Information Technology Reference
In-Depth Information
9.4.1 A General Model of Gamification
We suggest a general model of gamification consisting of the following four compo-
nents:
•
Atask
T
that needs to be performed.
•
A set of game design elements
g
∈
G
.
•
A set of users
u
∈
U
processing task
T
enhanced by
G
.
•
A task-dependent ground truth
f
∗
:
U
ₒ
G.
•
A function class
F
consisting of functions of the form
f
:
U
ₒ
G.
The gamification design problem is the problem of selecting a function
f
∈
F
that
best approximates the supervisor
f
∗
.
The ground truth
f
∗
is a function that assigns each user
u
a game design element
g
that maximizes the expected contribution of
u
to achieve a prespecified goal. For
users that best perform without any of the game design elements contained in
G
we
include a distinguished symbol
denoting the absence of any design element.
Typically, the ground truth is unknown for most users and therefore needs to be
approximated by a function from some function class
ε
F
based on a small subset
Z
= {
(
u
1
,
g
1
),...(
u
n
,
g
n
)
} ↆ
U
×
G
of training examples. The training set
Z
consists of
n
users
u
i
with corresponding
design elements
g
i
for which the ground truth is known.
Note that we do not want to memorize the training examples but rather find
(learn) a function
f
=
f
∗
(
u
i
)
∈
F
that predicts the best fitting design elements for new users
not considered in
Z
.
9.4.2 Learning Problem
There are different ways to select (learn) a function
f
from
F
in order to approximate
the ground truth
f
∗
.
One approach describes users
u
by a feature vector
x
u
. The components of
x
u
measure different properties of that user such as, for example, click behavior,
mouse movements, and other features. Then a classifier such as the support vector
machine [
5
] is trained to learn a model that predicts the best fitting game design
element for new users.
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