In designing the Learning Engine, there were many factors we worked to keep in mind and that influenced our decisions. One of the big ones was the Goldilocks Rule, as defined by James Clear, the author of Atomic Habits. He defines it as follows:
The Goldilocks Rule states that humans experience peak motivation when working on tasks that are right on the edge of their current abilities. Not too hard. Not too easy. Just right.
And has illustrated it as follows:
When you hear people talk about personalization, they often frame it as “meeting each student where they are.” Which is really another possible way of framing this Goldilocks Rule. That said, depending how it gets applied (and implemented) you can find students are either not being challenged enough (it’s closer to too easy) or being challenged too much (it’s closer to too hard). It can be difficult to stay in this “zone” because the zone keeps moving.
With edtech, there’s another major factor that often doesn’t get weighed in fully by the app’s learning engine/personalization algorithm: the classroom. Your students are learning every single day, regardless of whether they are using our app or not. You’re teaching them and helping them improve their skills. That means, that each time they return to the app, they may no longer be where we thought they were. Their skills may have grown leaps and bounds (and also may have slid a bit, from an extended break or other interruption/complication in their life). To assume that the old “Goldilocks zone” was correct, is to ignore the life of the student outside the app.
There’s something else that the Goldilocks Rule misses: that we need variation. To have everything always be “just right” means we never get rest breaks or a chance to really challenge ourselves.
All of this is a way to explain where we started from, and why we aim to not simply find this zone and stay there.
Every time your player opens the question window, the learning engine is at work to determine which content it should give them next. And it takes a few pieces into consideration, including:
- Where do we think they are right now? (Or their Reading Comprehension Level (RCL))
- How have they done on their latest question attempts on a specific skill? (What is their Skill Tier?)
- Do they appear to be guessing? (Are they answering unexpectedly fast?)
Given these variables (and others), the learning engine chooses whether to give them content we think is “just right” for them, a little bit easy, or a little bit challenging.
Just Right:
This is content right at or near their RCL or Skill Tier. It’s the edge of where we expect them to be (which means it’s pushing at their limit). This is not content we expect them to get 100% on. They are still learning this content.
A little bit easy:
This is content a bit below their RCL or Skill Tier. We expect them to already know this content. It’s like getting to a view point as you climb a mountain. You get to rest for a bit, build up your confidence, and then dive back into harder material. If they struggle on this, it can be an indication of some back sliding and needing to reduce the difficulty a little bit.
A little bit challenging:
This is content a bit above their RCL or Skill Tier. We expect this to be difficult and for the player to likely fail. This is hitting that steep hill on your mountain climb. You get to test your limits and possibly surprise yourself by going further than you expected. If they succeed on this, we increase their difficulty, because we likely missed a growth bump that happened outside the app, but if they struggle, no worries, they’ll get back to the Just Right and A little bit easy content.
The result of our Learning Engine taking all of this into consideration, is that at the end of the day, learning in our app doesn’t look like acing every assignment. It’s not about having a consistent 80% or higher accuracy rate on the questions. In fact, based on our research, players on Shoelace start showing real growth above 50% overall accuracy.
Looking for some ideas on how to use the game features to motivate your students to focus on their accuracy? Read more here.