AGENTS BASED ADAPTIVE HYPERMEDIA SYSTEM WITH THE COMPETENCY APPROACH

8/12/2019 AGENTS BASED ADAPTIVE HYPERMEDIA SYSTEM WITH THE COMPETENCY APPROACH

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Agents Based Adaptive Hypermedia System with the

Competency Approach

Jose Sergio Magdaleno-Palencia1, Mario Garcia-Valdez

2 , Manuel Castanon-Puga

3

and Bogart Yail Marquez4

1,2Tijuana Technological Institute, Mexico3,4 Baja California Autonomous University, Mexico

{1sergio.magdaleno, 3 puga, 4 bmarquez}@uabc.edu.mx, [email protected]

Abstract. This work is motivated by the need to propose a model for the study

of personalized hypermedia systems with a competency approach, which is one

of United Nations Educational, Scientific and Cultural Organization(UNESCO) and General Administration of Higher Education Technology(DGEST) recommendations. We use computer agents, to construct a student

model with emphasis on his learning style, multiple intelligences and thecompetency evaluation to adapt the course material according to the student’sneeds. The system will also use artificial intelligence techniques. The systems

will be used at a university level; the primary goal is to help each student learn

and thus reach a suitable competency level.

Keywords: Agents, Adaptive Hypermedia System, Competency model,Learning style, Multiple intelligences.

1 Introduction

Each human being perceives and processes information differently; hence the

importance to create a system to adapt these individual characteristics, for instances a

system that works as a personal tutor for each individual. Studies shows that when

presented information to the people according to their learning style, they learn better[1].

The goal of this project is to create a system that takes a novel approach to usingagents based in personalized educational hypermedia components using the students

profiling and the competency model. The system will be applied with programming

courses with the competency approach in a university of higher education in Mexico.

One of the UNESCO recommendations in education is to promote lifelong learningas well from the DGEST to seek comparability, compatibility and competitiveness of

the plans and study programs in a national and international context [2]. To create the

system is necessary to know about the following topics.

International Journal on New Computer Architectures and Their Applications (IJNCAA) 1(4): 1110-1117 The Society of Digital Information and Wireless Communications, 2011 (ISSN: 2220-9085)

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1.1 Multi-agent model.

An agent is “A computer program, or part of a program, that can be consider to act

autonomously and that represents an individual, organization, nation-state, or other

social actor.”, [3]

An agent is situated in some environment, within which it can act independently

and flexibly to meet its main objective. The agent will receive data from itsenvironment and must choose an appropriate action. According to the authors

Wooldridge and Jennings, agents have certain properties such as: autonomy,

sociability, reactivity, pro-activity, intelligence, rationality, consistency, and

adaptability,[4]. “A multi-agent system (MAS) can therefore be defined as a

collection of, possibly heterogeneous, computational entities, having their own

problem solving capabilities and which are able to interact among them in order toreach an overall goal”, [4].

The MAS system will be used to learn about the learning process while the student

uses the system. This process involves different types of agents; in a group of students

each one has its own personality, learning style, background, motivation, culture, etc.

Hence de MAS can be a powerful tool to model this kind of systems, because it canrecreate and simulates this behavior.

1.2 Adaptive hypermedia system.

Adaptive hypermedia systems (AHS) have been used as software tools in the teaching

of courses; they allow for adaptation according to the users learning style by making

the necessaries adjustments in the way the course material is presented [5], that its

why it is called a personalized hypermedia system. The classical architecture for an

adaptive hypermedia system includes: the user, includes the learning style and

multiple intelligences, as well the evaluation during the process; the adapter, the

necessary rules to adapt the course material; and the domain model, that contains the

material itself according the educational model.

Each one of them to know the student’s profiling, the process to adapts the domainaccording the students needs and the domains itself.

1.3. User model

The user model includes for their profiling the learning style, multiple intelligences as

well the evaluation of the course material during the process of learning the coursematerial.


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1.3.1 Learning style

Each student has their own learning style; each one processes and perceives

information differently. According to work done by Neil Fleming at Lincoln

University in 1987, through this work the word VARK was used to stands for Visual,

Aural, Read/write, and Kinesthetic sensory modalities that are used for learning

information [6]. These preferences are used by the learner according the ways that

they want to take-in and give-out information.

Also Alonso, Gallego and Honey in their research indicate that students learn betterwhen there are studying when they know their predominant learning style [1].

1.3.2 Multiple Intelligences

The theory of multiple intelligences was developed in 1983 by Dr. Howard Gardner;he suggested that the traditional notion of intelligence, based on IQ testing, is far too

limited. Instead, Dr. Gardner proposes eight different intelligences to account for a

broader range of human potential in children and adults. These intelligences are:

linguistic, logical-mathematical, spatial, bodily-kinesthetic, musical, interpersonal,

intrapersonal and naturalist[7]. Intelligence depends on the context, the tasks at handand the demands that life presents us, not only an IQ score.

1.4 Competency model

The educational model of competency is one of the UNESCO recommendations

to promote lifelong learning and competency building, as a result of a globalized

world of communications. This model integrates the types of known learning

outcomes known, the theoretical domain, declarative or relating to knowledge,

procedural skills or know-how relating to (executing or doing something, using

techniques, methods, skills and strategies [8]) and the affective or attitudinal, knowing being, which are three of the four pillars of education promoted by UNESCO

worldwide [9].

The competencies are distinguished by their types, the most common are conceivedas: basic, generic and specific. The components are broken down into

three levels such as: general competency, the unit of competency and elements ofcompetency [20].

The system will use Bloom's Taxonomy to help adaptation to the student’s learning

style; it considers the cognitive domain, in respect to any area of knowledge. The

types of learning’s domains suggested by Bloom are: cognitive (knowledge


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understanding, application, analysis, synthesis and evaluation), affective and

psychomotor [11].

1.4.1 Competency evaluation.

In the traditional teaching evaluation is usually of knowledge through objective

evidence or essay. In the competency based education assessment can be based on

competency rules if any, but especially in the testing of evidence through

demonstrations, product design, checklist, scales, simulations and portfolios [10].

The project will be using the competency approach, with competencystandards and professional requirements derived from manufacturing and service

sector; it forms the learner through a teaching methodology that emphasizes the

know-how and uses an organization and infrastructure similar to the area where these

skills will be used [10]. For the evaluation during the process of the student learning

we will be using the competency evaluation according the work done by Guzman s

[10].

2 Agents Based Adaptive Hypermedia System with the

Competency Approach.

The proposed system architecture is shown in Figure 1. We proposed a model based

on three agents:User Agent. This agent maintains the model of the user, which includes his learning

style, type of intelligence and background knowledge, this information is gathered

from other agents or systems, in form of formal tests, or inferred by the User Agent,

from the User’s interactions. Bloom's Taxonomy will be used to help the Hypermedia

Adaptation to the student’s learning style. The taxonomy considers the cogniti ve

domain, in respect to an area of knowledge. The types of learning’s domainssuggested by Bloom are: Cognitive (knowledge understanding, application, analysis,

synthesis and evaluation), Affective and Psychomotor [10]. For the student ’s learning

style, we will use the Fleming’s questionnaire, and for the type of intelligence the

multiple intelligence Gardner’s questionnaire; also for the competency evaluation we

will use the evaluations proposed according the work done by Guzman [10].

Adapter Agent. This Agent is responsible for the selection of the appropriate

learning activities in accordance with the competency approach selected by theCompetency Model Agent. This Agent also defines the possible sequence of activities

and the navigational options available to students according to their profile and

competency approach. This Agent is responsible for the Adaptation of the

Navigational options and Content presented to students. The content delivery systemsinteract with this Agent to get the data that is going to be presented to Students. Here

artificial intelligence techniques are used.


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Competency Model Agent. This Agent is responsible for the Competency

Approach, has didactic rules and personalized lesson plans tailored to different

student’s profiles. This Agent can also be replaced with a different Agent that follows

a different approach according to the student’s needs during the learning process.

Environment. The environment is another Agent that keeps track of the context in

which the learning activities are taking place. This information is taken into

consideration by other Agents to accommodate the learning activities to better suit the

environment.

Figure 1. Proposed Model

The learning process will be adapted and monitored by Agents and Instructors untilthe user can reach the competency level required. The system evaluation will use the

competency evaluation approach [10]. It is important to know that the adaptation

process will also consider the domain, the profiling and the student’s performance that

will be measure by the competency evaluation. Each Agent will have a set of rules,

initially specified by Instructors to personalize the material accor ding to the user’s

profile and the results of the evaluation for each topic of the course. This process will be in a loop until the user can reach the competency level required.

3 Case study

One of the commitments’ of the DGEST in Mexico, is to seek comparability,

compatibility and competitiveness of the plans and study programs in a national and

international context [2]. Hence the case study will be implemented at the University

level, in this case in Tijuana Technological Institute, Mexico, wich primary goal is to

help each student learn and thus reach a suitable competency level thus according the

UNESCO and DGEST recommendation. The proposed model will use programming

language courses material with the competency approach.


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3.1 Applications of adaptive hypermedia in education.

Advances on information systems and technology shows that it is very important to

focus on developing competency-based learning models [12]. Till this day the

development of AHS, has been instrumental in helping students achieve meaningful

learning; also by the new needs of life imposed by globalization, advances in modern

psychology and changes in the aims of education [9], [13].

New e-courses tend to be oriented to support the activities according to the

constructivism theory [14], hypermedia systems have became popular in the lastyears as tools for student’ learning [15]. Some projects have been carried out

for competency-based training standards, instructional technology education[16],[17],

and competency-based systems for recommending study materials from the Web to

learners (CBSR). It explores the benefits of a competency model for an improved

pedagogical approach to e-learning [18] and also Adaptive Learning Objects

Sequencing for Competence-Based Learning [19].

Some systems adapt A.I. techniques for the recommendations [21], but they do not

use the competency model for the pedagogical material. Also some hybrid system

architecture for learning objects [22] had been done.

With the implementation of this research, we pretend to know the following:

Which are the main characteristics of adaptability that the educational resourcesshould have with a competency approach?, which are the main characteristics of

adaptability that the evaluation mechanism should have for the student's knowledge

level based on the competency model?, which Artificial Intelligence techniques are

for diagnosing and evaluating the student's knowledge in the competency approach?

3.2 Design and implementation

The implementation of the model follows a Model-View-Controller (MVC)

architecture, and is implemented using the Django Web Development Framework, for

the server side, using PosgreSQL as the Database Management System (DBMS). Also

in the client side the technologies used are standard CSS3 (Cascade Style Sheets 3),

XHTML 5.0 and JavaScript; Browsers supported are Firefox from Mozilla as well

Internet Explorer from Microsoft. The Python language is used for other components.The Jade Framework is also used in prototypes.

4 Conclusions


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According to the UNESCO recommendation of to promote lifelong learning and

competencies building [9], the adoption of the model by the education systems [23]

and one of the commitments’ of the DGEST in Mexico, to seek comparability,

compatibility and competitiveness of the plans and study programs in a national and

international context [2].

The proposed methodology allows the creation of an agent based personalized

system with the competency approach, using a precise diagnosis of students learning

with their profile and adapts the best strategy using A.I. techniques to obtain better

result for each student.

Moreover, the technology of multi-agent systems has created opportunities forimproved, AHS, to incorporate the features characteristic of an agent, such autonomy,

sociability, reactivity, pro-activity, intelligence, rationality, consistency, and

adaptability [4].

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AGENTS BASED ADAPTIVE HYPERMEDIA SYSTEM WITH THE COMPETENCY APPROACH