ELEARNING VIRTUAL ENVIRONMENTS MULTI-AGENT

MODEL FOR MEDICAL STUDENTS

Luis Gaxiola Vega, Bogart Yail Márquez, José Magdaleno-Palencia, Manuel Castanon-Puga

Baja California Autonomous University, Chemistry and Engineering Faculty

Calzada Universidad 14418, Tijuana, Baja California, 22390, Mexico

Miguel A. Cadena Alcantar

Baja California Autonomous University, Cisalud Palm Valley, Blvd. San Pedro # 1000, Tijuana, Baja California, Mexico

Keywords: eLearning Environments, Multi-agent systems, Simulation Virtual Reality, Knowledge, Education Virtual,

Medical Students.

Abstract: In today's education it is becoming normal to talk about virtual learning, online and at a distance. Such

environments are incorporated daily attendance practices that know, greatly enriching, with the potential of

media and technology, educational opportunities in all areas, this paper explains how to implement the use

of multi-agents. It will discuss how the curriculum can be enriched by activities involving problem-based

learning, case studies simulations and virtual reality. This new model provides multiple uses for exploring

knowledge and supporting learning-by-doing. It engages users in the construction of knowledge,

collaboration, and articulation of knowledge in a virtual environment, especially in the teaching - learning.

1 INTRODUCTION

1.1 e-Learning

e-Learning comprises all forms of electronically

supported learning and teaching. The information

and communication systems, whether networked or

not, serve as specific media to implement the

learning process. The term will still most likely be

utilized to reference out-of-classroom and in-

classroom educational experiences via technology,

even as advances continue in regard to devices and

curriculum.

Electronic education is considered one of the

most promising options for today and the future of

education, which is why the development of learning

environments needs to increase. A virtual learning

environment consists of a digital space that

interrelate various aspects of communication,

education, technology and emotions, which helps

students learn. Learning environments usually

covered four areas: information, exhibition,

production and board area: interaction.

E-learning is essentially the computer and

network-enabled transfer of skills and knowledge. E-

learning applications and processes include Web-

based learning, computer-based learning, virtual

classroom opportunities, and digital collaboration.

Content is delivered via the Internet,

intranet/extranet, and others such as information

technologies. It can be self-paced or instructor-led

and includes media in the form of text, image,

animation, streaming video, and audio.

1.2 Computer-based Training

Abbreviations like CBT (Computer-Based Training),

IBT (Internet-Based Training) and WBT (Web-

Based Training) have been used as synonyms to e-

learning. Today one can still find these terms being

used along with variations of e-learning, such as,

elearning, Elearning, and eLearning.

E-learning has become common in specialties

that use standardized treatment pathways, such as

emergency medicine (Berne 2001). However,

learning programs based on simulation using virtual

369

Gaxiola Vega L., Yail Márquez B., Magdaleno-Palencia J., Castanon-Puga M. and A. Cadena Alcantar M..

ELEARNING VIRTUAL ENVIRONMENTS MULTI-AGENT MODEL FOR MEDICAL STUDENTS.

DOI: 10.5220/0003497503690372

In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 369-372

ISBN: 978-989-8425-53-9

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

rooms are still scarce, as they are expensive and

laborious (Morin 2008)

1.3 Virtual Room

Simulation of Virtual Rooms (VR) today is a new

technology applied to research of new methods,

forms, techniques, and architectures that provide

solutions to problems that occur in both medicine

and industrial engineering, and thus create

experience when actual cases are confronted.

Today education is heading toward virtual learning,

online, and distance learning. These types of

environments are incorporated into classroom

practices that as we know, are greatly enhancing,

with technology, the possibilities of teaching in all

areas. There is no doubt that within a classroom

learning environment, a process that always takes

place is communication. Interaction takes place

within the media.

1.4 Multi-Agent System (MAS)

MAS Multi-agent systems: it consists of

autonomous agents working together to solve

problems, characterized in that each agent has

incomplete information or capabilities for solving

the problem, there is no global system control, data

is decentralized and computation is asynchronous.

The agents dynamically decide to undertake tasks

(Gilbert 2007).

MAS including the integration of global and

individual perspectives and the dynamic adaptation

of systems to environmental changes. As growth

systems that include hundreds or thousands of agents

(Artikis, Boissier et al. 2009).

Formal theories are needed to describe interaction

and organizational structure and understand the

relationship between the organizational functions of

these agents. there are different definitions of a

multi-agent system, several authors define it as a

system in which multiple autonomous agents,

heterogeneous, interacting with the environment,

each seeking their own goals (Gilbert 1999).

However it must be heterogeneous to be a multi-

agent system, some authors (Gómez 2003), defined

from the construction of programs that make up the

distributed system applying a technology closely

related to artificial intelligence.

This technique is accomplished by being

autonomous and intelligent agents. It is when the

systems become more distributed. The model using

the MAS help the study of knowledge in virtual

environments.

Building on the Learning Virtual Learning

Environments, expert systems, social simulation

systems, robotics etc. among other areas. Research is

performed after the creation of a multi-agent model

that allow us to represent the process of learning and

artificial intelligence techniques in the area with

Student Health.

This work can be part of the beginning of a

multidisciplinary learning process, in order to

achieve meaningful learning (Ausubel 1983), there

will be iterative work between students of the

Department of Health CISALUD Palm Valley,

Tijuana, Mexico, where knowledge can be generated

through the simulation of learning environments.

Needs that are required are able to have an RV, for

working in collaborative or cooperative, also were

used computational techniques intelligent hybrid

techniques for the rules are changed adaptively.

2 BACKGROUND

For over twenty years, the simulation has been used

to solve health problems in the U.S. and the United

Kingdom. For example, (Pitt 1997) flows simulated

patient in a hospital based on State Transition

Networks. (Spry and Lawley 2005) developed a

model to assess the pharmacy staffing and work

scheduling and (Jun, Jacobson et al. 1999) applied

multidimensional performance measurement of a

Family Medicine Clinic and Community Health by

simulation. Jun (Jun, Jacobson et al. 1999) presented

a study of 117 applications of simulation

applications in health care clinics.

Most simulation studies on health have focused on

relatively well-constrained operational environments

of care (eg, organization of accident and emergency

departments (Miller, Ferrin et al. 2004) or have been

necessary to greatly simplify the domain modelling

to produce usable results. In general, these studies

have been directed to specific problems of interest

within the institutions identified in attention (Pitt

1997).

One of the common areas of concern in any

healthcare institution is to reduce the waiting time or

length of stay of patients. This is also part of the

management quality of health care organization.

Today, many health organizations have adopted

various quality management techniques, such as

Business Process Reengineering, Total Quality

Management and continuous improvement to

improve their processes. As simulation can model

complex and highly variable environments, it is a

useful tool in these studies.

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

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A significant percentage of hospital admissions

come through the Emergency (A & E) Unit and also

serves the most urgent cases is now essential that the

service department is efficient at all times. A number

of case studies have been performed on A & E units.

(Garcia, Choren et al. 2005) proposed the use of

simulation to study the possibility of reducing the

time in an emergency room via a fast track.

Some other studies, suggests the use of an additional

patient care coordinator at peak

times, making an alternative space for patients.

(Takakuwa and Shiozaki 2004) simulated the flow

of patients with ARENA and found that patients in

A & E unit spent most of his time waiting for

treatment and the wait in emergency beds, doctors,

drips and beds accounted for the bulk of the timeout.

(Miller, Ferrin et al. 2004) illustrates the use of

simulation for continuous improvement in A & E

Unit and in particular EXTEND used to apply

experimental design techniques.

2.1 Issues in Virtual Rooms

One of the problems of simulating clinics such as an

A & E is the unit that accurately represents the

arrival rates of patients. Random walk-ins "(and

emergencies) are superimposed on scheduled

appointments (for review, etc.) and it is very

difficult to predict and therefore manage patient

arrivals at any time. In previous studies patients are

usually grouped in situations of appointment and

Random Walk-ins. In patients who have scheduled

appointments leads to hours, while arrivals from

walk-in patients are randomly generated. The

developed a probabilistic model to predict potential

patient arrivals to an emergency department.

However, these approaches do not take into account

the possibility of peaks and troughs in the day called

"random" arrivals. For example the peak in arrivals

of patients often occurs in late morning and

afternoon.

2.2 Modelling

The purpose of the paper is on modelling through

simulation in order to strengthen their student’s

meaningful insight into virtual environments. Using

a multi-agent model that allows it to be the

computational tool to help us shape the learning

problems and to evaluate or measure, the proposed

model of meaningful learning.

The aim is to represent the knowledge of agents to

help shape the problems faced by students. And

being able to interpret the knowledge learned in the

simulation of multi-agent systems. The development

of a multi-agent model implemented in a virtual

environment to assess and demonstrate significant

learning in the area enabling students to improve

their health knowledge is required.

3 IMPLEMENTATION

Using the NetLogo software, StellaThing for

interpretation of agents implemented in multi-agent

model and compares the students to see the results

generated. First identify the problems that may occur

in the process of model development. In reproducing

the student's behavior in different scenarios, through

intelligent agents. And so to establish a method or

technique of intelligent computation according to the

proposed model is applied to social simulation

eLearning Virtual Environments NetLogo (Wilensky

1999).

Figure 1: ELearning simulation for medical students.

When creating the virtual cave for VR simulation.

(Rooms Virtuals) where students work health area,

simulations are carried out in the virtual cave where

they can reproduce the behavior of students and to

obtain data that can compare with actual behavior.

Experiments are underway to train students who

work in the laboratory of virtual teaching and

learning in the area of health sciences in the State of

Baja California, Mexico.

4 CONCLUSIONS

The proposed Project to have objective to

investigation and research on theoretical and

practical about how to behave in a group of students,

such as learning in different learning environments

for troubleshooting. We can adapt to our needs.

We propose further research, develop meaningful

learning. Hence, the project aims to develop tests

ELEARNING VIRTUAL ENVIRONMENTS MULTI-AGENT MODEL FOR MEDICAL STUDENTS

371

results with agents in the students, what is learning

and performed as research and also be elearning

assessment to develop, under the proposed multi-

agent model.

This would provide us with feedback and

intelligence using meaningful and collaborative

learning in different environments.

REFERENCES

Artikis, A., O. Boissier, et al. (2009). Handbook of

research on multi-agent systems: semantics and

dynamics of organizational models New York,

Hershey Pa. : Information Science Reference.

Berne, E. (2001). The structure ans dynamics of

organizations and groups. Beaconsfield, Australia,

Freemantle Publishing.

Garcia, A., R. Choren, et al. (2005). Software Engineering

for Large-Scale Multi-Agent Systems - SELMAS

2005: workshop report, ACM.

Gilbert, N. (1999). The Simulation of Social Processes.

SMAGET Conference. Clermont-Ferrand, France.

Gilbert, N. (2007). Computational social science: Agent-

based social simulation. Agent-based modelling and

simulation. D. Phan and F. Amblard. Oxford,

Bardwell: 115-134.

Gómez, J. (2003). Metodologías para el desarrollo de

sistemas multi-agente. Inteligencia Artificial, Revista

Iberoamericana de Inteligencia Artificial. No.18

(2003), pp. 51-63.

Jun, J. B., S. H. Jacobson, et al. (1999). "Application of

Discrete-Event Simulation in Health Care Clinics: A

Survey." The Journal of the Operational Research

Society 50: 109-123.

Miller, M. J., D. M. Ferrin, et al. (2004). Fixing the

emergency department: a transformational journey

with EDSIM. Proceedings of the 36th conference on

Winter simulation. Washington, D.C., Winter

Simulation Conference: 1988-1993.

Morin, E. (2008). "Restricted Complexity, General

Complexity."

Pitt, M. (1997). A generalised simulation system to

support strategic resource planning in healthcare.

Proceedings of the 29th conference on Winter

simulation. Atlanta, Georgia, United States, IEEE

Computer Society: 1155-1162.

Spry, C. W. and M. A. Lawley (2005). Evaluating hospital

pharmacy staffing and work scheduling using

simulation. Proceedings of the 37th conference on

Winter simulation. Orlando, Florida, Winter

Simulation Conference: 2256-2263.

Takakuwa, S. and H. Shiozaki (2004). Functional analysis

for operating emergency department of a general

hospital. Proceedings of the 36th conference on Winter

simulation. Washington, D.C., Winter Simulation

Conference: 2003-2011.

Wilensky, U. (1999). "NetLogo Software." from

http://ccl.northwestern.edu/netlogo.

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