We have a lousy product.
In the article, Thrun says that MOOCs, massive open online courses that gained popularity a couple years ago when introduced by professors from Stanford University, didn’t live up to their hype in democratizing education for the whole world.
Personally I’d been anticipating the start of a particular MOOC for several months–there isn’t very much educationally-oriented material on the topic in existence. Recently, on the week it finally came out, I finished Portal 2 instead of the first assignment, which involved installing, troubleshooting, and navigating a complex program and hunting down the dataset within the MOOC software–all before the deadline.
Ain’t nobody got time for that.
What can MOOCs learn from Portal 2 about making a compelling product? Let’s take a look.
Why am I playing this game at all? Plot. I’m stuck in a dystopian science facility being avenged by the evil computer system GLaDOS. The startling setting and crazy characters immediately draw me in.
Each level in Portal 2 has a clear goal: open the door. Generally I need to learn one new thing to complete the level while integrating what I’ve learned before, providing incremental difficulty. Furthermore, the environment that you interact with has many affordances, guiding you to play with tools like blocks, buttons, and
magical scientific bouncy goo.
Even if I’ve discovered the tools to use, it takes some trial to succeed in the level. The game provides feedback when something isn’t working right: I fall into a pit and drown in toxic water instead of reaching the other ledge when I haven’t figured out how to jump far enough.
Progress is concrete: I finish a level in about 10 minutes. Further, I receive a reward at the end in the form of taunting from GLaDOS that’s genuinely funny as I ride the elevator to the next level.
The “why?” of a MOOC is usually confined to the professor droning on a few minutes during the first lecture giving a list of ways the subject has been applied. There’s lots to say about storytelling, but there’s a reason that “vague list” isn’t a story archetype. Plots are, partly, about fantasy–we can put the learner in the applications and make it big and dramatic. Language learning? Take me to a foreign land. Applied math? Let me be that guy from Numb3rs. At least in college, I was a student on a four-year quest for a degree with my classmates. In a MOOC, I’m just a registered user who gets a lot of annoying emails.
Online learning has yet to go very far with this idea. One example is Codecademy, where you at least have a larger objective of completing a project.
MOOCs often ask you to complete a complex task in a complex environment. You need to switch back and forth between the software and slides for step-by-step instructions, and you don’t even understand what you’ve achieved at the end.
DragonBox teaches algebra using the principle of clear goals. Each level has the same goal of isolating the spiral, but they incrementally teach all aspects of solving algebraic equations.
Professors seem to love to jump into applied knowledge. Before making sure you get the definition of something, they’re asking you to transform and apply it.
In contrast, DuoLingo succeeds in incremental difficulty: it typically presents one new word at a time.
Check out Quill: it presents a textbox claiming “There are nine errors in this passage. To edit a word, click on it and re-type it.” I have no desire to learn anything more about grammar, yet I corrected several errors during my first visit to the page. The textbox, the existence of errors, and even the typography and the way individual words are selected when clicking, all afford me to play with it.
While it’s true that multiple choice prompts common on MOOCs are an affordance for providing an answer, these are generally removed from the environment and tools you’d actually be working with.
One of my major takeaways from interviewing many users of online learning systems is that the loop of instruction, practice, and feedback is way too long. Imagine that I watch several hours of video lecture over the course of a couple days, then I come back another day to do the assignment. Of course there are key ideas in the lecture I didn’t understand or remember, so I have to go hunt them down within those hours of video. Of the dozens of concepts covered in the videos, I get about 10 questions worth of practice on the quiz. Finally, I might not even receive immediate feedback on that quiz–I have to wait until after the quiz deadline to see what I missed anything and understand why. If I even come back to look it.
Based on Bret Victor’s principle that creators should immediately see the effects of their changes, Khan Academy’s computer programming environment allows you to adjust variables in the code and see the results on screen.
In other words, you get feedback as you adjust the code. However, this feature is only responding on one very minor aspect of programming. Imagine an environment that gives feedback about a misunderstanding of conditionals or recursive, and then we’re getting somewhere. Indeed Victor responded with an article about how they got it all wrong. You should read it.
In the Power of Habits, Charles Duhigg explains that concluding an interaction with a reward is a powerful way to instill habits. The trend of gamification has driven this effect through badges and points. But as Portal 2 shows, rewards are an opportunity to entertain and drive the plot forward, not just pad pockets with a fake currency.
CodeCombat (disclosure: friends with one of the founders) is a new effort to teach programming that uses this idea well. Once you’ve successfully programmed your soldier, you get to watch him execute his program and kill the ogre. You also get to see the “spells” that you learned in that level. It’s like collecting badges but also uses the opportunity to allow you to reflect on what you’ve just learned.
Some of these principles apply to developing better tools for us to do our work. If a tool is already well designed, learning it is easier. However, it is still important to understand the learner’s state, that is differences between what different users already know and understand. Considering the learner’s state implies we should set goals of incremental difficulty and indicate and reward when those goals are achieved, just as good games put sequence levels with clear goals in incremental difficulty for the player.
There’s plenty more to consider for an ideal learning environment. I’ve written before about spaced repetition, mnemonics, and multimedia. But I believe that solid execution on these principles gets us 80% of the way there. As Sebastian Thrun’s resignation demonstrates, we have a very difficult job ahead in that.