Game Development Reference
In-Depth Information
Soldato & Du Boulay (1995) formed a basis for
representing the learner's effort. Jaques & Vicari
(2007) do not present any results on the accuracy
of their approach. To date, no system is able to
identify the relevant learner's emotions.
Del Soldato & Du Boulay (1995) applied a
qualitative and quantitative approach to infer
the learner's motivational state from observable
behavior. This approach was based on Keller's
Motivational Design Model (Keller, 2006). The
approach focuses on analyzing the learner's ac-
tions and decisions and mapping them qualitatively
and quantitatively to effort, independence and
confidence. This approach was employed by M-
Ecolab, which is an educational game for teaching
ecology and it proved to be effective (Rebolledo-
Mendez et al., 2006). Self-efficacy was signaled
as an important measure of the student's disposi-
tion to learn (Del Soldato & Du Boulay, 1995).
Self-efficacy involves the learner's beliefs about
being able to perform a specific learning task,
achieving a specific level of performance and
the perceived control over the task, e.g. skills and
knowledge. Therefore, McQuiggan, Mott, and
Lester (2008) built a learner's self-efficacy model.
The model infers the learner's self-efficacy from
observable behavior and physiological data. Using
only observable variables, the model achieved
70% accuracy and the accuracy of the model
increased by 10% on incorporating physiologi-
cal data. Models that infer the learner's emotions
from observable behavior are not effective, but
inferring the learner's motivational state and
self-efficacy level using observable behavior has
been more successful. Furthermore, to date, there
is no student model that can completely integrate
motivational and emotional aspects. Hence, this
research is mainly focused on building a student
model that can use motivational and cognitive
variables to infer the student's emotional state
for intelligent gaming. In the section, 'An Affec-
tive Student Model for Intelligent Gaming', the
series of steps performed for building the affec-
tive student model will be discussed in detail. In
addition, the motivational and cognitive theory
employed to infer emotion from cognitive and
motivational variables is described.
Multimodal Output Communication
and Adaptation
Providing feedback is a key factor in the teaching-
learning process. The pedagogical strategies that
can be selected may be focused on enhancing
understanding or motivation. Research has shown
that it is important to classify pedagogical actions
in order to be able to identify when motivational
and cognitive strategies are in contraposition,
complement each other or are independent (Lep-
per, Woolverton, & Mumme, 1993). The aim is to
select the action that will maximize understanding
or motivation. To enhance the communication
of pedagogical responses, research has been fo-
cused on the creation of Embodied Pedagogical
Agents (EPAs) (D'Mello et al., 2008; Conati &
Maclaren, 2009) and Synthetic Characters (Dias
et al., 2006). The ultimate challenge is to attain
believability (Johnson, Rickel, & Lester, 2000),
which has encouraged research in the state of the
art of animation, emotional intelligence, common
sense, sociology, distributed architectures, mul-
timodal output adaptation and cinematography.
EPAs that comprise the communication module
of ITSs (D'Mello et al., 2008; Conati & Maclaren,
2009) convey a feedback response, which includes
data derived from the student model.
The synchronization of the behaviors of Her-
man, the EPA of the learning-environment Design
a Plant , is done through establishing hierarchical
dependencies between behaviors and classifying
audio and visual media through setting diverse
types of indexes (Stone & Lester, 1996). The
EPAs' animation space, which comprises their
behavior, can be defined by full-body and com-
positional animations, such as Cosmo's animation
space (Lester, Voerman, Towns, & Callaway,
1999). Visual languages are derived to achieve
synchronization (Cassell, Högni Vilhjálmsson, &
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