![](bg2.png)
factors, the ones witch interest us are all that is related
to the story. We aim to have consistent, non-boring
scenarios. This point raises the problem of what mo-
tivating factors to consider in our system and how.
As we are in highly complex and dynamic sys-
tems, the learner does not have the time to understand
everything that happens in real time. So, it is useful to
comeback on it in debriefing, even if the trainer had
all informations in real time. To do this, we added to
each session a trace of performed activity. This point
raises the problem of trace content and its replay.
3 CONTEXT AND RELATED
WORK
3.1 Pedagogical Scenario Control in
Virtual Environments
Most virtual environments designed for training pur-
poses are used in training sessions as part of a peda-
gogical scenario. These scenarios are the sequences
of learning activities. In some cases, several learning
activities are simulated inside the VE. The transposi-
tion of the subsequence of the pedagogical scenario
in the VE generally consists in branching tree struc-
tures, containing predetermined sequences of scenes
(Magerko et al., 2005), or tasks the user has to exe-
cute (Mollet and Arnaldi, 2006). Yet, in order to stay
within these paths, these VEs offer a strong guidance
to the trainee, often stopping them whenever they de-
viate from the training scenario.
On the other hand, some environments are used
in pedagogical scenarios as a single learning activity.
These environments opt for the “sandbox” approach,
letting the user act freely as the simulation evolves
and reacts to their actions, like in (Shawver, 1997).
However, without any real-time pedagogical control,
the efficiency of the training is not guaranteed.
One approach for ensuring both user agency and
pedagogical control is to define a multilinear graph of
all possible scenarios, In (Delmas et al., 2007), the set
of possible plots is thus explicitely modelized through
a Petri Network. However, when the complexity of
the work situation scales up, it becomes difficult to
predict all possible courses of actions.
3.2 Scenario Adaptation
Adaptative scenarisation is the process of reacting to
users actions to provide content fitted to their need.
In videogames, it might be used to adjust difficulty
according to learners level without using typical dis-
crete mode such as Easy,Hard, etc. With adaptive fea-
tures, learners are always in the flow (Csikszentmiha-
lyi, 1991): the difficulty remains high enough to pro-
pose a suitable challenge, yet, learner can overcome it
so that they do not get bored or frustrated. The adap-
tation can be made at different level of granularity. A
first approach is to have a global adaptation : a whole
scenario has been written (Marion, 2010) or generated
(Niehaus et al., 2011) and the outcomes of the events
were scripted beforehand.
The simulation where the adaptation take place
can be run with opposite approaches : the controlled
approaches versus the emergent approaches. The con-
trolled approach aims to provide a very efficient learn-
ing by orchestrating each part of the simulation : state
of the object, virtual character, action possibilities of
the learner, etc. It make possible Pedagogical control
(Gerbaud et al., 2008). Moreover, such an approach
demands an exhaustive modeling of the world func-
tionment which handicap the evolutivity of the sys-
tem. The whole modeling has to be reconsidered to
avoid incoherence each time an author add new con-
tents. By a clever modelling of small behaviors of the
world, emergent approaches allow new situations to
arise (Shawver, 1997). The issue with emergent ap-
proachs is the lack of pedagogical control.
3.3 Motivation’s Factors
There are several motivation models in video game.
These range from expectancy/valence approaches
(Mathieu et al., 1992) to Kellers (1983) Attention,
Relevancy, Confidence, and Satisfaction (ARCS)
model. Behavior can be intrinsically or extrinsically
motivated. Most models have emphasized intrinsic
motivation, focusing on the motives to perform a task
that are derived from the participation itself (Malone,
1981). Malone (1981) proposed that the primary fac-
tors that make an activity intrinsically motivating are
challenge, curiosity, and fantasy and specifically ap-
plied this framework to the design of computer games.
Others have examined extrinsic motivation, in which
someone engages in an activity as a means to an end
(Vallerand et al., 1997).
4 OUR TROPOSAL
4.1 General Architectur
For each observable action or effect in the VE, a
message is send to our tracking and scripting sys-
tem. Thanks to task recognition technique, our sys-
ICAART2014-InternationalConferenceonAgentsandArtificialIntelligence
680