final doc 4 e-learning

8/8/2019 final doc 4 e-learning

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A design of distributed distance e-learning systems based on software agents based

technology

Aim:

The main aim of the project to propose an intelligent answering system for

distance e-learning system with help of agent based technology search in distance

learning systems.

Objective:

The main objective of the project to design an intelligent answering system for

the distance learning system, which are understands the user queries and gives the query

detail automatically to the users. The answering systems are integrated with some agents

like student agent, teacher agent, etc., and databases like knowledge databases, keyword

databases, etc. The functional systems of the agents are reducing the tutors work and the

students work in distance e-learning systems.

Abstract:

Distance learning are popularly increasing because of two main reasons, that is

the students are not enough in a single location there may be come from different natives

so the institutions have the responsibility to overcome this problems like arranging

accommodations etc,. Then the other thing is the shortage of the courses that means thestudent needed course may not be available in their native, but the institutions are

focusing only their infrastructure, unfortunately they are not to concentrating about the

teaching services like arranging the experienced faculties like that. For these kinds of

problems the e-learning services are increased rapidly. But the e-learning systems are

facing lot of problems now like lack of answering system, then the tutors are working lot

to know about students details like course detail, profile details etc,. For these types of

reasons our work are proposing to design an intelligent answering system with agent

based search engines using in distance learning systems, that means this paper proposes a

integrated system architecture for agent-based distance learning. The architecture gives

users the ability to collect, analyze, distribute and use e-learning knowledge from various

knowledge source databases. Here our agents using as an interface between the tutors and

students to reduce the tutors work like student agent, discussion agent, teaching agent

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etc., by using these types of agents based search in distance learning systems the students

can get exact answers which they are required from the tutor, further it also reduces the

searching time. In order to increase the performance of answering system the intelligent

agent based search using some algorithms like word segmentation, full text based search

for retrieve the user needs from the various databases like mentioned above. It reduces

the cost of estimation and improves the resources usage and give efficient workload

optimization.

Title justification:

This entitled is justified A design of distributed distance e-learning systems

based on software agents based technology used to design an intelligent answering

system with agent based search, which are reducing the tutors and students work in

distance e-learning systems.

Project category:

Networking

Definition:

"A network of data processing nodes that are interconnected for the purpose of data

communication". The term "network" being defined in the same document as "An

interconnection of three or more communicating entities. A computer connected to a non-

computing device (e.g., networked to a printer via an Ethernet link) may also represent a

computer network, although this article does not address this configuration. This article

uses the definition which requires two or more computers to be connected together to

form a network. The same basic functions are generally present in this case as with larger

numbers of connected computers. In order for a network to function, it must meet three

basic requirements, it must provide connections, communications and services.

Connections refers to the hardware, communications is the way in which the devices talk

to each other, and services are the things which are shared with the rest of the network.

Network node (NN), is a grouping of one or more network elements (at

one or more sites) which provides network related functions, and is administered as a

single entity. A single site may contain more than one network node. For the purpose of

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this glossary, a network node is considered synonymous with a network element, and is

usually at a single site. This restriction simplifies the definition of the network node

interface (NNI) and INI, which would not apply between network elements.

TYPES OF NETWORK:

A personal area network (PAN)

Main article: Personal area network

A personal area network (PAN) is a computer network used for communication among

computer devices (including telephones and personal digital assistants) close to one

person. The devices may or may not belong to the person in question. The reach of a

PAN is typically a few meters. PANs can be used for communication among the personal

devices themselves (intrapersonal communication), or for connecting to a higher level

network and the Internet (an uplink). Personal area networks may be wired with computer

buses such as USB and FireWire. A wireless personal area network (WPAN) can also be

made possible with network technologies such as IrDA and Bluetooth.

Local Area Network (LAN)

Main article: Local Area Network

A network covering a small geographic area, like a home, office, or building. Current

LANs are most likely to be based on Ethernet technology. The defining characteristics of

LANs, in contrast to WANs (wide area networks), include their much higher data transfer

rates, smaller geographic range, and lack of a need for leased telecommunication lines.A

LAN network can be defined by the manual IP Address or by the selection of Auto IP

option.

Campus Area Network (CAN)

Main article: Campus Area Network

A network that connects two or more LANs but that is limited to a specific (possibly

private) geographical area such as a college campus, industrial complex, or a military

base. A CAN, may be considered a type of MAN (metropolitan area network), but is

generally limited to an area that is smaller than a typical MAN.

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management system (DBMS). The central concept of a database is that of a collection of

records, or pieces of knowledge. Typically, for a given database, there is a structural

description of the type of facts held in that database: this description is known as a

schema. The schema describes the objects that are represented in the database, and the

relationships among them. Databases are used in many applications, spanning virtually

the entire range of computer software. Databases are the preferred method of storage for

large multi-user applications, where coordination between many users is needed. Even

individual users find them convenient, though, and many electronic mail programs and

personal organizers are based on standard database technology. Software database drivers

are available for most database platforms so that application software can use a common

application programming interface (API) to retrieve the information stored in a database.

Two commonly used database APIs are JDBC and ODBC.

Introduction of the project:

Lecture style and delivery of courses are the traditional teaching approaches laying in

the basis of most of the existing e-learning systems. Limitations of such approaches are

numerous. Incompatible mode of delivery does not work well for all categories of

students. Application of delivered knowledge is complicated. Yet another limitation:

knowledge required for us to be competitive changes very fast and may become obsolete.

In recent years, a lot of attention has been given to automating of the content acquisition

and distribution processes, but personalized content delivery, access, and interaction

remain research challenges. Several e-learning management software tools are available

commercially, for example, WebCT, Blackboard. These tools offer passive and static sets

of services. As a result of the time-consuming maintenance requirements of distance

learning courses instructors spend more time teaching a course on-line than the same

course in the classroom. Instructors are expected to check students log files, grade books,

etc. They do not have time, budget, or technical skills to build true instructional

interactivity into their online learning programs.

The emergence of the Internet has great significance for distance learning

and training, as it is an effective and economical medium for making information

available to dispersed individuals. It has radically changed the way in which we learn,

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teach and train. It has also altered the way in which learning resources are developed.

Distance learning at Internet speed requires less time for preparing courses and more

frequent updates of courseware, and allows just-in-time delivery of this courseware to

students anywhere, at any time, while maintaining high levels of functionality and

quality.

Most of these e-learning systems are designed to facilitate the work of the

instructor, not to support the learning process of the students and their current needs. The

diversity of the students' backgrounds and skills is ignored. The same teaching strategies

are applied to students who have different profiles. All these factors lead to emergence of

new special requirements to a teaching process. In order to improve the quality of

distance education, we may help students solidify the learned knowledge through an

answering system besides studying the content in lessons. In this aspect, the answering

system is one of the important composing parts of network instruction platform.

The problems existing in the already used answering systems are as follows: It

is simple and less effective. It lacks intelligence. As to the aspect of answering mode, it

can be divided into manual answering and automatic answering. The manual answering

adopts the forms of e-mail, message board, BBS, chat room and so on; while the

automatic answering can not return satisfying results since it lacks understanding for

natural language though the answer comes from the automatic search of question

database and knowledge database. Most of the answering systems give only the reference

answer without knowledge points which need to be solidified and advises on learning

strategy. To overcome these deficiencies in answering system, Agent technology can be

used to improve the automatic answering technology from the aspects of natural language

word segmentation algorithm and information searching. This increases the intelligence

of a system and makes the system learn according to the learner's needs, thus improving

the performance of the answering system.

As a new kind of computing model in the field of artificial intelligence, the main

characteristic of Agent (Intelligent Agent) technology is the continuity of its function and

self-determination, i.e. Agent can continually perceive the changes of both external and

self state, and then produce reactions itself. Agent is an encapsulated module with

independent functions. It includes its own data and algorithms of operating these data; it

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can accept and process the messages from other Agents and can send messages to other

Agents. So it is an entity that has its independent problem-solving abilities and can

change according to the hanging environment. The answering systems are understands

the user queries and gives the query detail automatically to the users. In this paper the

answering systems are integrated with some agents like student agent, teacher agent, etc.,

and databases like knowledge databases, keyword databases, etc. The functional systems

of the agents are reducing the tutors work and the students work in distance e-learning

systems.

Existing system:

Most of the existing e-learning system relies too much on the traditional learning

approaches, the lecture style and remote distribution and delivery of courses. The time-

consuming maintenance requirements of distance learning courses instructors spend more

time teaching a course on-line than the same course in the classroom. Instructors are

expected to check students log files, grade books, etc. They do not have time, budget, or

technical skills to build true instructional interactivity into their online learning programs.

Most of these e-learning systems are designed to facilitate the work of the instructor, not

to support the learning process of the students and their current needs. The diversity of

the students' backgrounds and skills is ignored. To overcome these strategies, the

answering system is one of the important composing parts of network instruction

platform. But it is simple and less effective in knowledge databases Most of the

answering systems give only the reference answer without knowledge points which need

to be solidified and advises on learning strategy.

Proposed system:

In our proposals are to design an intelligent answering system using intelligent

agent based search for distance learning systems. Here we are using lot of agents as an

intermediate between the students and instructors. The main proposals of this project are

to design intelligent answering model, which contains of agents. The agents are like

answering agents, answering controller, and answering databases. Answering agents like

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student agent, teaching agent etc. The function of answering controller is to analyze the

question and search the question from the databases. Answering databases are like

knowledge databases, resource databases, question databases, etc. In our proposals work

the agents are using to reduce the tutors work and students work in distance e-learning

education.

Hardware requirement:

Processor : Pentium IV

Clock speed : 550 MHz

Hard Disk : 80 GB

RAM : 512 MB

Cache Memory : 512 KB

Operating System : Windows 2000 prof.

Software requirement:

FRONT END : Java 1.6

BACK END : MS-ACCESS

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Overall block diagram:

Students registrationTeacher agents and

student agents

Student queries

Intelligent answering

agents

Segmentation algorithms

Question analysis

Databases

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Modules:

Implementation of network

Designing of an intelligent agent model answering system

Designing of the answering agent model system

Implementation of word segmentation algorithm

Implementation of full text search algorithm

Implementation of network:

Network node (NN), is a grouping of one or more network elements

(at one or more sites) which provides network related functions, and is administered

as a single entity. A single site may contain more than one network node. For the

purpose of this glossary, a network node is considered synonymous with a network

element, and is usually at a single site. This restriction simplifies the definition of the

network node interface (NNI) and INI, which would not apply between network

elements.

Search engines have become an essential component of everyday life in modern

society. Most of the applications involve some interaction with search engines one

way or the other. The client server based search engine specifically addresses thesituations where centralized indexes are unfeasible and proposes the development of a

decentralized search engine built on agents based search technology. It has the design

goal of solving problems too big for any single computer which are connected in the

network, at the same time it has the flexibility to work on multiple smaller problems,

it provides a multi-user environment with many client. In most organizations, there

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are large amounts of underutilized computing resources. Most desktop machines are

busy less than 5% of the time. In some organizations, even the server machines can

often be relatively idle. A multi agent architecture in which the components that

facilitate diagnosis and support for online knowledge sharing behavior are integrated

with coaching agents and a system for collaborative work. Any collaborative distance

learning system requires a networked communication interface, so that the

participants can interact via text chat, voice, or some other channel. The architecture

also includes a shared workspace, where the students can jointly construct a diagram.

As the students are collaborating, the analysis team should be running in the

background, overhearing the interactions among students, dynamically assessing the

situation, and recommending actions to the individual and group coaching (or peer)

interface agents. These interface agents would ideally be online, monitoring and

interacting with the students while they are learning. In here the networks are

established with many clients for distance learning schemes. And creation of student

login form for to access the distance learning systems. Every user must need anauthorization. Authorization is for only prescribed users entering the network rather

than unauthorized access. Client and Server have an authorization entry. Sometimes

by mistake, the user gives wrong user name and password, the server generates the

warning to every mismatch inputs. If it is matched, then the user get the connection

otherwise the server quits the unauthorized person. If the user doesnt have any

authentications with the distance learning systems they have to create the new

authentication account.

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User Login

Checks the

user status

Enter the user name & password

Existing User

Non Existing

user

Create the useraccount

User Requests

Creation of user

authentication form

Implements the clients andserver network

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Designing of an intelligent agent model answering system:

As a new kind of computing model in the field of artificial intelligence, the

main characteristic of Agent Intelligent Agent) technology is the continuity of its function

and self-determination, i.e. Agent can continually perceive the changes of both external

and self state, and then produce reactions itself [1]. That is to say, Agent is an

encapsulated module with independent functions. It includes its own data and algorithms

of operating these data; it can accept and process the messages from other Agents and can

send messages to other Agents. So it is an entity that has its independent problem-solving

abilities and can change according to the changing environment. Like a black box,

Agent's construction model is as Figure 1. Agent perceives the external environment and

interacts with it through the interface; Agent accepts outer information by sensor, filter

and sorts the input information, then sends it to the reasoning machine, which can do

reasoning and make decisions according to knowledge and rules in the knowledge

database; eventually, hands action instructions to the effectors and produces operations

on the external environment through the interface. The intelligent answering system

model based on Agent is as Figure 2.

Teaching agent:

After a teacher logs on the distance teaching system, the system will automatically

generate a Teacher Agent. The Teacher Agent answers questions by exchanging

information with the Student Agent. At the same time it exchanges information with

Question Analysis Agent to learn the student's learning situation and difficult problems

and then makes instructional strategies.

Student Agent:

After a student logs on the distance teaching system, the system will

automatically generate a Student Agent. The Student Agent offers the student an

interactive interface and automatically answers questions by Answering Agent. When it

cannot satisfy the request, the Student Agent makes the teacher answer the questions by

communicating with the Teacher Agent. At the same time it memorizes how the

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knowledge points are mastered according to the answering process, so it can afford clues

for the students further study.

Fig1: Agent model system

Teacher Answering Agent:

The Teacher Answering Agent will hand in the questions which the Automatic

Answering cannot give proper answers, to the teacher. The question will be kept in the

Question Database after being answered by the teacher.

Externalenvironment

Interface

Effectors Sensors

Reasoning machine

Knowledge databases

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Question Analysis Agent:

The Question Analysis Agent analyzes the Question Database to find out the

frequently asked questions and then reflects the mastering situation of the students.

And the teacher adjusts instructional strategies according to this. Meanwhile, the

Question Analysis Agent communicates with the Student Agent, learns the mastering

situation of individual student, and gives related knowledge for him/her to learn.

Discussion Agent:

The Discussion Agent provides a platform for discussing questions, so the

students may learn by each other. The teacher may learn the students questions by this

platform and arrange the problems which the students are interested in to enrich the

Question Database.

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Fig2: Intelligent answering system model with agents

Question

databases

Knowledge

databases

Keyword

databases

Intelligentanswering agent

Questionanalysis agent

Teacheranswering agent

Human interface agent

Student Teacher

Student agent Teacher agent

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Designing of the answering agent model system:

The Answering Agent is the core of the whole system. When the students ask

with natural language, the Automatic Answering Agent separates it into key words one

by one through word segmentation technology, and analyses the type of the question,

then extracts the focus of the question, matches the questions with answers in the answer

database, makes the first level search, and outputs the right answer after finding it. If

there is no right answer can be matched, it takes out the corresponding keywords, makes

fuzzy search according to certain algorithm, and then outputs the answers in descending

order after finding it. If still there is no answer, it gives a related answer through full text

search, which depends on the user whether to take this answer or not. If the user is

satisfied, the search is completed; if not, experts will answer the question. After the

expert teachers answer the question, it will renew and enrich the Answering Database to

make its resource added. And in this way the system will be improved and can answer

more questions asked by students more accurately.

Agent can be defined as a 5-dimensional entity, namely, Agent=< A, I,

S, T, K>, among them: A represents name or mark, which should be unique and describes

the Agent's type; I represents interface, which describes the human-machine interface and

communication interface (including communication protocol and I/O interface), and

usually follows the principle of separating interface from function; S represents status set,

which describes the Agent's inner status, actually, Agent's behavior is the process from

one status to another; T represents transaction set, which defines Agent's behaviors and

embodies Agent's functions; K represents knowledge source, which describes knowledge,

data, reasoning rules and related resources required by Agent's behaviors in the form of

data structure, database (or knowledge base), etc.

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Question analyzer

Question answer

matching

Full text match answering

Interface

Whetherbe

satisfied

Knowledgedatabases

Keyworddatabases

ERING

CONTROLLER

Question

databases

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Implementation of word segmentation algorithm:

Users input the reasonable and logical one which can be used in query. Some

work need to be done after the part of question analysis has been elaborated: segmenting

the question and noting the features of the words, making sure the type and focus of the

question; extracting the keywords of the question; properly expanding the keywords

according to the kind of answers and other factors. The key of word segmentation

technology is keyword dictionary. Professional keyword dictionary, synonym dictionary

and the dictionary of common question type are set in the system. The dictionary of

common question type mainly means the types of the users questions, such as

difference, reason, how, what is, why, etc. The principle of word segmentation

is: firstly matches the professional word database and synonym database, then the

common word database, and the left character string will be abandoned. The biggest

inverse match algorithm is adopted in the word segmentation algorithm. In the following

flow chart Start=1 means assigning the strings start location as 1. Length=m means

assigning the strings length as m. In loop function if the starts location is greater then the

length of the keyword its start the search process, that is search_str= the string begun

from start whose length is length and search the keyword in the dictionary and stores

in the set of word segmentation results. Else the keyword length will be added with the

strings start position as start=start + length and again start the loop. If the string search

search_str are successfully searches the string from keyword dictionary the string

length will get decrement as length= length-1, and starts the loop again. Distilled

keywords and the kind of questions are matched with question database by this algorithm.

If not successful, full text search will be executed.

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Set the starting location of the question string

as start=1

Extract the keywords from the question

Calculate and assign length of the question

string length=min

Search the string search_str in

dictionary

do whileStart


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Implementation of full text search algorithm:

In answering system, full text search is to be done if the question

database cannot satisfy the needs of users. The concrete steps are: extract the keyword

from the full text and make weighted calculation firstly, and then calculate the

relevance of the input question and the full text of the knowledge. The texts whose

relevance exceeds the given threshold by system will be returned and the texts are

arranged in descending order according to the degree of relevance. The distribution of

weight value is relevant to the length of character string and the times it occurs in the

text. The function of weight is W=F* L3 (F is the times the key word occurs in the text

and L is the length of the keyword). The relevance algorithm is: Suppose N keywords

are extracted from a document Dj, T1 T2 Tn. The document can be described as:

Dj (w1j w2j wnj) w1j w2j wnj are the weight value of the keywords T1 T2

Tn in the document Dj. For the users the question can be described as Q (w1q w2q

wnq) w1q w2q wnq are the weight value of the keywords T1 T2 Tn in

the question. The relevance between the document and the users can be calculated by

relevance formula of vector space model.

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Calculate the length of the keyword

Calculate the weight of the keyword

Calculate the relevance of the input

question and full text of knowledge

Set the threshold value for the texts

occurring in the question

If relevance

exceedsthresholds

Arrange the texts in descending order

according to the degree of relevance

Search the keywords from the keyworddatabases

Finally search the texts fromknowledge databases

Yes

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Data Flow Diagram:

Students

enters the

questionwhich they

want from

tutor

Students andteachers

login in to

the distance

educationsystem

Student agentTeacher agent

Question analysis agentDiscussion agent

Intelligent Answering

agent

Human interface agent

Login

verification

A

Implementation of word

segmentation

algorithm

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Question

databases

Knowledge

databases

Keyworddatabases

A

Answering

controllerextracts the

keywordsQuestion analyzer

If notsatisfied

implements

fullsegmentation

algorithm

Question answermatching

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Flow chart:

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User Login

Checks the

user status


Existing User

Non Existing

user


After login of student the student

agent will create

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User Login

Checks the

user status


Existing User

Non Existing

user



agent will create


agent will create

Students enters the questionwhich they want from the tutor

A

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A

Teacher agents and student agents

interact with human interface agent

Question analysis agents analyze the

question in question databases

Discuss agents will discuss abut the

question with teacher and student agent

Discussion agent search questionsanswer from the question databases

Intelligent answer agents verifies the

answers and sends to the student agents

If notsatisfie

d

Intelligent agent again process the questionwith help of word segmentation algorithm

B

Yes

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Set the starting location of the question string

as start=1


Calculate and assign length of the question

string length=min

Search the string search_str in

dictionary

do whileStart


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D

Word segmentation match the

keywords from keyword databases

Question analyzer analyzes the

keywords from keyword databases

Its match the answers for the given

questions from the question databases

If

satisfie

d

Implementation of full text basedsearch algorithm and search questions

from knowledge databases

Interacts with human interface agentsand give the answers to the student

agent and teacher agent

E

YesNo

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Calculate the length of the keyword

Calculate the weight of the keyword

Calculate the relevance of the input

question and full text of knowledge

Set the threshold value for the textsoccurring in the question

If relevanceexceeds

thresholds

Arrange the texts in descending order

according to the degree of relevance

Search the keywords from the keyword

databases

Finally search the texts from

knowledge databases

Yes

E

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Overview of the Project:

Lecture style and delivery of courses are the traditional teaching approaches

laying in the basis of most of the existing e-learning systems. Limitations of such

approaches are numerous. Incompatible mode of delivery does not work well for all

categories of students. Application of delivered knowledge is complicated. Yet another

limitation: knowledge required for us to be competitive changes very fast and may

become obsolete. In recent years, a lot of attention has been given to automating of the

content acquisition and distribution processes, but personalized content delivery, access,

and interaction remain research challenges. Several e-learning management software

tools are available commercially, for example, WebCT, Blackboard. These tools offer

passive and static sets of services. As a result of the time-consuming maintenance

requirements of distance learning courses instructors spend more time teaching a course

on-line than the same course in the classroom. Instructors are expected to check students

log files, grade books, etc. They do not have time, budget, or technical skills to build true

instructional interactivity into their online learning programs.

Most of these e-learning systems are designed to facilitate the work of the

instructor, not to support the learning process of the students and their current needs. The

diversity of the students' backgrounds and skills is ignored. The same teaching strategies

are applied to students who have different profiles. All these factors lead to emergence of

new special requirements to a teaching process. In order to improve the quality of

distance education, we may help students solidify the learned knowledge through an

answering system besides studying the content in lessons. In this aspect, the answering

system is one of the important composing parts of network instruction platform.

As a new kind of computing model in the field of artificial intelligence, the main

characteristic of Agent (Intelligent Agent) technology is the continuity of its function and

self-determination, i.e. Agent can continually perceive the changes of both external and

self state, and then produce reactions itself. Agent is an encapsulated module with

independent functions. It includes its own data and algorithms of operating these data; it

can accept and process the messages from other Agents and can send messages to other

Agents. So it is an entity that has its independent problem-solving abilities and can

change according to the hanging environment. The answering systems are understands

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the user queries and gives the query detail automatically to the users. In this paper the

answering systems are integrated with some agents like student agent, teacher agent, etc.,

and databases like knowledge databases, keyword databases, etc. The functional systems

of the agents are reducing the tutors work and the students work in distance e-learning

systems.

A multi agent architecture in which the components that facilitate diagnosis

and support for online knowledge sharing behavior are integrated with coaching agents

and a system for collaborative work. Any collaborative distance learning system requires

a networked communication interface, so that the participants can interact via text chat,

voice, or some other channel. The architecture also includes a shared workspace, where

the students can jointly construct a diagram. As the students are collaborating, the

analysis team should be running in the background, overhearing the interactions among

students, dynamically assessing the situation, and recommending actions to the individual

and group coaching (or peer) interface agents. These interface agents would ideally be

online, monitoring and interacting with the students while they are learning. In here the

networks are established with many clients for distance learning schemes. And creation

of student login form for to access the distance learning systems.Every user must needan authorization. Authorization is for only prescribed users entering the network rather

than unauthorized access. Client and Server have an authorization entry. Sometimes by

mistake, the user gives wrong user name and password, the server generates the warning

to every mismatch inputs.

Teaching agent:

After a teacher logs on the distance teaching system, the system will

automatically generate a Teacher Agent. The Teacher Agent answers questions by

exchanging information with the Student Agent. At the same time it exchanges

information with Question Analysis Agent to learn the student's learning situation and

difficult problems and then makes instructional strategies.

Student Agent:

After a student logs on the distance teaching system, the system will automatically

generate a Student Agent. The Student Agent offers the student an interactive interface

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and automatically answers questions by Answering Agent. When it cannot satisfy the

request, the Student Agent makes the teacher answer the questions by communicating

with the Teacher Agent. At the same time it memorizes how the knowledge points are

mastered according to the answering process, so it can afford clues for the students

further study.

Teacher Answering Agent:

The Teacher Answering Agent will hand in the questions which the Automatic

Answering cannot give proper answers, to the teacher. The question will be kept in the

Question Database after being answered by the teacher.

Question Analysis Agent:

The Question Analysis Agent analyzes the Question Database to find out the

frequently asked questions and then reflects the mastering situation of the students.

And the teacher adjusts instructional strategies according to this. Meanwhile, the

Question Analysis Agent communicates with the Student Agent, learns the mastering

situation of individual student, and gives related knowledge for him/her to learn.

Discussion Agent:

The Discussion Agent provides a platform for discussing questions, so the

students may learn by each other. The teacher may learn the students questions by this

platform and arrange the problems which the students are interested in to enrich the

Question Database.

Answering agents:

The Answering Agent is the core of the whole system. When the students ask

with natural language, the Automatic Answering Agent separates it into key words one

by one through word segmentation technology, and analyses the type of the question,

then extracts the focus of the question, matches the questions with answers in the answer

database, makes the first level search, and outputs the right answer after finding it. If

there is no right answer can be matched, it takes out the corresponding keywords, makes

fuzzy search according to certain algorithm, and then outputs the answers in descending

order after finding it. If still there is no answer, it gives a related answer through full text

search, which depends on the user whether to take this answer or not. If the user is

satisfied, the search is completed; if not, experts will answer the question. After the

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expert teachers answer the question, it will renew and enrich the Answering Database to

make its resource added. And in this way the system will be improved and can answer

more questions asked by students more accurately

Word segmentation algorithm:

Users input the reasonable and logical one which can be used in query. Some

work need to be done after the part of question analysis has been elaborated: segmenting

the question and noting the features of the words, making sure the type and focus of the

question; extracting the keywords of the question; properly expanding the keywords

according to the kind of answers and other factors. The key of word segmentation

technology is keyword dictionary. Professional keyword dictionary, synonym dictionary

and the dictionary of common question type are set in the system. The dictionary of

common question type mainly means the types of the users questions, such as

difference, reason, how, what is, why, etc. The principle of word segmentation

is: firstly matches the professional word database and synonym database, then the

common word database, and the left character string will be abandoned. The biggest

inverse match algorithm is adopted in the word segmentation algorithm.

Full text based algorithm:

In answering system, full text search is to be done if the question

database cannot satisfy the needs of users. The concrete steps are: extract the keyword

from the full text and make weighted calculation firstly, and then calculate the relevance

of the input question and the full text of the knowledge. The texts whose relevance

exceeds the given threshold by system will be returned and the texts are arranged in

descending order according to the degree of relevance. The distribution of weight value is

relevant to the length of character string and the times it occurs in the text.

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Architectural design of the project:

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Students

Human interface agents

Student agent

Teacher agent

A G E N

T

MO

D E L I N G S Y S T E MANSWERING AGENTS

Question analyzer

Discussion agent

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Techniques involved and its advantages:

Word segmentation algorithms:

Students

Human interface agents

Student agent

Teacher agent

A G E N

T

MO

D E L I N G S Y S T E M

ANSWERING AGENTS

Question analyzer

Discussion agent

DATABASES

Keyword

databases

Question

databases

Knowledge

databases

Tutors

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Professional keyword dictionary, synonym dictionary and the dictionary of

common question type are set in the system. The dictionary of common question type

mainly means the types of the users questions, such as difference, reason, how,

what is, why, etc. The principle of word segmentation is: firstly matches the

professional word database and synonym database, then the common word database, and

the left character string will be abandoned. The biggest inverse match algorithm is

adopted in the word segmentation algorithm. In the following flow chart Start=1 means

assigning the strings start location as 1. Length=m means assigning the strings length as

m. In loop function if the starts location is greater then the length of the keyword its start

the search process, that is search_str= the string begun from start whose length is

length and search the keyword in the dictionary and stores in the set of word

segmentation results. Else the keyword length will be added with the strings start position

as start=start + length and again start the loop. If the string search search_str are

successfully searches the string from keyword dictionary the string length will get

decrement as length= length-1, and starts the loop again. Distilled keywords and the

kind of questions are matched with question database by this algorithm. If not successful,

full text search will be executed.

Step1: Start=1 //set the starting location of the question string

Step2: Length=min (n, the length of the question string) n is the biggest length in the

dictionary

Step2: do while start


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Step5: Else Save search_ str //word segmentation succeed, save into the set of word

segmentation results

Step6: Start=start +length

Step7: Loop

Full text search algorithm:

The distribution of weight value is relevant to the length of character string and

the times it occurs in the text. The function of weight is W=F* L 3 (F is the times the key

word occurs in the text and L is the length of the keyword). The relevance algorithm is:

Suppose N keywords are extracted from a document Dj, T1 T2 Tn. The document can

be described as: Dj (w1j w2j wnj) w1j w2j wnj are the weight value of the

keywords T1 T2 Tn in the document Dj. For the users the question can be described as

Q (w1q w2q wnq) w1q w2q wnq are the weight value of the keywords T1

T2 Tn in the question. The relevance between the document and the users can be

calculated by relevance formula of vector space model.

Disadvantage of previous techniques:

Previous distance education learning systems are simple and less effective

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Moreover existing e-learning systems have problems sharing knowledge in a

distributed environment.

Most of the answering systems give only the reference answer without

knowledge points which need to be solidified and advises on learning

strategy.

Previous distance education learnings answering systems are lack of

intelligence.

Previous automatic answering systems can not return satisfying results since

it lacks understanding for natural language.

Though the answer comes from the automatic search of question database and

knowledge database but in systematically it is weak.

Advantages:

Agent technology can be used to improve the automatic answering technology

from the aspects of natural language

And in this way we can increase intelligence of a system and make the system

learn according to the learner's needs.

Learner needs will help to improve the performance of the whole answering

system.

Agent can continually perceive the changes of both external and self state, and

then produce reactions itself.

Teaching agents and student agents will reduce both students and tutors work

in distance learning schemes.

Agent technology can be used to improve the automatic answering technology

from the aspects of natural language word segmentation algorithm and

information searching.

Literature survey:

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The emergence of the Internet has great significance for distance learning

and training, as it is an effective and economical medium for making information

available to dispersed individuals. It has radically changed the way in which we learn,

teach and train. It has also altered the way in which learning resources are developed.

Distance learning at Internet speed requires less time for preparing courses and more

frequent updates of courseware, and allows just-in-time delivery of this courseware to

students anywhere, at any time, while maintaining high levels of functionality and

quality. This paper proposes a flexible architecture for internet based distance learning

using an agent-mediated mechanism to create and deliver educational materials

effectively. Our work makes advances in the areas are effective delivery of courseware to

students consistent remote editing of courseware by educators and/or training participants

hardware and software heterogeneity [1]

In recent years, numerous Web applications have been developed,

such as portal websites (AltaVista; Google; Yahoo; YAM), news websites (CNN; Google

News, Taiwannews), various commercial websites (Amazon; eBay), and so on,

demonstrating the increasing maturity of the Internet. Consequently, the rapid growth of

information on the Web (Lawrence & Giles, 1998) has created a problem of information

overload (Berghel, 1997; Borchers, Herlocker, Konstanand & Riedl, 1998), such that

Internet users are unable to find the information they require (Arasu, Cho, Garcia-Molina,

Paepcke & Raghavan, 2001; Kobayashi & Takeda, 2000; Lawrence & Giles, 1999). The

reason for the growth of is that it provides a convenient and efficient learning

environment and practical utilities at anytime and anywhere. Many universities (E-

learning in the University of Maryland), corporations (E-learning in Cisco), and

educational organization (Distance Learning Resources Network (DLRN)) are

developing distance learning platforms to provide course materials for Web-based

learning. Therefore, many researchers have recently endeavored to provide

personalization mechanisms for Web-based learning (Brusilovsky, 1999; Kao, 2001;

Khan, 1997; Lin, 2001; Liu, Chen, Hongchi, Spyridon & Chen, 2002; Kyparisia and

Maria, 2002; Myung-Geun Lee, 2001; Chien Chou, Yi-Fan Chang and Yi-Ying Jiang,

2000). Therefore, to provide personalized learning strategy is urgently needed for most e-

learning systems currently. Nowadays, most recommendation systems (Kao, 2001;

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Balabanovic & Shoham, 1997; Fu, Budzik & Hammond, 2000; A. M. Rashid, I. Albert,

et al., 2002; Kyparisia and Maria, 2002; Myung-Geun Lee, 2001) consider learner/user

preferences, interests, and browsing behaviors when analyzing learner/user behaviors for

personalized services. These systems neglect the importance of learner/user ability for

implementing personalized mechanisms. [2].

The rapid advance of distance learning and networking technology has enabled

universities and corporations to reach out and educate students across time and space

barriers. This technology supports structured, on-line learning activities, and provides

facilities for assessment and collaboration. Structured collaboration, in the classroom, has

proven itself a successful and uniquely powerful learning method. Most online

collaborative learners, however, do not enjoy the same benefits as face-to-face learners

because the technology provides no guidance or direction during online discussion

sessions. Integrating intelligent individual and group facilitation agents into collaborative

distance learning environments may help bring the benefits of the supportive classroom

closer to distance learners. This research aims to support groups of online distance

learners by demonstrating a new Hidden Markov Modeling method for analyzing online

knowledge sharing interaction. It is intended to assist an intelligent coaching agent in

mediating situations in which new knowledge is not effectively assimilated by a

distributed group of humans. The key roles of the new analysis engine include

recognizing when students are having trouble learning the new concepts they share with

each other, and determining why they are having trouble. This paper explains how we

addressed both of these issues, and proposes a multi agent architecture integrating

coaching and analysis agents. We begin by discussing a few open issues in developing

agents for mediating learning communication, and introducing our software and

experimental method. We then describe our modeling approach to analyzing peer

knowledge sharing, summarize our results, and discuss future research directions [3].

Reference:

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[1]. Building a Next-Generation Infrastructure for Agent-based Distance Learning

Junichi Suzuki Department of Computer Science, Graduate School of Science and

Technology, Keio University Yokohama City, [email protected] Yoshikazu

Yamamoto Department of Computer and Information Science, Keio University

Yokohama City, [email protected]

[2]. Personalized E-learning System Using Item Response Theory, Chih-Ming Chen1,

Hahn-Ming Lee2, and Ya-Hui Chen2 Graduate Institute of Learning Technology

National Hualien Teachers College1 Department of Computer Science and Information

Engineering National Taiwan University of Science and Technology, Taipei, Taiwan2

123 Hua-His Rd., Hualien.

[3]. An Intelligent Agent Architecture for Facilitating Knowledge Sharing

Communication Amy Soller and Paolo Busetta ITC-IRST Via Sommarive Povo,

Trento, Italy +39 0461 314 358 [email protected],[email protected]

[4] Asoka S Karunananda, An Intelligent Agent for Distance Learning, Proceedings

of the Philippine Computing Science Congress, 2000:13-17.

[5] Koyama, Barolli, Tsuda Zixue Cheng, An agent-based personalized distance

learning system,Information Networking, 2001.2:895899.

[6] Yang Wei,Yuan Rong, An Adaptability ELearning system Based on Web, The

8th Joint International Computer Conference,November Zhejiang University Press,

November, 2002.

[7] Asoka S Karunananda, An Intelligent Agent for Distance Learning, Proceedings

of the Philippine Computing Science Congress, 2000:13-17.

Conclusion:

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Agent is a rising technology in the field of AI and computer software. This paper

presents a design of intelligent answering system based on Agent technology with higher

self-adaptation and real-time, and then provides the key technologies in its realization. By

this system, efficiency and precision may be increased and learners efficiency can be

enhanced. With the development of artificial intelligence (especially natural language

comprehension technology) and distance education, this intelligent answering system

must have broad perspective and great value in practice.

Enhancement:

In the future, apart from searching in LAN appliances could be plugged into

the Internet, tapping into thousands of high-performance computers, allowing us to do

word processing, spreadsheet calculations, email, and so on with a very low-cost

computing device. With the development of artificial intelligence and distance education,

this intelligent answering system must have broad perspective and great value in practice.

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