April 29, 2013

Adding time travel to a teacher’s bag of tricks

One Christmas when I was a little bonehead in 1st or 2nd grade, I got a chemistry set.  It came in a triptych-like case that unfolded with rows and rows of blue plastic bottles containing all manner of chemicals.  Some of the bottles contained scientifically labeled table salt and baking soda, but among those 60 something chemicals there must have been something toxic, like Red Dye #2 or something.  That was a different time; I survived. It included various vials, flasks, weighing spoons and the like.  I’d carefully unfold it in the corner of the dining room secretly harboring 8 year-old visions of alchemy. But mostly I mixed together chemicals willy-nilly to see what colors they made, usually pasty white or colorless.  When I ran out of the few chemicals that made blue, yellow or red, I abandoned my precocious chemistry career somewhat disenchanted.

A few years later in high-school: chemistry lab.  After a couple of weeks of droning by the teacher– I don’t recall ever actually reading a textbook, but I was a lazy student (and actually, he was a great teacher)– we finally get to the first lab.  I come in and scattered around the room are Erlenmeyer flasks, Bunsen burners, plastic hoses and test tubes.  The look in the eyes of the students ranges from warily curious, to mischievous excitement to sigh-ridden boredom. The look in the teacher’s face somewhere between chipper resignation and taut dread.   After we pair off with the usual adolescent social anguish, we get our lab instructions and are left to our own devices, under the watchful eye of Mr. Miner, the recently certified fire extinguisher conspicuously mounted in the corner.

A brief, concealed rush of excitement (I was 16, excitement unrelated to football or female breasts was inexcusable) as the child within thought now I get to do some real chemistry, my alchemical delusions reawakened.  But as I followed the instructions step by step, weighing out .5 grams of such and such, 1.8 grams of this and that, combining with 300 milliliters of water, heat for x minutes . . . I felt the same disappointment and boredom that had descended on the 8 year-old me in the dining room.  This was just a bunch of boring steps, and the concoctions weren’t even colorful, no yellow at all.  Writing up my lab notebook seemed fun at first, scribbling like a real scientist, making tables, drawing graphs, but that too became tedious, a paint by numbers science exercise with all the thrill of a spelling quiz.  Even the junior high frog dissection, anticipation building all semester, proved anticlimactic. I felt sorry for the frog.

Before raising the ire of high school chemistry teachers everywhere, I want to defend their valiant efforts.  I was a recalcitrant brat.  I admit it.  Salt in the wound, I eventually became a scientist.  The idea behind lab courses is noble: let students have an actual, physical encounter with the science they are learning about, to see it in the world, abstract concepts ground into powders, weighed in balances, poured from flasks, bubbling in beakers– doing, seeing, hopefully having fun.  But it’s like trying to be a chef by following recipes . . . the joy of cooking is opening the refrigerator and creating, not reading and measuring.  The joy of science is that little scratch beneath the surface of reality, a glimpse of the unseen and the world of possibilities it spins in the mind, the mad scientist feel of seeing the world differently.

I first had this experience, the non-disappointed chemistry set experience, in graduate school.   My encounter with protein science, an awakening.  Amazing little things, proteins.  If Blake saw the world in a grain of sand, looking at a three-dimensional protein structure would have made his batty little head pop.  I again encountered expanded consciousness with computer science and programming, where abstractions are not throwaway thoughts but, like Plato’s ideal forms, a sort of distilled reality– when poured into trillions of silicon transistors, transformative of on-the-ground reality.  The Holy Grail, of course, was neuroscience, the universe in our billions of heads, the 21st century frontier.

For the most part, inspiration, at least for me, rarely arose from hands-on experience.  The actual work of science is a frightful grind.  In fact, it’s the hands-on lab work and the toil of experiments, including the unbelievable amount of prep and follow-up work, that keeps scientists anchored securely on earth.  Aside from the drudgery, which eventually falls on others as one progresses in a scientific career, there’s this issue of being constantly wrong. Occasionally, an experiment will turn out exactly as predicted.  Such outcomes make me nervous . . . what did I do wrong? What am I missing? Because the expected outcome of any experiment, I’ve come to believe, is neither a positive or negative result, but some entirely unexpected result that leaves you scratching your head and discovering something much more interesting than you’d ever thought about before. No matter how complex and nuanced one’s hypothesis and experiment, reality is always more complex and more nuanced.  But it’s precisely this trekking back and forth from the heights of Olympus down to the messy muck of reality that is the heart and soul of science (you decide which is which).  It’s Sisyphean with moments of delusion.

Creating this sense of discovery in a high school chemistry or biology lab is a tall order.  And unrealistic.  Mucking with actual chemicals and doing an experiment is not ethereal, not castles in the sky.  It’s an experience of reality that like gravity pulls you to the ground, makes you heavy and keeps you from floating or flying away.  Hands-on experience is really important, but where does the inspiration and excitement come from? How can students get a taste of Olympus?

Not, I think, from lab exercises. It happens in their heads. It’s that cognitive layer, the development and shaping of a representation of the world in their brains that gives rise to alchemic magic.  So this brings us to video games.  Reflecting back on my chemistry set, a virtual chemistry set embedded in a game would probably have inspired– and educated– my 8 year-old brain far more than my mixtures of table salt and Red Dye #2.  The challenge is how to do it.

The answer, I think, is time travel.  No, I’m not crazy.  In a virtual world you can take students back to a time before we understood, for example, calculus.  What problems existed at that time that could not be readily solved? By virtually regressing a student into the past in a game context, you can recreate for them a deeper understanding of ‘the problem’ and the thrill of discovery of solutions, allowing them to ‘discover’ calculus for themselves (with a little scaffolding help, of course).  Many textbooks have little ‘special interest’ boxes where some historical tidbit is conveyed to students to pique their interest.  An example might be the challenge of telling time and its importance to navigation. In an educational video game, the relation between special interest boxes and main text would be reversed: students face the challenge of navigation with unreliable time keeping devices in a game, and this becomes the heart of the learning.  In this inverted approach to education, the ‘special interest’ boxes become the historical solutions, ie., the accumulated knowledge of mankind we want them to cotton on to in the first place.

Gamification rhetoric often centers on ‘make it a game and kids will like it.’ This often sounds like disguising education to make it palatable to kids, too often with little discussion of the quality of learning.  An underlying, unspoken desperation appears to permeate much of this discourse– despite its cloying optimism– as if just getting kids to do anything educational is an accomplishment, never mind the quality and depth of the learning.  Making education ‘fun’ is not what it’s about.  Sneaking educational content into games is a great way to make games not fun.  The potential of educational games must start from the premise that learning is fun and build around and scaffold the excitement that comes from discovery and mastery.  Rather than disguise learning, to reach into the heart of what is best about learning, that glimpse, however brief, from Olympus.

Despite all we hear about declining literacy, dwindling attention spans, addiction to video games and on and on, the problem is not the kids.  Their brains have not devolved.  Their synapses pop and crackle like any generation.  The kids are alright. Chemistry, calculus and physics have never been blockbusters, textbooks never the most engaging material.  If anything, we have a brighter, more savvy set of young minds than ever before and tremendous opportunities to engage and educate them in ways we’ve never had before.

Posted by on April 29, 2013 in education, learning mechanisms, motivation, science games and tagged , , , , .

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April 15, 2013

F = ma, Angry Physics

Angry Birds.  Undoubtedly one of the most addictive time-sink video games ever produced.  If you haven’t played, you should.  Aside from irresistibly cute animations and sound effects, the game is a technical accomplishment, built on a physics engine (Box2D by Erin Catto) that simulates how objects interact (eg., collide) in a real-worldish way.

Interestingly, there’s lots of talk about the educational value of Angry Birds.  Rovio seems to be starting an educational series called Angry Birds playground targeted to younger children.  They are also billing their recent Angry Birds Space as educational, though some have been critical of this claim.

Some enterprising teachers have used Angry Birds to teach physics.  By using screen tracking software, they do analysis of the trajectories of the birds and ask questions like ‘when the birds split, was momentum conserved?’  Or, a personal favorite, what is ‘g’ (the force of gravity) in the Angry Birds world?  Kudos.  Here we go beyond the ‘make learning fun with games’ and apply an analytical framework to understanding the Angry Birds universe, one that is not dissimilar to our own, but as it turns out, not exactly the same either.

The teachers are to be commended, but this is also an example of where game designers could really help out.  In the teachers’ use of Angry Birds to teach physics, the game is an object or virtual world to be analyzed.  The actions involved in playing the game are entirely separate from the classroom exercise of analyzing the physics within the game.  An alternative would be to create a truly educational version of Angry Birds that integrates this cognitive analytical activity with playing the game; that is, makes the thoughts and concepts an action that becomes a useful tool.  This might be thought of as first externalizing and then internalizing a concept and pairing it with procedural learning.

So how could game designers help these teachers make Angry Birds into a better teaching tool?  Here are several ideas:

1. An analytical layer could be built into the game.  The physics engine is making a zillion calculations.  There is no reason the data from a subset of those could not be cached in temporary arrays for analytical purposes.  For example, there could be a toggle that turns on ‘data analysis mode’ that then saves the data (or a portion of it) from the physics calculations.  The trajectory of the objects on the screen and select parameters of their movement could be cached.  There can be an analysis interface that allows the user/student to show these trajectories and select points on them.  Once a point is selected, particular parameters could be selected and displayed.  If a designer wanted to get really fancy, they could enable a pause in the midst of the birds flying at the pigs so that, midflight or mid-collision, the forces at work could be examined. This analytical mode might be exceptionally helpful, but doesn’t integrate this educational function into the game as part of gameplay.

2. Similar to the analytical mode above, there could be a ‘quantitative mode’ during game play that displays factors that will contribute to the launched birds trajectory.  This would include the mass of the bird, the potential energy of the drawn back sling shot, launch angle and, when launched, the resulting acceleration.  Throughout the trajectory, the forces working on the bird’s travel (eg. gravity, momentum, air resistance) could be displayed, as well as the force of final impact.  As above, a pause allowing examination would be helpful.  This allows a player to transfer implicit learning (how far back to draw the slingshot and at what angle) into explicit, quantifiable knowledge (30 degrees with x amount of force) creating a cognitive, mathematical layer of understanding game play.  Would this be torturous and boring? Award double points if the player can position the launch by typing parameters in rather than pulling the slingshot back.  As the player types, the visual of the sling being drawn back and aimed are the same as always, but the player is controlling it numerically instead of spatially.  My guess is that for many students, once they began to really understand the physics and could use that understanding to excel in the game, being able to rapidly wipe out loads of pig formations by typing in numerical parameters would be seen as a mark of distinction: this is how the real pros do it.  No amount of end of chapter problems could replace this learning experience.

3. A third approach could be to allow players to adjust critical parameters in the physics engine, for example changing the gravity or air friction within the game.  This could be implemented as a strictly an educational tool, having no role in the game per se– just qualitatively ‘see what happens’.  But it could also be integrated into game play and made quantitative.  When firing off a bird, the player is using not only the force of the slingshot, but whether they realize it or not, the force of gravity.  Without said gravity, the bird would not arc and instead maintain a straight course flying right over the pig formation going on forever.  So different levels in the game could provide situations where the player has to adapt to different conditions, such as different gravity or air resistance.  Even more interestingly, levels could be designed where the player must adjust the physics of the world to succeed.  For example, the pig formation could be sufficiently high or far away that in the normal gravity of the game there is no way a player could launch a bird and hit the target, gravity would pull it to the ground first.  In order to avoid this, the player has to adjust the gravity.  Of course, this introduces an entirely different set of trajectories.

There are, of course, limits to the physics engine and limits to the computing and memory capacity of the devices on which an educational version of Angry Birds could be played.  Such limits, however, are not likely to be the obstacle in creating an educational Angry Birds.  The limits seem to reside in economics: such a game would not be trivial and require talented and committed designers and developers, likely working in conjunction with educational consultants.  Investing the resources to do this, without the promise of making big bucks, is risky and an obstacle to aggressive development of high-quality educational games.  Non-educational games are easier to make and provide vast potential for financial gain, if successful.  Of course, thousands of games fail.  Perhaps some enterprising developer will see a freely available physics engine and the need for quality educational games as an opportunity to dive into the deep blue sea and try something different.  And maybe it will be successful.

It is encouraging that Rovio seems to be diving into educational games, though much of it appears to be geared toward younger children– even their particle physics project with CERN (3-8 year olds).  Games for bigger kids and adults are, let’s face it, much much harder to design and develop, with substantially greater risks.  Some might call this an obstacle, others . . . an opportunity.

Posted by on April 15, 2013 in design, education, learning mechanisms, science games and tagged , , , , .

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April 11, 2013

What would Vygotsky have thought of Play Station 4?

I would be remiss to not reference Lev Vygotsky, a long dead but genius dead Russian psychologist who has been one step and the better part of a century ahead of me on the core ideas being developed here.  Many of the ideas I explore and the language I use echoes Vygotsky’s insights and work, including ideas about tool use, scaffolding and the entire idea of using play as a form of intellectual development.  Giving credit where credit is due, in this post I will highlight some of the key concepts Vygotsky developed and how these ideas apply to video games, which only came into existence four to five decades following his death.  I can only imagine he would have had much to say about educational video games had he teleported half a century into the future.

 

Practical and symbolic intelligence

Though Vygotsky elaborates a developmental theory and applies it to learning and education, his starting point is fundamentally the question of what comprises intelligence and how does it arise and develop?  Based on primate and infant studies from the time, he argues that tool use represents a fundamental characteristic of intelligence. At its most rudimentary, we might think of our own appendages as tools.  But of course, tools can range from an arm to a stick or rock to a MacBook Pro.  He attributes to non-human primates ‘practical intelligence’ that arises from ‘practical activity.’  In the terms I have been using, this is equivalent to ‘how to do.’  So a monkey, for example, who wants to reach a banana beyond arm’s length will eventually figure out that he can take a stick and knock it down.  This represents a form of trial and error, procedural learning: what works and what doesn’t.  This form of practical intelligence is independent of speech and language and arises more from associations formed and reinforced between particular stimuli (like bananas and sticks) and particular actions (Thorndike had a bit to say about animal intelligence, which I have written about in my work here).

In contrast, we as humans obviously have a different kind of intelligence that arises from our ability to abstractly represent ideas and think through possible actions, outcomes and solutions in our head.  This is an intelligence that arises from a capacity for symbolic representation, reflected in speech and language.  One of Vygotsky’s fundamental premises is that these two kinds of intelligence and their mutual development are intertwined (described more below).  In previous posts, echoing this idea, I have sort of argued for two ‘intertwined’ levels of learning– procedural, action learning about how to do things and a cognitive, representational learning.  Unlike Vygotsky, I am inclined to see these less as ‘intertwined’ and more as two inseparable sides of a single coin, but I’m not going to quibble.  The key point is that there is an intimate link between practical activity– doing things– and cognitive activity, symbolically representing or thinking about things.  Video games abstract both of these and allow them to be arbitrarily paired.  “Doing” is abstracted into controller manipulations (ie., not really serving a tennis ball, but doing abstract motor movements with a joystick).  The symbolic or cognitive level– the dragons, keys, beachballs, jumping and sword fighting in our games– are also arbitrary abstractions.  Thus, in a video game, the field is created where we can link arbitrary ‘doing learning’ with arbitrary symbolic representation, or ‘thinking learning.’  You cannot do this in the real world where actions and things in the world are neither arbitrary nor abstract.  (I also quibble with Vygotsky’s characterization of the limits of animal intelligence, but that is another discussion, irrelevant here*).

Cognitive development: Internalization of culture through social interaction

I’m going to put a slightly different spin (see Wikipedia for standard rendition) on Vygotsky.  His work focuses on how, through speech, social interactions become internalized to become symbolic, representational cognitive systems.  He noted that because cultures differ, so too will the social interactions and the characteristics of the internalized representational system.  I want to abstract this just slightly and view the social interaction as a mechanism for cultural transmission of knowledge in the form of language and symbolic systems of signs.  What is being internalized, though, is effectively a cultural cognitive system– of course, with lots of individual variations, but still . . . a system of symbolic representation that is fundamentally cultural.  So while Vygotsky talked a lot about social interaction referring to direct interactions such as between parents and child or teacher and child, I would argue that the essentially same process can occur through cultural artifacts– watching movies, reading, surfing the internet, seeing billboards and advertisements every day.  These sorts of things represent an indirect social interaction.

In a sad nutshell that doesn’t do justice to Vygotsky’s genius: speech in children functions initially as a means of communication between child and adult, allowing for teaching and guidance.  Young children then adopt ‘self-speech’ where they talk themselves through particular actions and problem solving.  Eventually, this self-speech becomes internalized (talking in one’s own head), resulting in language transforming from a primarily interpersonal medium to one that is intrapersonal; that is, all that we learned in interactions through talking and acting we start to internally represent through symbols that allow us to think independently of acting.  This internalized representational system frees us from the demand of responding to our immediate sensory field, from a sort of tyranny of stimulus-response learning.  Critically, we can withhold a response and think beyond our immediate environment.  We can predict, envision, and plan.  In short, we can self-regulate our responses and explore options and possibilities in a virtual, representational world in our heads, basing our behavior on these mental operations– otherwise known as ‘thinking and cognition’– rather than emitting immediate learned responses to stimuli in the environment.

Though volumes can and have been written on internalization– not just as Vygotsky portrays it, but more generally– space precludes delving into the topic.  The critical point here is that interaction between an individual and his/her culture becomes mediated by symbols, primarily language, which take up residence inside the brain.  Those symbols are inextricably linked with actions and experience and come to form a cognitive system that allows us to put actual action on hold while  we engage in virtual action before deciding upon what actual action to take.   Though Vygotsky might not see it exactly this way, my argument is that such representational systems are, effectively, a great big ‘ol tool in and of themselves– and their use, ie., thinking, constitutes action.  Because our daily lives might consist of often attempting to get the cookie jar down from a high shelf, we frequently engage in interactions, including speech, to this end and eventually acquire elaborate cognitive representations of how to get the cookies.  We have considerably less opportunity to engage in interactions and internalize symbolic representations on how covalent bonds form between molecules.  Video games provide a virtual world where things like solving derivatives and understanding the effect of gravity on an object in motion can be as common as getting the cookie.

Zone of proximal development, zone of proximal discovery

The last Vygotsky topic, for which he is best known, is the ‘zone of proximal development’ (ZPD).  This is simple: the ZPD is the domain of tasks and problems that a person can successfully tackle with assistance.  Example:  Imagine a preschooler who has learned to count.  They are confronted with a task of deciding whether they have enough pennies to buy a candy bar.  They can count the pennies and have an answer.  This is a problem they can solve without help.  Now imagine they want to get a candy bar, a pop and a cookie but need to figure out whether they have enough pennies for all three and, if not, which ones they can buy.  There are several ways to solve this problem, none of which they may be able to do by themselves.  They could decide which they want first, then count out the pennies for that.  If they have pennies left over, they can decide which they want next and count out those pennies.  They are essentially grouping their pennies to match the costs of the items. If they are able to add, they could add up the prices and count their pennies for the total cost.   In fact, they could use the grouping process to learn about and come to understand addition. An adult can walk them through this process. After going through it a few times, they will internalize the procedure and be able to do it themselves.  This task, that they can do with help, lies within the ZPD.  In contrast, if they had to solve a differential equation in order to purchase the candy bar, no amount of adult guidance will enable them to do it– that is simply beyond their capability, and thus lies outside the ZPD.

Though Vygotsky never used the term, this has led to the idea of ‘scaffolding’: the support points necessary to enable someone to complete a task or solve a problem.  In the example above, ‘grouping pennies’ might be a support point, part of the scaffolding an adult provides. Eventually they will be able to do it without help.  In previous posts, I draw upon and extend this concept of ‘scaffolding’ to the development of motivational structures, but more generally, in its more traditional sense, it boils down to providing assistance that facilitates the ability to complete a task, wherein learning occurs and ‘how to do things’ gets internalized symbolically and becomes part of a cognitive structure.

Video games offer a rich opportunity for scaffolding.  In the junk food buying example above, a video game could easily be designed that teaches the child to group pennies (placing one at a time in a tray in front of the object they want), which could then be extended to abstracting the concept of addition.  As the child progresses up levels, the grouping opportunity (the scaffolding) could be removed requiring the child to perform the grouping process, ie., addition, internally before selecting an action.  The ‘grouping tool’ could be made available as long as the child needs it.

In the ZPD idea, the child is assisted by an adult.  However, the same principles can be translated to trial and error discovery. Imagine a simple task, analogous to the Wisconsin Card Sorting task, where the child has to learn to group objects according to their color for a while and then, later in the game, according to shape (an crude example of category learning, as well as an exercise in set-shifting).  If you arrange the elements of the game at a level where the child can discover what needs to be discovered, learning will occur.  If the child has to ‘discover calculus’, only about 1 child every several hundred years will be likely to succeed.  In short, in the same way in which there is a zone of tasks that the child can accomplish with assistance, there is reasonably a zone in any given environment with a set of available actions in which a child is likely to discover by trial and error some principle to guide their actions, even without adult assistance.   In principle, educational video games could work within both these zones– the traditional ZPD with peer and adult assistance in navigating the game, but also a proximal zone of discovery.  Either way the child is associating actions with symbols and constructing a layer of cognitive tools for mentally manipulating the environment, improving their ability to understand and act on that environment.  Quintessentially, learning.

Play and cognitive development

Finally, though I won’t go into details here, Lev had a lot to say about the importance of play to cognitive development.  In fact, a lot of people have a lot to say about this.  One of the ideas in Vygotsky’s work germane to this discussion is that play and its pretend objects (pivots, he called them), similar to speech and interactions with adults, gets internalized, not only contributing to cognitive structure, but allowing ‘play’ to occur increasingly in the form of mental representations.  Put another way, play can become increasingly cognitive.  The relevance to video games is obvious.  A video game is an entire world of pretend objects, or pivots, which gradually become internalized and incorporated in cognitive structure, potentially shaping the representational lens through which a person views the world. The value of Mario Bro’s or Grand Theft Auto’s contribution to a developing cognitive structure through which a child views the world is suspect, but that is hardly the video game’s fault.

Lev_Vygotsky

If he had known this pic would be viewed millions of times across the world for decades, he would’ve fixed that collar.

Summary

Both psychology and neuroscience are replete with brilliant people, going back to the 19th century all the way to the hundreds of brilliant scientists living and working today.  By rights, the list of people who have contributed to my thinking that deserve to be highlighted is quite long.  I single out Vygotsky not because I am ‘Vygotskian’ or because his work ‘supports’ my ideas, but because I think he was a visionary.  And because I think he would really dig video games.  And because his life was far too short, but his work continues to resonate.

* Vygotsky saw the ability to interpret the immediate environment through the lens of a cognitive structure that anticipates future events as unique to human intelligence, eg., he says ” . . . a view of the future now an integral part of the approach to surroundings.”  However, this is arguably true of animal intelligence as well.  Prior experience is structurally assimilated into the brain through learning and mechanisms of synaptic plasticity such that an animal perceives its current surroundings through the lens of expectations of the future.  The issue here is the degree to which human cognition, distinguished by language and symbolic representation, is a discontinuous departure from animal intelligence (ie., something wholly different) or merely an extension and elaboration of rudimentary cognitive function present in mice and monkeys.  Vygotsky tended toward the former, I tend toward the latter.

Note: primary Vygotsky text: http://www.amazon.com/Mind-Society-Development-Psychological-Processes/dp/0674576292

Posted by on April 11, 2013 in education, intelligence, learning mechanisms, problem solving, science games, social intelligence and tagged , , , .

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April 5, 2013

Tool use in video games: extending cognition

In the prior post, I talked about how actions can map onto cognitive learning.  In this post, I’ll extend this and look at tool use in video games.  There are an endless number of tools in video games:  swords, keys, maps, armor, potions and so on.  These can, like elemental actions, be grouped into some rough categories.

First, there are tools that extend actions.  The sword is an archetypal example, extending reach and adding the ability to cut and stab.  Another example might be a magnifying glass that extends the ability to see.  Tools like swords and magnifying glasses seem simple and basic, but they have an important effect of expanding both ‘sensory’ and ‘motor’ function creating a more complex understanding of the virtual world and increasing players’ response repertoire, enriching the world with which they interact.  Like elemental actions, there is a procedural, motor element to tool use, such as learning how to wield the sword, but also a cognitive element. Each tool has different properties such that a player needs to learn what tools can and cannot do and when and how they should be applied, not to mention how they work.  The player not only learns the properties and uses of the tools, but gains a deeper understanding of the virtual world and its characteristics, such as, for example, that swords don’t go through rock.  Similar to elemental actions, the cognitive layer is, within the game, arbitrary– a story– and it can be any story, including stories that teach math or physics, for example.  Tools that extend actions increases the player’s ability to act upon the environment within the game and, in the process, can yield greater understanding of that environment.

Second, many games include tools that might be thought of a ‘direct’ cognitive tools.  The archetypal tool of this genre would be the map.  As players navigate through a game, they form a representation in their minds of the virtual world, including its spatial layout as well as more abstract relationships and rules about how the virtual world works.  A map extends this mental representational system and can serve several purposes.  One, it can be a learning tool that extends memory.  Before someone has a solid representation in their mind, they can keep referring back to the map until they more thoroughly internalize its information.  Two, a map can highlight relationships that might not have been apparent in a players internal representation:  oh, that swamp is just around the corner from the castle, I didn’t realize they were so close.  Third, a map can direct future exploration and learning, increasing efficiency.  ‘Maps’ can be metaphorical are not limited to virtual geography, but are representations of any set of relationships.  For example, a lineage of goblins and how they segregate into good and bad goblins is a representation that maps out categorical relationships.  Video games contain many forms of ‘cognitive extensions.’  Another example might be finding scrolls or clues that slowly tell part of a story, piecing together a narrative, which is another form of accounting for how and why a virtual world works as it does.  As with tools that extend action, cognitive tools can represent anything, including educational curricula.  What is critical is that the content of ‘maps’ in the broad cognitive sense are not simply a bunch of information players are forced to learn and remember, but that the information is relevant and functions as a tool to solve a problem in the virtual world.

tool table png

Keys comprise a third class of tools.  Keys turn locks; we all know that.  But there are two aspects of keys that are more fundamental to learning.  First, they are a specific solution to a particular problem.  Usually, this is a set of teeth that match a tumbler within a lock.  But more broadly, unlike a sword or magnifying glass, they represent a class of tools with very specialized properties that fit a specialized problem.  Another example might be a special amulet that kills the seemingly invincible dragon, or a mathematical operation that makes a particular thorny problem go away.  Using a key requires understanding the relationship between its special properties and the problem at hand.  In the case of a lock, this is pretty simple.  But a key could be anything, really, including a method for finding derivatives.  The more remarkable aspect of a key is that once applied to the specialized problem, it opens up access to parts of the world not previously available.  The key is, in other words, the quintessential tool of discovery that expands knowledge and understanding and begins with solving a specific problem.  In designing educational video games and mapping action and cognition onto a particular domain of learning, keys are key, unlocking of understanding one specialized problem at a time.

In the same way that tools extend the capabilities of a player, tools expand the opportunities for teaching domain-specific content in video games.  To achieve an educational purpose, tools, like elemental actions, require mapping their use onto cognitive representations; tools, however, provide greater flexibility and offer the opportunity for opening up ever more complex aspects of a virtual world and its corresponding curricular content.

Posted by on April 5, 2013 in design, education, learning mechanisms and tagged , , .

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March 31, 2013

Thought is action: mapping video game actions onto cognition

We tend to view abstract thinking and language as ‘higher cognitive function’ and procedural, motor learning as, well, less higher.  As abstract thinking and language seems to be the basis of advanced education, we value higher cognitive function over, say, skills at ping-pong.  A tendency to liken video game skills more to ping-pong more than calculus may preclude recognizing their full potential as an educational tool.  However, the distinction between higher cognitive function and ping-pong may not be as clear as we think.

Our brains have evolved to act: to respond to on-going stimuli, learn from experience and adapt to our environment in order to survive.  Vast tracts of our brain are dedicated to putting sensory information together with potential actions, selecting and executing one response among many and subsequently learning from the outcome.  Language and abstract thinking can be thought of as an extension of this action system, a mental tool.  Though we usually feel like our actions arise after a thinking process– think before you act– in a sense, thinking itself is an action.  Educators already treat thought as action; wrong answer = wrong thought.  In many ways, we learn to think the same way we learn everything else– through trial and error and reinforcement.  And much like motor learning, such as ping-pong, our thinking can become as automatic and habitual as our backhand.  Again, this is precisely what education often tries to achieve– an automaticity in thought tools, such as how to solve integrals or recognizing chemical structures.  In this sense, thought does not precede action, thought is action.

Video games offer the opportunity to map thought-as-action onto literal actions in a virtual world.  Through this dual mapping, we facilitate abstract learning by yoking it to procedural, action learning to harness the millions of years of evolution that has designed brains, first and foremost, to act not think.  In more psychological terms, we are dually mapping learning onto an explicit, declarative learning system and, simultaneously onto an implicit, procedural learning system allowing the two to strengthen and reinforce each other.

What sort of actions in a video game can map onto cognitive skills and functions?  The key to understanding dual mapping is to separate the literal and the virtual, which corresponds to sensorimotor processing and cognitive representation, respectively.  A video game, at its most fundamental requires the player take in sensory stimuli– ie., the screen– and respond with a motor output, pressing a key or moving the joystick.  In Mario Bros, when a player ‘jumps’, they are really just pushing a button.  The same with stabbing a dragon.  And steering a race car around a curve– all these actions are literally simple motor movements of the hand and fingers that cause things on the screen to change which, through a combination of the programmers art and our cognitive apparatus, we come to think of as jumping, stabbing and steering.  The same holds true for the sensory stimuli.  Faced with the evil gate-keeping dragon, we cognitively perceive a dragon appearing in our path.  But the dragon could be replaced with a giant lizard, or a big red block or an enormous, teeth baring beach ball and our required response– stab it!– could be identical.  In the game, the characteristics of the sensory stimuli– like a dragon versus a beach ball– has nothing to do with the programmed requirement that the player tap the left button in response.  All the jumping, stabbing and steering we do in response to all the cars, dragons and growling beach balls is not intrinsic to the mechanics of the game, but an overlaid cognitive layer that assigns meaning to both sensory stimuli and our motor responses.  And these meanings can be anything.  The opportunity, then, is to take this simple sensorimotor learning– which our brain is very good at– and to map it onto a virtual world of cognitive representation that teaches chemistry or math instead of dragons and beach balls.

For the moment, I am going to set aside the mapping and look at just the literal, sensorimotor part of the learning.  It is in this stripped down view that the learning opportunities become more apparent.  For discussion, I have listed some basic, or elemental types of actions in video games in the table below.

ElementalActions

The table is not intended as a strict typology but just as some examples. By elemental, I refer to actions that do not involve any tools, like a sword or potions. I’ll address tool-based actions in a subsequent post.  Even simple actions like moving require both a motor and cognitive component: figuring out where to move, when and how fast, or in what manner, such as walk or jump.   Other basic actions include positioning and targeting, where the cognitive component can range from the simple, like positioning oneself under a falling banana, to the complex, like identifying the weak point in a configuration in Angry Birds.  Sequencing also can range from simple motor sequences, such as learning to hop across a series of ledges in a particular order, to obviously cognitive things like learning the order in which a series of doors need to be opened in order to solve a puzzle.

All of these elemental actions engage a fundamental process of adapting to an environment.  First the player has to learn which stimuli in the video game environment are actionable: which things can I interact with and cause something to happen?  Second, the player has to learn the sort of responses can be applied to these different stimuli.  Finally, the player has to learn the various consequences associated with each action applied to different stimuli.  In doing this, the player shapes attention processes– what should I pay attention to?  They begin to recognize patterns, discern cause and effect and make predictions– when this occurs, that is likely to happen.  As their learning deepens, they begin to make discriminations– only the red dragon is dangerous, the green ones give gifts– as well as learn categories– grouping together stimuli that share properties in common.  As the player acquires a deeper cognitive understanding of the video game environment and starts to learn the effects of their actions, they can begin to acquire sequences of behavior and through prediction, begin to plan series of actions, reflecting a well-developed mental representation of the virtual world. Finally, with this cognitive framework in place, they can begin to develop decision-making skills, assessing risk, uncertainty, and the relative cost-benefit ratio of different actions and choices.

We might be inclined to think of things like chemistry and calculus as representing ‘real’ cognitive learning while dismissing the cognitive component in video games as trivial. From the point of view of neural processing, however, I’m not convinced of the difference. In fact, one might make a case that the video game represents more complex, multi-dimensional learning.  Before discussing how to leverage this learning to map onto things like chemistry and math, in the next post I will talk about tool-based actions in video games.

Posted by on March 31, 2013 in education, learning mechanisms and tagged , , .

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March 14, 2013

Pathways of discovery: games as models of learning

The most successful video games are master teachers.  The main problem being that what they teach is generally not very helpful and a poor substitute for the math, science and language skills we might wish for our future generations– notwithstanding reports of gamers being in high demand as trainees to man military drones.  We can probably do better.  Rather than applying learning theory to video games, I will take video games as models of learning par excellence.

The expression ‘pathways of discovery sounds like a marketing slogan, but I mean the expression literally: a sequence of steps that gets someone from point A to point B– manifest in an educational game, these steps are experiential actions taken by a student/player toward some goal– embodied as movement in a virtual landscape.  This literal movement in the game parallels movement through knowledge/skill space in some domain of learning. Really, I mean pathways.

I will use two popular games– The Legend of Zelda and Mario Brothers– as examples.  In Zelda, the player has to navigate through a fantasy world that essentially a maze.  On their journey, they encounter obstacles and puzzles but pick up useful information and objects that will subsequently aid their quest.  As they acquire knowledge and tools, they typically have to discover a required order or sequence to effectively use what they find.  Piece of information C doesn’t make any sense until the player first discovers piece of information A and B.  In MB, the players learn skills on how to jump, dodge and pounce and how to use elements in the virtual environment to their advantage.  These skills also usually comprise a set of sequences: first jump on this ledge, then do this then do that.

On the surface, the types of learning in these two games appear different. In Zelda, the player acquires knowledge that guides their efforts.  In MB, the player acquires skills they deploy again and again in different circumstances.  This is analogous to two types of learning and memory, declarative and procedural.  Declarative memory is memory for facts, concepts, ideas, like when Columbus discovered America and how atoms are structured.  Procedural learning/memory is learning how to do things, like riding a bike or playing a clarinet.   In this sense, these two games illustrate how games can induce two different kinds of learning.

The distinction between these types of learning may be overstated. In particular, declarative memory/knowledge creates a framework that shapes how we perceive and respond to the world.  In a sense, that framework becomes a procedure for seeing and thinking.  Most people have probably had an experience where some learning ‘clicks’ causing ‘everything in their head’ to shift. Suddenly they see a particular problem entirely differently, together with new options for approaching tackling it.  Applying that ‘way of seeing’ again and again is a skill.  Conversely, a skill can be a way of understanding.  Despite its emphasis on proofs and logic, much of mathematics is about applying procedures for manipulating quantities and symbols.  These procedures become so integrated with one’s ‘understanding’ of math as to become effectively indistinguishable from ‘math as declarative learning.’  In short, the line between ‘knowing’ and ‘doing’ is thin.

One of the beneficial aspects of video games is that regardless of the type of knowledge or skill they teach, they are always about doing.  In both Zelda and Mario, players acquire a repertoire of responses to stimuli, learning when to apply what response.  And in both cases, responses generally need to be sequenced.  In this sense, learning is always about skill, the ability to do and perform.  That is, how to do is the heart of education: concepts, facts, principles . . . ‘understanding’ generally . . .  is meaningless in the absence of an ability to do something with that knowledge. Well-designed games inherently focus on doing.  One definition of ‘understanding’,  might be the ability to generalize a skill to new problems and situations . . . that is, apply a principle or concept to a new problem.

This is what video games can potentially do.  Allow a player to pick up pieces of knowledge and skill, learn to put these together into a sequence of responses appropriate to different situations and then re-apply that knowledge/skill to more and more situations, deepening their understanding through use.  Within the video game, a student’s movement through a virtual landscape toward some goal can parallel knowledge and skill acquisition within a specific content domain, from history to chemistry to algebra, providing pathways of discovery and activities of skill building.

There is a pitfall, a deep dark swamp to be avoided: apps that focus only on practice and drill.  Procedural learning– learning how to do– is not the same as practice.  You can drill declarative learning, like memorizing state capitals and dates.  And you can teach procedures, like solving integrals or modeling chemical reactions.  Procedural learning is learning how to do something.  Games that focus on practice/drill do not provide pathways of discovery and learning.  They presuppose teaching has already occurred elsewhere and that what is needed now is simply repetition.   Although such games may have their place, they are antithetical to the use of games in education proposed here built around creating motivation (prior posts) and providing pathways of discovery and knowledge/skill building.

In the next post, I will explore a typology of the types of skill building pathways found in many video games and consider how each provides a useful tool for embedding educational content into a game.

Posted by on March 14, 2013 in education, learning mechanisms and tagged , .

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March 5, 2013

A trip to the app store: in search of pathways of discovery

I took a trip to the app store and window-shopped educational apps.  There’s good news and bad news.  You know this educational gap people complain about where the United States is falling behind other developed countries in math and science? Well, if the availability of educationally oriented video games on the AppStore is any indication, we are going to come out on top when it comes to addition, subtraction, multiplication, naming colors, states, flags and identifying animal sounds. We are going to kick ass.  We’ll probably do pretty well at word searches too. The bad news: there appears to be a scarcity of educational games oriented beyond preschool and early elementary education.

Certainly early education is important and lays a foundation for later learning.  No argument there.  But let’s face it, naming colors and animal sounds or learning times tables is probably not our fundamental difficulty.  The challenge arises in subsequent years: getting a greater proportion of students to understand algebra, calculus, chemistry and physics.  And we should not be techno-centric.  An understanding of the humanities and social sciences and a dash competence with written language couldn’t hurt anything either.  The challenge lies in advanced skills.

There are plenty of opinions on why education is ‘failing’– ranging from arguments about what and how we teach, to arguments about how we fund and organize schools– but the basic challenge remains the same as always: to engage students with a subject so that they acquire skills rather than sleep-walk through a course mindlessly parroting back answers to pass a test.  Can games help with this?

I have talked about how games can potentially be used to engage non-motivated players (aka students) and, over time, create motivation.  The development of skills represents a critical component of that process.  Although I have talked about approaches to creating motivation, I have not discussed pathways to skill acquisition, which will be the focus of the next few posts.

Surveying educational apps on the app store, most do not use the game format to introduce pedagogical innovations.   Instead, there is a surprising lack of imagination, with most games being no more than electronic versions of ‘the same old thing.’  For example, there are a surprising number of ‘flash card’ games, numerous electronic ‘hangman’, plenty of word searches:  the same tools that have been conventionally used for decades dressed up in an electronic game format.

Most of these educational games do not use the game to teach or develop skills but settle for practicing skills.  That is, they don’t teach at all: they drill.  Of course, sometimes rote learning is appropriate. Yellow is yellow is yellow. But even apps tackling more advanced subjects fail to teach.  I encountered an algebra app that simply puts up algebraic equations for the player to solve and awards points when they are correct; absolutely zero teaching– and an exceptionally tedious ‘game’.  I found an app about the periodic table where you click on an element and up pops an animated cartoon ‘element’ bouncing across the screen with a summary of the same information on the periodic table.  The animated atoms are merely cute, illustrating no meaningful information (ie., electrons, protons, etc.).  There is no way to combine elements and see how they do or do not go together, why/why not, and what they might form when they do bond.  In short, there is absolutely no teaching (though to be fair, this isn’t really a game at all but more of a pointless reference app).

So what is missing? The pathways of discovery.  Video games fundamentally provide a problem to be solved.  Skill at the game means learning ways to solve the problem better and more efficiently, whether this be killing a dragon or finding the Golden Chalice to save the Princess.  Even games of chance require a player to develop an implicit understanding of the probabilities involved to make better choices.  In an educational game, the problems in the game are designed so that the skills to be acquired happen to be, for example, algebra or chemistry.  This does not mean pitting the player against a dragon to solve a quadratic equation in order to cross the bridge– there’s no teaching there, just boring practice.  And if the player doesn’t understand quadratics in the first place, they simply become more frustrated.  An educational game, like the Legend of Zelda, has to contain pathways of discovery that, through twists and turns, trial and error, eventually lead to winning the game. And like Zelda, these discoveries often have an order and build upon each other.  These pathways are the curriculum of the game and their discovery the acquisition of skills. And winning is effectively learning, not merely practice.  In the next post, I will develop the idea of pathways of learning, eventually relating this back to Mr. Basal Ganglia, reinforcement learning and motivation.  For skill learning, it always comes back to the basal ganglia (never mind all that wriggly cortical stuff).

PS- I should note, this was not a comprehensive survey of the state of the educational game industry, but a casual stroll through the Apple app store. I am in search of excellent educational games and companies, particularly targeting post-elementary school curricula. If you know of something, let me know.

Posted by on March 5, 2013 in education, science games and tagged , , .

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February 28, 2013

Inducing habit and fabricating motivation: from Farmville to algebra

In several previous posts, I talk about the idea of creating motivation (here, here and here).  If a player comes to a game expecting to like it, then the motivation is already there and the designer needs to make an exciting game that meets players’ expectations.

It is an altogether different challenge to engage players that come to a game with little to no interest and no (or negative) expectations.  Games that engage people initially lacking motivation can be profitable, as companies like Zynga achieve tens of millions of monthly average users, very few of whom ‘eagerly awaited the release of the game.’  Creating motivation where it is lacking is critical challenge in educational or training games.

The idea that presenting algebra in a game format will miraculously make algebra fun and increase math literacy is optimistic.  Algebra is still hard.  And if a student isn’t interested, it is still boring.  And no amount of animation, dragons, or experience points is going to make solving a quadratic equation easier.  If saving the princess means going through swamps of integrals and derivatives in the land of calculus, “that b* is history.”  I am skeptical that teaching algebra using a game format will improve math education by making it fun.  Instead, a game format provides a scaffolding to create motivation where there was none before.  But to reiterate: the motivation does not arise because suddenly a game has made algebra fun.

Motivation arises when the stimuli a person encounters become relevant and, I would argue, associated with a set of skills that empower them to respond to those stimuli to their advantage.  To an important extent, fun arises from competence.  So how do you engage a non-motivated player to develop competence and acquire motivation?  You have to do it incrementally.  And in order to do it incrementally, you need to achieve habit and persistence, where the game becomes a part of a person’s daily routine.  The techniques used by Zynga in the various *ville games illustrate several critical components of inducing habitual game play and creating motivation where there was none before:

summary

1) limit time commitment.  In a traditional view, a game that engrosses the player so that they become obsessed and find it difficult to stop playing, is considered a success.  This inducement of obsessive game play, however, is a detriment to forming habit.

(a) First, as play extends, players become gradually sated and tired, reducing the reward value of continued play.  Like a child allowed to eat all the cake he/she wants, it becomes less valuable, even aversive, after, say the 8th piece.  Enough cake binges and the kid may come to not like cake at all.  Long, engrossing sessions of gameplay may actually decrease the future value of the game, working against the development of persistent, daily habitual gameplay.

(b) Second, obsessive game play can induce player regret.  “Wow, I can’t believe how much time I wasted on this game.”

So a game that engrosses and exhausts a player actually ends on a reinforcement down note: less rewarding and more aversive– effectively decreasing the likelihood of repeating the performance tomorrow.  In contrast, a game that offers 15 minutes of fun does not create player regret, ends with a positive reward value– “that was amusing”– and provides an incentive rather than disincentive for returning to the game tomorrow.  Not ending with satiety, the player may actually anticipate returning to the game, without fear of regret.

2) encourage consistent engagement with the game.  If our objective is persistent, regular play across a extended periods of time (weeks, months), we need to allow and encourage players to determine how the game can fit into their daily routine.  If the required time commitment for a single session is minimal, it is easy to make demands that players check in regularly, allowing them to determine the frequency of play.  Farmville provides an illustration of this.  Players can check in every two hours, every few hours or daily, and make their crop selections to conform to their schedule.  The ‘demand’ that players play regularly is part of the monetization scheme: keep people coming back.  But monetization is one use of getting players to integrate gameplay into their schedule in a regular way.  This regularity of behavior establishes the groundwork for developing habit.

3) keep the game in the players mind.  For the brief period I played Farmville, I would find ‘check my crops’ mysteriously appear on my mental list of things to do in the morning.  Ridiculous, yes.  Nonetheless, the idea that ‘check crops’ makes it onto my mental list of tasks indicates an important outcome of limiting time commitment and inducing regular game play:  the game finds a place in players’ minds when they are not playing.  Unless you are temporarily obsessed with a game, in general you don’t think about it until you have free time and ask yourself, ‘hey, what should I do?’  Then, maybe, game x comes to mind.  In Farmville, the game essentially makes its way onto your to do list.  It is not a leisure option, but something you think about and remember to do.  This arises from making the incentives easy to obtain (ie., as above) while adding disincentives for forgetting:  punishing players when they forget.  In Farmville, if you ignore the game too long, your crops wither.  In the future, you (a) tend to remember and (b) select crops with turnaround times appropriate to your schedule.

4) develop incremental player investment.  Traditionally we think of the value of a game arising from its intrinsic fun.  An alternative is to think of the value as arising from a players’ investment in it.  It’s similar to the ‘waiting for the bus phenomena.’  When the bus is delayed, we tend to become increasingly annoyed and, perhaps, consider hailing a cab.  But then you think that as soon as you climb in the cab, the bus will arrive and you have wasted both your time waiting for the bus and your money paying for a cab.  So you wait longer.  And the longer you wait, though you increasingly fantasize about a cab, the more loath you become to risk the double waste.  We tend to protect our investments.  By analogy, after several months of playing Farmville, it may gradually dawn on you that, well, this is really a stupid game.  However, when you look at your vast farm, it feels like an accomplishment, stupid or not, and you experience just a little reluctance to quit . . . maybe not just yet.  In some games, such as educational games, an incremental investment may provide motivation to continue.  I hate algebra and really think this game is stupid, but look at what I have done: maybe I’ll continue just a bit longer

5) low demand, high reward.

If players are not motivated to play a game, a designer cannot make significant demands upon them up front.  You have to win them over, bit by bit, creating incremental investment.

(a) simplicity.  Many games have cluttered interfaces, lots of indicators, things to figure out, etc.  For someone already interested in the game, this is all fun stuff to figure out.  To everyone else, it is a tedious bother.  Many games ask people to figure out the game before playing.  For a non-motivated player, this is asking way too much.  An alternative would be to develop a minimalist interface with as little clutter and complication as possible, presenting only those things players can interact with and obtain reward– that is, things to click where something novel and interesting happens.   Instead of a daunting screen full of indicators and meters, hide these.  Mask the artificial ‘game’ components and reveal them in the course of player’s exploring and interacting with the game . . . when they become necessary. Reduce clutter, both on-screen and in-head.  This doesn’t mean these critical elements of a game should be eliminated, only that they should be introduced incrementally in response to player activity at a time when the player is likely to go ‘ah, I see,’ effectively making learning about the game rewarding rather than burdensome.

(b) orient the game toward discovery.  Typical games are presented to players as follows: ‘here’s the game, the objective, the rules, the meters, etc . . . now go to it.’  Designers have become brilliant at replacing a bunch of reading with interactive tutorials that walk a player through the game.  What is proposed here is taking this to the extreme: even a tutorial, to a non-motivated player, is instruction– boring and demanding.  Instead, a game can be designed such that players ‘uncover’ or discover the game and its elements as they go, without starting with an overall understanding of how the game works.  Exploration and discovery is intrinsically rewarding and tends to generate interest and investment.  Of course, it has to be fun.  I am not suggesting a blank screen with a bunch of click points that slowly reveal a game.  Rather, whatever game we design, we reduce it initially to its bare essentials—what is fun for players to interact with—and reveal the complexities of the game and allow it to unfold as they do this.  Again, the Zynga games illustrate this quite well.  You can begin playing and discovering, without any notion of how the game works: you start clicking and figure it out.  In contrast, some games, for example the online Oregon Trail, demand players spend some time figuring them out before starting to play, an obstacle to engaging the non-motivated player.

This may sound like ‘tricking’ someone into liking a game. Perhaps.  But the fundamental idea behind many educationally oriented games amounts to ‘tricking’ students into liking academic work by ‘making it fun.’  The argument here is that for most students, algebra is not– and will never be— intrinsically fun.  To make learning algebra fun requires going deeper than framing educational content in a game format.  Rather, one needs to recognize that ‘fun’ and ‘motivation’ arise when things in the world around us, or in a video game, become meaningful to us and actionable . . . things we can respond to successfully.  Competence requires persistence, practice and investment.  Thus, when you start with a lack of motivation and hope to engage someone in something that is, at its core probably not fun, at least at first . . . the single most important thing is to create persistence and engagement.  From this, skill will gradually develop and with it, motivation.

The five principles briefly described above go beyond the idea of making algebra palatable by making it fun and instead represent a strategy for inducing incremental, habitual gameplay that creates the conditions under which a player can persist in skill development and gradually acquire a motivational structure that supports learning algebra.  You don’t create motivation by making algebra fun; you make algebra fun by creating motivation, even and especially when it is initially absent.  That is, games provide motivational scaffolding that gradually creates motivation, not by disguising hard work as fun and games. . . but by transforming the hard work of skill acquisition into the fun of success: an age old educational principle that games allow us to implement with profound sophistication and individualization–   as long as we recognize what it is about games in education that is really important.

Posted by on February 28, 2013 in design, education, motivation, Uncategorized and tagged , , , , .

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January 16, 2013

Is randomness antithetical to skill-based games or a crucial element of mastery

Daniel Cook recently posted an interesting piece on the incorporation of random noise into skill-based games.  He starts with the distinction between games of skill and games of chance and suggests that introducing noise– an element of randomness– into games of skill can potentially enhance their value. In particular, he suggests that one can develop skills at ‘mastering randomness.’  He provides a theoretical framework for thinking about randomness and noise in games, identifies different types of noise, and talks about considerations to keep in mind in terms of player skill acquisition.

At its root, he argues that players can develop a statistical model of uncertainty in a game and select actions based on that model.  So even in games of chance, there are probabilities associated with different random events and a good player learns those probabilities and makes game decisions accordingly.

What is more interesting is the introduction of a degree of randomness in games of skill.  Some of the comments to his piece objected to the introduction of an element of chance into a game of skill, arguing that a skill should be a skill and introducing probability decreases the value of developing a skill in the first place.  To an extent, this is a reasonable objection.  If the acquisition and deployment of a skill– let’s say killing the monster– is rendered pointless by an invisible dice roll that ‘cancels’ out the skill, then the game of skill is not really a game of skill, or is a game of skill only with a certain probability.

However, rarely is a skill simply a matter of emitting a precise learned action at the precise right time. There is almost always some degree of uncertainty and variance.  When the ping-pong ball comes flying at me from the other side, I can assess its velocity, trajectory and spin, but my assessment will always be within a range, not exact.  Subtle differences from one time to the next in, for example, the spin on the ball. . . which may be beyond my ability to directly assess and detect. . . will require subtle differences in how I hit the ball.    So while mastering ping pong requires the development of specific skills, there is always a degree of uncertainty in applying these skills to a specific instance of the ball coming at you.  In this sense, mastery of ping-pong requires not only the development of skills such as a good backhand, but also a model of the success of these different skills under different circumstances.  The best players maximally reduce uncertainty as to which circumstances require which subtle variation in response.  In short, even with the most highly skilled ping-pong players, there is an element of guessing, but for masters of the game it is a very refined guessing within a very narrow range of uncertainty.

So in this sense, introducing a degree of uncertainty or randomness into skill based games can make these games more like real life.  But why? As one comment pointed out, being like real-life is not a requirement for excellent games.  I would argue that building games where killing the dragon is more analogous to ping-pong– that is, where the development of skill requires working within some space of uncertainty – provides a better mechanism for titrating the difficulty and challenge of the game for players and, in general, is likely to generate more engaging games across a broader spectrum of players.

Let’s take killing the dragon as an example.  As players acquire skill, how do designers make dragon-killing more interesting and keep the game challenging? You could make it more difficult by requiring increasingly more precise blows, by requiring faster actions or by requiring the player to kill more than one dragon at a time.  At its core though, this amounts to acquiring one repetitive action and being able to perform it very well and very fast. And there is a limit to how fast and accurate it can be performed. . . both a limit for individual players (some will simply have slower processing/reaction times) as well as an overall average limit for human beings.  The player with an intrinsically faster reaction time will do better than one with an intrinsically slower reaction time, having little to do with skill acquisition per se.

When you introduce a little noise or random variation to killing the monster, you make the skill acquisition a little more like ping-pong.  Specifically, instead of ‘skill’ consisting of performing the exact same movement ever faster and more precisely– and a game that requires ever faster and more precise execution– the skill represents a range of variations on a action that generates a range of feedback.  So the number of times the monster has to be stabbed may vary, the precision of the required targeting may vary, where exactly ‘bullseye’ is may vary and so on. The skill being acquired is not the execution of a single maneuver, but a range of responses that always includes a degree of uncertainty on which exact response would be most effective.  Though some players may find the need to pound the controller ever faster to be exhilarating, undoubtedly there are some that may find this tedious.  With the introduction of noise, gameplay can be made more interesting and challenging independent of the precision and speed of a single, specific maneuver.  Even beginners tend to enjoy ping-pong.

The challenge with introducing noise is to provide in-game information that players can use to begin to discern or model that noise.  A ping-pong player may learn that when he observes his opponent smack the ball with sharp flick of the wrist, the on-coming ball is likely to have a severe spin. A game needs to provide information players can use to model uncertainty; that is, what information can I use to predict likely outcomes, what Dan Cook called learning variables: things that have some correlation with the probability distribution the players are trying to figure out. Such variables can be highly artificial and contrived, such as the color of the dragon reflecting the mean number of stabs required to kill it.  However, the learning variables can be more naturalistic as well. For example, if you have a dragon that moves and takes on different body positions during the confrontation with the player, the probabilities associated with different aspects of a successful kill could vary with the dragons body position.  For example, when the dragon’s arm is extended from his body, the target region effective for striking could enlarge and/or shift.  Alternatively, the target region could shift with each subsequent blow.

Of course, these linkages do not require noise or probability.  If the dragon’s arm is extended, only one blow will kill him.  If his arm is at his side, it will require 3.  A simple rule that players can learn.  Proficiency then requires applying this rule ever faster and more efficiently.  When noise is introduced, however, it makes it more challenging to discern the rule. Say for example that for each blow landed on the dragon, whether the dragon dies or not is drawn from a probability distribution (eg., poisson).  With each successive blow, the probability changes; however, a blow delivered while the dragon’s arm is extended increases the probability of death on the subsequent blow to a greater extent than one delivered with the arm down.  The skill here, then, becomes how to strategically battle the dragon, not simply increased precision and skill of a single movement.  This allows players to learn and improve at the game independently of just doing a single movement ever faster.  It also introduces the possibility that a player who relies on speed may initially do better than a slower player.  However, as the demands of the game increase, the slower player may excel through having a better understanding of how to effectively engage the game, much as a slower moving tennis player may defeat a quicker opponent by virtue of having greater control over the ball.

Daniel Cook’s discussion of introducing randomness into games of skill is not only a provocative idea, but prompts reflection on what we mean by ‘skill’ in the first place.   Insofar as ‘skill’ is thought of as doing something fast and precise, noise degrades skilled gameplay. However, if ‘skill’ is a matter of doing the right thing at the right time in a field of uncertainty, perhaps together with doing that faster and more efficiently, then introducing noise into games of skill may actually enhance the degree to which such games are, actually, games of skill instead of games of speed and motor repetition.

Posted by on January 16, 2013 in Uncategorized.

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October 10, 2012

TuneOut! New iPhone/iPad app by Lola Productions

So it’s not a game exactly, but we recently released our first app, ‘we’ being me and my business associates at Lola Productions.  I’m writing about it here mostly for self-promotion, but there is a neuroscience perspective on its design.

TuneOut! is a very simple entertainment app: you select songs from your own music library and then tap along; your taps generate drum and percussion sounds along with visuals.  Presently, there are four sound packages included, a djembe sound, a traditional drum-set sound package, a latin package and one we call ‘bag of goodies’ which is just an assortment of percussive sounds, including a bang on a pot sound.

The app is meant to tap into that natural tendency people have to engage music they are listening to.  People like to sing or hum along, play air guitar or, often, tap and drum their fingers. . . on their pant legs, the steering wheel, their desk. . . or here, on the iPhone.  It’s a way to get involved and participate in the music.  There is no game to it. . . no points, or score, or competitive aspect . . . just the sheer joy of drumming along with your favorite music.

In designing the app, we avoided images of drums and percussive instruments.  Being confronted with a set of drums, even virtual ones on an iPhone, creates a pressure of ‘knowing what to do.’  Which drum do I hit. . . I don’t know how to play drums.  Having virtual instruments can be inhibiting and make a person self-conscious about their ‘performance.’  We opted instead for no visual images of the drums, just a blank screen that responds with a sound and a simple visual when you tap.  In this way, the challenge of facing a set of drums and not knowing what to do is replaced by discovery.  That is, you just tap.  You tap according to the music and your rhythmic response to it and as you tap you discover different sounds located in different regions of the screen.  In this way, instead of being faced with a snare drum and cymbal and trying to play a basic rock rhythm, you start to learn that when you want a cymbal sound, you tap over ‘here’ and when you want a cowbell, you tap over ‘there.’  As you learn these locations, they come to represent a learned repertoire of movements and sounds that you deploy to fit the sounds and rhythms you are creating.

Anyway.  If you happen to read this, try it.  The first 10,000 downloads are free.  If you like it, let others know. If you don’t like it, let us know and we’ll make it better.

Posted by on October 10, 2012 in Uncategorized.

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