Creative Education
2011. Vol.2, No.5, 452-457
Copyright © 2011 SciRes. DOI:10.4236/ce.2011.25065
A Multiagent System to Support Problem-Based Learning
Laysa Mabel de O. Fontes1, Francisco Milton Mendes Neto1,
Alexandre A. A. Pontes2
1Postgraduate Program in Computer Science, University of State of Rio Grande do Norte,
Federal University of Semi-Arid, Mossoró, Brazil;
2Superintendent of Information Technology and Communication, Federal University of Semi-Arid,
Mossoró, Brazil.
Received October 15th, 2011; revised November 14th, 2011; accepted November 28th, 2011.
The problem-based learning (PBL) is a learning theory that emphasizes collaboration and teamwork to solve a
problem. However, a problem that occurs frequently in the implementation of PBL is the out-of-context conver-
sation, which is a situation in which the students lose focus and start talking about topics that are not related to
the discussion. Another important aspect is related to the formation of groups in PBL. It might be difficult for
the facilitator to assign students to groups at a distance, since the lack of presential contact makes it difficult to
perceive important characteristics of the students’ profiles involved in the process. Thus, this paper presents a
Multiagent system to support PBL, with the objective of detecting and correcting problems inherent in the im-
plementation of this learning theory.
Keywords: Problem-Based Learning, Multiagent System, Agents
Introduction
Distance Learning (DL) is a modality of teaching and learn-
ing that has grown and produced good results. Group activity is
an important component in classroom teaching. Interactions
between students over the course of some educational activity
are crucial to the learning process, as each student shares with
the others his/her knowledge, questions and impressions of
what was discussed in class, thus enriching the learning process.
This form of learning is called Collaborative Learning (Cou-
tinho, 2007).
The computer support to collaborative learning is called
Computer-Supported Collaborative Learning—CSCL (Dimi-
tracopoulou, 2005). Systems that implement collaborative learn-
ing must include cooperation and communication mechanisms
so that the learning process can be performed with quality. Also,
such systems must have group creation mechanisms and ways
of monitoring for the facilitators (teachers).
In the traditional approach, the teaching is teacher-centered.
The foundation of said stance is that the teacher has the know-
ledge that must be transfered to the student. In this modality of
teaching, classroom activities are developed under the exclusive
direction of collective activity and the students are responsible
for their learning. The discussion of ideas among members of
the group increases interest and promotes critical reasoning.
The Problem-Based Learning (PBL), according to (Hmelo-
Silver, 2004), is a method through which students learn while
solving a problem that usually doesn’t have a trivial solution
and only one correct solution. The learning is student centric
and self directed. The students, organized in small collaborative
groups, work towards identifying what they must learn in order
to solve the problem. The teacher acts as a facilitator in the
learning process, instead of just transmitting knowledge (Fontes
et al., 2010). In the collaborative learning approach, the stu-
dents work together in small groups towards a common goal
(Coutinho, 2007). Software agents have been used in an educa-
tional context. That technology has proven to be very promising
at aiding collaborative learning environments, boosting the
process. They can be used, for instance, to support the fulfill-
ment of a learning theory in a collaborative environment (Pon-
tes, 2010).
A problem that occurs often in the process of applying PBL
on distance learning is the dispersion of students. That is caused
mainly by the physical absence of a teacher to direct the discus-
sions.
Another important aspect in this context is group creation.
On PBL, the members of a group are responsible for solving a
problem and, in order to do that, they must have complement-
tary competences regarding the matter. Also, it could be diffi-
cult for the facilitator to assign students to groups, since the
lack of physical presence makes it difficult or him to know
certain important features of the students involved in the pro-
cess.
That way, aiming to solve the problems related to student
dispersion and support group creation, a PBL-support multi-
agent system is presented.
This work is structured as follows: on Section 2 the main
PBL-related concepts are described; Section 3 presents an ex-
planation on multiagent systems; on Section 4, the agent-based
approach to PBL support is presented; on Section 5, the related
works are presented; at last, on Section 6, the final remarks and
future work are presented.
Problem-Based Learning
The role of the facilitator is to guide students in this process,
identifying possible deficiencies in their knowledge and skills
necessary to solve the problem proposed. Thus, in this learning
theory, rather than the facilitator simply transferring the know-
ledge to the students and then testing them through evaluations,
he causes the students to apply their acquired knowledge in new
situations. In this approach, students often face ill-structured
problems and are motivated to discover, through investigation
and research, useful solutions.

L. M. DE O. FONTES ET AL. 453
For successfully applying of PBL as pedagogic strategy the
following stages must be accomplished (Hmelo-Silver, 2004): 1)
the facilitator proposes an ill-structured problem to the students
group; 2) the students try to generate facts and identify hy-
potheses about the problem through an initial brainstorming; 3)
Then, the students formulate and analyze the problem aiming to
generate ideas for problem solving; 4) After this, the students,
supported by the facilitator, identify knowledge deficiencies for
solving the problem by explanations and justifications; 5) In the
following, the students look for new knowledge related to the
domain, for following try to generate facts about this new
knowledge; 6) at the end of each problem, the students reflect
on the acquired knowledge. Figure 1 illustrates the PBL deve-
lopment cycle (Hmelo-Silver, 2004).
When applied, PBL offers some benefits, among which these
stand out (Hmelo-Silver, 2004):
 Develops critical thinking and creativity on the student;
 Improves his ability to solve problems;
 Improves motivation;
 Helps students apply the knowledge they acquired to new
situations.
Multiagent Systems
According to (Russell & Norving, 2002) agents are autono-
mous software entities that perceive their environment through
sensors and perform actions on the environment through actua-
tors, processing information and knowledge.
Multiagent systems can model complex system, allowing
agents to have common or conflicting goals. Those agents can
interact with each other in two ways: directly (via communica-
tion and negotiation) or indirectly (acting upon the environ-
ment). The agents can cooperate in order to achieve mutual
benefits or compete to serve their own interests (Bellifemine,
Caire, & Greenwood, 2007). Sensors are the agent’s data inputs
and the actuators are the ways through which the agent per-
forms its actions and interacts with the environment.
A percept sequence is everything that has been picked up by
the agent. The agent’s behavior is defined by an agent function
that maps any percept sequence to an action (Russell &
Norving, 2002).
There are several types of agents. They can be of software or
Figure 1.
PBL cycle.
of hardware, stationary or mobile, persistent or non-persistent,
reactive or cognitive (intelligent). One of the most important
classifications of agents is as reactive or cognitive.
Reactive agents are simple agents that note changes in the
environment and react without any knowledge of previous ac-
tions. Since these agents have no memory, they are unable to
plan future actions. Simple reactive agents select an action
based on its current perception of the environment, ignoring
previous perceptions. Cognitive agents are more complex be-
cause they have an explicit representation of both the environ-
ment and the other agents. This agent type has a memory,
which enables it to plan future actions based on situations that
took place previously (Russell & Norving, 2002).
A middle ground between the simple reactive agent and the
cognitive agent is the reactive agent with internal state which,
in order to achieve a more rational performance, have an inter-
nal state with aspects of the domain that may not be evident in
the current perception. This state depends of previous percep-
tions of the environment, and is defined in a set of possible
current internal states, ∆ = {δ1, ···, δ1}.
This agent structure assumes that: 1) the agent receives in-
formation, though sensors, regarding the environment’s state,
defined in a set of possible states; 2) the agent has a perception
sub-system and a decision-making subsystem; and 3) the agent
executes the selected action on the environment through actua-
tors (Pontes, 2010).
Agent Based Approach for Support PBL
Figure 2 presents the architecture proposed in this work.
The following subsections present the agent-based approa-
ches to out of context conversation detection and group creation
on PBL.
Agent Based Approach for Out-of-Context
Conversations in PBL
Intelligent agents can perform many tasks in computer-sup-
ported collaborative learning, such as monitoring students’
participation in discussions, facilitating the selection of topics
for discussion, and assessing student performance in relation to
the use of communication and cooperation tools available in the
environment, among others. The use of agents to assist with
these tasks is becoming increasingly important, mainly due to
the increasing number of students who interact in learning sup-
port systems, which makes it very difficult to the facilitators to
manage these activities at distance. The approach to out of con-
text conversation detection is shown in Figure 3.
According to the approach presented in Figure 3, three types
of agents are proposed: a Problem Detector Agent (PDAg), a
Student Agent (SAg), and an Animated Interface Agent.
The Problem Detector Agent is responsible for detecting out-
of-context conversations based on the environment’s collabora-
tive tools and an ontology. After detecting the students’ focus
has been lost, it notifies the Student Agent, which searches its
history base to verify whether this student has already been
stimulated.
If he has not been previously stimulated, the SAg will trigger
the animated interface agent that will search for the first stimu-
lus in the base of stimuli previously registered.
A stimulus is a text message shown by the interface agent.
Then the animated interface agent will try to motivate the stu-
dent with this stimulus. In the following, the interface agent
will record the type of stimulus used in its historical base and

L. M. DE O. FONTES ET AL. 454
Figure 2.
General architecture of agents.
Figure 3.
Agent based approach f o r o u t -of-context convers a t i o n s i n PBL.
notify the facilitator about the student’s situation.
However, if the SAg detecting in its historical base that the
student was previously stimulated, it will check whether it has
already passed the deadline (previously registered by the facili-
tator) given to the student improve their collaboration on the
learning environment (ex. at the end of each day). If the dead-
line has not passed, it does nothing. But if the deadline has
passed, it will notify the animated interface agent, which will
search for the next stimulus in the base of stimuli by motivating
the student in a different way. Then it records this information
in its historical base and it notifies the facilitator about the stu-
dent’s situation, which is done through a message that is auto-
matically sent via e-mail.
The agents will repeat this process while there is stimulus
registered in the base of stimuli. The animated interface agent is
responsible for motivating students to focus more of the discus-
sions and to use the tools available in the virtual learning envi-
ronment.
The PDAg is also responsible by detect students with unfo-
cused behavior during the PBL’s interactions.

L. M. DE O. FONTES ET AL. 455
Agent Based Approach for Group Formation in PBL
The interaction in PBL plays a very important role on the
learning process. In this context, the workgroups creation pro-
cess in the learning environment is very important to the overall
performance of the process. In presential learning, the students
are very close to each other and, usually, the teacher knows
each one of them, and the students know each other. In distance
learning, the students are geographically distributed, therefore
even the facilitator doesn’t know all of the students, and the
students don’t know each other either. The facilitator must cre-
ate the workgroups in PBL, but in distance learning he doesn’t
have enough information regarding the students in order to
perform this task on his own. In the approach proposed in this
paper, an agent is used to help the facilitator in this task. The
workgroups creation process is illustrated in Figure 4.
The workgroups creation process happens as follows: the
students, through a web-based interface, fill their profiles in at
the beginning of the process. This process feeds a profile base
that will be used in the workgroups creation process. The stu-
dents’ profile is composed of skills, acquirements and deficien-
cies, where each of these has a level, which can be either low,
medium or high. A student can have several skills, deficiencies
and acquirements.
The facilitator defines the profiles of the workgroups for
each problem that must be solved through a web-based inter-
face, similar to the one used by the student. The groups’ profile
is composed of skills, acquirements and deficiencies, where
each of these has a level, which can be either low, medium or
high, and a fuzzy value that varies from 0.1 to 1. After the fa-
cilitator to create the groups’ profiles, there will be a groups
profile base that can be accessed by the WCAg. It is important
to note that the desired profile, created by the facilitator, is what
best matches the problem’s resolution, and therefore, a student
that has an approximate profile should have the competences
needed to solve the proposed problem.
The WCAg is responsible for the automatic creation of
groups. It was implemented using two programming languages:
Java (Ken, 1996) and Prolog (Pereira, 2002). The Java section
of the agent is responsible for the creation of candidates that are
apt to participate in a workgroup. This process is done by ana-
lyzing the students’ profiles and the groups’ profiles. After this
analysis, it generates a file that will be the input for the Prolog
section.
The generation of candidates is performed as follows: 1) the
WCAg analyzes the profile of the group and checks whether
there is a candidate that possesses some required skill; 2) if
there is a candidate that has at least one required skill, this can-
didate is enclosed in a list of suitable candidates to compose the
group. This process is done similarly for acquirements and
deficiencies. Thus, candidates that have at least one skill, ac-
quirement or deficiency are included in the list of suitable can-
didates to join the group; 3) next, the WCAg generates an input
for the section in Prolog. This input is called perception, and it
is actually a text file that contains parameters that will be read
by the agent session in Prolog and thus constitutes the interface
between the two sections. An example of the file structure can
be seen in Table 1.
The first parameter is the number of candidates. The second,
a measure of similarity. The third is the amount of the universe
of discourse. The fourth is the desired situation (sources and
importance values) that represents the profile of the group and,
finally, the fifth parameter is the current situation or the pro-
file(s) of student(s) able to compose the group.
The Prolog section of the agent is responsible for the as-
signment of students to groups. At the end of this process, a file
containing the workgroups is generated. The facilitator must
analyze the result and decide whether he will accept the agent’s
suggestion.
Related Works
Multiagent Systems (MAS) have been widely used in educa-
tional applications. This technology has been quite promising
as an aid in collaborative learning environments, making these
environments more proactive and autonomous. MAS can be
used, for example, to assist in the implementation of a particu-
lar learning theory in a collaborative environment.
In (Vizcaíno, 2005), a simulated student architecture is de-
Figure 4.
roup formation process . G

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.
References
Arnold, K., Gosling, J., & Holmes, D. (1996). The Java™ Program-
ming Language. Upper Saddle River: Prentice Hall, 352.
Bellifemine, F. L., Caire, G., & Greenwood, D. (2007). Developing
multiagent systems with JADE. Hoboken: Wiley, 5.
doi:10.1002/9780470058411
Coutinho, C. M. P., & Bottentuit, J. B. Jr. (2007). Collaborative learn-
ing using Wiki: A pilot study with master students in educational
technology. Proceedings of World Conference on Educational Mul-
timedia, Hypermedia and Telecommunications (pp. 1786-1791).
Vancouver.
Dimitracopoulou, A. (2005). Designing collaborative learning systems:
Current trends & future research agenda. Proceedings of the 2005
Conference on Computer Support for Collaborative Learning:
Learning 2005: The Next 10 Years! (pp. 115-124). International
Society of the Learning Sciences.
Felix, Z. C., & Tedesco, P. A. (2008). Smart chat group: Ferramenta
ciente de contexto para formação de grupos. Fortaleza: Anais do
Simpósio Brasileiro de Informática na Educação—SBIE, 19, 643-654.
Fontes, L. M. O., Mendes Neto, F. M., Pontes, A. A. A., & Campos, G.
A. L. (2011). An agent-based architecture for supporting the work-
groups creation and the detection of out-of-context conversation on
problem-based learning in virtual learning environments. Proceed-
ings of the 2011 ACM Symposium on Applied Computing (pp.
1175-1180). TaiChung, Taiwan: ACM SAC Proceedings. New York,
NY: ACM.
Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do
students learn? Educational Psychology Review: 16, 235-266.
doi:10.1023/B:EDPR.0000034022.16470.f3
Lima, M. R. C., Labidi, S., Bastos Filho, O. C., & Fonseca, L. C. C.
(2005). Aprendizagem cooperativa e o problema de formação de
grupos. Renote—Novas Tecnologias n a Educação, 3.
Moisil, I., Pah, I., Barbat, B., & Popa, E. M. (2006). Socio-cultural
modelling of the student as the main actor of a virtual learning envi-
ronment. Proceedings of the Wseas, International Conference on

L. M. DE O. FONTES ET AL. 457
Mathematical Methods and Computational Techniques in Electrical
Engineering, 8. Bucharest.
Pereira, F. C. N., & Shieber, S. M. (2002). Prolog and natural-language
analysis. Microtome Publishing, 64, 627-631.
Pontes, A. A. A., Mendes Neto, F. M., & Campos, G. A. L. (2010).
Multiagent system for detecting passive students in problem-based
learning. Advan c es i n S o f t C o m p u t i n g, 71, 165-172.
doi:10.1007/978-3-642-12433-4_20
Russell, S., & Norving, P. (2002). Artificial intelligence: A modern
approach. Upper Saddle River: Prentice Hall, 1132.
Sendag, S., & Ferhan, O. H. (2009). Effects of an online problem based
learning course on content knowledge acquisition and critical think-
ing skills. Computers & Education: Elsevier, 53, 132-141.
Silveira, S. R., & Barone, D. A. C. (2009). Formação de grupos
colaborativos em cursos a distância via web: Um estudo de caso
utilizando técnicas de inteligência artificial. Revista Brasileira de
Informática na Educação, 14.
Strobel, J., & Van Barneveld, A. (2009). When is PBL more effective?
A meta-synthesis of meta-analyses comparing PBL to conventional
classrooms. Interdisciplinary Journal of Problem-based Learning, 3,
4.
Tseng, K., Chiang, F., & Hsu, W. (2008). Interactive processes and
learning attitudes in a web-based problem-based learning (PBL)
platform. Comput ers in Human Behavior: Elsevier, 24, 940-955.
doi:10.1016/j.chb.2007.02.023
Vizcaíno, A. A. (2005). Simulated student can improve collaborative
learning. International Journal of Artificial Intelligence in Education,
15, 3-40.