Using IMS LD for Characterizing Techniques and Rules in

Using IMS LD for Characterizing Techniques and Rules in Adaptive Educational Hypermedia Systems

Adriana J. Berlanga and Francisco J. García Department of Computer Science, University of Salamanca

Plaza de los Caídos, s/n, 37008 Salamanca, Spain [email protected]; [email protected]

Abstract

Adaptive Educational Hypermedia Systems (AEHS) have the potential of delivering instruction tailored to students’ characteristics. However, despite of many years of research in the area, this kind of systems has been used only in a few real learning situations. Reasons for this are their use of proprietary semantics in the definition of adaptivity and educational elements, and their lack of interoperation among courses and applications. We claim that an option to define AEHS elements might be the IMS Learning Design specification and, in this way, define them using a common notational method and support their reusability and exchangeability. This paper, presents our current work with IMS LD to define “declaratively” learning designs with adaptive characteristics. First, it introduces AEHS, their characterization, elements, taxonomy, techniques, and presents the elements that specific AEHS take into consideration for performing adaptivity. Then, it reviews briefly IMS LD and explains how the main characteristics of an AEHS can be modelled by means of this specification. Afterwards, it describes our research work towards the definition of adaptive learning designs compliant with IMS LD, and the authoring approaches we are developing for novice and expert users of the specification. Subsequently, it concludes pointing out some issues of utilizing IMS LD in AEHS, and exposes further work.

Keywords: Learning Design, Adaptive Educational Hypermedia System, Authoring IMS LD, Authoring Adaptive Rules 1. INTRODUCTION

Adaptive Hypermedia is defined as an alternative to the ‘one size for all’ approach in the development of hypermedia systems. These systems construct a model through interaction with the user, with the purpose of adapting to the needs of that user (Brusilovsky, 2001).

The objective of an Adaptive Hypermedia System (AHS) is that the system should adapt to the user and not the user to the system, as occurs in “classical” hypermedia systems which show the same content and links to all the users (De Bra et al., 1999a). To achieve this, an AHS constructs a model for each user that represents her/his characteristics (e.g. goals, preferences knowledge, etc.), and uses it and modifies it according to the interaction of the user with the system. Hence, it adapts the information and the links presented to the specific needs of each individual.

The main application areas of AHS are educational hypermedia, on-line information systems, and information retrieval hypermedia (Brusilovsky, 1996). The former is the most popular area.

Adaptive Educational Hypermedia Systems (AEHS) support the student’s understanding of the learning material through paths and content tailored to their characteristics and preferences. They can behave in different ways according to the type of user, and thus provide unique learning space for each student facilitating the comprehension of the learning material.

Research in the area of Adaptive Hypermedia for web-based education has been conducted since 1996. Some pioneer AEHS are ELM-ART (Brusilovsky et al., 1996a), InterBook (Brusilovsky et al., 1996b), and 2L670 (De Bra & Calvi, 1998). Up to now several AEHS and authoring tools have been developed. Some examples are KBS-Hyperbook (Henze & Nejdl, 1999), TANGOW (Carro et al.,

Adriana J. Berlanga and Francisco J. García

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1999), AHA! (De Bra & Ruiter, 2001), ALE (Specht et al., 2002), and INSPIRE (Papanikolaoua et al., 2003).

Nevertheless, the use of AEHS in real classrooms or e-learning environments is rare. Reasons for this include their high cost of production, lack of credible evidence of benefits, and limited subject matter coverage (Murray, 2004). Additionally, we found that normally these systems:

Do not consider the adaptation of the pedagogical strategy.

Do not include instructional designers or teachers (different from the AEHS developer team) in the creation and design of the adaptive mechanisms of AEHS.

Do not have authoring tools that allow teachers or instructional designers to define the adaptivity actions that would occur.

Do not have mechanisms or possibilities to reuse or exchange educational elements, adaptive strategies, contents, adaptive rules, and so on.

Consequently, Adaptive Educational Hypermedia researchers are pointing out the need of a “common language” in order to facilitate adaptive applications, systems and services to “understand” the semantics of the adaptation rules and learning resources. In this line, efforts are attempting to converge Adaptive Educational Hypermedia and the Semantic Web (Berners-Lee et al., 2001) (e.g. Brusilovksy & Nejdl (2004), Cristea (2004), De Bra et al. (2004), Henze (2003)).

By contrast, our perspective is that the IMS Learning Design specification (IMS LD) (IMS LD, 2003) can be used to annotate adaptive predefined rules, techniques, and learning elements in AEHS. The use of this specification will emphasize the importance of instructional strategies in AEHS, and could guide the description of pedagogical approaches tailored to both students’ characteristics and knowledge properties.

To support our argument, this paper outlines our proposal to define learning designs with adaptive characteristics that can be modelled by means of IMS LD. The rest of this paper is structured as follows: First, it introduces AEHS, their characterization, elements, taxonomy, techniques, and the elements that specific AEHS take into consideration for performing adaptivity. Then, it reviews IMS LD, and explains how the main characteristics of an AEHS can be modelled using this specification. Subsequently, describes our research work towards the definition of adaptive learning designs as well as the approaches we are designing to build tools for authoring them. Afterwards, it points out the strengths and drawbacks of utilizing IMS LD in AEHS, and references related work. Finally, the last section exposes conclusions and further work. 2. ADAPTIVE EDUCATIONAL HYPERMEDIA SYSTEMS 2.1. CHARACTERIZATION OF AEHS Two well known approaches to model AHS are the AHAM model (De Bra et al., 1999b) and the Munich Reference Model (Koch, 2000). Both approaches state that an AHS contains principally three components: the domain model, the user model, and the adaptation model. The domain model stores and structures the knowledge, the adaptation model considers the domain model and the user model to adjust the content and learning paths to the user’ characteristics, and the user model stores information about the user’ characteristics and preferences which is taken into consideration to carry out the adaptation. A clear separation between these components must exist. Likewise, Henze (2003) claims that an AEHS is a quadruple:

(DOCS, UM, OBS, AC)

Where:

DOCS (Documents): It represents the document space of the hypermedia system and its information associated. This include annotations (e.g. metadata attributes), domain graphs that model the structure of the documents (e.g. document structure, hierarchical relations), or knowledge graphs that describe the knowledge of the document collections (e.g. domain ontologies).

UM (User Model). It represents, stores and infers information about a particular user (e.g. knowledge, preferences, interest, etc.). Information in the UM is updated by observations (OBS). Examples of UM include users’ stereotypes and overlay models (where users’ knowledge is described as a subset of the experts’ knowledge).


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OBS (Observations). They represent the observations about the interaction of the user with the AEHS. Examples of OBS are visited documents, visited document for a period of time, knowledge tests, and so on.

AC (Adaptation Component). It includes the adaptivity rules (e.g. suggest a document, generate paths, etc.) and/or the adaptive treatment (e.g. link order or document annotations considering their utility for a particular user) that will be performed. Wu et al. (2000) mention that there are two levels to control adaptation: the author level and the system level. In the former, a person (teacher, expert on the topic, etc.) defines and specifies the adaptation rules that will govern the system. In the latter, all the rules defined on the author level by means of an adaptation engine are executed. 2.2. ELEMENTS FOR PERFORMING ADAPTIVITY Brusilovsky (1996) and Kobsa et al. (2001) suggest a set of elements that adaptive hypermedia systems (in general, not only for AEHS) take into account for archive adaptivity. Table 1 shows them.

Brusilovksy (1996) Kobsa et al. (2001)

- User knowledge

- User objectives

- User experience in other fields of study (profession, experience, etc.)

- User data: demographic characteristics, knowledge of the subject, preferences, objectives, etc.

- User browsing experience in the WWW

- User preferences of links

-Use data: how is the user interaction with the AHS (opinions, actions, use frequency, etc.)

- Environment data: technical information of the user that affects the functionality of the AHS (e.g. software, hardware, bandwidth, etc.)

Table 1. Elements for performing adaptivity

The most used element for performing adaptivity in AEHS is the learner knowledge. Table 2 shows the elements that some AEHS take into consideration for performing adaptivity, and the adaptive techniques they use (see next subsection). 2.3. ADAPTIVE HYPERMEDIA TECHNIQUES Adaptive hypermedia techniques are procedures to archive adaptivity. Abstractions of one or more adaptive techniques are known as adaptive methods. Therefore, the same method can be implemented using several techniques and the same technique can be used to implement more than one method. For a detail description of adaptive methods, techniques, and their relationship see (Brusilovsky, 1996). Brusilovksy (2001) classifies adaptive hypermedia methods/techniques in adaptive presentation (the adaptation of the content) and adaptive navigation support (the adaptation of the links) (see Figure 1).

Adaptive presentation methods provide users with prerequisites, comparative or additional explanations, give alternative information (present information in different ways), and sort the information according to the user model. To implement these methods it is possible to adapt the multimedia material, the modality of the medium used for presenting the information (sound, text, image, etc.), or reconstruct the text by inserting, removing, altering, or highlighting fragments of information.


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System Domain Elements for performing adaptation

Adaptive Techniques

ELM-ART (Brusilovsky et al., 1996a)

Computer Science

LISP Programming

Student knowledge

Learning stage

Link annotation (traffic light metaphor)

Sorting annotation

Help with prerequisites

Curriculum sequencing

InterBook (Brusilovsky et al., 1996b)

Domain independent

Authoring adaptive contents

Prerequisites

Student knowledge

Learning state


Direct guidance

Help with prerequisites

AHA! (De Bra & Ruiter, 2001)

Domain independent

Authoring AEHS

Attributes associated with concepts (e.g. access, knowledge, interests, visited, learning styles, etc.)

Inserting/Removing fragments

Link annotation (colours/recommendation: blue/good; violet/neutral; black /bad)

KBS-Hyperbook (Henze & Nejdl, 1999)

Domain independent

Authoring Educational Hypermedia Books

Prerequisites

Student knowledge

Student preferences


Direct guidance

Routes/project adaptation

TANGOW (Carro et al., 1999)

Domain independent

Authoring of adaptive courses

Student stereotype (e.g. age, language, previous knowledge)

Student preferences on learning strategy (theory vs. practice, level of detail, etc.)

Learning styles (uses the approach of Felder & Silverman (1988))

Curriculum sequencing (using student stereotypes and task and rules)

Inserting/Removing fragments

Curriculum sequencing (using learning styles and traffic light metaphor)

INSPIRE (Papanikolaoua et al., 2003)

Computer architecture

Learner knowledge

Learning styles (uses the approach of Honey & Mumford (1992))

Curriculum sequencing

Adaptive navigation (navigation routes are proposed)

Adaptive presentation

ALE (Specht et al., 2002)

Learning Management System

Learner knowledge

Learner preferences (e.g. learning style, language)

Learning style (uses the approach of Felder & Silverman (1988))

Link annotation (icons, alternative texts)

Table 2. AEHS and the elements they take into consideration for performing adaptivity

Adaptive navigation support methods assist users’ when they are navigating in order to prevent them from follow navigation paths that are irrelevant with their goals. Methods for adaptive navigation include global or local guidance, global or local orientation, and the generation of personalized views. Techniques for navigation support manipulate web page links and anchors to present relevant and appropriate information for each user. The different ways of manipulating the links are:


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Direct guidance. Taking into account the user’s characteristics and the learning objectives, the system decides which is the “best” link to follow.

Adaptive link sorting. Considering the user’s model and a characteristic of value, it sorts the links on a page by relevance. Its drawback is that inexperienced users may be disorientated, since the order in which the links are presented can vary due to the system adaptive characteristics.

Adaptive link hiding. Hides, disables or removes links that are not relevant for the user, insignificant for the learner’s objectives, or it presents information that the student is not ready to understand.

Adaptive link annotation. Marks the links to give users visual or text clues of their content or current state. Marks include, for instance, the traffic light metaphor for highlighting pages (e.g. green for “ready to read”, red for “not ready to read”, or yellow for “recommended for reading”), as well as icons or different colours that represent current learning state of the link.

Adaptive link generation. Includes discovering useful links between documents and adding them permanently to the existing set of links, generating links for navigation based on similarity between elements, and producing dynamically recommendations of relevant links.

Map adaptation. Modifies the structure of hypermedia maps for each individual according to his or her user model.

Adaptive Hypermediatechnologies

Adaptivepresentation

Adaptive navigationsupport

Adaptive multimedia presentation

Adaptive text presentation

Adaptation of modality

Direct guidance

Adaptive link sorting

Adaptive link hiding

Adaptive link annotation

Adaptive link generation

Map adaptation

Hiding

Disabling

Removal

Natural language adaptation

Canned textadaptation

Inserting/removingfragments

Altering fragments

Stretchtext

Sortingfragments

Dimming fragments

Adaptive Hypermediatechnologies

Adaptivepresentation

Adaptive navigationsupport

Adaptive multimedia presentation

Adaptive text presentation

Adaptation of modality

Direct guidance

Adaptive link sorting

Adaptive link hiding

Adaptive link annotation

Adaptive link generation

Map adaptation

Hiding

Disabling

Removal

Natural language adaptation

Canned textadaptation

Inserting/removingfragments

Altering fragments

Stretchtext

Sortingfragments

Dimming fragments

Figure 1. Taxonomy of adaptive hypermedia technologies (Brusilovsky, 2001)

It should be noted that other authors have suggested classifications for adaptivity in learning environments. For instance, Paramythis & Loidl-Reisinger (2004) propose a high-level categorization that includes:

• Adaptive Interaction. To adjust the system interface without modifying the learning content.

• Adaptive course delivery. To tailor a course to a learner. This category includes the group of adaptive hypermedia technologies defined by Brusilovksy (see above).

• Content discovery. To use adaptive techniques for discovering and assembling learning material.

• Adaptive collaboration support. To use adaptive techniques to facilitate interaction and communication between users.

Van Rosmalen & Boticario (2005) prefer to distinguish between design time adaptation and runtime adaptation. The former deals with the creation of the course structures and predefined rules, and the latter observes the user’s behaviour –using Artificial Intelligence techniques such as data mining or machine learning– in order to harvest the user’s model with collected or inferred data that will be taken into account to perform adaptivity. These approaches can be used separately or as a combination.


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3. IMS LEARNING DESIGN (IMS LD)

The IMS Global Consortium Inc. (www.imsproject.org) is a non-profit international organization of different universities, software and hardware companies, research centres, and institutions in order to promote distributed learning environments. This consortium is defining an e-learning framework that includes specifications for learning resources metadata (IMS LOM) (IMS LOM, 2001), for create content packages (IMS CP) (IMS CP, 2003), for define tests and assessments (IMS QTI) (IMS QTI, 2002), or learner information (IMS LIP) (IMS LIP, 2003), among others.

IMS LD belongs to this framework. Its objective is to describe formally any design of the teaching-learning process. Specifically, it aims at meeting requirements such as completeness (to fully describe the teaching-learning process), pedagogical flexibility (to be able to express all kinds of pedagogies), personalization (to perform adaptation based on learner’s preferences, portfolio, pre-knowledge, and/or educational needs), interoperability (to be able to exchange and use information among different applications), and reusability (to reuse learning elements in other contexts) (IMS LD, 2003).

IMS LD has three levels of implementation and compliance that are complementary (but not independent from each other): Level A contains the vocabulary to support pedagogical diversity, Level B adds properties and conditions to Level A, and Level C adds notifications to Level B. Figure 2 shows (abridged) the hierarchical order of IMS LD elements. A learning design modelled in IMS LD contains objectives, prerequisites, components (properties, roles, activities, sequences) and a method of learning. The latter is formed by a play, which represents the “learning flow”, and conditions (if-then-else statements) that define the behaviour of the method.

Notice that IMS LD models the learning and teaching process but it does not include learning resources. That is to say, it separates the learning process structure (i.e. pedagogy) from the resources (i.e. content). As a result, a learning design should be included in a Unit of Learning (UoL) (preferably compliant with IMS CP), which then will be considered as a learning unit (e.g. course, lesson or curriculum) that contains a learning design and its related resources (e.g. test, learning resources, URI, etc.).

Learning-design Learning-objectives Prerequisites* Components Properties* Role learner* staff* Activities learning-activity* environment-ref* activity-description support-activity* activity-structures {sequence|selection} environment-ref* activity-ref* activity-structure-ref* Environments environment* learning object* services*{mail-send|conference} Method Play* Act* Role-parts* role-ref activity-ref Conditions* {If-then-conditions} Metadata

Figure 2. IMS LD elements (IMS LD, 2003).


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3.1. ADAPTIVITY AND IMS LD One of the most promising features of IMS LD is its ability to annotate adaptation characteristics based on learner’s preferences, portfolio, pre-knowledge, and/or educational needs. Although, IMS LD Level A contains some personalization options such as selections of learning activities, IMS LD Level B should be used to define personalization characteristics, and more elaborated sequences and learning interactions based on students’ portfolios.

IMS LD Level B includes elements as properties to store information about users, global elements to set and view the information of the properties, monitor services to read the properties, and conditions to manage and change properties values (Koper & Burgos, 2005).

Properties, which are essential elements for adaptivity, are divided into local and global properties. In the former case they can be only managed and accessed within the UoL, and in the latter they can be managed and accessed in any UoL. Local properties include local properties (<loc-property>), local role properties (<locrole-property>), and local personal properties (<locpers-property>). Local properties have the same value for all the users, local role properties contain information about each role in a UoL, while local personal properties contain information about each person, thus they have a different value for each user in a UoL.

There are two types of global properties: global personal (<globpers-property>), and global (<glob-property>). The former contains information about the user (e.g. portfolio information) and the latter contains a single value for all users in all UoL.

Likewise the element <role-parts>, which describes the activities to be performed by a role in an act, could be used to group students in stereotypes. In this way, every role-part covers a set of learning activities for specific learners’ characteristics (e.g. learning style, knowledge, etc.). Students’ stereotypes has been used in pioneers AEHS as C-Book (Kay & Kummerfeld, 1994) but also in recent approaches that consider students’ learning styles for performing adaptivity as in TANGOW (Paredes & Rodríguez, 2003), or to model adaptive web services (Garlatti & Iksal, 2003). 4. AEHS AND IMS LD The attempt to extend and use existing standards and specifications to support learning personalization has been conducted in projects such as OPAL (Conlan et al., 2002), OLO (Rodriguez et al., 2002), KOD (Karagiannidis et al., 2001), Edutella (Dolog & Nejdl, 2003), WINDS (Kravcik et al., 2004), and aLFanet (Van Rosmalen & Boticario, 2005). Nevertheless, due to the relatively novelty of IMS LD, aLFanet is the only effort that address directly the implementation of AEHS using IMS LD (see related work below). As we already mentioned, we claim that IMS LD might be an option for defining AEHS. In the rest of this section we will sketch how this specification can be used to characterize AEHS and, particularly how it can be used in order to predefine, at design time, adaptive navigation support techniques and rules for adaptive course delivery. 4.1. CHARACTERIZATION OF AEHS USING IMS LD If AEHS use IMS LD to structure and annotate the teaching-learning process, then this specification can be seen as an ontology of the process (Koper, 2004), where a clear separation between the learning flow and its components (i.e. learning activities, resources, etc.) exists. Moreover, the defined elements could be exchanged and reused among different systems compliant with IMS LD and/or be modified, run and stored in IMS LD tools. The characterization proposed by Henze (2003) explained before can be modelled using IMS specifications as follows:

DOCS. Using IMS LD elements such as <components> (i.e. learning activities and activity sequences) and <act>, and for learning resources IMS LOM. (Note that in this case, the modelling will be based on learning activities and not in documents that contain concepts).

UM. Using IMS LD element <properties> (i.e. <locpers-property>, <globpers-property>, and <localrol-property>). Ideally, IMS LIP might be used to store and retrieve users’ characteristics, and IMS QTI to obtain users’ knowledge, characteristics, or preferences.

OBS. Using IMS LD elements <properties>, <on-completion>, <time-limit>. Ideally, IMS QTI should be used to retrieve users’ preferences, knowledge, characteristics, etc.


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AC. Using IMS LD element <conditions> to define adaptive rules that can include UM and OBS (using <properties>) and DOCS (using <learning-activity>, <activity-sequence> and <act>). Ideally, AC should upgrade the UM (or the IMS LIP repository). 4.2. ELEMENTS FOR PERFORMING ADAPTIVITY USING IMS LD Elements for performing adaptivity using IMS LD are primarily a conjunction between <properties> and <conditions>(i.e. IMS LD Level B). Koper (2005) categorizes learning conditions as: Learning objectives: knowledge, skill, attitude, competence.

Learner characteristics: pre-knowledge, motivation, situational circumstances.

Setting characteristics: individual work, group work, work at school, work at home.

Media characteristics: bandwidth, synchronous/asynchronous, linear/interactive, media types.

Based on these categories and the elements for performing adaptivity mentioned by Brusilovksy (1996) and Kobsa et al. (2001) (see Table 1), it is possible to define a set of property elements to perform adaptivity. Table 3 shows an example. Generally, these elements can be modelled using the <property> element of IMS LD, but media characteristics are more suitable to be modelled using IMS LOM elements.

ID Category Options Data Type Examples

[LO] Learning Objectives

String, boolean Knowledge, skills, attitude, competence

Age Integer [LD] Learner Demographics Language String, Boolean Spanish, English, Dutch

Student pre-knowledge

String, integer, boolean, percentage

[LC]

Learner Characteristics

Learning style String, boolean Sensitive/ intuitive, visual/verbal, sequential/global (Felder & Silverman, 1988)

Level of detail String, boolean Basic, medium, high

Learning style String, boolean Activist, Pragmatist, Reflector, Theorist (Honey & Mumford, 1992)

Interactivity type String, boolean Linear/interactive

[LP]

Learner preferences

Learning strategy String, boolean Theory/practice; learning by example/learning by doing

Technical characteristics

Boolean, integer OS, bandwidth, hardware

Communication String, boolean synchronous/asynchronous

Media type String, boolean Video, text, graphic

[MC]

Media characteristics

Interactivity type String, boolean Linear/interactive

Work type String, boolean Individual/group [SC] Setting characteristics Work place String, boolean Home/school

Table 3. Elements for performing adaptivity using IMS LD


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4.3. ADAPTIVE HYPERMEDIA TECHNIQUES AND IMS LD Adaptivity can be performed in plays, acts, role parts, activity sequences and learning activities if IMS LD elements such as <conditions>, <hide>, <show>, <locpers-property>, <locrole-property>, <globpers-property>, or <on-completion> are included for showing certain learning activities, annotating learning sequences (using the XHTML “class” attribute to annotate links in adaptive navigation support), and so on. For instance, Table 4 shows how some adaptive techniques can be modelled using IMS LD. It includes in the first column the name of the adaptive technique; in the second, the IMS LD elements that can be taken into account to perform adaptivity; in the third the properties for performing adaptivity; and in the last column, the type and options of each adaptive technique. The separation of the elements by a vertical line (“|”) represents the conjunction OR (e.g. Play or Act or Role-Part, etc.).

The IMS LD elements of the second column, which contains the IMS LD elements that can be suitable to perform adaptivity, can be taken as “wrappers”. For instance, if a play element is selected, then the adaptivity technique will be performed for all the elements included in that particular play. Likewise, if an activity sequence is selected then the adaptive technique will be performed for the elements included in that particular activity sequence (e.g. learning activity, support activity, activity sequence).

Note that the elements of the third column, which contains the elements for performing adaptivity (or properties), are the same as those defined in Table 3. Also, a prerequisite ([PRE]) element is included, this means that the adaptivity technique could be performed taking into account if the learning activity’ prerequisite has been completed.

Adaptive Technique Element Elements for performing adaptivity (properties)

Type/options

Direct Guidance Play | Act | Role-Part| Activity Sequence

[PRE]| [LC]| [LP]| [LD]|[MC]

Type: {Sequence| Selection}

Curriculum Sequencing

Play | Act | Role-Part| Activity Sequence

[PRE]| [LC]| [LP]| [LD]|[MC]

Type: {Sequence| Selection}

Options: {On-completion| Time-limit}

Show/Hiding Links

Play | Act | Role-Part| Activity Sequence| Learning Activities

[PRE]| [LC]| [LP]| [LD]|[MC]

Link annotation Play | Act | Role-Part| Activity Sequence| Learning Activities

[PRE]| [LC]| [LP]

Type: {Traffic-light metaphor | Boolean| Icons}

Inclusion of pages Play | Act | Role-Part [PRE]| [LC]| [LP]| [LD]

Table 4. Adaptive Techniques and IMS LD

Giving the reasoning explained before, in the next section we present our current work towards the definition of learning designs with adaptive characteristics, or Adaptive Learning Designs (ALDs). 5. DEFINITION OF ADAPTIVE LEARNING DESIGNS (ALDS) Our research objective is to propose a new approach for defining ALDs that might be included in AEHS. An ALD is a learning design that contains predefined rules and declarations, which might consider students’ characteristics (i.e. knowledge, learning styles, etc.), in order to deliver to each


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student a personalized learning flow (Berlanga & García, 2005). In order to permit reutilization, ALDs are semantically structured according to IMS LD. Therefore, ALDs aim at combining the personalization and reusability characteristics of IMS LD. The definition of an ALD follows the Lego metaphor. We claim that the separation of elements is crucial in order to support their reusability and exchangeability. Each element of IMS LD, such as learning objectives, prerequisites, components (i.e. learning activities and activity structures), methods, personalization properties and adaptive rules (i.e. conditions), is defined and stored as a separate object. This allows authors to reuse and interchange a complete ALD, but also each one of its elements in different learning contexts, lessons, and courses. Figure 3 represents this approach. Each element is stored in a different repository, and different colours mark the different authoring process steps. For readability reasons, not all relationships among elements are presented; see Table 5 for a full list, including the ID of the element, its name, the elements in which it can be included, the elements it can include, and the elements where a learning object can be attached.

Figure 3. ALD Lego metaphor

For instance, an author creates the learning object LO-v that can be attached to the learning activity LA-w. Then, LA-w can be included into activity structure AS-x that can be integrated in act ACT-y, and so on. In the same way, AS-x could be incorporated in ACT- z. In this manner different IMS LD components can be reused and exchanged among different AEHS, applications and tools.

Moreover, the definition of a new method of instruction does not imply the creation of learning activities, roles, objectives, etc., that have been created before for other ALDs.

Notice that in Figure 3 properties are connected to the student model (user model) in order to manage, update and retrieve users’ information, and adaptive rules are connected to a test repository that contains assessments or forms to evaluate properties. Ideally, the user model and the test repository should be compliant with IMS LIP and IMS QTI in order to update and retrieve useful information for performing adaptivity.


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ID Name It can be included in It can include Learning Object (LO) in IMS LD element(s)

LO Learning Object LO, OBJ, PRE, LA, SA, AS, EN, R, ACT, PLY, MET

LO <item>

OBJ Learning objective LA, ALD LO <item>

PRE Prerequisite LA, ALD LO <item>

LA Learning activity AS, RP, RUL LO, OBJ, PRE, EN, PP

<activity-description>

<feedback-description>

SA Support activity AS, R, RP, RUL LO, RP, EN, PP <activity-description>


AS Activity structure RP, AS, RUL LO, LA, EN, SA, AS, ALD

<information>

EN Environment EN, LA, SA, AS, RP, RUL

LO, EN <learning-object>

R Role RP, ACT LO, SA <information>

RP Role-Part ACT, SA R, LA, SA, EN, AS, PP, ALD

ACT Act PLY LO, RP, PP, R, ALD


PLY Play MET, RUL LO, ACT, PP <feedback-description>

PP Properties RUL, LA, SA, RP, ACT, PLY, MET, ALD

RUL Rules (or conditions)

MET, ALD PP, EN, LA, SA, AS, PLY, MET, ALD

MET Method RUL, ALD LO, PLY, RUL, PP <feedback-description>

ALD Adaptive Learning Design

RP, ACT, AS, RUL OBJ, PRE, PP, RUL, MET

Table 5. ALD Elements

5.1. AUTHORING ALDS We aim at supporting authors on the creation of ALDs by means of a tool, which does not prescribe any instructional approach, properties, or adaptive rules for adjusting learning to students’ characteristics. As a result, we are extending the functionality of the Hypermedia Composer (HyCo) (García & García, 2005) –a tool for authoring hypermedia books– in order to use it as the ALD Editor. Figure 4 shows the HyCo ALD screen for defining learning activities. Similar interfaces are provided for depicting other elements such as learning objectives, prerequisites, roles, learning activities, and so on. The editor uses a tab structure that gathers sets of attributes that might be described to annotate the


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element. It presents, when possible, default values and combo-boxes. Moreover, it is connected to a LOM Editor, which is included in HyCo in order to provide authors with a tool for creating metadata for learning resources.

Figure 4. HyCo ALD Editor

Currently, we are working on analyzing and designing the variables needed for defining personalization properties and adaptive rules (i.e. IMS LD Level B). Personalization properties contain information about the users that, afterwards, can be included into adaptive rules, while adaptive rules are prescriptions defined by authors that will be taken into account to adjust the learning design, and that can be included into learning methods. We are developing two approaches for defining these elements: one for novice users in IMS LD and other for expert users in the specification. The rest of this section explains these approaches. 5.2. AUTHORING ALD FOR NOVICE USERS

We are defining a wizard for supporting novice users in the definition of adaptive navigation support techniques, which can be managed as separate objects and included into different learning methods. We are depicting the wizard following the definitions of adaptive techniques presented on Table 4 and the elements for performing adaptivity presented on Table 3 . Figure 5 shows a prototype of the adaptive technique wizard. First, a name for the adaptive technique needs to be typed. Then, the definition is divided according to Table 4, hence it considers each column of the table as follows:

Element level. In this section, authors should select the IMS LD element from which the adaptation will be performed (i.e. second column of Table 4). It is inferred that if an element of higher order is selected, then all the sub-elements it wrappers will be included in the adaptive technique. For instance, if an act is selected, then all the learning activities and activity sequences included in that act will be annotated with the same adaptivity hypermedia technique definition.

Based on. In this section authors should select the element that will be the base of the adaptivity. The options are those presented on Table 3 (elements for performing adaptivity) –that match with the third column on Table 4 (Adaptive Techniques and IMS LD)–, therefore, if the author selects a category (i.e. second column on Table 3), then the list box will display the elements that contain that category (i.e. third column on Table 3). Then, authors need to indicate the operation, data type (i.e. fourth column on Table 3) and value of the selected element, and indicate the test (or form) and property from where the value of the element will be taken. Note that this is necessary because currently we are


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not considering the use of a particular AEHS, IMS QTI (for assessments) or IMS LIP (for learners’ characteristics). In such cases, properties and tests must be handled differently.

Type or options. In this section authors should select the type or options that are available for defining the adaptive technique (i.e. fourth column on Table 4).

5.3. AUTHORING ALDS FOR EXPERT USERS Expert users might need more flexible and open schemas for authoring personalization properties and adaptive techniques. Therefore, for this kind of authors the definition of properties is not restricted; authors can define whatever they want. To define them authors indicate their name, data type, restrictions, and initial value. Afterwards, they can include personalization properties into adaptive rules. Likewise, for defining adaptive rules authors could use an expression-builder tool. The tool, currently under development, in based on the definition of adaptive statements that follow a formalism (Berlanga & García, 2004), which includes learning design elements, personalization properties, and logical and relational operators. The formalism is as follows (BNF notation):

<adaptive-statement> ::= IF <condition> THEN <action>

<condition> ::= <element-set> [<unitary-op-set>] “(“ <expression> “)”[<binary-op-set> <condition>]

<expression> ::= [<spec-element> “,”] [<value> | <binary-op-set> “,” <value>] [“,” <relational-op-set> “,” <value>]

<action> ::= <action-set> “(“ <expression> “)” [<binary-op-set> <action>]

<spec-element> ::= specific-element-identified-by-its-id (learning-design-structure-set; student-set)

Figure 5. Adaptive Hypermedia techniques wizard (Prototype)


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<value > ::= [<data-set> |<integer> | <string> | <percentage>]

Table 6 shows the elements that can be included in the definition of adaptive statements. They are divided into “sets” based mainly on IMS LD elements. This will help authors to easily identify the elements of the learning design structure, and the characteristics of the student or learning activity s/he wants to use to define an adaptive statement. In addition, the definition of sets would make possible that the formalism could be used in other AEHS, players or engines compliant with IMS LD.

Observe that the student-data and attributes-data subsets are not part of IMS LD. The AEHS or an IMS LIP repository has to provide the values of these elements. The student-data set is included in order to permit the definition of rules that consider learner’ knowledge (not just visited pages), and her/his learning style (to define effective instructional strategies (Merrill, 2002)). The attributes-data-set will facilitate the authoring process and the student follow-up. However, this set includes elements for defining adaptive statements for adaptive navigation support techniques, but excludes techniques such as map adaptation or adaptive link generation. At this stage of the work, we preferred to reduce the number of adaptivity options and test and evaluate them before incorporating new options.

Set Sub-set Elements

learning-design-structure Prerequisite; Learning-objective; Learning-activities Activity-sequence; Support-activity element-set

student-element-set Student

student-data-set Initial-knowledge; Current-knowledge; Final-knowledge; Learning-style

attributes-data-set Completed; Visited;

Recommend; Sequence; Selection data-set

time-data-set Time-unit-of-learning-started;

Date-time-activity-started

binary-op-set And; Or logic-operators-set

unitary-op-set Not

relational-operator-set

relational-op-set Greater-than; Less-than; Equal;

Greater-or-equal-than; Less-or-equal-than

action-set

Show; Hide;

Show-menu; Hide-menu;

Sort-ascending; Sort-descending;

Number-to-select

Table 6. Collection of sets to describe adaptive statements

Two reasons motivated the way we defined the sets and their elements. Firstly, to take advantage of IMS LD possibilities, and to be able to exchange and reuse the definition of adaptive rules within different learning designs. Secondly, to give authors a flexible manner of defining adaptive statements and include nested conditions.

For instance, by means of the collection of sets on Table 6, authors could create an adaptive statement that establishes that if the initial knowledge of the student is equal to 5 and her/his learning style is “Reflector” (Honey & Mumford, 1992), then a particular introduction learning activity for reflectors will be shown as in the following statement:


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IF <student>: (initial-knowledge, equal, 5) and (learning-style, equal, “reflector”)

THEN show (learning-activity-introduction-reflectors)

Once authors create their rules (i.e. adaptive statements, techniques or stereotypes) or adaptive techniques they need to save them. Afterwards, authors should build-up an ALD by selecting the appropriate rules, learning activities, learning objects, roles, and so on. Then, an automatic assembling of a package –compliant with IMS CP– will join together all the definitions (i.e. resources, rules, properties, etc.), and produce a .zip file that could be exchanged among different systems compliant with IMS LD. In this way, users can share and re-use their ALD among different colleagues, tools, and AEHS. For instance, the open source engine Coppercore (http://www.coppercore.org) could process the ALD.zip file, which later on could be delivered as an adaptive learning design in an AEHS or in a web-based learning environment compliant with IMS LD. 6. STRENGTHS AND DRAWBACKS OF USING IMS LD IN AEHS This paper we maintain that IMS LD might be an option for depicting ALDs. Although, the use of a specification can be somehow restrictive and we are in early stages of the investigation, the proposed approach presented in this contribution might bring the following benefits: Incorporation of an existing annotation (i.e. ontology) to describe learning knowledge and pedagogical strategies into AEHS, therefore it stresses the learning and pedagogy aspect of AEHS.

Assure separation between pedagogical strategies, adaptive logics, and domain knowledge.

Feasible reutilization and interoperation of learning design elements among AEHS.

Rapid incorporation of learning design components and resources in different courses, applications, and AEHS.

Quick AEHS prototyping and testing (e.g. edit an ALD and incorporate it in IMS LD tools).

Although IMS LD can be used to model and annotate ALDs, design more complex adaptivity behaviour might be not too easy. For instance, it is not possible to annotate learning content (in order to perform adaptive presentation techniques), or define students’ roles that consider their characteristics (e.g. knowledge, preferences, etc.). Moreover, Towle and Halm (2005) argue that in IMS LD it is difficult to support multiple overlapping interactions, enforce the learning flow, and change the learning strategy once a IMS LD package has been delivered (due to the IMS LD “manifest-centred” representational schema). In the same line Paquette et al. (2005), has proposed to take out Level B and Level C –restricting IMS LD only to Level A– in such way that the adaptivity conditions can be stored outside the host system, and not inside the learning design definition.

The Lego metaphor presented in this paper might be an option to avoid the creation and annotation of the same elements (e.g. learning activities, adaptive rules, activity sequences, etc.) for different ALDs. One step further is that those repositories could be distributed in different servers, and ALDs include URL references anchoring to adaptive conditions. It could be a solution to store adaptivity conditions outside the learning designs, as well as to avoid static adaptation forced by IMS LD and its manifest-centred schema suggested by (Paquette et al., 2005) and (Towle & Halm, 2005), respectively. 7. RELATED WORK The work presented in this paper is mainly related to two domains: authoring AEHS where a clear separation between the learning flow and its components exists (i.e. semantic web perspective), and using IMS LD to create predefined rules and declarations for AEHS. In the area of authoring adaptive hypermedia from the semantic web perspective, within the EU founded project ADAPT (Cristea & De Bra, 2002) the LAOS authoring model (Cristea & De Mooij, 2003) has been defined. It is based on AHAM, thus it does not contain a layer for pedagogical strategies, and the user model is placed inside LAOS, therefore, it is not clear its separation from the model for performing adaptivity (e.g. use of a common user model repository among AEHS). Furthermore, the grammar for authoring adaptive rules (Cristea & Verschoor, 2004) might be difficult for those without computer background. Our approach, in contrast, defines two proposals for supporting novice and expert users in the definition of adaptive techniques or statements (i.e. a wizard


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containing main steps and elements, and a formalism that uses a flexible structure of sets to define conditions and actions). Additionally, within this project investigation of authoring learning styles in adaptive hypermedia has been conducted (Stash et al., 2004). However, the focus is concept-based (not learning activities-based), thus the adaptive strategy does not consider learning objectives or prerequisites. Moreover, the ADAPT project does not consider reusability or exchangeability of elements and rules among different AEHS, or the use of learning technology specifications, as our proposal does.

In the area of using IMS LD to create predefined rules and declarations for AEHS, the aLFanet project (2005) aim at offering intelligent personalization and adaptivity capabilities in a learning management system. The adaptation is defined in three areas: instructional design, interaction, and presentation. The idea is based on a framework that supports active and adaptive e-learning, and is open to any type of learning model. Although, this project investigates many aspects that are not related to our work (e.g. adaptive collaboration, agents, assessments, runtime adaptation, etc.), the aLFanet LD Editor, as our proposal, closely represents the IMS LD structure. However, the authoring process requires deep knowledge of IMS LD, and does not consider the definition of adaptive hypermedia techniques. Conversely, our proposal is focused on the definition of adaptive techniques which might be created for novice or expert users of IMS LD. 7. CONCLUSIONS AND FUTURE WORK It is clear that pushing forward the benefits of AEHS for a wide range of applications and systems requires using a common notational method. Although, IMS LD is in its early phases of development and dissemination, it could be an option for depicting AEHS. One could argue that this specification is not indented for adaptivity and that it is more appropriated developing a particular notational method or ontology. However, the dissemination and development of a specific notational method for AEHS it is not likely to be as widespread as specifications proposed by an international consortium. Moreover, there are more chances that commercial products will use IMS LD, than a particular notational method. For instance, Blackboard (Etesse, 2004) is projecting the incorporation of IMS LD. This article sketched how IMS LD could be used to author adaptive learning designs and, in particular, predefine at design time adaptive navigation support techniques for course delivery. It introduced AEHS and explained how the main characteristics of this kind of systems can be modelled using IMS LD. Afterwards, it presented the definition of adaptive learning designs and an authoring tool we are developing. Finally, it provided a general idea of the strengths and weakness of IMS LD as a notational method for AEHS, and referenced related work.

Currently, we are finishing the HyCo ALD Editor. Afterwards, we will incorporate adaptive characteristics by providing users with authoring tools (an adaptive hypermedia technique wizard, for novice users, and an expression-builder tool, for expert users). Subsequently, we will verify if HyCo is able to reuse other learning designs compliant with IMS LD and vice versa. After that, we will use HyCo ALD to test if the Lego approach we are proposing is valid, and verify if reusability and exchangeability of ALD, and its components, is achievable.

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