Game Development Reference
Figure 3. Setting for the Prototyping Study of the
Affective Student Model
changes in real-time according to the pedagogical,
motivational and affective actions, whilst a dy-
namic module changes according to the decisions
taken by the presentation content manager mod-
ule . According to this classification, the function
of each module is described below.
Static interaction modules:
1. Physics and collisions module contain all the
physics-and-math driven objects increasing
the level of realism during simulation.
2. Input detection module senses and handles
the user's input.
3. Networking module transmits data across
4. Utilities module contains other tools such
as timers and resource managers.
5. Scripting module enables external control
of the application.
Olympia . In addition, the game-design, the cogni-
tive student model and the selection and imple-
mentation of pedagogical responses are discussed.
Dynamic interaction modules:
Affective Olympia Architecture
1. Emotional feedback module uses sound and
color tailored to the student's mood.
2. Interactive AI module determines the be-
havior of non-player characters in the scene,
3. Graphics Look & Feel rendering module
contains the real-time visual resource
PlayPhysics is an application of the Affective
Olympia architecture shown in Figure 4. Olympia
is an enhanced version of the generic architec-
ture introduced by Noguez & Sucar (2005) and
refined in Sucar & Noguez (2008). Olympia has
proven effective for building game-based virtual
learning environments and teaching Physics at
undergraduate level (Muñoz et al., 2009). Here,
Olympia is enhanced to enable it to infer the
learner's emotional state and implement cogni-
tive, motivational and affective actions, which
will improve the learner's level of engagement,
learning and understanding. The motivational and
affective actions will be delivered using game
elements such as visuals, sounds and colors.
Olympia is comprised of static and dynamic
interactive modules . These modules are incorpo-
rated to enable a virtual learning environment to
depict the interaction level of an educational game.
The difference between a static and a dynamic
module is in its adaptability. A static module
Olympia represents a semi-open learning en-
vironment (Bunt & Conati, 2003) where specific
learning goals direct the learner's interaction with
the virtual world and the simulator. Olympia com-
bines a game-based learning environment with
an ITS. The action-challenge relation is handled
by the Game mechanics module through the
game rules. The Teaching & Learning AI module
comprises an ITS. The interface analysis module
decides which events are relevant to be analyzed
by the behavior analysis module . Once evaluation
of the events has been completed, the evidence
provided is propagated to th e student model . The