SALO

Sharable Auto-adaptive Learning Object

Ignacio Gutiérrez Menéndez, Mª del Puerto Paule Ruiz and Juan Ramón Pérez Pérez

Universidad de Oviedo, Dpto. Computer Science, Oviedo, Spain

Keywords: e-Learning, Dynamic languages, Adaptation.

Abstract: New pervasive computing scenarios such as ubiquitous learning (u-learning) and mobile learning (m-

learning) demands real time adaptation. Getting such adaptation couldn’t be possible with the current

technologies. It is necessary a change in the development of pervasive e-learning systems using dynamic

technologies and including them in both: platforms and e-learning content specifications. In this paper we

define and develop the concept of Sharable Auto-Adaptive Learning Object (SALO) like a Learning Object

which includes content and describe its behaviour thanks to dynamic languages. Such features allow it to

change and include new resources and behaviour, in a dynamic way and using the user’s context at 'anytime,

anywhere, from any device'.

1 INTRODUCTION

The reuse of electronic resources is a complex

matter that should guarantee more attractive,

effective, efficient and accessible learning

experiences for students (Campbell, 2003).

Ubiquitous learning allows students to learn in any

place and moment and with any device (Abarca et

al., 2006). A context-aware approach is needed

because each learner has unique needs and context,

but also provides focused and structured learning

(Farrell et al., 2004).

The interoperability in the educational content

has allowed the creation of the learning objects

(IEEE, 2002) (Willey, 2000). The use of open

standards in course construction has brought about

Content specifications such as Sharable Content

Object Reference Model (SCORM;

http://www.adlnet.gov), IMS Learning Design (IMS-

LD; http://www.imsglobal.org) and IMS Common

Cartridge (IMS-CC; http://www.imsglobal.org/cc/).

The specifications work with an object model

that allows serializing the learning objects,

independently of the tool or application which they

have been created or processed. However, the actual

specifications present, under our point of view, a

limitation: the impossibility of storing or serializing

the objects behaviour. Stored and distributed

learning objects are just content containers without

any own behaviour. Actual Learning Management

System (LMS) must provide the behaviour.

From our point of view, this way of

implementing the behaviour makes impossible an

adaptation in real time, restricting the creation of

ubiquity learning (Weiser, 1993), mobile learning

and context-aware learning systems. Our proposal

follows changing this approach. The aim is to

generate learning objects with data and behaviour.

This idea is not new, in (Bailey et al., 2002) appears

the concept of the Fundamental Open Hypermedia

Model (FOHM) and in (Zouaq et al., 2008) presents

an ontology-based approach for the dynamic

generation of learning knowledge objects (LKO).

We have developed and implemented a SALO’s

(Sharable Auto-adaptive Learning Objects), objects

which are fully autonomous, where both content and

behaviour are stored. In order to achieve this

approximation, it is necessary to use dynamic

adaptation (Yang et al., 2002), which allows the

possibility of adapting the learning objects to the

context in which they are being executed.

2 RELATED WORK

2.1 Adaptive Educational Systems

In general, the Adaptive Educational Hypermedia

Systems (Brusilovsky, 2001) are based on the use of

Adaptive hypermedia techniques (Brusilovsky,

387

Gutiérrez Menéndez I., del Puerto Paule Ruiz M. and Ramón Pérez Pérez J..

SALO - Sharable Auto-adaptive Learning Object.

DOI: 10.5220/0003303103870390

In Proceedings of the 7th International Conference on Web Information Systems and Technologies (WEBIST-2011), pages 387-390

ISBN: 978-989-8425-51-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

1996) to solve a number of the problems associated

with the use of educational hypermedia. Different

ways of implementing the adaptation has been used

in educational systems (Paule et al., 2008):

1. Use of rules that activate one another when

certain actions happen, these rules can also create

new rules, e.g.: AHA! (http://aha.win.tue.nl) and

MOT

(http://www.dcs.warwick.ac.uk/~acristea/mot.html)

are both systems that belong in this first category.

2. The second kind is the creation of proprietary

objects with a particular format created for each

application. AuldLinky (Michaelides et al., 2001) is

a system that belongs to this category.

3. In (Cristea et al., 2007) the authors describe two

new representation languages that emerged in the

process of adaptation: a common format for defining

the static material, CAF, and an extended adaptation

language for the description of the dynamic

behaviour, LAG.

In general, the use of rules is important to support

the adaptive capabilities of the system; however, the

adaptive applications generate a content that cannot

be reused by other systems. Regarding the creation

of proprietary objects for each application, sharing

these objects among different applications is

difficult since these objects do not follow any

standard format. The use of LAG is a step forward in

this field, although it is not compatible with the

existing specifications on the market.

2.2 Adaptation in the e-Learning

Content Specifications

Merging adaptive hypermedia with e-learning seeks

to adapt e-learning information according to the user

needs and context. The specifications use the

sequencing in order to get the adaptation, SCORM

has the SN (Sequencing & Navigation) which by

means of rules and depending on user model it

allows the LMS to show the right content. The

decision taken by the sequencing is done by a static

engine and a number of rules that are executed

according to some data returned by the learning

object.

The learning objects are mere data containers,

but without any related behaviour, leaving this

behaviour to the LMS, so developers need to build a

set of sequencing rules and an API defined by the

content specification. It would be a great advance to

provide learning objects with a complex and

independent behaviour from the platform. With this

goal, LMS just needs to care about the content

administration task, releasing it from the task of

managing object relationships.

3 OUR PROPOSAL:

SHARABLE AUTO-ADAPTIVE

LEARNING OBJECTS

We consider that an auto-adaptive learning object

must have the following features:

1. Own content according to the learners’ needs and

context

2. Own behaviour allowing:

- To be sufficiently aware of the learners’ context

- To be able to take full advantage of pervasive

computing

3. To be reused many times independent of

software and platforms versions

4. To be interoperable; compatible with the content

specifications and with current LMSs

The first three features require dynamic adaptation.

In order to get dynamic adaptation, we need to use

reflective and reflection techniques (Pattie, 1987)

and those techniques are offered currently by

dynamic programming languages (Ortín et al.,

2005). From our point of view, it is necessary the

addition of dynamic features to a LMS to get

context-aware, ubiquity and multi-mode e-learning

systems. These features implies many changes in

design and development of actual learning

platforms, however these changes are compatible

with the actual e-learning frameworks such as

OKI(http://www.okiproject.org), ELF

(http://www.elframework.org) or IMS Abstract

Framework(http://www.imsglobal.org/af/), because

these frameworks define a set of services which are

independent of the programming language.

3.1 Our Proposal:

Sharable Auto-adaptive

Learning Object (SALO)

We define as a Shareable Auto-Adaptive Learning

Object (SALO) as a learning object which is able to

describe its own behaviour, being independent from

the LMS, adapting itself to the context and being

reusable for other e-learning systems.

A SALO contains data and behaviour (Figure 1),

packed both of them with SCORM specification,

which makes it independent of the LMS.

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388

LearningObject

Content

<item>

SALO

Content

Behaviour

SALO

Content

Behaviour

SALO

Content

Behaviour

Sequencing

Currentspecifications Proposalofanewspecification

LearningObject

Figure 1: Transition from LO to SALO.

The creation process of a SALO is formed by an

editing process plus a run time process and it is

divided into four parts:

- To choose the content: pdf, html, or any kind of

educational resource.

- Addition of behaviour: It consists on a set of

techniques and methods of adaptive hypermedia and

behaviour rules, which define the instructional

design of the LO and they are implemented in Ruby.

Teacher has an edit tool in order to include such

techniques and rules into a SALO.

- Packing: SALO’s learning content is packed in a

similar way as SCORM does, but also our process

packs its behaviour. Later on, content and behaviour

will be used by the dynamic engine inside a LMS to

make the user adaptation possible.

- Adaptive environment: It is done by the LMS and

the adaptive engine. The adaptive engine loads the

content and behaviour of the LO in memory, starting

its execution. The engine also processes the SALO´s

learning content and returns it to the LMS. Once the

interaction between SALO and the user has finished,

a terminate request is gotten and according to the

object’s behaviour the next SALO is selected and

the process begins again. If the user decides to quit

the course, the state is stored in the system.

The engine has two main functions: load and run the

SALO. Firstly the loading process connects to a

repository and it will take every data and resources

building a current LO. Also, it will take the

behaviour which will be transformed, using

reflective techniques, into methods that will be

attached to the LO previously built producing a

SALO in memory. Secondly, the SALO is executed

and it uses its new methods to change its view to the

user.

The previously described process adapts the

SALO to the user at runtime and can be modified

just adding/removal behaviour in the repository. At

this point it is interesting to highlight that every

addition we have done to the SCORM specification,

to create a SALO, is compatible with the current

SCORM format, because, this extra information will

be ignored by the current LMSs.

3.1.1 Preliminary Results

We have tested the implementation of a SALO with

20 learners of University of Oviedo. The goal of this

test is to show that the adaptation technologies

proposed is feasible.

For the test every user had to complete two

courses - with and without adaptation - and they

must be done twice each one. The subjects chosen

for the courses have been related to “golf” and

“basics of programming”.

The results, which are not conclusive, shows that

most learners appreciate the combination of

adaptation technologies adopted and the support

offered by the implementation because they notice

as significant visual changes between the adapted

course and the course without adaptation. These

results go in parallel with other similar studies done

by other researchers in the Adaptive Hypermedia

Educational Systems.

4 CONCLUSIONS

AND FUTURE WORK

Nowadays, researches related to ubiquity and

context-aware e-learning systems are in great

expansion. These systems need adaptation to the

current user context in real time. Dynamically typed

programming languages are able to include dynamic

adaptation to the e-learning content, as it has been

shown in this paper, however using dynamic

technologies means a change in design, develop and

implementation of the current LMS´s.

A SALO changes the meaning of the current

LO’s, giving them behaviour and making them

independent of the LMS´s where they are being run.

They are compatible with current content

specifications like SCORM, and also with LMs´s

like Moodle. Nowadays there are LMS´s

implemented with dynamic languages like Moodle,

implemented in PHP. This kind of implementation

of LMS makes easier to add the adaptation engine

which allows the SALO’s execution.

Currently we are exploring the possibility of

creating new adaptive strategies in runtime

according to a user model and how to do the

SALO - Sharable Auto-adaptive Learning Object

389

connection between the user model and its

translation in adaptive rules.

ACKNOWLEDGEMENTS

This work has been partially funded by the

Department of Science and Technology (Spain)

under the National Program for Research,

Development and Innovation; project TIN2008-

00276.

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