Game Development Reference
In-Depth Information
INTRODUCTION
ITSs encourage independent learning and provide
adaptable responses according to each student's
pace, form of learning and history of interaction.
On the other hand, emotion has been shown
to influence learning and performance. Emotion,
cognition and motivation are deeply interrelated
(Pekrun, Frenzel, Goetz, & Perry, 2007). Hence,
research is focused on enhancing the learner's
understanding and engagement by intelligently
and effectively managing the affective aspects of
educational games. The aim is to communicate
effectively an emotional state, which will change
the learner's disposition and emotional states to
those that facilitate learning and understanding
(Conati & Maclaren, 2009). However, to know if
the desired effects will be achieved, it is necessary
to enable these systems to identify the learner's
affective or motivational states. Finding a solution
to this challenge has been the focus of several re-
search areas, e.g. Affective Computing, ITSs and
Game-based Learning Environments. Affective
Computing focuses on enabling computers to ex-
press and recognize emotion (Picard et al., 2004).
ITSs are currently being enhanced to incorporate
the emotional dimension into their framework.
This chapter focuses on the specific challenges
of how to design effective game-based learning
environments, how to identify the learner's emo-
tional state and how to adapt and respond to the
learner's actions and disposition. To attain this
objective the state of the art of Intelligent Game
Learning Environments, ITSs and Multimodal
Output Adaptation related to Affective Comput-
ing is first discussed. An affective student model,
which infers the learner's emotional states from
cognitive and motivational variables, is proposed.
To infer emotion an approach focusing on analyz-
ing the learner's observable behavior, qualitatively
and quantitatively, is applied. A methodology
of designing an affective student model using
Probabilistic Relational Models (PRMs), e.g.
Bayesian Networks (BNs), is described. The affec-
tive student model is based on the Control-Value
theory of 'achievement emotions'. PlayPhysics ,
Space and flight simulators signaled the begin-
ning of a new revolution for training, teaching and
learning (Bergeron, 2006). Technology has made
remarkable progress in computing and electronics,
which have converged in the game development
area. Students have grown up playing video games,
exposed to a large quantity of visual and acoustic
stimulus and inhabit a world strongly influenced
by Information Technology (IT). As a result,
achieving knowledge, understanding and motiva-
tion during the teaching-learning experience has
become more challenging (Oblinger, 2004). The
ultimate goal has been to create enhanced learning
environments that will be able to deal successfully
with the learners' expectations. Intelligent game-
based learning environments, e.g. educational
games, facilitate teaching through experience by
offering immediate feedback and engaging the
learner's attention (Squire, 2003). As a result, an
emotional link is established between the learner
and the game. This feature results in these game
environments becoming straightforward motiva-
tional tools.
Game-based learning environments must
follow design approaches and principles that
make them capable of offering effective learning
(Schaller, 2005). Learning goals must lead and
encourage the learner's exploration. An assess-
ment criterion must be incorporated to evaluate
the learner's skills and performance, and to dis-
tinguish the knowledge topics that are understood
and mastered by the student from those which are
lacking. It is important to note that learning to play
a game effectively does not ensure mastering the
domain knowledge, since games are composed
of a set of rules, which define the gameplay, that
are not necessarily related to the learning con-
tent. Suitable feedback must be provided when
a learning need is identified. Therefore, Intel-
ligent Tutoring Systems (ITSs) are incorporated
into the architecture of intelligent game learning
environments (Conati & Maclaren, 2009), since
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