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Moving From Learning Objectives to Game Mechanics

Whenever we start a project, it’s pretty common for us to not have a formal finished pitch. Early concepts and proposal aside, there is still a giant gap between “here’s what we’d like our users to know” and “this is what the game is like”. It’s nuts! We love it!

Years ago, Matt Haselton and I worked on a cool little paper that outlined our general Filament philosophy on how to actually migrate learning objectives into gameplay mechanics. It was short, simple, and fun to write. What I didn’t expect is that a “one simple trick” idea like this would become such a durable and effective methodology for our studio for years to come.

Filament’s design philosophy generally evaluates learning objectives in terms of their potential for making engaging gameplay- essentially, games are great at creating scaffolded, engaging experiences around specific types of “interesting problems”. We generally categorize them into three groups:

1. Identity – “Who am I in this game?”

Games are great at asking you to become someone (or something) other than yourself.

This can mean a job (scientist, urban planner, lawyer, wizard), or a specific personal identity (Master Chief, Mario, Gandhi). Games bestow a perspective and set of skills on the player, and ask them to understand and master it. If the learning objectives speak to a specific identity, then an identity strategy will have great traction for making something really impactful and fun.

2. Verbs – “What do I do in this game?”

Play is driven through action.

Players are granted specific means and constraints that govern how they interact with the game. These actions are then scaffolded and rewarded. This can be as simple as placing a tetris piece or as complex as constructing a budget for a railway empire.

If the learning objectives speak to a specific action, and we can identify a way to build a digital equivalent (or metaphor) of that action, then it’s possible to wrap that action with feedback, rewards, increasing challenge, and complexity. The result is a core gameplay mechanic that deeply embodies your learning objectives.

3. Systems – “How does this game work?”

Games are constructed from a constellation of rules that players inhabit.

Every game is governed by rules. Some learning objectives (like your environmental objectives) speak to understanding how a complex system works. If we can identify a way to build the rules of the game to illustrate those systems, then mastery of the game’s rules will grant players deep access to understanding the objectives as a system, in motion, with interesting and sometimes unexpected outcomes.

So these are super handy, it turns out. By using these three categories, you have a tool not only for discovering mechanics, but for defining the playful anatomy of each learning objective- which clarifies what an objective really is *and* adds depth at the same time!

Here’s a quick example. Let’s say we’re making a game with some objectives taken right out of the NGSS:

Analyze and interpret data to provide evidence for the effects of resource availability on organisms and populations of organisms in an ecosystem.

So let’s start with “Identity”.


It obviously speaks to a science identity, but not in a particularly unique way. Furthermore, the reason it connects to a science identity is that it’s using verbs associated with science- and verbs have their own turn at the wheel here.

So at first glance I’d say we’ll look for ways to construct a positive, affirming sense of identity around science use and practice, but this is not our core interaction for the game.

Next stop: “Verbs”!

Well we’ve got “analyze and interpret data”, and we have “to provide evidence for…”. We could indeed make the game focused around these activities. The only challenge here is that in terms of the objectives content- the ecosystem and organism populations, we haven’t really dug deep yet. A content agnostic tool for analyzing and interpreting data would be possible, but this game isn’t about ALL analysis, it’s about SPECIFIC analysis on ecosystems, resources, and animal populations. And we always want to assume that the objective is written for the purpose of being unique from other objectives- otherwise why write it in the first place? Right?

So it’s likely that we will create actions in the game that involve analysis and evidence-driven argumentation. But we need more…

Which brings us to our third and final stop- “Systems”.

This one has got systems written all over it- it’s about understanding the ins and outs of an ecosystem! It’s pretty clear we’re going to want to make a light simulation environment that can model organism populations and resource scarcity. This will create the “sandbox” in which we can create our player verbs of analysis and evidence. And our reward structures can be based on setting up identity goals and rewards around thinking like a scientist. If we can pull that off, we’ll pretty much have made a living, breathing incarnation of the objective itself! Hot diggity dog!

So before we knew almost nothing, and now we definitely know something. The specific mechanics of evidence collection, the details of the sim environment…there’s still plenty of work to do, but our mechanical trajectory is set up for success!

This process can be applied over and over, crunching on just about any stated objective. If you see zero traction between your objectives and these strategies, it’s quite possible you’ve got objectives that aren’t very well aligned for translation into game-based learning. But that’s ok too- it’s certainly better to find that out at the beginning of the project rather than the end.

So whether you’re a teacher looking to whip up a classroom game, a designer working on a new game design document, or a curriculum developer pondering game integration, I think you’ll find this thought experiment tool to be useful, flexible, and easy to apply. Enjoy!


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