by Jeroen Bouterse
I know teachers who imagine the tune is what they have on repeat in hell, but I myself am strongly pro-Kahoot. If (like me) you were born too early to have your own school experience center around a large screen, and (unlike me) you have one of those boring non-teaching jobs, a brief explanation is in order. Kahoot is an app that lets you ask multiple-choice questions on your class screen, and have students answer them on their own devices to earn points. With its bright colors and some other bells and whistles, it hits a sweet spot in the teenage brain that magically makes it care about getting mathematical terminology right. It’s the best thing.
Or perhaps the best thing is actually Blooket. A self-paced quiz app, where getting questions right can give you the edge in a larger game in which you are trying to catch fish or steal crypto from your classmates. To get a genuine sense of what playing a Blooket is like, you will have to wait until Generation Alpha starts producing its own great literature. My own grasp of the different game modes is primarily inductive, based on the yelps and cries of my students rather than on first-person experience. I agree with my colleagues that ‘Café’ works well, but only if you can give enough time. Else, students complain their investments don’t pay off; they get to upgrade different breakfast ingredients to higher levels, you see, in order to make more money.
These apps embrace an important fact about school life, namely that students and teachers don’t want the same things at the same time. Though in the end we are all interested in demonstrating that learning has happened, some teenagers apply a steeper discount function to that outcome than I do. Gamifying ‘kahootable’ skills is one way of harmonizing our short-term aims.
Rule-based conflict
Absorbing in a collective activity the variable, distinct and potentially conflicting impulses, understandings, and goals of different participants is a nice social accomplishment, and games help achieve it without coercion. In his 1938 cultural-historical study Homo Ludens, Johan Huizinga, preparing to analyze the ways in which major cultural phenomena such as law, philosophy, and art, are rooted in our human (and animal) ‘playful’ tendencies, treats this as a first essential feature of play: “it is free, it is freedom” (35).
Relatedly, Huizinga says, play is distinct from ordinary life: it temporarily creates an activity which is bound in time and in its own space (in a broad sense of the word space). Within this space, it imposes an order and a pattern of rules. In Huizinga’s analysis, this results in a strong analogy – and indeed, genealogical relation – between play and social institutions such as legal proceedings or religious rituals.
Politics is another area to which Huizinga’s perspective – understanding culture and society in terms of play – could conceivably stretch. Here the situation is ambiguous. On the one hand, politics is clearly serious; it does not stop, as a game does. Also, though Huizinga saw the politics of his time as dominated by “the temperament of adolescent lads and the wisdom of the boy’s club”, this did not make it playful. On the contrary:
“the absence of a sense of humor, excitement about a word, far-reaching negativity and intolerance against non-group-members, the boundless exaggeration in praise and blame, the openness to any illusion that flatters self-love […]” (237),
all of these were puerile, but they were not typical of children playing. Children, as Huizinga observed and as any teacher who has had to invigilate an overcrowded playground can attest, are not ‘childish’ while they are playing; they behave in a childish way only once they get bored or don’t know what game to play.
On the other hand, games as a way of processing different interests, and establishing winners and losers in an orderly manner, clearly bear a resemblance to what happens in politics. Indeed, Huizinga sees definite game-elements in both national and international politics: British Parliamentary debates, American presidential elections, and the principle that promises and treaties mean something in the relation between sovereign states; in all these, politics is treated as a sphere with rules to be respected.
Philosopher Bernard Williams later ventured that the British parliamentary tradition as simultaneously “adversarial and rule-governed” rested on a world-view in which the world itself was adversarial and rule-governed.[1] It is easy to see (and Williams of course understood) that such rule-governed antagonism is in fact a special case requiring its own space. We are also reminded, in our time as in Huizinga’s, that such a space is easier to maintain if no single player has the power to change or ignore the rules at will.
If the world itself is not governed by norms, it is tempting to conclude it is adversarial only. Huizinga warned against this perspective, represented by the thinker Carl Schmitt, who saw others not just as fellow players or competitors, but as enemies to be destroyed. Huizinga called this idea “an inhumane figment of the imagination” (242), which is a profoundly succinct reply to it. Friends and foes cannot be identified without reference to the goals we have and the constraints we face. The concept of an adversary, though serious, has an abstract, game-like aspect; it does not reach beyond all social constructs to deeper, biological necessities. Yes, nature is red in tooth and claw; but to add to that image the thesis of unceasing and omnipresent mortal conflict with every other being in it is to confuse a powerful but limited abstraction with a universal truth.
Certainly, game metaphors can be applied to the struggle for biological survival, even in very precise and fruitful ways; but the identification of enemies only makes sense once it is clear what game we think we are playing. It is based on our awareness of the parameters of this game that others come to seem like potential allies, competitors, or resources.
Nice or Mean
I host a weekly after-school ‘math club’, where several 7th– and 8th-graders come for activities that involve attempts at clear thinking. Recently, I devoted a few sessions to repeated prisoner’s dilemmas. We set a payoff table for “being Nice” and “being Mean”, then played several rounds between different duos and reflected on what worked well.[2]
After the first session, the main takeaway was that though in a single round being Mean always gives you more points than being Nice, long-term mutual trust pays off: players who are in duos who manage to be Nice leave the game with more points than players who were consistently mean or who got stuck in harsh retaliatory patterns. Since both situations had occurred in almost pure form, clear lessons seemed to be drawn. In the second session I gave them multiple simple algorithms to choose from – “always pick Nice”, “pick Mean every even turn”, “pick Nice until the other player picks Mean; then always pick Mean”, and others (later including the option to design a custom algorithm). I split the students into two groups so that each confrontation had everyone’s full attention.
Under this setup, cynicism prevailed. A team that had been Nicer than was good for them in the previous round would make sure to choose a Mean strategy in the next. In many cases, this led to them scoring fewer points than a Nicer strategy would have brought them. Though we had established in advance that what we were after was points, not ‘beating the other team’, students were still glad they had not been exploited this time. “At least we did better than them now”, was the dominant feeling.
I believe I learned something here. First, how difficult it is not to see other players as opponents by default. Seconds, about the incentives that may have led the class to be ‘Meaner’ than the week before. I have thought about how to get them back into comparing decision algorithms for maximizing their own scores, rather than for ‘beating the opponent’; to think of the other player not as the enemy, but as an unknown agent whose attitudes could make them into a potential cooperator.
One thing I want to try out is to have students design an algorithm that has to do as well as possible when confronted with a certain poule of other players (I got the idea from this Veritasium video). ‘Doing better’ means obtaining more points than other algorithms would have done in the same circumstances; and since those competing algorithms are now represented by the actual choices of their classmates, doing better also means getting to win in that inevitable other game: outperforming your peers.
For the third lesson is that I cannot dictate completely what everyone wants to get out of the game. Even with participants who accept the stated rules and aims, there will always be more ‘at play’ than only achieving those aims. This is not necessarily because of a lack of understanding on their part (although that might of course be relevant), but because human playfulness is not exhausted by the shared space of the game.
Especially in a well-known game, it can be fun simply to try out something new, even if another strategy would have a higher chance of success. There is a mild tension between this and the acceptance of the objectives of the game; an extremely frivolous experiment with no shot at winning could indicate a lack of respect for the game. In general, however, my own (board and online) gaming experience suggests that allowing yourself some room for play within the game – to find your own style, to try out pathways to victory that may fail but will be satisfying if they work – does not break the game at all.
Even to the straightforward, uncreative, individualistic multiple-choice quiz that is Kahoot, some of my students recently found a way to add another game element. Before the questions started, a group changed their avatars into an eagle; now, their shared goal was to get as many eagles as possible on the leaderboard. Make of that what you will; I am sure that straightforward aim still does not exhaustively describe what games they were playing.
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[1] Bernard Williams, Truth and truthfulness. An essay in genealogy (Princeton, Oxford 2002) 108-109.
[2] Scores were 10-0 for Mean-Nice (and 0-10 for Nice-Mean), 7-7 for Nice-Nice, 3-3 for Mean-Mean.