
L. M. DE O. FONTES ET AL.
456
Table 1.
A file structure example.
1st Parameter 1
2nd Parameter 2
3rd Parameter 11
4th Parameter [a, m, b, a, a]
[0.8, 0.4, 0.2, 0.7, 1.0]
5th Parameter [John’,[m, m, b, m, b’, 1]]
signed to detect and avoid three situations that decrease the
benefits of learning in collaboration: off-topic conversations,
students with passive behavior and problems related to stu-
dents’ learning.
As a distinctive feature of our work, we highlight the fact
that our approach uses an animated interface agent with socio-
affective features, i.e., when the problem detector agent identi-
fies unfocused behavior, the animated interface agent tries to
solve or minimize the problem by motivating the students to
participate in activities and discussions. For this purpose, it uses
text messages.
In (Moisil et al., 2006), a model for virtual learning envi-
ronments is presented that employs intelligent agents to imple-
ment Vygotsky’s sociocultural theory, focusing on the social
aspect of interaction. The proposed model has several agents,
among which we highlight the following: 1) the social agent,
whose main goals are the construction of models for groups of
students and the identification of groups of students that can
cooperate in good conditions; 2) the tutor agent, which evalu-
ates the student’s educational goals and recommends some type
of activity; and 3) the personal agents for assistance to the stu-
dents, which monitor their activities and then inform other
agents of the results of the monitoring.
As a distinctive feature of our work, we highlight the fact
that our approach uses the PBL, which is a learning theory that
has been proven to be effective (Tseng, Chiang, & Hsu, 2008;
Strobel & Barneveld, 2009; Sendag & Ferhan, 2009).
In (Lima et al., 2005), the authors present an approach to
group creation that is based on genetic algorithms, in which are
applied the group’s acceptance factors, by the teacher, taking
into consideration the group’s cohesion and the students’ pro-
files, using socio-metric techniques. This approach is used on
the NetClass cooperative learning environment.
In (Silveira & Barone, 2009), the authors apply multiagent
techniques to collaborative groups creation in an Interactive
Multiagent Environment for Learning on the Web. They pre-
sent the definition and implementation of an agent architecture,
modelled with genetic algorithms, as well as its integration with
the TelEduc environment.
In (Felix & Tedesco, 2008), the Smart Chat Group tool is
presented. It employs an intelligent agents society to create,
suggest and monitor small learning groups based on the stu-
dents’ context’s data.
As a distinctive feature of our work, we highlight the fact
that the Group Creator Agent, proposed in this paper, performs
the group creation based on both the students’ and the groups’
profiles, the latter having been created by the facilitator.
Final Remarks and Future Works
PBL is a learning theory that has been successfully applied to
virtual learning environments. This theory emphasizes team-
work and collaboration in order to solve a problem. However, a
problem that often occurs is the dispersion of students during
discussions on collaborative learning environments, which
greatly influence their productivity. Another important problem
regards the group creation process on PBL. It could be difficult
for the teacher to assign students to groups without physical
presence, since that lack of physical presence makes it difficult
to perceive certain important features of the students involved
in the process.
This paper presented an approach that uses software agents
to avoid allowing the students to lose focus during interactions
with other students and support group creation, providing the
facilitator with support to solve these problems. Using the pro-
posed approach, it is possible to achieve a reduction of student
dispersion, as upon detecting the focus has been lost, it notifies
the facilitator, who can take appropriate action. The architecture
also provides support for group creation.
As the work’s contribution, we highlight the development of
an agent-based architecture that supports PBL on out of context
conversation detection and group creation.
As future work, we intend to improve the out of context con-
versation detection approach and the group creation process
presented in this paper, through literature study of other possi-
ble approaches to said problems. We also intend to approach
other questions regarding PBL, as presented in (Pontes, 2010).
Finally, we aim to perform a case study as a way to validate the
solution presented in this work and a quantitative analysis in
order to obtain statistical data that may verify the effectiveness
of the proposed solution.
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