Using Learning Styles and
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Neural Networks as na Approach to
eLearning Content Layout Adaptation
Keywords: Adaptative Hypermedia; eLearning; Adaptative
Educational Systems; Navigation Support; user modelling
Systems; Navigation Support; user
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 1

Agenda
g
• Introduction
• Problem Statement
• Hipotesis
• Learning Styles
• Personalized and Dynamic Content Presentation
and N i ti
d Navigation
• Prototype CeLIP – Cesae eLearning Intelligent
Player
• Conclusions and Future Work
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 2

Introduction 1/2
L d h
eLearning´s trend is changing:
´
Content has become the key
issue.
issue
Learners don´t identify their
g y
own learning style with most of
actual eLearning content.
High impact use of technology
in support of learning, like :
fl lk
Web 2.0 technologies and
services and social networks.
Microlearning become a
Research Topic.
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 3

Introduction 2/2
eLearning´s trend is changing:
New oportunities with SCORM
2004 (LO, adaptative navigation
paths)
USA and south A
d h America
i
elearning market is growing
very quick 60% of American
corporations and large
companies have already applied
these solutions [www.elearning-
online.com, 5-2-2008]
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 4

Problem Statement
The eLearning in Portugal, as in many other
countries, is not yet so widely used as an alternative
i i id l d l
to other forms of training: as is the case of traditional
classroom.
classroom
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 5

Hipotesis
This is because learners don’t identify their own learning
style in the way the presentation of education content
are done in the majority of eLearning material
produced today, or not feel enough customization in
the content to their own needs. From our point of view,
p
the appropriate modeling of the learner’s needs and
preferences, representation of pedagogical strategies, learning
designs and assets as well as the runtime reconciliation of
these elements, are the key issues to solve some problems of
this generation eLearning. This can be done with the help of
some kind of learning styles classification and a mechanism to
produce personalized content.
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 6

Learning Styles
g y
We use a learner model based on Kolb learning
W l d lb d K lb l i
style inventory classification[1] .
Some other work in this field:
ALE Fraunhofer Inst. for Applied Information Technology Felder ‐Silverman
INSPIRE Honey & Munford
TANGOW Felder & Soloman
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 7

Kolb Learning Styles
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Kolb set out four preferences for learning:
Feeling (“Concrete Experience” – CE)
Watching (“Reflective Observation” – RO)
Thinking (“Abstract Conceptualization” – AC)
Doing (“Active Experimentation – AE)
The Combination of this styles gives us
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 8

Kolb Learning Styles
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Four Learning Styles or Types:
Reflector (Watching and Doing, Concrete‐Reflective)
Theorist (Watching and Thinking, Abstract Reflective)
Theorist (Watching and Thinking, Abstract‐Reflective)
Pragmatist(Thinking and Doing, Abstract‐Active)
Activist (Doing and Feeling, Concrete –Active)
RO: 13
AC: 14
1º ACTIVIST
1º ACTIVIST AE: 17
Predominant area CE: 17
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 9

Kolb Learning Styles
g y
RO: 13 In his research Kolb
AC: 14
concludes that no learner
no learner
AE: 17
1º ACTIVIST CE: 17 has one single style
Our implementation of
Kolb’s self‐administered
K lb’ lf d i i t d
questionnaire[9].
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 10

Personalized and Dynamic Content
Presentation and Navigation
In our design, we use two types of adaptability:
Adaptive Presentation
Adaptive Navigation
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For the first type we use three methods of adaptivity: Kolb
yp p y
Learning Styles, individual and global performance and
user’s preferences. For the second type we use a subject
matter pre-test mapped to each l
d h learning object in the
b h
repository.
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 11

Personalized and Dynamic Content
Presentation and Navigation
Learning element Sequencing Recomended topics
Optional topics
LS Kolb Adaptivity in learning experience is
actual User Preferences accomplished by choosing the
learning paths that suit the
values knowledge level and the acquired
competencies of the learner.
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 12

Personalized and Dynamic Content
Presentation and Navigation
Depending on the subject,
their topic might have more
or less presentation layout
options.
Other important
p
sequencing strategy is
imposed by the kind of
hidden options dictated
by the initial diagnosis
and ontological maps
representing the
curriculum
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 13

Personalized and Dynamic Content
Presentation and Navigation
Content/Presentation Adaptation
We use four type of pedagogical layout strategies mapped to the four basic
main styles defined by Kolb. We use sets of didactical elements composed in a
main styles defined by Kolb We use sets of didactical elements composed in a
way that the learner “feels at home” in their learning style.
AUDITIVE – Verbal instructions; Reading; Debate; Brainstorming; oral
presentations; Music, Video, Television; Repeat, audio recordings; audio
books; This learners retain knowledge by using the language …
VISUAL – Watching, observing; remenber things using visual images and
h b b h l d
patterns, learn using color codes; understand better using shapes, drawings,
tables, oral instructions must be accompanied by video, graphics, images.
“TACTIL‐CINESTÉTICA” – Learn with practical activities, and Ph i ll
“TACTIL CINESTÉTICA” L ith ti l ti iti d Physically
envolved in projects or activities. Better learning performance using
movement. (example: listening a mp3 Learning Object while walking).
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 14

Prototype (ongoing work)
CeLIP ‐Cesae eLearning Intelligent Player
CeLIP architecture integrates new principles and
tools in the field of Learning Design and some
Artificial Intelligence. This player uses a MLP ‐
Artificial Intelligence. This player uses a MLP
Multilayer Perceptron neural network to predict
the next presentation layout.
We use Three steps :p
1 – Train the network (feed‐forward repeated until
MSE (mean‐squared error is suffficiently small)
2 – Use the network to predict the best layout
presentation for learner.
3 – Retrain on‐the‐fly??? the network with a
backpropagation algorithm (backpropagate error by
algorithm (backpropagate error by
adjusting weights through the hidden layer and back
to the input layer)
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 15

Prototype (ongoing work)
On‐the‐fly learning with portions of Backpropagation
Algorithm.
To include the ability to learn during the use of the neural
network, we use portions of Backpropagation Algoritm
network, we use portions of Backpropagation
and the delta (∆), value obtained from pre and post‐test
(performance) and the actual choice made by the learner.
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 16

Prototype (ongoing work)
Player Prototype.
Test it
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 17

Prototype (ongoing work)
Example Workflow
Firstly, CeLIP determines and
y
employs a diagnosis in order to
create a structure of LO’s
(Learning Objects) to cover the
unit of study
study.
For each step advance in the
g
navigation structure, CeLIP
searches and finds learning
objects that best suit learning
style of the learner, their
preferences and performance
performance.
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 18

Results and Future Work
Results : We had implemented a first prototype of CeLIP and we are now
producing a course for central region of Portugal local authorities that become the
first eLearning content using this technologies in Portuguese Language.
Future Work :
Neuronal network learning parallelism;
Neuronal network learning parallelism;
IMS LIP compatibility (by IMS Global Learning Consortium Inc.) ;
Multi model approaches to model learner;
time based learning (historical); short and long time learning duality.
time based learning (historical); short and long time learning duality.
Some authors expressed skepticism concerning the viability and validity of
g g y p p g
using learning style of the learner to adapt or personalize a learning
environment to suit the needs of the learner [ Melis, E.M., Rachada, They
Call It Learning Style But It's So Much More. 2004]
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 19

Conclusions
In the current presentation we have described the implementation of possible
adaptive methods for content sequencing and adaptive presentation based on:
learning styles preferences.
l l f
adaptive hiding result of a diagnosis test.
an AI engine using a neuronal network that process the predictions of the best
presentation layout for the next LO (learning Object) in the navigation sequence.
t ti l t f th t LO (l i Obj t) i th i ti
This architecture is currently in the phase of implementation.
In our first public presentation for local authorities we received good feedback
In our first public presentation for “local authorities”, we received good feedback
from them.
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 20

Conclusions
THANK YOU
Questions?
Q ti ?
museu8bits@gmail.com
jm@mail.cesae.pt
l
DSIE08, 8 Fevereiro PRODEI ‐ Jorge Mota 21