Developing the Range Trainer
I've been thinking a lot about how we came up with the mechanics of the Range Trainer. In the end, Matt and I put a lot of thought into how people were going to interact with it and how we could build it in a way to maximize the impact. We went through a couple of initial ideas, but none of them really felt right until we landed on what we ended up releasing; a close to real-world training simulation of actually playing poker. I'm not going to go into all of the different approaches we looked at taking, but I do want to go into one of our possible alternatives and compare it to our current Range Trainer.
First, if you haven't experienced the Range Trainer, let me very quickly explain how it works. After you select what ranges you want to study, you're given a graphic of a table that shows you at a specific position and stack depth with a hand. Given all the information, you then tell the Trainer whether you should fold, call, raise, or go all in. Essentially, it's as close to playing as you can get without actually playing at a real table.
So, what was the other main option we considered? Basically, it was a system that would have asked users to re-create a range from memory. The system would have given you a scenario (i.e. a position, stack depth, and the action that had occurred ahead of you) and asked you to fill in a range chart. Simply, it was a digital version of the flashcard method.
Ultimately, we decided this was an exceedingly inefficient way to go about studying. It was clunky and frustrating to use and, to be perfectly honest, completely contrary to the mechanics of playing. We quickly realized that the idea of trying to picture hundreds or even thousands of different combinations of range charts in one's mind was frankly a massive waste of time and wasn't fun.
Happily, I think we found the perfect system for the Range Trainer. But, it wasn't easy and there was a significant amount of research that went into forming what ultimately has been released. This isn't a scientific article, so I won't go into some of the finer points that went into our research. However, I do think it's important to point out a couple of key points. Our solution employs what is generally referred to as the Learning-by-doing Theory of Education or Experiential Learning. We've built the Range Trainer based on the idea of creating "The strategic, active engagement of students in opportunities to learn through doing, and reflection on those activities, which empowers them to apply their theoretical knowledge to practical endeavors..." (as discussed by Jennifer McRae of Simon Fraser University).
Put another way, you absorb the ranges in a way that you will be using them: at a table versus (simulated) opponents. There is significant data to further back up the idea of employing an Experiential Learning model, but by far the most important aspect is this idea of learning through the experience of playing.
In the end, RangeTrainerPro has spent an enormous amount of time thinking about the best way to help our users learn and get better and I'm confident that we've come up with a system that is efficient, valuable, relatable, accessible, and, most importantly, fun.
