Architecture for Monitoring Learning Processes using
Video Games
N. Padilla-Zea, R. Lopez-Arcos, F. L. Gutiérrez-Vela, P. Paderewski and N. MedinaMedina
GEDES Research Group, University of Granada, Spain
[email protected], [email protected], [email protected],
[email protected]
Abstract. PLAGER-VG is an architecture to design, execute, analyze and adapt
educational processes supported by video games, especially, those that include
collaborative activities and use collaborative learning techniques. In this paper,
we have focused on the monitoring and adaptive processes in order to customize activities both in the game and in the learning to improve results obtained
from using these collaborative video games. To perform these processes we
propose a mechanism based on the use of a set of specialized agents included
into this architecture to collect relevant information and to process it in order to
obtain the necessary adaptation actions.
Keywords: Video games, Architecture, Learning processes
1
Introduction
The incorporation of Game Based Learning (GBL) into learning processes has already
become a reality, both in schools and for research purposes. In particular, playing
these games in groups has been discovered as a desirable way of using them since
players are used to do it when they play commercial video games. By joining these
two aspects, we have focused our research on the use of group activities within Educational Video Games (EVG) in order to promote collaborative skills in students and
to promote the several advantages that both elements involve.
Although several aspects of designing and using EVG have been developed in our
research, in this paper we have fixed the focus on the personalization of the learning /
playing process. Assuming that an educational video game includes recreational
activities that hide some educational contents, the monitoring and adapting of the
game are closely related to the monitoring and adapting of the learning process.
Starting from this assumption, we think that it is necessary to monitor relevant
activities in the game in order to analyze and to adapt it according to the features of
the player or the group who is playing.
In this context of using collaborative learning and EVG as tools for teaching, we
think our previously proposed architecture PLAGER-VG [1] (PLAtform for
managinG Educational multiplayeR Video Games) needs to be modified. PLAGERadfa, p. 1, 2011.
© Springer-Verlag Berlin Heidelberg 2011
VG helps teachers and designers to obtain more efficient video games and is able to
monitor and to adapt the learning processes underpinned by them.
The problem of monitoring and analyzing learning processes is of particular
interest in Computer Supporter Collaborative Learning (CSCL) environments where
learning is usually based on the interactions and communications occurring among
students.
Many CSCL systems offer functions to study the way in which collaboration takes
place. For this purpose, they usually record the actions carried out with the
interactions with the system [2], the communications between collaborators [3],
and/or the changes carried out in the shared workspaces [4]. However, we have
checked that some more information is needed to contextualize the performed actions
and automatic mechanisms are needed to collect this information and to adapt the
games.
Thus, we propose a modificaation of the architecture PLAGER-VG using agents as
active entities in order to collect information generated by the collaborative activities
performed by the players during the game. In addition, these agents help teachers to
improve the learning process by proposing adaptation actions. An adaptation action
allows changing some aspects in the game, for example, modify (decrease) the
difficulty level if the player or the group can not overcome a challenge in the stated
time.
In this paper, we use the terms “game” and “video game” as synonymous.
2
Architecture PLAGER-VG
The architecture PLAGER-VG [1] is composed of five interrelated and
interconnected subsystems: Personalization Sub-system, Design Sub-system, Groups
Sub-system, Monitoring Sub-system and Game Sub-system. Both educational and
recreational contents are designed using functionalities included in the Design Subsystem. Components designed as result of this process are stored in a central
repository, as can be seen in Figure 1.
The Personalization Sub-system accesses the designed elements and customizes
them according to the needs of each of the users. Such changes, which customize both
the learning and game processes, are also reflected in the central repository.
The Personalization Sub-system communicates with the Game Sub-system which,
given a set of educational specifications for a student or group, generates a personal
Game Instance, which runs consistent with the educational restrictions.
Fig. 1. PLAGER-VG Architecture
During the game, a set of “interesting events” occur, from both an educational and
recreational viewpoint, which are collected for processing by the Monitoring Subsystem. As a result, the Monitoring Sub-system generates a set of recommendations,
which are reported to the Personalization Sub-system and to the central repository.
The Personalization Sub-system adapts the game and learning features to each of
the users and is therefore responsible for implementing these recommendations.
Finally, the Groups Sub-system manages both the design and creation of groups
and stores this information in the central repository. Information about groups allows
both the Personalization Sub-system, Monitoring Sub-system and Game Sub-system to
manage collaborative activities.
Although the PLAGER-VG architecture is composed of five interrelated and
interconnected subsystems, in this paper we are going to focus our attention into the
Monitoring and the Personalization Sub-system.
3
Using Agents to Retrieve Relevant Information
In order to analyze learning processes and, is that way, to improve them, we need to
select properly the information to be studied, but also to decide how to process and to
use it.
Based on our previous works [5,6], we have decided to use agents in order to
obtain the functions to monitor and adapt the game.
In those works we presented an architecture for dynamic and evolving cooperative
software agents. We defined a model that allowed communication between agents and
preserved system activity while it was running. A central blackboard was used to
communicate and coordinate agents and to store the information needed by those
agents. This blackboard was controlled by special agents with specific functions to
store and retrieve information when needed and to perform evolution actions over a
software system.
Following this idea, and to facilitate the analysis of learning processes, we propose
to include two type of specialized agents in the architecture (PLAGER-VG), which
will be responsible for monitoring (Monitoring Agents) and for providing information
to teachers about the activities to be performed on the game and groups to adapt and
improve learning process (Facilitator Agent). Figure 2 shows the integration of these
agents with other elements in the architecture.
Fig. 2. Multi-Agent architecture for PLAGER-VG.
Since EVG claims for implicit learning, we need to establish a relationship
between what a student is doing in the game and the learning implicit in such activity.
To do that, we need to determine what activities are relevant in the game and which
information is relevant for each of them.
Thus, we use interesting events. These events can be individual, if information is
only related to one player; or for a group, if the activity is being done by several
players and includes information about interaction, besides the one related to the
learning process. The information that the agent has to collect of each event are: event
identification, game identification, game mode, video game task, educational task,
educational goal, task starts, success/failure, added score, player, group and task end.
Information collected by agents while the game is running is stored in a global
structure, called State of Game. This structure contains details about every interesting
event and allows us to analyze sequences of the game in order to study patterns of
behavior or repetitive actions.
In our platform, players have a set of tools (Synchronous Communication Tools,
Asynchronous Communication Tools, Information about the group members,
Scheduler, Voting system, Map of the game, Common Warehouse) that enable group
communication, coordination and improve the awareness.
Using these types of tools, players can maintain contact with their group partners to
do the group activities but also to obtain advice or information while performing
individual activities, if they need help to overcome them. Therefore, all elements
previously stated could appear in both individual and group activities.
One of the most important problem is how to identifying interesting events. The
Monitoring Agent use a set of events based on the 3 C’s model [7], and clasifies them
according to a message classification we have proposed was adapted to interaction
analysis in EVG. This classification fall out of the scope of this paper, but can be
found in [8].
In general, making an automatic classification of messages produced during the
game is difficult. A way to reduce this difficulty is to defining the set of tools to be
used and how this classification can be done by using them. In our system, defining
specialized agents with specific information associated to them makes the process
easier, because the frame of information that every agent collects includes what kind
of event it is.
Facilitator Agent use some analysis mechanisms based on Social Network Analysis
[9] (SNA) focusing the analysis on the educational process that students are doing,
specifically, on the collaborative process. From the results of this analysis, we can
make adaptations to the learning process to improve the process itself and, therefore,
skills of students and learning outcomes.
There are diferent types of adaptation actions. Depending on how they are
performed, they can be automatic, performed by the teacher (directed) or semiautomatic, if the teacher agreement is required. Depending on the duration, they can
be temporal or permanent. Finally, depending on the applicability scope, adaptations
can affect only the current game, or the next game or until another change.
4
Conclusions and Further Works
In this paper we have presented our proposal of using agents 1) to analyze interaction
between players in EVG in order to asses their learning processes and 2) to control the
adaptation of the game. These agents have been included as an extension of the
architecture PLAGER-VG.
We have defined the concept of interesting event and we have presented the
information to be collected in order to classify and analyze the learning process
during the game and based on the widely known model of the 3 C's.
To monitor the interaction during the game, we have proposed the inclusion of
additional information (context) referred to conditions under which events occur. This
information is collected by a set of special agents of Monitoring Agent type.
Once the previously mentioned information has been analyzed, other special agent
called Facilitator Agent decided whether some adaptation actions have to be
performed or whether the teacher has to be informed. In this last case, the teacher
could accept or not the proposed changes.
Our immediate future work is to refine and to implement modifications of
PLAGER-VG prototype with the agents defined. We want to integrate the prototype
with a modular design which allows the design, execution and analysis of educational
video games with group activities.
We are also intended to improve the adaptation mechanism. We are going to
complete the list of adaptation actions and to include the corresponding pre and post
conditions to them. Pre-conditions and Post-conditions will warranty the integrity of
the game when these new adaptation actions are carried out. This process is
dynamically performed while the game is running.
5
Acknowledgments
This study has been financed by the Ministry of Science and Innovation, Spain, as
part of the VIDECO Project (TIN2011-26928) and Vice-Rector's Office for Scientific
Policy and Research of University of Granada (Spain).
6
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