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facilitate knowledge learning and skills acquisition.
The canvas is then used by the other part of SELDON,
a scenario planner, DIRECTOR (Barot et al., 2013) to
constrain the simulation. This paper presents our con-
tribution on activities selection based on belief about
learner’s aptitudes and pedagogical needs. In section
2 we present how our contribution positions in rela-
tion to different approaches on adaptive scenarisation.
Then, we introduce the overall process of situation
constraints generation and present a detailled method
of selection of the constraints depending on pedagog-
ical needs in section 3. Section 4 shows an illustration
of this selection through the case of nannies training.
Then we will present the perspectives we foresee to
extend this work and conclude over the whole contri-
bution.
2 RELATED WORKS
Adaptive scenarisation is the process of reacting to
user’s actions to provide content fitted to their need.
In videogames, it might be used to adjust difficulty ac-
cording to player’s level without using typical discrete
mode such as ”Easy”,”Hard”, etc. With adaptive fea-
tures, players are always in the flow (Csikszentmiha-
lyi, 1991): the difficulty remains high enough to pro-
pose a suitable challenge, yet, players can overcome it
so that they do not get bored or frustrated. Such a con-
cept might be used to adapt difficulty in a training ses-
sion so that learners keep a high level of motivation.
The system can propose activities that are always dif-
ficult enough to challenge the learner but always man-
ageable to prevent frustration and loss of motivation.
Our objective is twofold: providing adapted content
(1) and presenting this content in such a manner it
does not cut the user from the flow and, moreover,
motivate him (2).
The adaptation can be made at different levels of
granularity. A first approach is to have a global adap-
tation: a whole scenario has been written (Marion,
2010) or generated (Niehaus et al., 2011) and the out-
comes of the events were scripted beforehand. This
approach allows the building of a scaffolding scenario
which present many advantages:
• Pedagogical Coherence: the scenario ensures a
progressive learning through the session, assis-
tance can be given easily at relevant key points;
• Narrative Involvement: it is therefore possible to
unfold the event as a story which will involve the
learner.
A main drawback is the lack of reactivity of the sys-
tem. As the whole session has been planned, the sys-
tem cannot reorient the scenario to adapt to the very
current learner’s state. The only way to cope with it
is to foresee each possible path which can represent a
huge amount of work. An opposite approach is to pro-
vide reactive adaptation by controling the outcomes
of learner’s actions. It enables:
• Dynamic Adaptation: the system triggers out-
comes of learner’s actions and provides assis-
tances depending on pedagogical needs.
For example, in the application V3S (Barot et al., ),
the triggering of a hasardous matter leak is computed
in real-time by HERA (Amokrane and Lourdeaux, ),
an intelligent tutoring system, according to a learner’s
model.
The simulation where the adaptation takes place
can be run with opposite approaches: the controlled
approaches versus the emergent approaches.
The controlled approach aims to provide a very
efficient learning by orchestrating each part of the
simulation: state of the objects, virtual character’s be-
haviours, possibilities of action the learner, etc. It per-
mits:
• Pedagogical Control: each element of the simula-
tion serves the scenario and pedagogical needs.
This approach which is used in the Generic Virtual
Training (Gerbaud et al., 2008) helps building peda-
gogically efficient scenarios but disables the possibil-
ity to encounter unintended - though relevant - situ-
ations. Moreover, such an approach demands an ex-
haustive modeling of the world functionment which
handicaps the evolutivity of the system. The whole
modeling has to be reconsidered to avoid incoherence
each time an author adds new contents. Any attempt
to interfere with the simulation can cause incoher-
ence for the learner: virtual characters become un-
predictible, states of objects changes with no coher-
ent reason. As a result, there is no way to explain
a posteriori the unfolding of events. These explana-
tions are critical for the learner to understand causes
and consequences of events and actions and they can
be provided at the end of the session or reviewed by a
teacher.
By a clever modelling of small behaviors of the
world, emergent approaches allow new situations to
arise (Shawver, 1997). It also enables:
• Freedom of Action: learners are not framed by the
task they are supposed to do, they can experiment
and discover the outcomes of their actions;
• Autonomous Virtual Characters: as they are not
being controlled by a supervisor, virtual charac-
ters maintain their autonomy and their behaviours
remain coherent throughout the simulation run.
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