

Let’s start with some examples to illustrate the concept. I’ll give three examples of learning loops from my personal experience.

EX1. As a youth, I always had good grades in math, but despite this, taking exams was a stressful experience. All this changed one summer back in 2004, during which the only source of entertainment I had was a calculus book. I started solving the problems in the back of the book out of boredom. Each problem that I solved gave me a little dose of “achievement buzz,” which motivated me to solve the next problem, then another one, and before I knew it I had gone through all the end-of-chapter problems in the book, including problems with a double-star difficulty rating. Since I had the answers to each problem in the back of the book, I was getting constant feedback about my performance. Each time I solved a problem correctly, I felt a little boost in confidence. By the end of this summer, I had developed a completely new attitude toward solving math problems: a general curiosity in the sense of “Let’s see what they are asking?” and there was zero grams of math anxiety left in me.

EX2. Another example of a learning loop from my life is how I learned to use the UNIX command line. As a kid, I had read a crime novel in which the protagonist (FBI special agent) used UNIX to look up some important data as part of her work, which gave me the idea that UNIX is cool. Later in life, there was a UNIX command line seminar offered at my university, which helped me get started with basic commands like ls, cd, cat, etc. I also had ssh access to a UNIX server on campus to practice the commands I was learning. Later on, I started installing GNU/Linux on old laptops, which required developing further command line skills, gradually learning about more and more programs, services, and config files. This non-curricular hobby turned out to be very useful later in life since every tech job I’ve had involves command line and scripting in one way or another. I’m still learning about servers and command line tools to this day, continuously benefitting from the instant feedback when commands fail, and from easily accessible docs, manpages, blog posts, and discussion forums.

EX3. The third example of a learning loop is how I got into web programming. I attribute my success in web programming almost entirely to the Django web framework, which has excellent documentation. Before starting with Django, I had tried to learn Flash, C, PHP, and JavaScript, but I never managed to get off the ground with any of these. Then one day I made an attempt at coding a project using Django and fell in love with it. I was productive from the very first day: I was able to create a simple view function and test it out, then added templates, and slowly built the project via small, incremental steps. Over time I understood the request-response cycle, learned to handle HTML form submissions via POST requests, login sessions, and all other features needed for a full website. The source of feedback was the manual testing process: navigating the webpages as I build them out and manually testing each new feature.

Here are some common aspects of all three examples:

Before the learning loop started, I had some minimum competency level with the task (i.e. I wasn’t a complete beginner).
In each case there was a source of rapid feedback: 1) answers in the back of the book, 2) command line errors, and 3) not-what-you-expect-to-see output when testing a new feature.
There was a progression of difficulty of the tasks.
There was no time constraint or “rush” to get through the tasks.
In each example, I was motivated by a personal goal: 1) to finish all the exercises in the book, 2) to get access to compute power by installing GNU/Linux on old computers, and 3) to build a website.

Note also what is missing from these learning experiences:

Not a course. All learning was nonformal, meaning it was outside the educational system.
No teacher (after the initial exposure).
No exams, grades, or any other external metrics: learning was happening naturally as needed to achieve a personal goal.
Not for a job, but motivated by a personal need.

I find it fascinating that three of the deepest skills I have developed in life are in no-way related to my formal education at university. It’s as if the formal education system is only good at achieving the basic level of competency, and achieving true depth with any skill requires active participation of the learner: learners need to get into a learning loop and pursue the skill on their own.

In the rest of the blog post, I’ll think on this idea further, and flesh out the implications for educators in general and specifically for content producers like Minireference Co.

Skill progression for a self-taught programmer

Let’s take a broader view to see where learning loops fit as part of the overall learning process. Let’s analyze the user-journey of a self-taught programmer, let’s call her Aisha, as she progresses through different levels of experience, from absolute beginner to domain expert.

Level 1. Exposure

Aisha hears about programming as a career choice either by meeting someone who works in tech or talking to a friend who is studying to become a developer or data scientist. Aisha could also be exposed to programming as part of some course.

This initial exposure is one of the most crucial moments in the overall learning journey, due to the emotional dimension. Does the person who is introducing Aisha to programming give the impression that programming is something cool? Do they look like they’re having fun when programming? Do they look confident and happy with what they are doing?

A successful exposure will draw Aisha’s attention to programming, raise her interest, and build a positive association, making her think of programming as something worth looking into. Ideally, the person through which Aisha gets her first exposure is a woman, which would help her see herself in that profession. This is why it’s so important to support (or at least not interfere with) women in tech—even if the field continues to be gender-imbalanced, the presence of women is essential to give role model examples to the next generation of tech women. It’s a very simple message: women can be tech bosses too, no hipster-beards required!

Level 2. Initial investment

This is where the actual journey begins. Aisha faces a number of barriers to enter the field. Does she have a working laptop? Does she have the computer literacy required to set up the software development environment? Does she have the language skills to follow an introductory tutorial (in case English is not Aisha’s first language).

Assuming she passes these starting hurdles, the initial efforts invested in learning to program (from tutorials, books, videos, apps, etc.) will feel like energy sinking into a black hole. Nothing makes sense at first. She’s just learning a bunch of disorganized facts and concepts relying on memorization and without understanding why they are important. The primary skill that Aisha must cultivate at this stage is persistence in the face of endless errors. This is a long and painful stage where stepping outside the tutorial and you end up with error messages. At this stage, having access to good tutorials[ex1,ex2,ex3] and helper tools that print friendly error messages (see explainer video) are very helpful.

Ideally, this initial “uphill” step shouldn’t be necessary if Aisha is learning in a beginner-friendly environment that allows her to figure things out on her own and get into a learning loop right away (see next level). I’ve included this “initial investment” level as a prerequisite for the “learning loop,” because for many people learning coding doesn’t come easy at first, so it’s important to recognize this difficulty (and plan for it).

Level 3. Learning loop

Assuming Aisha survives the initial “information gathering” step, she will eventually start to feel more comfortable with coding, spend less time stuck, and finally start to productively use her programming skills. Aisha is no longer a beginner, so the learning resources that become accessible to her have increased: lots of textbooks, online lecture notes, video tutorial series, school projects, and personal projects are available to you once you know the basics.

The success, duration, and productivity of Aisha’s learning loop will depend on the type of tasks/projects she picks. Personal projects are particularly good at this stage because they allow the integration of ideas: the disorganized facts turn into experience. In the ideal case she can find numerous challenges that are at an appropriate level and is also able to find additional resources for just-in-time learning of new concepts. Each time she completes one level/task/assignment, there is an immediate positive feedback, which in turn motivates her to do the next level/task/assignment, thus creating the learning loop.

Persistence in the face of errors continues to play a major role at this level—not quitting even when being stuck. A real-world mentor that can help her get unstuck, choose projects/tasks of appropriate difficulty, fill-in knowledge gaps, and provide feedback on her performance would be very useful to have.

Thanks to her efforts, Aisha will gain enough experience to get a job, which is the next level.

Level 4. Work experience loop

The work-experience loop is similar to the self-teaching loop, but now Aisha is learning on the job. She’s fighting bugs, finding workarounds for problems, and taking on medium-size projects. Educational projects are replaced by real-world business objectives, which means the tasks become much more concrete and well-defined. This is where Aisha will develop auxiliary collaboration skills like: version control (git), bug tracking, personal time management, project management, and team communications.

Initially each task is going to be an opportunity to develop new skills (learning on the job for the win!). Over time Aisha starts to see reusable patterns for solving problems. Throughout this work experience, Aisha will build her confidence and skills for solving more and more problems using the “I have done this before” feeling. Work experience level can last for many years, and some people transition to the next level which is mastery and specialization.

Level 5. Mastery and specialization

At this level Aisha knows how to plan, scope, and manage big projects. She doesn’t need supervision or technical management anymore, since she’s capable of planning work on her own and getting things done. She knows how to deal with very hard bugs, and becomes comfortable reading other people’s source code (e.g. look under the hood at the source code of libraries) and understanding problems from first principles.

With enough work experience and debugging, she builds an inventory of common solutions in her head and no longer needs to google things. In fact she probably knows the domain better than 90% of people out there on the internet.

I’ve written out this whole user journey in order to highlight the importance of the learning loop in the overall career trajectory. Once Aisha gets a job and starts learning on the job, she’s all set to advance and continuously develop new skills (unless she makes particularly bad choices of employers). The learning loop (Level 3) is the key step that brings her up to employable level, and hence it’s worth thinking about how we can make life easier for Aisha to get there—not as a lucky coincidence of circumstances, but as a repeatable, reliable, and enjoyable process.

Of course we must not forget the prerequisite levels that come before the learning loop: the exposure to coding as something cool and worth considering as a career (Level 1), and the initial time investment needed to reach basic proficiency (Level 2). Both these levels have the potential for learners to drop-out, so we must think about them in parallel with the focus on the learning loop.

Generalizability of this approach

Before we get excited about learning loops, let’s check if self-directed learning is not an exception that is only applicable to the domain of computer programming, or only for people with a specific background.

I think self-directed learning loops will work for all STEM fields: basic math, physics, chemistry, biology—all fields where exact answers exist. These are necessary to give learners the immediate feedback and “achievement buzz” of getting the correct answer which is required to keep them going. Within the STEM fields, computer science is the easiest case; it doesn’t require anything more than a computer of some sort. These days, thanks to tools like jupyterlite, basthon, and thebe you can learn to code without leaving the browser! Other theory topics will be equally easy to learn. However, learning advanced biology, chemistry, and other subjects that require a “wet lab” would not be practical for at-home autodidacts. Still, there is a lot of learning that can happen before the need for specialized equipment becomes the limiting factor.

Artistic fields like writing, painting, singing, and playing an instrument are also amenable to learning loops, at least at the beginner level. Unlike STEM subjects, there is no “right” answer for a given artistic performance, but the inner critic is enough to keep learners motivated. If you’re learning to play the guitar and you like the sounds you’re producing when playing your favourite songs, then you’ll play more and more. The greatest guitarist of all times learned to play on his own, without taking music lessons. Reading literature also leads to strong self-reinforcing loops. I know many people who get addicted to reading: they read one novel, enjoy the story, then read more and more. That’s why we have bookstores: they are the SQDCs of reading.

It’s not all good though. Developing advanced-level skills is difficult using self-directed learning loops. The more advanced learners’ skills get, the higher the need for fine-grained feedback. We’re not talking about getting the right answer anymore, but about polishing and improving specific performance aspects. This kind of improvement only happens when you have detailed feedback from an expert teacher or mentor. For an example from the math field, it’s easy to know when you have the right answer to simple numeric questions, but much harder to know when you have all the right steps in a lengthy math proof.

In summary, the learning loops approach may not be fully general, but it’s pretty generalizable and applicable in many fields. The fact that we hear a lot about self-taught programmers and not other professions is not because programming is special somehow, but rather because it’s the current “hot” field and attracts a lot of attention. Generalizability test passed! There is no reason the same dynamics that make self-taught programming work can’t be applied to other fields.

Game mechanics

The concept of a learning loop is very common in the gaming world. In fact we could even say getting players into a “learning loop” is the primary mechanic behind most games. Players come into the game world and are presented with a background story and a clear mission (motivation). After an initial onboarding tutorial to explain the game controls, players enter a compelling game world with clear objectives and path for advancement. Progress in the game requires some special skill, and the player faces progressively more difficult challenges.

Games contain a microcosm of reinforcement loop in which players can spend hours and hours engaged with game-world tasks. Some people can get addicted to these game worlds and spend multiple days playing and having fun, completely immersed in the game world. Clearly, there is a lot to learn from the gaming industry about user experience, the power of storytelling, and motivating players to complete missions.

Zooming into the genre of educational games specifically, we find a rich history of projects that use game mechanics to teach mathematics, geography, history, business, physics, and other academic concepts. Indeed, the idea of using computers as learning devices has been around since the early days of computer hardware.

As an educational platform, computer games have some unique characteristics as compared to traditional methods of instructions (textbooks and lectures):

Game worlds usually offer a compelling story that keeps players engaged and motivated for hours.
The learning aspect is often only part of the game and not at the center. The experience still feels like playing a game and not a class. This game-like feeling is often a source of criticism for games—the kids look like they are having too much fun!
Players are often presented with the problem before the solution, which is the natural order of events. Indeed, players are naturally interested in developing the skills needed to solve the problem.
Educational games use exploration and non-linear storylines, which contrast with the rigid curriculum of the traditional school system.
Games offer an opportunity for learners to develop higher order skills like: problem-solving, creativity, collaboration, planning, systems-thinking, resource allocation. These are all skills that are difficult to teach in a classroom, but easily developed as part of the game progression.
Simulation games are particularly good at teaching system dynamics which allow students to gain hands-on experience in domains that they normally wouldn’t have access to. See the serious games wikipedia page and the games developed by Molleindustria for example.
Online games are often an opportunity to build a community of peers to share knowledge, earn respect of one’s peers, and also have fun.
Games teach students that it’s OK to fail. The low-stakes game world makes it easy for students to try tasks at the limit of their capabilities without the fear of failing. No grades, no stress.

I hope you’ll agree with me that these characteristics of game-based learning are very interesting and hold the potential for making education fun and enjoyable. The problem with games is that they require lots of resources to create (programming, level design, educational expertise, knowledge of curriculum standards, artswork, music). It’s not like every teacher can sit down and create an edu-game for their students, but when the teacher has the skills, the results can be amazing. Perhaps we can make edu-games easier to create by providing “game templates” that teachers can fill-in using their course materials? I’m also optimistic about the potential for “lightweight gamification” where a teacher keeps using their traditional handouts and exercises, but renames them as “knowledge scrolls” and “missions” and embeds them in a game-like storyline.

Dynamics of learning loops

Let’s think about the mechanism that makes learning loops possible, and look for ways we could make the learning more efficient. We’ve already identified the three core requirements for a learning loop:

learner must have a base level of knowledge (prior skill) and be motivated to learn
learner has access to small, self-contained challenging tasks
learner received fast and accurate feedback on each task

A loop is formed because learners who complete a task successfully receive positive reinforcement that motivates them to start the next task. The positive reinforcement can be intrinsic (self evaluation, building confidence, personal satisfaction) or extrinsic (game points or grades awarded by a teacher). A crucial aspect for a learning loop to form is for the tasks to be of the right granularity (think one-hour-long tasks and not one-week-long tasks) so that learners get to experience some “achievement buzz” at regular intervals.

The steps in a learning loop: from problem to solution, to check, and if successful feeling of knowledge buzz, then onto next problem Figure 2: To enter the learning loop, learners need some initial motivation and a series of problems to solve. The learning happens as learners work toward a solution, and the feedback lets them know when they have found a good one. The feeling of success then motivates them to go around the loop another time.

These three core requirements for a learning loop are similar to the ingredients for optimal skill development identified by Anders Ericsson and Robert Pool in their book Peak: Secrets from the New Science of Expertise. The authors studied top-performers in various fields in order to identify the secrets of their success. The ingredient for developing mastery at a given task are:

many hours of focused, deliberate practice working on the task
have a clear, specific goal in mind, and a plan to get there
receive feedback after every attempt (ideally from a coach who is an expert in the field)
monitor one’s progress and develop new learning strategies (develop new mental models when performance improvements flatten out)
always work on challenging tasks that are slightly outside their comfort zone (a.k.a. zone of proximal development)
figure out a way to maintain motivation

I don’t think I’m doing justice to the book with this point-form list, but I encourage you to look into it and read about this further by consulting this condensed book summary here. For me, the main takeaway from Peak is the importance of fast, accurate feedback. Without this clear “error signal” from an experienced coach, it is much harder for learners to fix the mistakes they are making, especially for more advanced skill levels.

Speaking of teachers…

Teacher’s role

Learning loops are primarily a learner-driven activity and not a teacher-led activity. Learners advance on their own without the need to be controlled or supervised by a teacher. However teachers, coaches, mentors still have a role to play in fostering and supporting students’ looping behaviour.

First, teachers and mentors are important to give the initial motivation for learning the subject. Why should learners care about learning subject X? Without this initial motivation, the new knowledge or skill will be perceived as inert and lifeless. If the teacher or mentor is excited about the subject X, then this excitement will rub off on learners. Basically, the learning process is not just about transferring skills. There is also the “romance” aspect, getting students to fall in love with the subject matter. This “romance” aspect is well described by Stefan Schindler in his paper The Tao of Teaching.

Teachers’ role is essential for setting up the foundational knowledge. Recall that entering a learning loop requires some minimal level of competency in order to make the learner independent and able to complete tasks on their own. An introduction to the subject by an experienced tutor can be an excellent way to get up to speed.

Teachers also play a crucial role of modelling the right attitude, exemplifying a chill mindset in the face of difficult challenges, confidence, showing examples of breaking down complicated tasks systematically, knowing when to go for details and when it’s OK to cut corners. Such “soft skills” and domain conventions are difficult to pick up on one’s own, so it’s great when learners can see examples of this behaviour.

When a teacher is nearby students will have access to a mentor whom they can ask for help. This help can come in the form of detailed feedback on the student’s performance to improve their skill, or helping learners get unstuck.

Note the teacher’s role described here is very different from the traditional role of the teacher as the unique source of knowledge and information, but still very valuable as a stop-gap (for learners who are behind) and accelerator (for learners who can move faster). A coach’s role is more of a “teacher on demand” and “learning loop support” service, rather than a principal educator taking centre stage.

Last but not least, working with a mentor has the potential to select challenges of appropriate difficulty level for learners. Optimal progress requires taking on challenges that are adjacent to their current skill level: what better way than to have expert teacher support in this process?

Learning groups

Not everyone has access to an expert coach (teacher or private tutor) available to help 24/7. In the absence of a dedicated teacher that caters to each learner’s specific needs, the presence of a group coach can be a good replacement. Think of an online forum or chat group on subject X where learners can ask questions when they are stuck and receive recommendations for task challenges. A mentor doesn’t need to invest many hours of their time, but just pop-in once in a while to answer questions and help learners get unstuck.

Peers can also play a useful role in the learning process. The hypothetical discussion forum on subject X doesn’t need to be populated by experts—other learners can also be helpful to answer questions, share resources, and give the general solidarity feeling in the face of difficult tasks. The self-organizing learning groups at the Recurse Centre are a good example of this: there are no instructors and there is no fixed curriculum to follow, just a bunch of people getting together to learn and practice programming. Recent trends in online education are for courses that leverage the power of learner groups and use cohorts of learners, instead of the traditional MOOCs where the assumption was each student learns independently.

Learning resources that foster looping

We finally get to the most important question when thinking about “learning loops” from the point of view of a textbook author and educational publisher. What types of learning resources are best for supporting and encouraging learning loops?

The optimal learning resources for learning loops might not be textbooks or lesson plans, which are the standard types of resources used in the traditional educational system. Instead what we need is a set of standalone resources that students can reach for when they need them, in the middle of a task. Accessing learning resources on-demand is called the “pull condition” by Nick Shackleton-Jones, see this video.

Let’s see what type of resources might be helpful in the next subsections.

Tutorials

Introductory tutorials are probably the best way to pick up the prerequisite skills, either before the learning loop starts, or in a just-in-time moment to fill in necessary background info. The goal of a tutorial is not to be exhaustive, but to be a short hands-on introduction that takes learners to the first “win” in the learning process. A tutorial doesn’t need to explain everything, but rather focus on the specific needs of beginners.

Recently interactive notebooks have been all the rage and rapidly being adopted by educators. For those who might not be familiar, Jupyter notebooks are collections of code examples embedded in a text narrative. Learners can run the code examples and easily modify them interactively. Getting started tutorials presented in the form of jupyter notebooks offer the perfect combination of hands-on and minimal, just-in-time explanations.

Textbooks

Textbooks can serve multiple purposes for learners: they can be a primary source of knowledge (async instruction), they can serve as a reference (just-in-time consultation), and when they contain exercises and problem sets, they can provide hands-on practice opportunities.

In order for a textbook to be a good reference, it must have a good table of contents and an index so that learners can navigate directly to the place in the book that is relevant for their current need.

Reference materials

Reference-oriented resources are perfect for the “pull condition” when a learner has a specific knowledge need. Simple, self-service resources like concept maps (e.g.: math, mathphys, LA), flowcharts, glossaries, checklists, FAQs, templates, quick start guides, infographics and other visualizations can be easier to consume than traditional textbooks and video lectures. Most people have a short attention span these days and will not read long walls of text, but if a learning resource provides a useful way to think about the material, then people will read. Basically, learners don’t have the time for any blah-blah, and just want the facts. Educators must be ready to give learners what they need.

Exercises and problem sets

Practice problems are by far the most useful part of any textbook or course. Most people erroneously believe that learning happens by listening to lectures and reading books. In reality the real value of a course or textbook is in the practice problem sets, which give learners the chance to apply the knowledge. In Eastern Europe it is common for students to buy a workbook (сборник) of problems and not a textbook.

Practice problems should be numerous, of varying difficulty levels, and also come with hints so that learners never get stuck for too long. We’re not talking about ANY problems here—the problems need to be interesting. We want the learners’ experience solving the problems to be fun by using real world scenarios, adding jokes, and generally choosing the questions that feel relevant to the learner’s context. Problems should not be difficult for no reason, the goal is for learners to actually appreciate the effort of solving a problem, not to suffer needlessly.

Completing a problem often has an interesting component (applying insights) and a boring component (manual labour). A good problem is one which has pre-filled the boring parts and leaves students to do only the interesting parts. This is what I call a “neat” task: a task that gives learners a good feeling, not only are they spared the boring component of the task, but they also feel their instructor really cares about them by putting in the effort to prepare well structured questions. A good example of “neat” problems are the assignments for the Stanford CS231n course, which contain elaborate notebooks with scaffolded classes and pre-filled Jupyter notebooks and test code, allowing students to focus on implementing only the interesting parts.

Projects

Projects are by far the best way to encourage learning loops. Projects need to have the right scope, be complex enough to be meaningful tasks, but not too complex so as to be overwhelming. Projects are an opportunity for learners to take end-to-end ownership of a complete task, including filling in knowledge gaps. In the education space, this is called project-based learning (PBL). PBL classes have a proven track record of improved learning outcomes. See this webinar about PBL to learn more, or browse these PBL curriculum samples. Some earlier research had cast doubt on the effectiveness of the discovery-based learning approach, but I don’t see a contradiction here. There is no doubt that scaffolding can be very useful: don’t need to go 100% constructivist on this. Direct instruction seem like the perfect tool for learning the basic skills (Level 2) required to enter a learning loop (Level 3 learning).

Projects are important because they allow learners to integrate knowledge—all the disparate facts, laws, equations, and rules to memorize must be used and combined into a coherent whole in order to ship the project. Working on a project is also an opportunity for learners to develop teamwork and collaboration skills.

Learning apps?

Is it possible to combine the best aspects of books, games, and project-based learning into a learning app? Imagine installing a learning app that starts with some tutorials to give beginners the minimum competencies in subject X, then guides them through a series of well-defined projects of increasing difficulty, just like a computer game. Such an app would be the holy grail of educational technology. This is what I’ve always dreamed of working on one day—a gamification layer on top of the No Bullshit Guide content that makes the learning process more fun. I had a lot of knowledge buzz learning all this stuff, and I know many more people will enjoy learning math and physics, if the material were presented as a set of challenges.

I’m thinking the app could contain a mixture of problems (short missions) and projects (long missions), and possibly additional team challenges to get some social aspects going, e.g., meeting people from around the world that are working on the same mission as you. I recently started the noBSmath community chat room on Gitter to give readers a place to ask questions. It could easily scale this up to separate channels for different chapters, sections, and project-based missions. It would be nice to introduce some mechanism that encourages advanced learners to play the role of mentor for beginners.

I realize this idea is not well defined at all, but I think there is something that can be done here. Introducing learners to different subjects and then letting the dynamics of “knowledge buzz” take over seems like a fruitful way to get people into learning on their own.

Conclusion

I like the idea of “learning loops” a lot because they represent a potential solution to the main problem in the formal educational system: apathy, or the fact that students don’t care! I know from my personal experience and the experience of teacher friends, that the bottleneck in formal education is not the lack of content, teacher skills, or other resources, but the lack of students’ interest in learning the material that is “forced” upon them. When the material is presented in a disconnected and unmotivated way, students don’t care. I wrote this blog post because I think it’s important to think about how self-directed learning dynamics can make formal schooling work better. If we move away from an education where teachers “push” knowledge onto students and instead let students “pull” the knowledge they are interested in, then the educational system can be salvaged.

Acknowledgments. I want to thank Jonathan Herman and Kevin Ollivier for our discussions about education where these ideas originate. I also want to thank Jonathan, Kevin, Edith, and Julia for their constructive comments that improved this blog post.

Minireference blog

Starting a revolution in the textbook industry

Learning loops