A set of adaptation patterns for expressing adaptive navigation in Adaptive Hypermedia

Abstract

This paper presents a set of 22 adaptation patterns, independent of any application domain and independent of any adaptation engine. They have been translated to LAG and GLAM adaptation languages in order to plug them on existing adaptation engines. Currently, they are used in the EAP framework, which allows de ning complex adaptation strategies in Adaptive Hypermedia. We also propose a typology for the elementary adaptation patterns in order to facilitate their use and their understanding.

Introduction

Over the last decade, Adaptive Hypermedia (AH) have been under development [ 6 ], particularly in education [ 1 ], where learners get access to particular resources according to their knowledge, preferences and goals. Such access are proposed through the de nition of adaptation, which is often considered as the most di cult part to author in AH [ 4, 2 ].

The de nition of adaptation is made through expressing multiple adaptation strategies. An adaptation strategy speci es which resources have to be proposed and how these resources will be proposed to a set of users who share the same characteristics [ 9 ]. For example, users being theorist, textual and sequential, will have access only textual resources related to theory before those related to samples according to a depth- rst navigational path on the relation successor.

Thereby, authors face numerous challenges when de ning their adaptation strategies. The 1st challenge concerns the expression of adaptation strategies. The 2nd challenge concerns the reuse of adaptation strategies from one system to another one, and the expression of adaptation strategies independently of any AH System. To do so, the paradigm "write once, use many " [ 8 ] has been proposed. It endorses expressing adaptation at a high level, independently of any AHS, then translating this adaptation into a particular AHS. The 3rd challenge concerns the granularity in writing adaptation strategies. Its target is to avoid writing the common parts of adaptation strategies several times.

As shown in [ 9 ], till now, there have been no works concerning building complex adaptation strategies, independent of any AHS by combining simple adaptations.

So, in this paper, we present a set of 22 elementary adaptation patterns (EAP), easy to understand, independent of any application domain and also independent of any adaptation engine. They have been translated to LAG [ 2 ] and GLAM [ 7 ] in order to plug them to existing adaptation engines. Note that, LAG is already plug on multiple adaptation engines and GLAM proposes its adaptation engine. These EAP are used in the EAP framework [ 9 ]. The EAP framework enables authors to de ne complex adaptation strategies, at a high level and independent of any adaptation engine. It assists authors to instantiate our EAP on their domain model, thus to de ne elementary adaptations. Each elementary adaptation is associated to a user characteristic. As a user has multiple characteristics at a time, the framework proposes a semi-automatic combination process of elementary adaptations to compose complex adaptation strategies3. 2

Description of an elementary adaptation pattern We propose the following de nition for elementary adaptation patterns (EAP), based on the de nition of design patterns [ 5 ].

De nition 1. An elementary adaptation pattern describes a generic solution for a generic elementary adaptation problem.

The solution is independent from any language, and it exploits the characteristics of the domain model.

De nition 2. A generic elementary adaptation problem describes a criterion to select resources to be proposed and a criterion to de ne in which order the selected resources are going to be proposed.

In the following, we de ne fundamental criteria to select resources and to organize the selected resources on which EAP are based.

Criteria used to select resources. They are based on the structure of the domain model. We argue that the general description of a domain model includes the following elements: a set of classes, which must contain the class representing all the resources to be proposed, most often known as Resource, and the class representing all the domain concepts, most often known as Concept. A set of relations between classes. Each relation de nes a graph on instances of classes on which it is de ned. The graph have to be navigated in order to reach user goals. A set of properties.

Thereby, we have di erentiated between criteria selecting resources and criteria de ning a navigational path on relations. Our criteria for selecting resources are: (a) their belonging to a class, (b) the values of some properties, or (c) the presence of a relation through the resources or (d) the presence of a relation through the concepts. Furthermore, our criteria currently considered for de ning a navigational path are either (a) depth- rst, (b) breadth- rst. 3 The most di cult part of the combination is done automatically.

Criteria used to order the selected resources. In [ 9 ], we have studied over works de ning adaptation methods, by giving a particular interest for adaptive navigation. We have retained four distinct and basic modes to select resources in a setting of adaptive navigation, as described below: a - Selection only mode provides a set of resources based on a criterion. Only the selected resources are proposed to users, the other ones are not proposed.

For example, we propose only textual resources. b - Recommend selection mode provides multiple sets of resources (at least two) that include knowledge to specify which set should be recommended rather than the others. For example, to recommend de nitions rather than examples. Both types of resources are accessible by users with distinct typographic indication to identify which resources are recommended. c - Ordered selection mode provides multiple sets of resources (at least two), accompanied with knowledge to specify the order in which they must be presented. Only one set of resources is proposed at a time, and the resources of a particular set are not proposed until all the resources of all sets of higher priority have been viewed by the user. For example, concepts can be selected and ordered using the successor relation de ned between concepts. d - Alternate selection mode provides multiple sets of resources (at least two), accompanied with data that speci es the order in which they must be presented, knowing that only one set is presented to the user. For example, we propose textual resources when they are available, and audio resources in the absence of textual resources.

Name: is the name of the elementary adaptation pattern described.

Intent: is a short statement about an elementary adaptation problem. It answers what is the elementary adaptation pattern supposed to do? i.e. what is its goal? Indeed, it indicates the way the resources are selected and the way they are presented. Solution: includes two elements:

{ Expressions: denote a set of resources to be proposed, and the conditions which have to be satis ed. These conditions can be represented in one or more logical expressions. Those to be considered simultaneously are gathered in the same expression, while excluded conditions are expressed in di erent expressions. { Meta-expressions: a binary relation between two expressions. Indeed, when using multiple expressions, we specify the way they have to be considered using metaexpressions. Let E1, E2 two expressions, we de ned the following meta-expressions using P2.2 if theory and sample are modeled as two classes in the domain model, or using P2.3 if theory and sample are modeled as an attribute of the Resource class in the domain model.

We describe below our library of EAP per selection mode. Table 2 describes EAP using the selection only mode. P1.1.1, P1.1.2, P1.2 and P1.3 in Figure 1.

Name: Ordered Selection - Depth rst- Relation - Concept

.1Intent: It proposes resources according to a depth rst navigational path on con.1cepts. 1 .2Solution:

P

Expression

{ E1: linked(currentR, concept', abstraction) ^ linked-transitive(concept, goal, relationi) ^ linked(r, concept, abstraction) ^ linked(concept, concept', relationi) { E2: linked-transitive(concept, goal, relationi) ^ linked(r, concept, abstraction)

According to E1 selected resources are linked to concepts using abstraction. The

concepts can reach the goal using relationi and are directly linked to current concept.

According to E2 selected resources are linked to concepts using abstraction. The concepts can reach the goal using relationi.

Meta-expressions

E1 E2

According to this meta-expression, the set of resources selected by E1 is proposed before the ones selected by E2.

Constituents: See row 1, row 2 in Table 6

Name: Ordered Selection - Relation - Concept - breadth rst

.2Intent: It proposes resources that are linked to concepts by abstraction, and where .1each concept can reach the concept named goal directly or indirectly using relationi 1 .2according to a depth rst navigational path.

PSolution:

Expression

{ E1: linked-transitive(concept2, goal, relationi) ^ linked(r, concept2, abstraction) ^ distance(concept2, origin, relationi) ^ distance(concept, origin, relationi) ^ linked(currentR, concept, abstraction) { E2: linked-transitive(concept, goal, relationi) ^ linked(r, concept, abstraction)

According to this meta-expression, the set of resources selected by E1 is proposed before the ones selected by E2.

Constituents: See row 1, row 2 in Table 6

Name: Ordered Selection - Relation - Resource - Breadth- rst

2Intent: It proposes resources that can reach the resource named goal directly or

. .2indirectly using relationi according to a breadth rst navigational path. .21Solution:

P Expression

{ E1: linked-transitive(resource, goal, relationi) ^ distance(resource, origin, relationi) ^ distance(currentR, origin, relationi) { E2: linked-transitive(r, goal, relationi)

Meta-expressions

E1 E2

According to this meta-expression, the set of resources selected by E1 is proposed before the ones selected by E2.

Constituents: See row 1, row 2 in Table 6

Name: Ordered Selection - Classes

Intent: It proposes ordered resources belonging only to subclasses of the class Re

.2source. 2 PSolution:

Expression

- E1: instanceOf (r, Class1) - ....

- En: instanceOf (r, Classn)

Meta-expressions

Ei Ej, i < j, i = 1..n and j = 1..n.

According to this meta-expression, the set of resources selected by Ei is proposed before the ones selected by Ej (i < j).

Constituents: See row 1, row 3 in Table 6

Name: Recommended Selection - Relation - Concept- Depth rst

.1Intent: It proposes recommended resources that are linked to concepts by .1abstraction, and where each concept can reach the concept named goal directly 1 .3or indirectly using relationi according to a depth- rst navigational. PSolution:

Expression

{ E1: linked-transitive(concept2, goal, relationi) ^ linked(r, concept2, abstraction) ^ linked(concept, concept2, relationi) ^ linked(currentResource, concept, abstraction) { E2: linked-transitive(concept, goal, relationi) ^ linked(r, concept, abstraction)

Meta-expressions

Name: Recommended Selection - Relation - Resource - Depth- rst

.1Intent: It proposes recommended resources that can reach the resource named goal .2directly or indirectly using relationi according to a depth rst navigational path. 1 .3Solution:

P Expression

Meta-expressions

E4 ] E5

According to this meta-expression, the set of resources selected by E1 is recommended rather than the ones selected by E2.

Constituents: See row 1, row 2 in Table 6

Name: Recommended Selection - Relation - Resource - Breadth- rst

Intent: It proposes recommended resources that can reach the resource named goal

directly or indirectly using relationi according to a breadth rst navigational path. Solution:

Expression

Meta-expressions

Intent: It proposes alternate resources that are linked to concepts by abstraction,

wher each concept can reach the concept named goal directly/indirectly using relationi according to a depth- rst navigational.

Solution:

Expression

{ E1: linked-transitive(concept2, goal, relationi) ^ linked(r, concept2, abstraction) ^ linked(concept, concept2, relationi) ^ linked(currentResource, concept, abstraction) { E2: linked-transitive(concept, goal, relationi) ^ linked(r, concept, abstraction)

Meta-expressions

.2Name: Alternate Selection - Relation - Resource - Breadth- rst .2Intent: It proposes alternate resources that can reach the resource named goal di1 .4rectly/indirectly using relationi according to a breadth rst navigational path. n r e t t a P Solution:

Expression

Name: Alternate Selection - Properties .3Intent: It proposes alternative resources, where each of them satisfy a value of the 4 nproperty propertyi. r teSolution: ta Expression P { E1: characteristicOf(r, propertyi , op, val1) { .... { En: characteristicOf(r, propertyi , op, valn)

Discussion and conclusion

Concerning the domain model, we argue that whatever the domain model, it is composed of a set of classes, of properties and relations [ 7, 4, 2 ]. Therefore our EAP are independent of any domain model.

Concerning the adaptive navigation, we have conducted a study [ 9 ] of supported types of adaptive navigation and we have included all of them in our EAP. In case, we didn't consider a type of adaptive navigation, our EAP are extensible to support missing types of adaptive navigation.

We also argue that a user model is always composed of a set of characteristics. For any user model, our framework is generic to consider any user characteristics. The EAP framework enables to associate a value of a user characteristic to an instantiation of EAP. Afterward, it generate complex adaptation strategies basing on combinations of user characteristics.

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