AUTO-COLLEAGUE

A Collaborative Learning Environment for UML

Maria Virvou and Kalliopi Tourtoglou

Department of Informatics, University of Piraeus, 80 Karaoli & Dimitriou St., Piraeus, Greece

Keywords: Collaboration, learning, collabo

rative learning, CSCL, motivation, motivation management, collaboration

management, group, team, role, stereotype, student modelling, user modelling, software engineering, UML.

Abstract: In this paper we present AUTO-COLLEAGUE, a collaborative learning environment for UML. AUTO-

COLLEAGUE is a Computer-Supported Collaborative Learning (CSCL) system. It is based on a multi-

dimensional User-Modeller component that describes user characteristics related to the UML domain

knowledge, the performance types, the personality and the needs of the learner. The system constantly

monitors, records and reasons about each learner’s actions. As a result of this process, AUTO-

COLLEAGUE provides adaptive advice and help to users so that they may use UML more efficiently and

collaborate with other members of their team more constructively.

1 INTRODUCTION

Collaborative learning is a situation in which two or

more people learn or attempt to learn something

together (Dillenbourg, 1999). There are many

theories to support the advantages of adapting

collaborative learning. As such, collaborative

learning has attracted a lot of researcher energy

lately and has resulted in many computer-supported

collaborative learning environments.

Collaborative learning has found its most

effective

application in software systems, as these

tools can be appropriately implemented. Software

systems that include mechanisms of collaborative

learning are widely known as Computer-Supported

Collaborative Learning (CSCL) systems. CSCL

systems have been widely used over the last decade

with the aim to encourage learners to collaborate

simulating the traditional class courses. This

simulation is oriented in two kinds of interaction: the

peer-to-peer and the teacher-to-learner interactions.

In view of the above, we have developed AUTO-

COLLE

AGUE (AUTOmated COLLaborativE

leArning Uml Environment). It is a UML training

environment for software engineers. The Unified

Modelling Language (UML) is an object-oriented

visual modelling language that is used to specify,

visualize, construct, and document the artifacts of a

software system (Rumbaugh, 1999). The use of

UML has grown enormously in many organizations

that develop software during the last decade. UML

is, also, very popular amongst education institutes,

that use it as a tool for software engineering training.

Being widely used in industry by now, proficiency

in UML is certainly a valuable asset for every

Computer Science student (Engels et al., 2006). For

the purposes of AUTO-COLLEAGUE, collaborative

learning seems appropriate for training users in

UML. As such, AUTO-COLLEAGUE provides a

multi-user, synchronous and remoted environment

for software engineers to collaborate during the

learning process.

The design and the architecture of AUTO-

OLLEAGUE are based on the principles of the

constructivism learning theory (Piaget, 1973).

Constructivism is based on the principle that human

learning is constructed. One of the main perspectives

of this theory is that the learners should be

autonomous and independent during the learning

experience. The sense of autonomy reveals other

aspects of the human personality, such as the

motivation. Motivation is defined as the forces

within an individual that account for the level,

direction and persistence of effort expended at work

(Schermerhorn et al., 2000). In the implementation

of AUTO-COLLEAGUE, we have taken into

account the impact of motivation of learners.

Thereby, we have chosen to apply principles of a

widely used motivation theory: the Maslow’s

Hierarchy of Needs (Maslow, 1954).

AUTO-COLLEAGUE bases its reasoning on a

User Mod

elling component implemented with

stereotypes. Learner modeling is the key concept

Virvou M. and Tourtoglou K. (2007).

AUTO-COLLEAGUE - A Collaborative Learning Environment for UML.

In Proceedings of the Second International Conference on Software and Data Technologies - SE, pages 35-39

 SciTePress

concerning the application of artificial intelligence

into educational software (Dillenbourg & Self,

1992). The stereotypes used in AUTO-

COLLEAGUE encompass the personality, the

performance type, the knowledge and the motivation

sources of the learners. Stereotypes constitute a

widely used technique of building user models.

Stereotypes are simply collections of facet-value

combinations that describe groups of system-users

(Rich, 1979). Stereotypes have else been widely and

efficiently applied to educational software

(Beaumont, 1994). The User Modelling component

will be explained in detail in a subsequent chapter.

2 RELATED WORK

AUTO-COLLEAGUE is a CASE tool, but its main

objective is to provide a collaborative environment,

to model users and to offer intelligent advice based

on stereotype-based reasoning. As such, it is not our

aim to compete with well-established and efficient

CASE tools for UML, such as Rational Rose.

There have already been developed quite many

efficient CSCL systems to support distant users to

work together (some of them into groups) and to

provide them with an assistant on their learning

process. However, after reviewing them, we have

concluded that AUTO-COLLEAGUE introduces

new aspects especially in the architecture and the

variety of user characteristics it evaluates. Most of

the CSCL systems base their inferences mainly on

the participation of the users in a chat system and the

outcomes of this participation. In particular, COLER

(González et al, 2000), LECS (Rosatelli & Self,

2004), COLLECT-UML (Baghaei & Mitrovic,

2005), DEGREE (Barros & Verdejo, 2000),

HABIPRO (Vizcaíno at al., 2000), FLE3 (Chen et

al., 2003) and Learning Environment (Or-Bach &

Van Joolingen, 2004) do not focus on including in

their inference mechanisms important learner

characteristics, such as personality. In contrast, they

focus on qualitative evaluations of learners’

participation in the system and the effects that this

participation had afterwards in their progress. In

contrast, AUTO-COLLEAGUE is is based on a User

Modeller, which models the learner in several

aspects. There have been other CSCL systems that

perform user/learner modelling, such as GRACILE

(Ayala & Yano, 1998) and HELP-DESK (

Greer et

al., 1998

). However, these systems too have not

taken into consideration human characteristics as

AUTO-COLLEAGUE. In specific, they successfully

use social user traits that are related mainly to the

collaboration. On the other hand, with the aim to

have an integrated approach to the human/learner

model, AUTO-COLLEAGUE uses characteristics

related to the performance type and the personality.

Another detected difference is the way that the

system estimates what has been done wrong. For

example, COLER (González et al, 2000), LECS

(Rosatelli & Self, 2004), COLLECT-UML (Baghaei

& Mitrovic, 2005) and HABIPRO (Vizcaíno at al.,

2000) evaluate the user performance (on the domain

knowledge and skills) based on submitted solutions

to specific problems. On the contrary, AUTO-

COLLEAGUE continuously evaluates the efficiency

of the learners. That is achieved by tracking every

action of the users even idleness.

We also remarked the absence of the precious

contribution of a human trainer (COLER,

COLLECT-UML, GRACILE, HABIPRO and

Learning Environment). AUTO-COLLEAGUE

provides the potential to a person to be assigned to

the system as a trainer. This gives him/her a special

role in the system. The trainer is responsible for

defining crucial attributes/parameters of the learners,

evaluating, applying, and contributing to the advice

and conclusions of AUTO-COLLEAGUE.

3 OVERVIEW OF THE SYSTEM

At the front end, AUTO-COLLEAGUE is an

environment in which software engineers can create

UML diagrams. Additionally, software engineers

can provide UML diagrams as input to the process

of generation of an XML file (descriptive of the

UML diagram), a database structure and a source

code project written in Borland Delphi. A screen-

shot of AUTO-COLLEAGUE is illustrated in Figure

1. The main form contains two toolbars, a status bar,

the forum section, the UML workspace and a

horizontal and a vertical ruler perimetric the

workspace. The two toolbars are the standard and

the project toolbar. The standard toolbar includes the

buttons for creating a UML diagram, opening an

existing one, saving, printing and clearing the

current UML diagram and an exit button. The

project toolbar has three buttons, which respectively

have the functionality to export an XML file, a

Delphi application and a database structure. The

status bar on the lowest part of the form is used by

the system to show additional and explicative

information to the user, such as the cursor position,

the log data and help messages. In the forum section,

the user can send messages to another user of the

system after selecting the purpose of message (to

make a question, to request help, to propose an

answer to a stated question, to state his/her

ICSOFT 2007 - International Conference on Software and Data Technologies

agreement/disagreement, to simply communicate, to

greet). The UML workspace provides the necessary

user interface to design UML diagrams. The user

can add UML classes (properties, attributes,

methods, method parameters etc), notes, inheritances

and compositions.

Figure 1: Screen-shot of AUTO-COLLEAGUE.

There are two human actors in the AUTO-

COLLEAGUE environment: the learner and the

trainer. The learners are the users who intend to

learn UML and the trainer is the coordinator/coach

of this process of learning. The trainer is the partner

of the system as his/her responsibility is to define the

roles, teams and groups of the learners and to

evaluate and apply appropriately the information

given by the Collaboration Module. The roles, teams

and groups are explained later in detail. In brief,

these concepts concern the structured organization

of the learners in the system.

The aim of AUTO-COLLEAGUE is to provide a

collaborative environment. To achieve this, it

includes the Collaboration Module. The

Collaboration Module constantly traces the

attributes, the operations and the progress of the

users in order to draw inferences on which may be

the most effective collaboration patterns between the

colleagues. The user attributes are maintained in a

stereotype-based structure by the User Modeller.

The User Modeller includes attributes that refer to

the UML domain knowledge, the performance type,

the personality and the motivation sources of the

users. The User Modeller will be described in detail

in a subsequent chapter.

AUTO-COLLEAGUE includes five main

components: the User Modeller, the Collaboration

Module, the Tracker, the Feedback Module, and the

Advisor. We have already mentioned the User

Modeller and the Collaboration Module. The

Tracker is the module that stores every action, every

message and every error of the user. The Feedback

Module is responsible for the interactive messages

of the system to the user. The answers to these

interactive messages are stored and used by the User

Modeller for further inferences. The Advisor is the

module that informs the trainer and the learners

about the conclusions of the Collaboration Module.

4 USER MODELLER

The User Modeller is of vital importance in AUTO-

COLLEAGUE. It is the module that performs the

essential reasoning for the users. This outcome often

is passed to another component, the Advisor. The

architecture of the User Modeller is based on

stereotype reasoning.

The user features that we decided to record are

the personality, the performance types, the levels of

expertise and the motivation sources. The User

Modeller is composed by four stereotypes: the

personality, the motivation sources, the performance

types and the levels of expertise.

Personality refers to some typical characteristics

of the software engineers that influence their

behavior in relation with the acquisition, the usage

and the sharing of knowledge. The personality

features used by the system are the self-confidence,

the leading potential, the diligence and the

willingness to help others, conformity, participative

and communicative. The self-confidence feature is

what the user believes about him/herself as

compared with the reality that AUTO-

COLLEAGUE has assumed. The leading potential

refers to the ability of the user to manage a group.

For example, a person who has leading potential

should be able to be influential within the group and,

at the same time, preserve a harmonious relationship

between them. The diligence measures the amount

of work that the user is willing to take. The

willingness to help others is the eagerness of the

software engineer to cooperate with others and help

them even if they do not belong in the same team.

Conformity expresses the ability of the user to work

in accordance with the established group in which

s/he belongs. For example, a conformist would not

have the tendency to disagree (without having a

substantial reason) with some particular colleagues.

A user who is participative is the one who seems to

have a share in the message forum. The same

definition applies to the communicative

characteristic. The difference between these two is

that the “participative” refers to messages of

knowledge content, in contrast with the

“communicative” that refers to message of general

content, such as greeting a peer.

AUTO-COLLEAGUE - A Collaborative Learning Environment for UML

Performance types are the main typical manners

of software engineers when using a program. The

defined performance types are: sceptical, hurried,

slip-prone, unconcentrated and efficient. Sceptical

refers to the user who frequently lets the program

idle for a noticeable amount of time, in his/her effort

to complete a task. Hurried is a user who completes

tasks quite quickly but not successfully. A user may

be characterized as slip-prone when although s/he

has the essential UML knowledge to complete a task

successfully, s/he makes mistakes due to

carelessness or imprudence. Unconcentrated is a

user who seems to be sceptical (delays to act), but

actually cannot be productive in UML probably

because of lack of ability to concentrate. Efficient is

the user who seems to accomplish successfully a

task at a reasonable speed.

A level of expertise represents the extent of the

software engineer’s knowledge on UML and ability

to construct a correct UML diagram. The UML

knowledge is stored in AUTO-COLLEAGUE

database, which means that the trainer can update it.

This reflects flexibility in the maintenance of such a

crucial element. The UML knowledge is stored in a

hierarchical structure of UML concepts. The UML

concepts are the principles of constructing UML

diagrams, such as Class Definition, Attributes

Definition and Inheritance. The levels of expertise

used by the system are No Knowledge, Basics,

Junior, Senior and Expert. Each level of expertise is

linked to some specific UML concepts. This link

represents the prerequisites on UML knowledge for

a software engineer of level of expertise

classifications. Additionally, there is a degree of

knowledge upon each UML concept. For example,

the Basics Level of Expertise can be linked to the

Attributes Definition with degree 1. The fact that the

structure of UML concepts is hierarchical

contributes to the inferences rules of AUTO-

COLLEAGUE stereotype-based user modelling.

The motivation sources can be defined as the

forces that lead the learners to strive for the

possession of knowledge and the development of

skills and talents in the framework of satisfying their

needs. The stereotypes of User Modeller include

these motivation sources, which are structured on

the basis of the Maslow’s Hierarchy of Needs

(Maslow, 1954). The levels of needs that our system

uses are the social, the esteem and the self-

actualization needs. The social needs refer to the

need of the learners to feel members of a group, to

have a common target and a pleasant

communication, to be accepted by colleagues and to

develop friendship relationships. The esteem needs

are linked to the satisfaction of the self-ego that is

the need to be a distinguished personality/entity. In

particular, the esteem needs are the needs of a

learner to be recognized, respected, admired or even

envied by others. The self-actualization needs

concern the achievement of the maximum of

everything one considers as admirable. The

motivation sources are implemented in the User

Modeller structure with two attributes: the need and

the strength of need. The motivation sources are

included in the stereotype structure as their values

can influence largely the inferences of the overall

system. For example, the satisfaction of the esteem

needs can trigger the user trait of personality by

defining the user as self-confident. On the other

hand, the dissatisfaction of the social needs may

influence the behavior of the user and prevent

him/her from being participative.

For the Inference Mechanism and the

Triggering/Retraction Conditions of the stereotype-

based User Modeller, we need to define certain

variables that we have used. These variables are

descriptive of all of the aforementioned

characteristics that constitute the user stereotypes.

These variables are: user actions, participation

frequency, help requests frequency, help offered to

others frequency, error frequency, average idle time

between actions, speed of completing a task,

relevancy of actions.

5 SUPPLEMENTARY MODULES

AUTO-COLLEAGUE includes six supplementary

modules that support the main functions of the

system. These are the Group-Role Module, the

Tracker, the Historian, the Feedback Module, the

Communicator, the Advisor and the Collaboration

Module.

The function of the Group-Role Module is to

allow the trainer to categorize the users into groups

based on certain roles. This categorization is

necessary for AUTO-COLLEAGUE to draw

inferences about the collaboration schemes between

software engineers. The Tracker is responsible for

tracking the sessions, the log data, the actions, the

errors and the messages of the users. The Historian

is the module that provides the users with a

supplementary functionality: the user can playback

the exact movements s/he did in the past. The

Feedback Module is responsible for requesting the

user opinion on the assumptions of the system. The

Communicator includes the mechanism of the

message forum. The message forum is a subsystem

that offers a chat utility. It enables the users to send

messages to each other and, simultaneously, observe

them. The Advisor is the module that undertakes the

task of notifying the trainer of the system about the

conclusions of the Collaboration Module. This is

ICSOFT 2007 - International Conference on Software and Data Technologies

done by means of window messages and report

forms. The Collaboration Module is a vital module

as it serves the aim of AUTO-COLLEAGUE to be a

CSCL system that is to offer an efficient

collaborative learning environment. In other words,

the goal of the Collaboration Module is to make the

necessary inferences to draw conclusions about the

most effective collaboration teams between the

learners. The effectiveness is relative to the

performance of all the learners as individuals and as

a whole.

6 CONCLUSIONS

The collaboration management is a very complicated

task as the aspects of the human nature are too

complicated. With the aim to contribute to the

scientific effort to find effective ways of building

CSCL systems, we designed and presented in this

paper AUTO-COLLEAGUE. It is a collaborative

learning environment for software engineers to be

trained on UML. The most crucial modules of

AUTO-COLLEAGUE are the User Modeller, the

Group-Role Module and the Collaboration Module.

The stereotype-based User Modeller includes

important descriptive user characteristics that have

effects on the learning process. These characteristics

are the knowledge, the performance types, the

personality and the motivation sources. The Group-

Role Module involves the structural classification of

the learners in the system. Using the data of the User

Modeller and the Group-Role Module, the

Collaboration Module draws inferences based on the

stereotypes of the learners and proposes the most

effective collaboration arrangements. One very

important functionality of AUTO-COLLEAGUE is

also the provision of assistance to the human trainer.

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AUTO-COLLEAGUE - A Collaborative Learning Environment for UML