HYBRID ONTOLOGY-BASED FEEDBACK E-LEARNING

SYSTEM FOR MOBILE COMPUTING

Ahmed Sameh, Abdalla Mahmoud and Amr Ibrahim

Department of Computer Science

The American University in Cairo

P.O.Box 2511, Cairo

Keywords: LOM, LMML, Dublin Core, Fragments, Metadata, RDF, Web Repositories, Mobile Computing.

Abstract: An E-Learning system that provides vast quantities of annotated resources (fragments or learning objects)

and produces semantically rich feedback is very desirable. It is an accepted psychological principle that

some of the essential elements needed for effective learning are custom learning and semantic feedback. In

this paper we are making use of a collection (ontology) of meta-data for the design of a custom E-Learning

system that also provides learners with effective semantic educational feedback support. The learning

domain is “Mobile Computing”. We define various concepts in the domain and the relationships among

them as the ontology, and built a system to utilize them in customizing the E-Learning process. The

ontology is also used to provide informative feedback from the system during learning and/or during

assessment. The focus in this research is on the representation of ontology using languages/grammars,

grammar analysis techniques, algorithms and AI mechanisms to customize the learning and create effective

feedbacks. The proposed mechanisms, based on Ontology; are used to assemble virtual courses and create a

rich supply of feedbacks, not only in assessment situations but also in the context of design-oriented

education. We are targeting feedbacks similar to ones in programming environments and design editors.

1 VIRTUAL COURSES

The proposed system aims to expose vast quantities

of annotated resources (fragments or learning

objects) that have been created over time and space

by educators and instructional designers to data-

mining end users in order for the latter to assemble

sequences of “learning objects” (virtual classes).

Towards this goal we are proposing an ontology-

based feedback model to achieve a number of high-

level objectives: Dynamically Generating On-

Demand Virtual Courses/Services, Providing

Component-based Fragments, and facilitating rich

Semantic Feedbacks.

An organization repository is a repository of course

components (fragments) at various levels-of-details.

As such, these fragments can be reused for several

courses and contexts and can be distributed over a

number of sites. A virtual course authoring process

points to various learning components (fragments).

A learning fragment is a self-contained, modular

piece of course material. It can be either passive or

active (e.g. live or recorded lecture). These

fragments are annotated (for example by author)

according to ontology metadata schema that provides

efficient mechanisms to retrieve fragments with

respect to the specific needs of a virtual course. With

similar details; administrators cooperate in building

various service repositories. This follows the new

direction in what is called “Semantic Web” .

An organization local architecture consists of

Repositories, a Mediator, Concept Storage

Fragments, Storage Systems, and Clients. The

Mediator provides transparent access for all client

requests to all distributed repositories fragments. The

huge amount of metadata we have to deal with

indicates the need for efficient storage and query

mechanisms. The repositories can be accessed either

synchronous or asynchronous. Customers can either

access their composed virtual courses/services either

on-line or off-line, in distance and/or conventional

education.

The development of electronic course material

suitable for different learners takes much effort and

incurs high costs. Furthermore, professional trainers

have huge expenses to keep course content up to

date. This problem occurs especially in areas where

knowledge and skills change rapidly, such as in the

Computer Science domain. Thus, we need new

approaches to support (semi-)automatic virtual

347

Sameh A., Mahmoud A. and Ibrahim A. (2006).

HYBRID ONTOLOGY-BASED FEEDBACK E-LEARNING SYSTEM FOR MOBILE COMPUTING.

In Proceedings of WEBIST 2006 - Second International Conference on Web Information Systems and Technologies - Society, e-Business and

e-Government / e-Learning, pages 347-356

DOI: 10.5220/0001247203470356

 SciTePress

course generation in order to keep up with current

knowledge and perhaps even to adapt materials to

individual user needs. In this proposal repositories

are used as infrastructure to support the automatic

generation of virtual courses from similar course

repositories, while also aiming to achieve a high

degree of reusability of course content. A popular,

promising approach is to dynamically compose

virtual courses “on-demand” within the course

repositories. The idea is to segment existing course

material (e.g., slides, text books, animations, videos)

into so-called learning fragments. Such learning

fragments represent typically self-contained units

that are appropriately annotated with metadata.

Tailored training virtual courses are requested and

generated "on-demand" by assembling single

fragments, such as in (Tendy,1998) (Kolb, 1984).

Using this approach that is similar to modularization

in software engineering, we intend to achieve a high

degree of reusable content, and have learning

fragments that can be used in new contexts. This

reduces costs for the development of real courses.

User adaptability is achieved by means of allowing

users to specify queries, and dynamically construct

courses. As a result, a course may contain fragments

from various sources such as textbooks, instructional

movies, or slides from several knowledge providers

(e.g. teachers) as well as fragments from similar

remote repositories. Note that there is a tradeoff

between granularity (size) of learning fragments,

reusability, and annotation effort. Having small units

of learning fragments increases the annotation effort,

but implies better reusability since small fragments

can be composed more flexible. The same can be

said about virtual services for

students/administrators.

Students can customize whatever they want to

learn according to their learning style, the amount of

time they wants to spend in a certain learning

session, the surrounding environment, their mode,

how deep they wants to dig into the subject, etc.

Based on the available metadata annotations, on-line

virtual courses are generated semi-automatically

from course repositories by selecting appropriate

course fragments and by structuring them into a

virtual course, which is a composition of fragments.

The selection and composition is based on a query

that specifies concepts to be taught and restrictions

(e.g., author, source, date of authoring). For these

specified concepts, any pre-knowledge concepts are

retrieved and appropriate fragments are selected.

This means that the virtual course structure is build

dynamically on the corresponding pre-knowledge

conditions of fragments to be selected. We call this

bottom-up approach, that stands in contrast to top-

down course composition, where the course structure

(e.g., a table of content) is static and given a priori.

Further, didactic, content-specific and course-

specific needs can be considered for the selection of

fragments. The structuring and selection processes

are strongly correlated. In case that no fragments are

found that map exactly to the restrictions (e.g., the

date of authoring is older than specified in the course

request), the algorithm selects fragments that do not

map optimally to the specification, but also fit to the

course. This proceeding avoids the ad hoc generation

of missing fragments for a requested course. As a

result, complete virtual courses can be generated. A

course end user also has the option to refine an

automatically composed virtual course. The

complete virtual course is then presented to the end

user who has access to a web portal.

In fact, research groups can open their own

repositories (e.g. Robotic-repository) where

fragments of group member’s contributions can be

stored in and annotated. Again, an end-user can

compose his virtual tour among these fragments. For

example, the AUC annual conference proceedings

can be fragmented and stored there.

2 SEMANTIC FEEDBACK

In a classroom learners and teachers can easily

interact, i.e. students can freely ask questions and

teachers usually know whether their students

understand (basic) concepts or problem solving

techniques. Feedback is an important component of

this interaction. Furthermore, educational material

can be continually improved using information from

the interaction between the lecture and the learners,

which results in a more efficient and effective way of

course development.

Feedback can be given to authors during virtual

course development and to learners during learning.

In the current generation of E-Learning systems,

automatically produced feedback is sparse, mostly

hard coded, not very valuable and almost only used

in question-answer situation.

In this paper we are introducing mechanisms –

based on ontologisms- to create a rich supply of

feedback, not only in question-answer situations but

also in the context of virtual courses composition.

Ontologisms are formal descriptions of shared

knowledge in a domain. With ontologisms we are

able to specify (1) the knowledge to be learned

(domain fragments and task knowledge) and (2) how

the knowledge should be learned (education). In

combining instances of these two types of

ontologisms, we hope that we (1) are able to create

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(semi-) automatically valuable generic feedback to

learners during learning and to authors during virtual

course development, and (2) are able to provide the

authors with mechanisms to (easily) define domain

and task specific feedback to learners.

Feedback describes any communication or

procedure given to inform a learner of the accuracy

of a response, usually to an instructional question.

More general, feedback allows the comparison of

actual performance with some standard set of

performance. In technology-assisted instruction, it is

information presented to the learner after any input

with the purpose of shaping the perceptions of the

learner. Information presented via feedback in

instruction might include not only answer

correctness, but also other information such as

precision, timeliness, learning guidance, motivational

messages, background material, sequence

advisement, critical comparisons, and learning focus.

Feedback is given in the form of hints and

recommendations. Both a domain

conceptual/structural ontology as well as a

task/design ontology is used. The ontologisms are

enriched with axioms, and on the basis of the axioms

messages of various kinds can be generated when

authors violate certain specified constraints.

In our research we are generating generic, domain

and task feedback mechanisms that produce

semantically rich feedback to learners and authors

during learning and authoring. We distinguish two

types of feedback: (1) feedback given to a student

during learning, which we call student feedback, and

(2) feedback given to an author during course

authoring, which we call author feedback. The

generic feedback mechanisms use ontologisms as

arguments of the feedback engine. This is important,

because the development of feedback mechanisms is

time consuming and specialist work, and can be

reused for different ontologisms. Besides generic

feedback mechanisms we will provide mechanisms

by means of which authors can add more domain

and/or task specific feedback. In this research, we

focus on “Mobile Computing” domain.

We designed an E-Learning environment for

Mobile Computing courses, in which: (1) learners

are able to design artifacts of certain domains using

different types of languages, and (2) authors are able

to develop virtual courses. Learners as well as

authors receive semantically rich feedback during

learning, designing artifacts and developing virtual

courses.

For example, a student first has to learn the

concept (communication) network. Assume that a

network consists of links, nodes, a protocol and a

protocol driver. Each of these concepts consists of

sub-concepts. The domain ontology ‘communication

technology’ represents these in terms of a vocabulary

of concepts and a description of the relations

between the concepts (see figures 1-3). On the basis

of an education ontology, which describes the

learning tasks, the student is asked to list the

concepts and relate the concepts to each other (see

figure 1). Feedback is given about the completeness

and correctness of the list of concept and relations

using different balloon dialog patterns.

In a second step the learner is asked to design a

part of a local area network (LAN) using the network

model developed during the first step (see figures 2-

7). Instead of concepts, concrete instantiations must

be chosen and related to each other. The learner gets

feedback about the correctness of the instantiations

and the relations between the concepts using

different star/lamb/scroll dialog patterns. Some

protocols for example need a specific network

topology. There are various sequences of activities to

develop a network, each of them with its own

particular efficiency. The student gets feedback

about the chosen sequence of activities on the basis

of the task/design ontology. Further, the student

receives different types of feedback, for example

corrective/preventive feedback, critics and guiding.

All these feedback types are further customized to

the learning style of the learner.

An author develops and optimizes a virtual course

from learning fragments. He/she has to choose,

develop and/or adapt particular ontologisms and

develops related fragmented material like examples,

definitions, etc. (see figure 1). Based on analyses of

the domain, education and feedback ontologisms, the

author gets feedback, for example about: (1)

Completeness: A concept can be used but not

defined. Ideally, every concept is introduced

somewhere in the course, unless stated otherwise

already at the start of the course. This error can also

occur in the ontology for the course. (2) Timeliness:

A concept can be used before its definition. This

might not be an error if the author uses a top-down

approach rather than a bottom- up approach to

teaching, but issuing a warning is probably helpful.

Furthermore, if there is a large distance (measured

for example in number of pages, characters, or

concepts) between the use of a concept and its

definition in the top-down approach, this is probably

an error. (3) Synonyms: Concepts with different

names may have exactly the same definition. (4)

Homonyms: A concept may have multiple, different

definitions, sometimes valid depending on the

context.

The E-Learning environment consists of four

main components: a player for the student, an

HYBRID ONTOLOGY-BASED FEEDBACK E-LEARNING SYSTEM FOR MOBILE COMPUTING

349

authoring tool, a feedback engine and a set of

ontologisms as pluggable components (see figure 1).

The player consists of a design and learning

environment in which a student can learn concepts,

construct artifacts and solve problems. The authoring

tool consists of an authoring environment where the

author develops and maintains courses and course

related materials like ontologisms, examples and

feedback patterns. The feedback engine

automatically produces feedback to students as well

as to authors. The feedback engine produces generic

feedback and specific feedback. Generic feedback is

dependent of the ontologisms used and is applicable

to all design activities and artifacts (e.g. critic,

guidance, and corrective/preventive feedbacks).

Specific feedback is defined by the author and can be

more course, domain, modeling language or task

specific. To construct feedback, the feedback engine

uses the four argument ontologisms (concept,

structure, task, and design feedbacks). Since the

ontologisms are arguments, the feedback engine

doesn’t have to be changed if an ontology is changed

for another. The feedback engine can produce the

two types of feedback mentioned (student and author

feedback). To produce student and author feedback,

student and author activities are observed and

matched against the ontologisms mentioned.

3 ONTOLOGISMS

In the experimental “Mobile Computing” prototype,

fragmented metadata-based repositories that are

making use of four standards: Resource Description

Framework (RDF), IEEE LOM Metadata, Learning

Material Markup Language (LMML), and XML-

based Metadata are used to build the prototype. The

proposed prototype is providing gateways among

these standards.

RDF/RDFS (1-4) is deployed as one of the

underlying modeling languages to express

information about the learning objects (fragments or

components) contained in the repository, as well as

information about the relationships between these

learning objects (the ontologisms).

Each repository can for example store RDF

(Resource Description Framework) metadata from

arbitrary RDF schemas. Initial loading for a specific

virtual course is done by importing an RDF metadata

file (using XML syntax for example) based on this

course's RDFS schema. A simple cataloguing

(annotation) of its fragments can be deployed using

the Dublin Core metadata set (1-4). We can also port

these metadata to the LOM standard, using the

recently developed LOM-RDF-binding (1-4). With

RDF, we can describe for our purposes, how

modules, course units, corselets are related to each

other or which examples or exercises belong to a

course unit, RDF metadata used in this way are

called structural or relational metadata.

The IEEE LOM Metadata standard specifies the

syntax and semantics of Learning Object Metadata,

defined as the attributes required to fully/adequately

describe a Learning Object. Learning Objects are

defined here as any entity, digital or non-digital,

which can be used, re-used or referenced during

technology supported learning.

LMML (1-4) proved itself as a pioneer in this field

providing component-based development,

cooperative creation and re-utilization, as well as

personalization and adaptation of E-learning

contents. Considering both economic aspects and

the aim to maximize the success of learning, LMML

is up to the new requirements of supporting the

process of creation and maintenance for E-learning

contents as well as supporting the learning process

itself. Each E-learning application has its own

specific requirements structuring its contents.

The ARIADNE (1-4) Knowledge Pool standard is

a distributed repository for learning objects. It

encourages the share and reuse of such objects. An

indexation and query tool uses a set of metadata

elements to describe and enable search functionality

on learning objects. To ensure simplicity,

understandability and adaptability for the ARIADNE

community, data elements are grouped into six

categories:

• General: groups the general information that

describes the learning object such as document title,

Document language, etc.

• Semantics: groups elements that describe the

semantic classification of the learning object like

the science type, main discipline, sub discipline etc.

• Pedagogical: groups elements that describe the

pedagogic and educational characteristics of the

learning object such as semantic density, interactivity

level, etc.

• Technical: groups elements that describe the

technical requirements and characteristics of the

learning object like OS version, required disk space,

etc.

• Indexation: groups elements that describe the

general information about the metadata itself of the

learning object such as the identifier of the metadata

instance, metadata creation date, creator, etc.

• Annotations: groups elements that describe people

or organizations notes about learning objects like

annotator, language of annotations, and date of

annotation.

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4 MOBILE COMPUTING

PROTOTYPE

We have built a prototype system in the domain of

mobile computing to demonstrate the ideas of the

proposed model. Hybrid fragments of annotated

resources taken from (Amjad, 2004) are used in this

prototype. Fragments are encoded in the four

representations described above: Dublin cores, IEEE

LOM, Learning Material Markup language (LMML),

and ARIANE. Figure 1 shows how the underlying

ontologisms are used in the prototype to build virtual

courses.

Figures 2-11 show snapshots of the implemented

prototype. Figure 2 shows part of the Domain

Concept Ontology of “Mobile Computing” in

acronyms terminology with semantic feedback (By

clicking on an Akron you get the Balloon feedback

with colored links to further explanation). Figure 3

shows part of Domain Design Ontology of the

“Mobile Computing” in hybrid wired/wireless

networking with semantic feedback (The Lamb

feedback is always on during hybrid networking

design). Figure 4 shows part of Domain Task

Ontology of the “Mobile Computing” in Sensor

Networking with semantic feedback (By clicking on

any element you will get the scroll feedback). Figure

5 shows part of the Domain Design Ontology of the

“Mobile Computing” in Bluetooth networking with

semantic feedback (the scroll feedback is always on

during a Bluetooth network design). Figure 6 shows

part of the Domain Design Ontology of the “Mobile

Computing” in Wireless PANs Networking with

Semantic feedback as well as critics and guidance

feedback (a Ribbon banner is always on during a

design of a wireless PAN network). Figure 7 shows

part of the Domain Structural Ontology of the

“Mobile Computing” in wireless networks

classification with Semantic feedback (a Balloon

provides rich feedback once you click on any class in

the classification). Figure 8 shows part of the

Domain Design Ontology of the “Mobile

Computing” in Wireless LAN Configuration with

Semantic as well as generic critic and guidance

feedback (a star banner is always on during the

design of a wireless LAN). Figure 9 shows part of

the Domain Design Ontology of the “Mobile

Computing” in Positional and Voice Commerce

Design with Semantic as well as

corrective/preventive feedback (a Balloon banner

popes up upon demand during the design of the

network). Figure 10 shows part of the Domain

Design Ontology of the “Mobile Computing” in

wired/wireless network Design with Semantic as

well as corrective/preventive feedback ( a Balloon

banner is always on during the design of the

wired/wireless network). Figure 11 shows part of

Domain Task Ontology of the “Mobile Computing”

in wireless/Internet heterogeneous networking

Design with Semantic task/conceptual feedback (

Balloon banners with colored links for further

explanations are displayed).

5 CONCLUSION

In this paper, we have presented a flexible

course/service generation environment to take

advantage of re-conceptualization and re-utilization

of learning/service materials. Virtual

courses/services are dynamically generated on-

demand from fragments’ metadata entries stored in

the Repositories along with semantically powerful

feedbacks. The proposed model achieve the

following benefits:

-Costlessness: Simple interface. No preparative steps

needed

-Comprehensiveness: Allowing all players to find

each other and the existing repositories easily

(totality is promoted by costlessness)

-Wideness: Inter-Repositories brokerage for wider

collaborative scope

-Heterogeneity: Gathering contributors with different

naming schemes (taxonomies) in one collaboration

model

-Promotion: Encouraging the unification of the

namespace (deepening the common part)

-Flexibility: Repositories can join even partially

-Abstraction: Repositories encapsulate their details

-Semantically Rich Feedback: Four types of visual

feedbacks are available

REFERENCES

(1)http://www.mccombs.utexas.edu/kman/kmprin.html#hy

brid

(2)http://www.w3.org/2001/sw

(3)http://www.indstate.edu/styles/tstyle.html

(4)http://citeseer.nj.nec.com/context/1958738/0

Tendy S., Geiser W., 1998, The Search for Style: It All

Depends on Where You Look, National Forum of

Teacher Education Universal Journal, 9(1).

Kolb D.,1984, Experiential Learning: Experience as the

source of learning and development, New Jersey,

Prentice Hall.

Amjad U., Mobile Computing and Wireless

Communications, NGE, 2004

HYBRID ONTOLOGY-BASED FEEDBACK E-LEARNING SYSTEM FOR MOBILE COMPUTING

351

Figure 1: Example of Resource Annotation Structure.

M-Business, M-Government

M-Commerce P-Commerce V-Commerce

M-SCMs M-CRMs SMS MMS M-Portal

Symbian WAP MM IT WML VXM L J2M E BREW

Mobile IP MANET OMA ITU ETSI FCC

Zigbee UWB FSO Bluetooth WLL DECT HomeRF

Wi-Fi GPRS UM TS 802.11 802.16 802.15 WSN

OFDM FEC TDM A CDM A

Figure 2: Part of the Domain Concept Ontology of “Mobile Computing” in acorns terminology with semantic feedback (By

clicking on an Akron you get the Balloon feedback with colored links to further explanation)

W irele s s L A N

Cell

W irele s s L A N

Cell

W irele s s L A N

Cell

CentrexCentrex

Router

and

Firewall

Link to

P ub lic In ternet

DSL

LAN Server

Fast Ethernet

LAN (Backbone)

1. N o physical net security

(server ID /PW )

2. N o physical net security

(server ID/PW + encryption)

3. Physical net security at APs

(optional

server ID /PW + encryption)

W ired Ethernet

LAN

Figure 3: Part of Domain Design Ontology of the “Mobile Computing” in hybrid wired/wireless networking with semantic

feedback (The Lamb feedback is always on during hybrid networking design).

Voice XML means markup

language for describing

Music and encoding musical

notes. Some of …. most

Server ID

is used fo

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352

Figure 4: Part of Domain Task Ontology of the “Mobile Computing” in Sensor Networking with semantic feedback (By

clicking on any element you will get the scroll feedback).

Figure 5: Part of the Domain Design Ontology of the “Mobile Computing” in Bluetooth networking with semantic

feedback (the scroll feedback is always on during a Bluetooth network design).

W ireless Sensor Networks

Network of

Tiny

Sensors

Network of

Tiny

Sensors

Network of

Powerful

Sensors

Powerful

server

Many R&D

efforts

Sensor = mote

Bluetooth

Cellular

Network

PSTN

Access

Point

Wired

LAN

Bluetooth Piconet

(1 M bps, 10 meters, mobile adhoc network)

No physical network security exists between this server and

the top layer powerful sensor nodes. Each node is a Mote dev

Such as Video

aud

Routing

Protocols of

sensors net

Bluetooth stack

include layers of

data, packets

control….

Devices

with

commun

ication ..

HYBRID ONTOLOGY-BASED FEEDBACK E-LEARNING SYSTEM FOR MOBILE COMPUTING

353

Figure 6: Part of the Domain Design Ontology of the “Mobile Computing” in Wireless PANs Networking with Semantic

feedback as well as critics and guidance feedback (a Ribbon banner is always on during a design of a wireless PAN

network).

Figure 7: Part of the Domain Structural Ontology of the “Mobile Computing” in wireless networks classification with

Semantic feedback (a Balloon provides rich feedback once you click on any class in the classification).

Wireless PANs: Home Networking

Wireless LAN2

(Master/slave)

First Floor

= W ireless Adapter

Wireless connection

Second Floor

Printer

Baby

Monitor

Phone

Laptop

xDSL, cable,

ISDN, or other

Main PC

Internet

Gateway

Players:

•Bluetooth

•Sensor networks

•UW B

•802.11

•Zigbees

•HomeR/F (dead)

XDSL and

ISDN are

fast Internet

service

provider

protocols

used to

connect in

between fl

W ireless Netw orks

Satellite

Systems

Cellular

Networks

Wireless LANs

Example1:

GSM , 9.6 Kbps,

wide coverage

Example2:

3G, 2 Mbps,

wide coverage

Example1:

802.11b

11 Mbps,

100 Meters

Other

examples:

802.11g,

HiperLAN2

W ireless W ANs

Personal

Area

Networks

Business

LANs

Example1:

Bluetooth

1 Mbps,

10 Meters

Other examples:

wireless sensor

networks, UWB

Example1:

Motorola

Iridium

up to 64 Mbps

globally

Example 2:

Deep space

communication

Wireless

Local Loops

(Fixed Wireless)

Wireless MANs

Example1:

LMDS

37 Mbps,

2-4 Km

Example2:

FSO

1.25 Gbps

1-2 KM

Paging

Networks

Example1:

FLEX,

1.2 Kbps

Example2:

ReFLEX,

6.4Kbps

Used in

6.4K

bps

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Figure 8: Part of the Domain Design Ontology of the “Mobile Computing” in Wireless LAN Configuration with Semantic

as well as generic critique and guidance feedback (a star banner is always on during the design of a wireless LAN).

Figure 9: Part of the Domain Design Ontology of the “Mobile Computing” in Positional and Voice Commerce Design with

Semantic as well as corrective/preventive feedback (a Balloon banner popes up upon demand during the design of the

network).

Part of Domain

Task Ontology of

the “Mobile

Computing” in

wireless/Internet

heterogeneous

networking Design

with Semantic

task/conceptual

feedback ( Balloon

banners with

colored links for

further explanations

are displayed)

W ireless LA N C onfigurations

W ireless L A N 1

(peer-to -p eer)

Wired

LAN1

= W ire le s s L A N A d a p te r

W ireless connection

W ireless L A N 2

(p e e r-to -p e e r)

A ccess P oint

as a repeater

W ireless L A N 3

(M aster/slave)

A ccess P oint

Wireless

LAN -LAN

Bridge

Wired

LAN2

Personal A rea

N etw ork (PA N )

Part of Domain Task Ontology Computing” in wireless/Internet

heterogeneous networking Design with Semantic task/conceptual

feedback (Balloon banners with colored links for further explanations are

displayed)

Data NetworkData Network

Voice O ver IPVoice O ver IP

W ireless

Phone

Network

P o sitio n a l a n d V o ic e C o m m e rc e

Wireless

Gateway

Com puter+

GPS

Wireless

Phone+GPS

GIS/Map

Voice

Portal

W ireless Phone

E n te rp ris e

A p p lic a tio n

Server

Partner

Network

Partner

Network

APPs

DBs

802.11 L A N

(o ffic e o r a

hotspot)

Web

Server

Public

In te rn e t

Public

In te rn e t

Mobile Computing in

wireless/Internet heterogeneous

networking Design with Semantic

task/conceptual feedback ( Balloon

banners with colored links for further

lanations are dis

)

Part of Domain Task Ontology of Computing” in wireless/Internet heterogeneous networking Design with

Semantic task/conceptual feedback ( Balloon banners with colored links for further explanations are displayed)

HYBRID ONTOLOGY-BASED FEEDBACK E-LEARNING SYSTEM FOR MOBILE COMPUTING

355

Figure 10: Part of the Domain Design Ontology of the “Mobile Computing” in wired/wireless network Design with

Semantic as well as corrective/preventive feedback ( a Balloon banner is always on during the design of the wired/wireless

network).

Figure 11: Part of Domain Task Ontology of the “Mobile Computing” in wireless/Internet heterogeneous networking

Design with Semantic task/conceptual feedback (Balloon banners with colored links for further explanations are displayed).

Router

C ellu lar

Phone (W AP)

Solaris

(D ata)

Ethernet

LAN

M o re D etailed V iew

TCP

/IP

C atalo g

M iddlew are

(ODBC)

Op System

(U N IX )

TCP

/IP

Purchase

System

Middleware

(W eb Serv er) (O D B C )

Op System

(N T )

WAP

Stack

WML

Middleware

(M icrobrow ser )

Op System

(C elullar)

(Business L ogic)

CGI

WAP

Gateway

W ireless

Network

P u b lic

In te rn e t

Router

wireless/Internet heterogeneous networking Design with Semantic task/conceptual

feedback ( Balloon banners with colored links for further explanations are displayed)

S am p le C o n fig uratio n

Wireless LAN1

(p e e r-to -p ee r)

Corporate

Backbone

= W ireless L A N A d ap ter

W ireless connection

W ired connection

W ire le s s L A N 2

(p ee r-to -p e e r)

A ccess Point

as a repeater

A ccess

Point

A ccess Point

Internet

Gateway and Firewall

Corporate

ATM

Network

= A T M S w itc h

Public

In te rn e t

Domain Task Ontology of the “Mobile Computing” in wireless/Internet heterogeneous networking Design with

Semantic task/conceptual feedback ( Balloon banners with colored links for further explanations are displayed)

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