Be the player, not the played: Advice for novice learners 

What chess really teaches us about the path to mastery

Play is so often shunned in formal learning environments. The standard maths curriculum, for instance, is like a purgatory for student-novices; before they are given the chance to do mathematics proper, they have to consume a years-long diet of facts and procedures. ‘Play’ is the bit that comes after (if at all), once these supposed prerequisites are embedded.

Arguments against play centre on the idea that novices think differently to experts. There’s a large extent to which this is true. In a series of studies half a century ago, subjects were asked to memorise and recall chess board configurations. Two key findings emerged:

  • If the arrangements represent plausible game-like scenarios, a subject’s recall improves in proportion to their level of chess expertise.1 

  • If the arrangements are random, then chess expertise has no predictive power for recalling the pieces.2

Expert chess players literally see the board differently to novices. More experienced players draw on their knowledge of the game to compress the arrangements of the board into more manageable chunks. For instance, if a bishop has pinned a knight against the queen; the three pieces can be seen as a single ‘pin’ rather than three separate positions. This compressive advantage was erased in the second experiment, where the configurations lost their structure. 

Two configurations. One represents an actual game scenario, the other is randomised. Chess enthusiasts will know the difference, and will exercise better recall of the authentic configuration (but not the randomised one). Source.

It’s this insight - that experts have a greater volume and variety of knowledge representations - that is used to justify cramming novice learners with knowledge. The thinking goes that novices can only be expected to perform like experts once they are armed with the same stores of knowledge. Otherwise they’ll suffer from ‘cognitive overload’ as they strive to piece together the most basic moves.

How memories are made

There’s no denying that knowledge consumption - even hardcore memorisation - plays a prominent role in many intellectual activities. Chess players don’t shy away from this fact. Bobby Fischer even went as far as to suggest that Chess boils down to memorising opening plays. Magnus Carlsen practices retrieval blindfolded. 

Yet in any discipline, when you trace the knowledge and skills of experts back to their humbler origins, you’ll rarely find that they were developed in a vacuum. The reason that pins, or elaborate openings, are familiar to more proficient chess players isn’t simply that they’ve consumed them through direct instruction. They will have tried out those moves on the board and, through hours of real or simulated gameplay, seen how they connect to other board positions. You don’t master chess by learning static configurations. You have to see the whole board, as a moving entity.

This is why blindly mimicking the moves of experts won’t get you very far. As Garry Kasparov explains in How Life Imitates Chess:

Players, even club amateurs, dedicate hours to studying and memorising the lines of their preferred openings. This knowledge is invaluable, but it can also be a trap...Rote memorisation, however prodigious, is useless without understanding. At some point, he’ll reach the end of his memory’s rope and be without a premade fix in a position he doesn’t really understand.

The trappings of rote learning are easily avoided if we tie knowledge with understanding. The connective tissue is play. This isn’t some whimsical ploy to make learning more fun. Play is a vital companion on the road to mastery. Artificial Intelligence, which has had Chess in its crosshairs from its very beginnings, has shown us as much. 

Kasparov famously fell victim to IBM’s Deep Blue, which was hard-coded upfront with millions of chess moves scripted by expert human players. So far so good for the knowledge-only advocates. But Deep Blue wouldn’t stand a chance against the machine learning programs of today. DeepMind’s AlphaZero (which has also mastered other high-profile games including the considerably more complex Go) relies on no inputs from human chess players and instead teaches itself strategies through repeated self-play. AlphaZero thinks several moves ahead and identifies subtle gameplay patterns that lead to success and failure. Its performances are as elegant as they are devastatingly powerful, suggesting a mix of intuition and reasoning. And it is all derived from elaborate trial-and-error mechanisms. Learning through play, not self-contained rules.

How we learn anything

Novices may not think like experts but they only level up by putting some skin in the game. In Bobby Fischer Teaches Chess (thought to be the best-selling chess book of all time), even the staunch proponent of memorisation guided his reader through carefully sequenced chess problems, starting with check-mates and working towards more advanced strategies. As early as the preface, he advises beginners to take grasp of a chess board and get stuck in. Fischer knew that play is the most active form of learning, and it makes the effort of memorisation that much easier by situating each move within a rich, interconnected scheme of knowledge. 

Isn’t this how we learn just about anything? My card magic books put me in the role of magician-in-making right from the off, guiding me through progressively more challenging effects. I can play a new board game within minutes of scanning the rulebook. Video games thrust me into action without a moment’s notice. Even my daughter, an aspiring footballer at all of twenty-six months, is permitted to take shots at goal during her Little Kickers sessions. Play is an inescapable part of the novice’s journey.

Play also orients us as learners. Win, lose or stalemate, engaging in authentic tasks elevates us from passive consumers to active participants. It’s only by sampling the real deal - even if in a simple, simulated context - that we develop our identities as learners; the gratification of a well-executed move, the disappointment of a failed strategy. 

In informal learning contexts, this all seems so obvious. Yet the ‘knowledge-first, play-later’ rationale remains pervasive in education. It is designed for compliance; keeping students in their place as rule-absorbers. Because God forbid they should stray from rigidly specified knowledge outcomes.

In school mathematics, we rarely get to play. Where most disciplines lend themselves to miniaturisations that simulate the actual thing to be mastered (such as check-mate puzzles for Chess), not enough thought goes into what authentic mathematical experiences entail. If we put actual mathematical thinking at the heart of the curriculum - reasoning, problem solving, exploration, pattern-seeking, questioning and more - whole swathes of the curriculum would face an existential crisis. Long division and trigonometric identities would make way for more playful, open mathematical experiences - the kinds currently found on the fringes of the subject, branded as ‘recreational’. More on this another time.

Novices need guidance, to be sure. They need a supply of problems that lie at the edge of their abilities and that nudge them towards ever-increasing levels of expertise. They need to learn worked examples and commit a host of items to memory. But there’s no substitute for the real thing. Learning without play is oxymoronic.

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1

de Groot, A.D., ‘Het denken van de schaker‘, PhD dissertation, 1946. Translated into Thought and choice in chess, The Hague, Mouton Publishers, 1965.

2

Chase W.G. and Simon H.A., ‘Perception in chess’, Cognitive Psychology, vol. 4, 1973, pp. 55-81.