THE COLLABORATIVE PROCESS OF DECISION-MAKING IN

THE COOLED LEARNING ENVIRONMENT

Patricia Cristiane de Souza, Eunice P. dos Santos Nunes and Guilherme M. Armigliatto

Institute of Computing, Federal University of Mato Grosso - UFMT, Cuiabá, Mato Grosso, Brazil

Keywords: Collaborative Virtual Environment, Decision-making, Intelligent Agent.

Abstract: This paper presents decision-making processes and collaboration and coordination mechanisms with the use

of an intelligent agent in the CoolED Environment. The CoolED is a collaborative learning environment

developed by the LAVI research group at the Federal University of Mato Grosso (UFMT). This system aims

to help computer science undergraduate students master the content of Nonlinear Lists, which is a subject of

the Data Structuring class, emphasizing insertion, removal and search operations. The group decision-

making is made by a leader chosen by the agent and it follows some evaluation criteria stipulated in a

leadership model developed on this work. This model allows any user a chance to lead the group at least one

work section and respects individual interests on the process of becoming a leader.

1 INTRODUCTION

This paper presents decision-making processes and

collaboration and coordination mechanisms with the

use of an intelligent agent in the CoolED –

Collaborative Learning Environment for the Study

of Nonlinear Lists. This environment is one of the

results of researches in collaborative environments

of the Research Group Laboratory of Interactive

Virtual Environments (Laboratório de Ambientes

Virtuais Interativos - LAVI) of the Computer

Science Institute at the Federal University of Mato

Grosso (UFMT).

Decision-making is a very important factor in a

collaborative environment, but working

collaboratively demands additional effort for

coordinating its members. Without coordination,

many communication efforts are not put to good use

during collaboration. That is to say, in order for the

group to satisfactorily act together and make correct

and coherent decisions, compromises must be made

during conversations among participants. In this

context, the agent plays the role of a mediator and

contributes to the decision-making process,

improving communication among participants and

helping develop collaborative learning.

This paper is divided into six sections. Section 2

describes how the decision-making process operates.

Section 3 presents a few studies related to the topic.

Section 4 presents the CoolED system and shows

how it works, describing the decision-making

process and the role of the agent in mediating this

process. Section 5 presents the conclusions of this

study and the next steps for continuing the research.

Lastly, Section 6 presents bibliographic references.

2 DECISION-MAKING PROCESS

The decision-making process characterizes a

situation that displays a certain degree of uncertainty

and a certain level of complexity. When occurring in

a group, this process also considers individual

knowledge based on each person’s values, creeds

and experiences. The process is initially based on

information exchange between individuals within

the group by the resources offered by the

environment. From this interaction and group

reasoning, a so-called collective knowledge is

created, which determines the decision through a

sequence of actions that will be executed. At this

moment, the decision-making process may be

considered pre-finalized because, when discerning

the result, a new discussion is generally initiated,

creating a certain unbalance within the group and

often producing changes in the collective knowledge

about a given situation. This experience, in turn,

leads the group to acquire new and richer

227

Cristiane de Souza P., P. dos Santos Nunes E. and M. Armigliatto G. (2010).

THE COLLABORATIVE PROCESS OF DECISION-MAKING IN THE COOLED LEARNING ENVIRONMENT.

In Proceedings of the 2nd International Conference on Computer Supported Education, pages 227-232

DOI: 10.5220/0002780202270232

 SciTePress

experiences, due to the debate generated by the

encountered problem.

Figure 1 illustrates a schema of the decision-

making process in a group, also called collective

decision-making process based on the work authored

by Rodriguez et al. (2007), which presents a social

software system for the collective decision-making

process called Smartocracy.

Figure 1: Collective decision-making stages.

According to Moron (1998), the decision-making

process is characterized by phases that ensues the

so-called decision in an election, for instance, where

people generally research, discuss and exchange

information. Decision-making can be considered an

attempt to resolve a certain problem.

Making a decision means choosing among many

well-considered alternatives. Thus a decision must

be made as a response to a problem that has many

possible solutions and each solution has positive and

negative sides (Gomes et al., 2006).

Also, when certain decision involves a high

degree of complexity, its resolution simultaneously

influences many different objectives. A decision is

made considering quantitative and qualitative

parameters and their impacts are not easily

predictable; they can result on the possibility or

necessity of other decision-making processes.

It is also worth noting that not all individuals

who participate in a decision-making process

necessarily have the power to decide. However,

these participants can contend with deciders in order

to influence them.

Therefore, the decision theory is neither a

descriptive nor explanatory theory, since it does not

attempt to describe or explain how and/or why

people act a certain way or make certain decisions

(Maciel, 2008). Conversely, Gomes, Gomes and

Almeida (2006) believe the theory to be at times

prescriptive and at other times normative, in the

sense that it intends to help people make better

decisions according to their basic preferences. The

decision theory thus assumes that individuals are

capable of expressing their basic preferences and are

rational when they face simple decision-making

situations.

There are different methods that aid decision-

making and the most commonly consensual is voting

and leader-based decision-making. The latter is the

method used in the CoolED system. This choice is

based on an experience that uses a voting tool

described in Souza’s work (2003) and also on the

fact that the proposed environment works with small

groups and with non complex problems, demanding

some discussion without, however, much time for

choosing, for instance, a consensual method. In

addition, this choice is also based on the fact that

leadership method allows verifying some behavior

aspects on students such as diligence and

responsibility, among others.

Leader-based decision-making relies on the

choice of one member to lead the group and this

person will be invested with the power to make a

certain decision, though this leader’s choice will be

influenced by other members. This method requires

the establishment of criteria for choosing the leader

who will not necessarily remain the whole time with

this function. The length a leadership will last

depends on the methodology employed – it may

have a fixed leader during the whole collaborative

process or other members of the group may also play

this role. A model of leadership was elaborated and

it is described on section 4.3.

After the decision is made, the group must

organize itself to assess the efficiency of the adopted

solution. This evaluation will serve as a parameter

for next decisions. Therefore, we can consider a

decision positive when it is democratically

accomplished and generates harmony, satisfaction

and motivation among participants.

3 RELATED STUDIES

The use of intelligent agents has been widely

explored by researchers of this field in order to

monitor learner behavior and to facilitate the

teaching-learning process in collaborative learning

environments, interchanging collaboration and

interaction (Wang, 2002).

Souza (2003) developed a collaborative learning

environment for problem solving in the field of civil

engineering. The presented prototype uses an agent

that simulates the behavior of a social constructivist

INPUT

pro

blem

individual

solution

ideas

generate

potential

solutions

collective

solution

rankin

final

solution

selection

OUTPUT

solution

CSEDU 2010 - 2nd International Conference on Computer Supported Education

228

professor aiming to mediate student interaction. This

agent possesses a plan-driven and information-based

behavior regarding learners that is dynamically built

during the interaction course. The role of the

mediator agent is to stimulate and verify the

existence of a learning situation, thus it monitors

individual and collective participation of the group

within the environment and acts according to

defined mediation strategies. The agent also assists

group members during the decision-making process

through voting tool. Despite the existence of this

tool, the agent uses its communication strategy to

stimulate discussion among members about the

decision being made before using the tool.

Goodman et al. (2005) presents the use of an

intelligent agent to recognize the dialog between

participants and interact with them in a collaborative

learning environment. The agent participates in the

discussion and interacts with participants when

detecting learning problems, for instance, when a

participant dominates the debate but does not

interact with other participants or when participants

do not understand the problem at hand. The agent is

also concerned with group interaction during

collaborative learning and encourages participation,

asking the group questions and stimulating members

to think. When a problem is detected, the agent posts

a question or comment in order to facilitate learning

or improving interaction, serving as assistance or a

clue to guide the group in solving the problem.

Therefore, the investigation’s key concern was

determining when and how the intelligent agent

should intervene in learning.

After the simulations, improvements were

identified in the collaboration process (hence

learning process) and showed that the role of the

instructor, represented by the agent, should not be

lost when the student enrolls in distance learning.

Therefore, studies by Goodman et al. (2005) show

that the dialogue between participants (students and

instructor) provides strong evidence to check

whether or not group dynamics in the teaching-

learning process is efficient.

Accordingly, Aguilar, Antonio & Imbert (2006)

propose an intelligent collaborative virtual

environment (ICVE) that incorporates a Pedagogical

Virtual Agent (PVA) to assist one or more team

members during task execution in situations where

the real learning environment is impossible or

undesirable due to costs or danger, while acting as

an advisor and coordinator in virtual group

meetings.

The Pedagogical Virtual Agent (PVA)

incorporated into ICVE will take as its bases for the

tutorial process, the knowledge of the task, as well

as a mechanism for modeling the group based on the

interaction process. It may communicate with the

apprentices or make them suggestions during the

execution of their activities, if necessary. The PVA

will offer its help to the team, giving preference to

activities that are critical for the task success. The

incorporation of a PVA as a team member (leader)

with capacity to carry out behaviors similar to those

of a human tutor will require integrating with the

IVET a cognitive architecture for the virtual agent.

The aim of the study was to develop a strategy

for the assisted formulation of small groups in

ICVEs called Team Training Strategy (TTS), in

order to assist the formulation process. The proposed

strategy consists four interdependent phases in

which the trained team follows an iterative self-

evaluation process for executing a certain task.

4 THE COOLED

The CoolED system is a collaborative learning

environment developed by the LAVI research group

at the Federal University of Mato Grosso (UFMT).

This system aims to help computer science

undergraduate students master the content of

Nonlinear Lists, which is a subject of the Data

Structuring class, emphasizing insertion, removal

and search operations.

4.1 Functions

The CoolED system includes two types of users –

student and professor – who are identified by login.

The professor’s first task is to make a pre-

registration of students containing only essential

data. At this point the professor can either create

work groups with two or three students or let the

system randomly perform this operation. The

professor’s main activities in CoolED are: including

new exercises in the environment, since CoolED

already has some available exercises; inserting

didactic texts to download; recording appointments

in a planner; creating topics for discussion in the

forum; and visualizing and/or printing reports of

student activity in the environment.

The agent that welcomes the student receives this

student’s first access in CoolED and provides a brief

description of the operation and aims of the

environment. The agent then asks the student to

finalize his/her registration through the option

profile, which is made available in the environment.

THE COLLABORATIVE PROCESS OF DECISION-MAKING IN THE COOLED LEARNING ENVIRONMENT

229

The main menu shows the options concerning the

domain (binary trees, AVL trees, and B trees),

didactic material for download, and an option to

activate the Help system and the following

submenus include concepts, tree operation

simulations and proposed exercises about insertion,

removal and search operations.

CoolED also presents chats to allow student

group discussion; a forum, which acts as a repository

of topics of interest for discussion, where students

and professors may participate; an agenda or

planner, where the professor posts deadlines for

activities and meetings; and an animated agent that

acts as a mediator in the collaborative process,

stimulating participation of all group members,

observing the development of activities, and

assisting the decision-making process as well.

4.2 The Agent

The agent is responsible for the student-environment

communication interface. It acts as mediator,

intervening when the student, for instance,

experiences difficulties in using a CoolED tool or in

conducting exercises and also when choosing the

group leader. The term mediator was constructed

from the concept of mediated relation described by

Vygotsky and is based on the behavioral analysis of

a teacher in a classroom who follows a social

constructivist approach.

In the developed prototype the agent’s figure is

represented by Peedy, an animated agent developed

by Microsoft that is available free of charge. Peedy

possesses a few movement functions, appearance

and text balloons. The remaining functionalities are

being developed by LAVI.

The following functions pertain to the agent:

 Warning – shown to the students every time

new information is included by the professor in

the Planner, when material is available for

download, and when interest topics are inserted

in the Forum;

 Assistance – provided whenever the agent

notices that a student faces difficulties in solving

the exercise, when there is lack of

communication between group members or when

communication is unilateral;

 Correction – at the end of an exercise, the agent

compares the group answer to the exercise

solution;

 Decision-making – for a solution of each

exercise, the agent uses the leadership model for

determining which student will lead the group.

4.3 CoolED’s Decision-making Process

In solving exercises collaboratively, group decision-

making is conducted by a leader, who is selected by

the agent, considering evaluation criteria described

in the leadership model. The student chosen as a

leader is the only one who conducts insertion,

removal and search operations in trees, and the role

of the remaining students is to participate in the

solution of the exercise, expressing their opinions

through the chat room.

In the model of leadership each student has a

leadership reputation grade that corresponds to the

probability of being chosen to be leader and a

variable that indicates how many times the student

has already been a leader. The system initially

bestows grade 5 to each student, hence all students

are equally likely to become leaders. As students

lead the exercises, their grades vary from 0 to 10,

where 0 means least likely to be chosen and 10

means most likely to be chosen. Grades start at 5

because, after each leadership, a student can have his

or her grade increased or decreased depending on his

or her performance. Whenever there is someone in

the group who has not yet been leader, this person

will always be chosen. The number of times a

student has been a leader will also be taken into

consideration, and priority is given to those who

were leaders the least often. In case of a tie between

the grade and number of times two people were

leaders, both will have the same chances of being

chosen, and the agent will randomly select one of

them.

At the end of each collaborative student-led

exercise, the group will answer an evaluation survey

that aims to analyze leadership performance, and

thus serve as a positive or negative weight for the

leadership grade. CoolED possesses two types of

surveys, one for group members lacking leadership

functions and another for leader self-evaluation. The

self-evaluation questions induce leaders to reflect on

their performance in this role and their interest in

repeating the experience. The other survey makes

the remaining participants of this work session to

analyze the leader’s role and investigate whether

they wish to work with this leader again. The result

after compilation is presented by the agent to the

leader.

4.4 Implementation

The CooIED system is in a final stage of

development taking as a basis the Document of

Requisites Specification, the Entity-Relation

CSEDU 2010 - 2nd International Conference on Computer Supported Education

230

Figure 2: Interface of the instructor’s module.

Diagram, and the Interface Project. The CoolED

interface project was elaborated according to

methodology proposed by Pressman (2006) to create

interface projects for web. Different criteria of

usability were adopted during the whole project and

a session of inspection was implemented using

heuristic evaluation.

After defining MySQL as management language,

the data basis was created, as well its tables and its

relations. In addition, some tests were taken in order

to verify their integrity. For its implementation Java

was the language selected and this choice was made

for its particularities, for non additional costs for the

project, and for been known for the development

team. After studies, the following frameworks were

defined: JSF (with facelets, richfaces, and Ajax4Java

– A4J), Hibernate JPA, and JavaMail. Proper

implementation started with the process of building

the classes which are related to the data basis entities

and its maps using Hibemate JPA specifications.

After that, insertions of random data were made on

the data basis and a few tests were taken as well.

In accordance to the CoolED Specification

Document, the implementation of the system

followed MVC architecture and the layer model was

subdivided on business and persistence. On the

persistence layer several interfaces (Java) were

defined to be implemented. A number of tests were

taken in order to verify the mapping made by classes

and the XLM file, the Hibermate framework

behavior inside the application, and also the Java

code necessary for access operations on data basis.

The layer of vision is on a process of

development based on images available for the

interface project team, where mapped actions and

attributes have been used on the pages. The

Controller has been developed as an intermediate

stratus among layers of vision, persistence and

business. It is responsible for providing required

information and registering it, applying some

validation rules.

After dividing responsibilities between creations

of data basis, its application and the classes that

support that structure, the construction of the pages

of web application was started attending to the

student´s and the instructor´s modules. The

construction of the pages and the application of the

tests have been finalized and soon the integration

between web application and the agent will be done,

concluding the construction of the first version of

the CoolED System.

To illustrate how the CoolED works, figure 2

presents an interface for the instructor’s module of

insertion of the binary tree activity. On the screen

the instructor determines what type of activity:

insertion, removing or search. In this example, the

teacher has chosen a sort of tree (null or not null),

and typed some keys and the complete statement of

the activity as well. After finishing (save key) the

activity it is storage on the CoolED system data

basis and ready for use at the student module.

THE COLLABORATIVE PROCESS OF DECISION-MAKING IN THE COOLED LEARNING ENVIRONMENT

231

5 CONCLUSIONS AND FUTURES

STUDIES

Investigation of related studies still leads us to

believe in the need for an environment that considers

collaborative learning, where the professor can be

present during the whole work session represented

by the mediator agent and where the professor’s role

is not limited to analyzing the end result of

collaborative activity. Every issue involving

communication, activity coordination, and above all

decision-making processes is related to the

opportunities that the environment bestows, favoring

constant dialogue between group members and

especially interest in teamwork. Leadership is a

crucial issue in this work since it is not fixed and

continuous in all work sessions and is thus able to

permeate all group members. Besides, there is an

attempt to respect individual interest in leading the

group by means of an evaluation conducted

according to previously established criteria in the

leadership model.

As regards the investigative aspects of this study, the

next step is to conduct an empirical study with a data

structuring class of students enrolled in the

undergraduate department of Computer Science and

Information Systems at UFMT, thus analyzing the

agent’s performance as a mediator of student

interaction and especially in the decision-making

context.

In this experiment the researchers intent to observe

the CoolED support during the learning process of

non linear lists (trees), the decision making process

behavior of the students, and also the efficiency of

the developed leadership model. The study is not

restricted to the environment, as we also intend to

use the interviewing method with users in order to

conduct a more accurate analysis.

In this final stage of the development of the system,

the methodology for this empirical study and

interview is on the process of elaboration. It is

expected that in middle of March those activities

will be done and thus the experiment will be

concluded in April of 2010.

It is worth noting that one of the members of the

research group LAVI is a professor who teaches data

structuring classes. She is the responsible for

elaborating the whole contend related to the domain

of the system: texts, examples, simulations, and tests

on trees.

ACKNOWLEDGEMENTS

CNPq and Propeq-UFMT.

REFERENCES

Aguilar, R. A; de Antonio, A.; Imbert, R. An Intelligent

Collaborative Virtual Environment for Team Training

– A Preliminary Report. (November 21-24, 2006). In

Proceedings of the 15th International Conference on

Computing CIC.IEE Computer Society, Washington,

DC, 236-239. doi:10.1109/CIC.2006.23

Gomes, L. F. A. M.; Gomes, C. F. S.; Almeida, A. T. de.

(2006). Tomada de Decisão Gerencial: enfoque

multicritério. (2nd ed) São Paulo: Atlas.

Goodman, B. A.; Linton, F. N. Gaimari, R. D.; Hitzeman,

J. M.; Ross, H, J.; Zarrella, G. (Mar, 2005). Using

Dialogue Features to Predict Trouble During

Collaborative Learning. User Modeling and User-

Adapted Interaction 15, 1-2, 85-134. doi:

10.1007/s11257-004-5269-x.

Maciel, C. Um Método para Mensurar o Grau de

Maturidade na Tomada de Decisão E-Democrática.

(2008). Doctoral Thesis in Computer Science.

Universidade Federal Fluminense. Niterói (RJ), Brazil.

Moron, M. A. M. Concepção, Desenvolvimento e

Validação de Instrumentos de Coleta de Dados para

Estudar a Percepção do Processo Decisório e as

Diferenças Culturais. (1998). Master Thesis in

Administration. Universidade Federal de Rio Grande

do Sul. Porto Alegre (RS), Brazil.

Pressman, R. S. (2006). Software Engineering: a software

practitioner´s approach. (6nd ed). Mcgraw-Hill.

Rodriguez, M. A.; Steinbock, D. J.; Watkins, J. H.;

Gershenson, C.; Bollen, J.; Grey, V.; de Graf, B.

Smartocracy: Social Networks for Collective Decision

Making. (2007). Proceedings of the 40th Hawaii

International Conference on System Sciences.

Souza, P. C. Diretrizes para a Construção de Mediadores

Sócio-Construtivistas em Sistemas de Aprendizagem

Colaborativa Apoiada por Computador. (2003).

Doctoral Thesis in Engineering. Universidade Federal

de Santa Catarina. Florianópolis (SC), Brazil.

Wang, F. Self-organising communities formed by middle

agents. (2002). In Proceedings of the first international

conference on autonomous agents and multi-agent

systems, Bologna,Italy.

CSEDU 2010 - 2nd International Conference on Computer Supported Education

232