A cautionary tale
Four years ago leading universities jumped into the bandwagon of massive open online courses. They didn’t get much more attention since then:
This is international data. In the US, interest in MOOCs declined, despite respectable institutions kept offering new courses on various topics. Is it a marketing failure, which best universities would be proud of, or a bad educational technology?
Let’s see. A typical MOOC consists of
- lecture slides and exercises
- a talking head that reads the slides
- a discussion board, barely alive
- an optional certificate
Despite many professors having good presentation skills, this technology is not different from a textbook. In fact, ten years before MOOCs, the MIT offered a much better solution: OpenCourseWare — a guideline how to study like an MIT student. It wasn’t tied to particular enrollment dates, pace, or lecturer. Instead, it showed what a diligent student should complete in one semester.
MOOCs became popular after Sebastian Thrun and Peter Norvig had released their open AI course. More than 100,000 students had enrolled, and universities decided to supply more courses. But the AI course was backed by new exciting technologies like self-driving cars and text recognition, while a standard university course covered boring rudiments available in any textbook.
The quality of online courses didn’t improve over time. Each professor appreciated his own brand and didn’t collaborate with colleagues from other universities. So each one had his own course, that is, slides and exercises. For example, a large MOOC provider offers 609 “data science” courses. Students enroll in just a dozen of them, when the lecturer already has a very good reputation. Like Andrew Ng and his machine learning course based on Stanford’s CS229 and available online since 1999.
The history of MOOCs shows how a lot of smart people keep making things that don’t work. Interestingly, it has to do with their core competencies and not online education itself.
Because someone else did better.
Y Combinator: Engaging educators
University professors have little motivation to work with students. Richard Feynman described teaching as “something [to do] so that when I don’t have any ideas and I’m not getting anywhere I can say to myself, ‘At least I’m living; at least I’m doing something; I’m making some contribution’—it’s just psychological.” So when it comes to research vs teaching, many professors choose research.
Anyway, most universities teach future workers, not researchers or educators. Normally, you expect workers teaching workers. Workers raised by professors are like Tarzan raised by gorillas. An innocent problem in a primary school, but the difference in interests increases as education progresses.
How to align the interests of educators and students? By involving the educator in the student’s real passion. That’s what startup accelerators do.
Y Combinator, the most prestigious of accelerators, invests in early-stage startups and puts their founders through a 3-month training program. The 5% stake that Y Combinator acquires for $120K ensures that the mentor’s wellbeing depends on the performance of his students.
Mentorship and apprenticeship are old business practices, of course. Startup accelerators add a social component by bringing many founders to one place. They also escape the research lab hierarchy, when a senior faculty member secures funding and employs graduate students as cheap labor force.
The MIT Media Lab is perhaps the most famous academic lab that operates like a startup accelerator. Professors join the companies founded by their graduates. That’s not a general practice in other universities, in which offering a stake for better mentoring sounds like an insult.
Khan Academy: Engaging students
Engaging students is the second most important task of an educator after engaging himself. This task takes time, so schools and colleges prefer to get rid of the least motivated troublemakers, instead. Many leave college because they see better options. How can educators decrease attrition?
Khan Academy was a one-man project done by a hedge fund analyst in his spare time. The founder taught math on YouTube years before universities started publishing videos of their own classes.
But arguably the best part of Khan Academy appeared later, when students started solving exercises online and getting immediate feedback. Happened before, but Khan Academy polished this technology with data:
- How Khan Academy is using Machine Learning to Assess Student Mastery
- Khan Academy: Machine Learning → Measurable Learning
- Khan Academy Mastery Mechanics
In brief, Khan Academy sets the sequence of exercises such that students are not discouraged by frequent failures. It’s part of Khan Academy’s gamification mechanism, which keeps learners motivated throughout K-12.
Stack Exchange: Asking and answering questions
Good educators teach the Socratic way, by asking leading questions. This technique does not scale well in a class with 100+ students. A good alternative is a Q&A website, like StackExchange or Quora.
StackExchange covers many academic subjects up to the graduate level. Its community encourages good questions and punishes for ill-prepared ones. Over time, a motivated person learns how to do preliminary research and ask right questions.
Answering these questions makes more sense than standardized tests or oral exams. Other advantages? Real problems, clear rewards, faster feedback.
Wikipedia: Accumulating knowledge
Wikipedia is fifteen year old, but the education system integrated only one half of it: students copy-paste Wikipedia content into their essays. It should be the other way around! Instead of assigning essays that no one reads, university professors could assign editing Wikipedia articles.
That’s a real contribution. Wikipedia editors check changes and reject the bad ones. It’s easy to track these edits. The Wikimedia Foundation always look for new editors and broader coverage. The content goes straight onto the front page of Google Search.
Despite all the advantages, I saw very few professors who practice this. That’s again about engaging educators, rather than students.
GitHub: Offering creative assignments
GitHub became a Wikipedia for code. Anyone can contribute to a project of interest. The list of open issues suggests possible contributions.
Like Wikipedia and StackExchange, GitHub addresses genuine problems, not synthetic exercises. Software engineers dominate, but any STEM project suits this platform.
Kaggle: Encouraging competition
Though the idea of 3,500 statisticians competing for $50,000 may seem irrational, Kaggle attracted thousands of math-savvy folks to practical problem solving. “Practical” is Kaggle’s key innovation. Competitive problem solving existed before in international olympiads and websites like Hacker Rank. Kaggle made such competitions useful, massive, and scalable.
Some CS departments encourage students to take part in Kaggle competitions. Why here and not on Wikipedia or GitHub? Kaggle challenges look much more like a standardized testing with clear-cut ranking. No need to evaluate whether the student made a useful contribution or just cheated.
Code4Startup: Learning for doing
Learning by doing is an old, popular, and effective technique. But task assignment is a trap. Stupid tasks kill motivation, and the rest dies by itself.
The simplest way to improve motivation is to increase the reward. Startup success stories turned to be a very effective one. More importantly, they are free.
Code4Startup turned this idea into a service. They offer courses showing users how to make a clone of a successful startup. Unlike MOOCs, these courses show how to turn coding and marketing skills into a useful product.
A honorary mention goes to McDonald’s and Walmart. These companies employ and train the people which top universities would never admit (and other universities get rid of these people after admission). Those who complain about students paying them $50K a year must try to teach a person working for the minimum wage.
Code Review: Giving feedback
Feedback prevents bad habits. In music, you want someone to hear you playing and to fix your techniques before you mastered them. Because one hundred thousand repetitions later you still may do it wrong. And music is complex enough to require a dedicated person sitting next to you and giving tips.
In other areas, technology provides a medium. Kind strangers from Stack Exchange Code Review help developers write better code. Duolingo fixes pronunciation. Show HN let the developer know if his MVP is good enough to keep on going. And elsewhere, video calls connect students with any teachers they can afford.
The calls are the best. Feedback is too complex for technology. Humans have to do it. Skillful people, really. So while lots of apps offer to teach you math or music for $20 a month, they sell new problems, not solutions. And any buck saved on teachers turns into hours of wasted time.
The services I mentioned have nothing to do with the formal education system. Many of them are not even labeled as educational. But they do what colleges are supposed to do, and do it better.
Three more things. (1) These services never associated themselves with colleges. More importantly, none attempted to reform the formal educational system. That’d be an interesting waste of time, as it was for John Dewey and other reformers. (2) These services scale and depend less and less on the limited supply of really good professors. (3) These services specialize. They don’t teach everything; they make narrow tools to improve specific skills.
Comparing their popularity with that of top universities (the MIT is much more popular outside the US; other terms are insensitive to geography):
Selected services (the two plots have different vertical scales and only trends are comparable; for more, check the links):
So if education is changing, it it’s changing outside traditional institutions.