Thanks for reading out the title for me, so I'm Dennis,
I come from the National University of Singapore.
So today, I want to try and convince some of you
as game designers to rethink the way we design games.
So as game design as one of the things
that we often strive for is this idea of an optimal game
experience, who want the best experience for the players.
And one particular theory that's quite popular,
and you might have seen it in some of the poster sessions,
is this theory of flow by Csikszentmihalyi.
So the idea of flow is, if your game is way too difficult,
then your players very quickly become frustrated.
But if it's way too mundane or too easy,
your players will eventually experience boredom,
and that is not what we want as game designers.
So there's a very nice Goldilocks Zone
according to the theory of flow, where we will basically
match your player skill with the game difficulty or game
challenge.
So, of course, it's not a nice little line
that we can stay within, but so as you can see from the graph
here, you might sometimes stay within that nice linear line,
sometimes you might deviate from it.
But then one of the things as game designers that we can use
is this thing called Dynamic Difficulty Adjustment systems.
So for Dynamic Difficulty Adjustment systems,
you basically use a computational system
to help you adjust difficulty on the fly,
so this should help keep the players within the flow zone.
In current research, however, I think there's a problem,
and I hope I don't offend too many people here
by saying that we often look at Dynamic Difficult Adjustment
systems with needing to use AI or some smart computational
system to do it.
So this was one of the motivations of my study,
I basically ask this question, well,
how else can we adjust difficulty
in a simplistic way of putting it aside
from the computational game system?
There are two other possibilities,
one is the player themselves, and if you're
talking about multiplayer games, it
can be opponents or teammates.
So the focus of this study is on two questions,
so how do we design for Dynamic Difficulty Adjustment choices
for the player?
So the players themselves will make these choices.
And how do coming up with designs
that allow the player to do that impact the player experience?
So we designed a small experiment
to try and do a little bit of innovation.
So what we did was two factors, the first factor
is representation of choice that means,
where do we put these difficult choices
presented to the players?
If we look at commercial games now, a lot of the Dynamic
Difficulty Adjusting systems, or maybe not even
dynamic, just difficulty systems, they give you menus,
and there's been existing literature that criticizes
such representations.
The other factor that I'm looking at
is also frequency of choice, how often?
If we do give players this difficulty choice.
So if you haven't guessed yet by that odd looking thing
on the corner of my slide, I basically
made use of a really popular, well, I'm not sure
whether it's too popular anymore,
but the well-known game, I think.
How many of you have this game before?
Or at least heard of it?
Good, so most of you.
So for those of you who haven't seen it,
snake is a very easy game to play.
You play as a snake, and you move
around an enclosed space trying to collect food,
and the more food you collect, the faster you go
and the longer you grow, and that's it to the game.
So we have a 3 by 2 within subject, counterbalanced
of course, game design, and we meet our participants,
play three games for each condition.
So they had to play a total of 18 snake games altogether.
And then at the end of playing those games,
we basically measured the nine components
of flow using Jackson and Marsh's flow state scale.
So how do we do representation of choice then?
We operationalized it by having two things embedded
in non-embedded controls.
So for non-embedded, we basically
provided keyboard keys, Z and X, to adjust for difficulty,
and what we had was a small note at the top that tells you when
you can make that decision.
Then, of course, we have embedded mechanics, some of you
might notice this is a bit similar to Jenova Chen's
actual game flow, where we have the different choices
of difficulty embedded within the game as part of the column
game mechanic.
And then, of course, we tested these two
and compare them with each other.
So in terms of frequency of choice then,
we decided to try to extremes.
So we started with ones, we only presented the choice
at the start of the game, and then
it disappeared after about, if I remember right, 15 or 8
seconds, 8 seconds actually.
And then we have periodic choice,
which means that after you have made a choice, shortly
after, you see the choice again.
And then the last one is constant need,
so immediately after you make the choice,
the next choice pops up.
So I'll just go to the findings, if you
want to read more about the Flow State Scale,
I refer to our paper, refer to the papers
before that we referenced as well.
So our first finding, whether we should embed or not
to embed difficulty choices, we actually
found that using embedded choice,
players are more likely to experience two things, at least
in the context of our game.
So spontaneous or automatic actions,
which we call action awareness merging
as well as an altered sense of time.
But more importantly, we did not find any negative impact
on the game experience, so trying
to use embedded choices, of course,
it depends on what kind of game you're
doing as we have discussed.
Depending on what kind of game you have,
there might be other effects, I'll discuss that later on.
So is once enough?
In our study, it's actually not enough
because if you compare our periodic condition
with the once condition, out of nine dimensions that we
measured, five of them were significantly improved
and they are quite important dimensions,
if you look at them.
However, there was one that was actually diminished,
which was unambiguous feedback.
So the interesting thing here is that providing more choice
within the game itself might actually
lead to this ambiguity of, what is the game trying to tell me?
So can there be too much choice in comparing
constant with the once condition?
It was marginally better, so we have
two components that were basically improved,
and one component, action awareness merging,
which is an important one, that was deproved.
But for constant and periodic comparison,
three dimensions were diminished,
so yes, they can be too much choice sometimes.
So this is an interesting thing that I would like to discuss,
if there are any psychologists or cognitive
scientists within this room.
I will try and speculate a little bit on that later.
So the most interesting finding of our paper
is that if you use both together,
meaning where you put the choice and how frequent you
provide choice, there's actually an interaction effect.
So as you can see, if you have embedded and constant choice,
which is this one, instead of having improved clear goals,
you will basically have a very large drop.
So we speculate then that the players might be actually torn
between trying to play the actual game and figuring out,
should I be making a difficult adjustment
at this point of time?
So when constant choice is required,
if you needed to use it, you might
want to consider direct control instead because
of this drawback.
So another thing that we didn't expect to find
was, when we use embedded with periodic conditions
there was actually a loss of self-consciousness,
so this is the part that I was talking about.
We believe that by having periodic choice in the game
mechanics, it actually makes the choice much more
important to the player.
Because it seems as if, when the player looks at the icons that
appear on the screen, suddenly they have this time frame where
they need to make this important choice of, am I doing well?
Do I need to make this choice right now?
If I don't do it now then the game
may be too difficult for me, and so on.
So that is basically how we try to explain this, and of course
if you have an embedded mechanic design, what
this means is you have to work with the mechanic itself.
So for example, in this case, you
have to move the snake to the actual food that increases
or decreases the difficulty.
So in conclusion, there are a few takeaways here,
so we really need proper design of difficulty choices.
Right now what's happening is, we have these interactions
where players make choices within our games,
changing the difficulty as an afterthought.
And sometimes it's good, sometimes it's bad,
sometimes we don't even know what happens at all.
Of course, embedding difficulty choices is generally
recommended, but I would like to exercise a bit of caution here.
It really depends on the kind of game that you're making,
but it is clear that one choice is obviously not enough.
So for game designers who are looking
at letting the player select a difficulty choice only
at the start of the game, you should probably
rethink that as well.
And then finally, when you are designing for player choice,
you should be cautious of unintended effects.
So as I have shown, if you have representation
with the frequency of choice, you
might have unintended impacts on the player experience.
So that's it for my presentation.
Thank you very much.
[APPLAUSE]
We've got time for two questions just before lunch.
Daniel.
Daniel Johnson, Queensland University of Technology,
thanks for the presentation, I'm really
excited to see more people doing some DDA stuff.
And so my question is not loaded,
but it might feel that way.
We've used that Flow Scale a lot,
and we've pride ourselves into pretzels quite a few times
trying to explain results that didn't make sense,
and eventually when--
I don't think Jackson's Flow Scale works that well in games.
Does that resonate for you at all?
Like do you have any sense of that being possibly--
So right now, at least in terms of my experience,
and I have re-validated the scales again,
for this particular study two years ago in CHI PLAY '17.
I actually use the Flow Scales as well
to test the fundamental assumption
that we can use such scales to measure the flow
experience in games.
So for that one, I basically used Tetris,
and there was a very nice effect that we can see,
and it's pretty strong as well.
So what you might be experiencing
might come from the games themselves.
You have to be really careful, and that explains why
I'm using such a simple game.
Because previously, we wanted to use way more complex games,
obviously, because of external validity,
but we realized that there are things that you don't often
associate with, it might impact these player experiences.
But it turns out that it confounds
the entire experiment, so maybe that's something
that is worth considering.
Let's chat about it because I'd love to.
If there's not a second question,
I've got a follow-up question, but I'll shut up and will--
OK, is there a second question?
[INTERPOSING VOICES]
So for me, then the curious thing
is, what do you think it is about?
Because we use more complex games, and every time
it went weird on us.
Do you have any speculation on what
it is about the more complex games that
makes the scale stop working?
I think it's a really complex question
to answer because it depends on what kind of mechanics
that you are putting within the game.
From my own observations, I haven't
done any official studies on this,
but depending on the kind of mechanic, for example,
whether or not there is Dynamic Difficulty Adjustment,
players may feel, for example, that that's
transformation of time.
So someone in here was playing a VR game, where
you had to do some navigation of a player down the slope
and skiing on something, you had to lean forward to play in VR.
And just by having mechanics like that because you don't
usually move your body like that,
there might be effects where you feel
as if all time is being slowed down,
even though there's no you know Dynamic Difficulty
Adjustment in this case.
Thanks, Dennis.
Looking forward to chatting more about it.
And with that we closed the session for lunch.
Thank you.