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Table of Contents
                            Preface
E-Learning – Organizational Infrastructure and Tools for Specific Areas
Part 1
01 Factors that Influence Academic Teacher's
Acceptance of E-Learning Technology in
Blended Learning Environment
02 Towards Economical E-Learning Educational
Environments for Physically
Challenged Students
03 Advanced Pedagogical Approaches
at Slovak Universities
04 Digital Faces on the Cloud
Part 2
05 Lego Based Computer Communication
for Business and Learning
06 Multimodal Intelligent Tutoring Systems
07 Using the Smith Chart in an
E-Learning Approach
08 Intelligent Tutoring System with Associative
Cellular Neural Network
09 Proposing Two Algorithms to Acquire
Learning Knowledge in Problem-Based
Learning Environment
10 E-Learning in Architecture:
Professional and Lifelong Learning Prospects
                        
Document Text Contents
Page 1

E-LEARNING –
ORGANIZATIONAL

INFRASTRUCTURE AND
TOOLS FOR SPECIFIC AREAS


Edited by Elvis Pontes, Anderson Silva,
Adilson Guelfi and Sérgio Takeo Kofuji

Page 2

E-Learning – Organizational Infrastructure and Tools for Specific Areas
Edited by Elvis Pontes, Anderson Silva, Adilson Guelfi and Sérgio Takeo Kofuji


Published by InTech
Janeza Trdine 9, 51000 Rijeka, Croatia

Copyright © 2012 InTech
All chapters are Open Access distributed under the Creative Commons Attribution 3.0
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Notice
Statements and opinions expressed in the chapters are these of the individual contributors
and not necessarily those of the editors or publisher. No responsibility is accepted for the
accuracy of information contained in the published chapters. The publisher assumes no
responsibility for any damage or injury to persons or property arising out of the use of any
materials, instructions, methods or ideas contained in the book.

Publishing Process Manager Ivona Lovric
Technical Editor Teodora Smiljanic
Cover Designer InTech Design Team

First published February, 2012
Printed in Croatia

A free online edition of this book is available at www.intechopen.com
Additional hard copies can be obtained from [email protected]

E-Learning – Organizational Infrastructure and Tools for Specific Areas,
Edited by Elvis Pontes, Anderson Silva, Adilson Guelfi and Sérgio Takeo Kofuji
p. cm.
ISBN 978-953-51-0053-9

Page 97

Multimodal Intelligent Tutoring Systems



85

illustrates one example of the interest areas. For each interest area, the interest score is
calculated. When the score for an area exceeds a threshold, the agent will react if a reaction
is defined.



Fig. 2. Example of “interest areas”.

The key functionality of the attention information processing module in our MITS is
characterized by three main components:
 Monitor the grounding. In human face-to-face communication, grounding relates to the

process of ensuring that what has been said is understood by the conversational
partners, i.e. there is “common ground”. During the learner-tutor interaction,
grounding is considered successful if the following condition is met: the student’s gaze
shows a transition from the screen area of the speaking tutor to the screen area of the
referent mentioned by the tutor. When positive evidence in grounding is observed, the
course will continue. And a window of contextual content, i.e. the related contend,
maybe popup according to the referent.

 Guarantee the attention. The agent will perform an interruption if the student attends to
interest areas that are not considered as part of the current content the agent is talking
about. An “alert” action will be performed if the student does not gaze at the display,
for example, gaze out of the window.

 Note the history. This function records which area and how much of the area has been
accessed by the student. If an important area that the student does not pay enough
attention to, this area might be proposed again. While the area previously has been
accessed for enough time, it is not very likely that the student intends to activate it
again.

The components aforesaid are all based on the modified version of the algorithm described
by Qvarfordt (Qvarfordt & Zhai, 2005), where it is used for an intelligent virtual tourist
information environment (iTourist). Two interest metrics were developed: (1) the Interest
Score (IScore) and (2) the Focus of Interest Score (FIScore). IScore is used to determine an
area’s “arousal” level, or the likelihood that the user is interested in it. When the IScore
metric passes a certain threshold, the area is said to become “active”. FIScore measures how

Page 98

E-Learning – Organizational Infrastructure and Tools for Specific Areas



86

the user keeps up his or her interest in an active area. If the FIScore for an active area falls
below a certain threshold, it becomes deactivated and a new active area is selected based on
the IScore. According to the key functionality of the attention information processing
module in our system, a simplified version of the IScore metric is sufficient for our purpose.
IScore basic component is eye-gaze intensity p :

ISon
IS

T
p

T
 (1)

Where ISonT refers to the accumulated gaze duration within a time window of size IST (in
our system, 1000 ms) and IST is the size of the moving time window. In order to account for
factors that may relate to user’s interest, Qvarfordt characterized the IScore as

(1 (1 ))isp p p   , where isp is the arousal level of the area and  is the excitability
modification defined as below in (Qvarfordt & Zhai, 2005).

f f c c s s a a
f c s a

c c c c

c c c c

   


  


  
(2)

Where f , c , s , a are constants empirically adjusted, they are defined as:

 f is the frequency of the user’s eye gaze entering and leaving the area
 c is the categorical relationship with the previous active area
 s is the relative size to a baseline area
 a records previous activation of the area
We modified the formula 2, only f , s , a were integrated into MITS. The factor f is

represented as swf
f

N
N

  , where swN denotes the number of times eye gaze enters and

leaves the area and fN denotes the maximum possible swN in the preset time window.
f is identified as one indication of a user’s interest in an area. Since some noise in the eye

movement signal, larger areas could have a higher chance of being “hit” than smaller ones,

s is defined to avoid this. s is represented by
b

s
S S

S



 , where bS is the area size of the

common areas which are also the smallest, and S represents the size of the current area. As
for the a , it is employed to indicate whether the area has been paid enough attention.

1a   when the area has been paid enough attention and 0 when it has not been paid
enough attention.

4. Affective information

Our interest in the emotion integrated in tutoring systems is motivated by the social
cognitive theory suggesting that learning takes place through a complex interplay between
both cognitive and affective dimensions. Researches in cognitive sciences argue that
emotion enables people to communicate efficiently by monitoring and regulating social
interaction, by evaluating and modifying emotional experiences. ITS would be significantly
enhanced if computers could adapt according to the affective state of the student. In order to

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E-Learning in Architecture: Professional and Lifelong Learning Prospects



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