Generating Multiple Hypermedia Learning Views Using OO Modelling

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Generating Multiple HypermediaLearning Views Using OOModellingC. Ghaoui Ghaoui & H. Ainsley AinsleyPublished online: 09 Aug 2010.

To cite this article: C. Ghaoui Ghaoui & H. Ainsley Ainsley (2001) Generating MultipleHypermedia Learning Views Using OO Modelling, Interactive Learning Environments, 9:1,1-32

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Generating Multiple Hypermedia Learning Views Using OOModelling

C. Ghaoui and H. AinsleySchool of Computing and Mathematical Sciences, Liverpool John Moores University,Liverpool, UK

ABSTRACT

This paper discusses an extensible authoring model called ExAM. ExAM guides the hypertisingof learning material to support the automatic generation of multiple instructional views. Thispaper emphasises the features of object-oriented (OO) methodology which bene®t the hypertextauthoring of learning material, and which are incorporated in ExAM. ExAM provides theauthor with a framework which enables him/her to elicit, extend, and store knowledge aboutconceptual information contained within learning material. Algorithms have been developedwhich access the resulting hypertext knowledge-store in order to generate multiple views to suitdifferent instructional objectives. The model provided is generic and extensible in order forsubject experts to develop tailored hypertext for most learning material from the scienti®cdomain.

INTRODUCTION

This paper presents an extensible authoring model called ExAM. ExAMguides the hypertising of learning material to support the automatic generationof multiple instructional views. To hypertise a document means to store it interms of a hypertext structure. We will often refer to this process as hypertextàuthoring'.

This paper emphasises the features of OO which bene®t hypertext author-ing. ExAM incorporates a specialised object-oriented (OO) model thatprovides generic structural elements for hypertising existing learning material.

Correspondence: Claude Ghaoui, School of Computing and Mathematical Sciences, LiverpoolJohn Moores University, Byrom St., Liverpool L3 3AF, UK. E-mail: [email protected]

Interactive Learning Environments 1049-4820/01/0901-001$16.002001, Vol. 9, No. 1, pp. 1±32 # Swets & Zeitlinger

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ExAM provides the subject expert with a method which enables him/her toelicit and store conceptual knowledge about the information contained withinexisting learning material. The models provided are generic and extensible inorder to enable the development of tailored views for most learning materialfrom the scienti®c domain.

Using ExAM, the process of hypertisation requires some manual authoringwhereby the subject expert is guided (using the model) to mark-up the subjectmaterial. This is augmented by automatic authoring whereby the model gene-rates additional structural components e.g. hypertext links. Once the authoringprocess is completed, a set of structure-building objects have been developedwhich can access the hypertext knowledge store and automatically generatemultiple views of the document to suit different instructional objectives.

Creating Hypertext StructuresExisting techniques for automatically building hypertext structures such asconceptual mark-up (Gains & Shaw, 1998; Kneece, 1996; Soderston & Kleid,1996), automatic indexing (Salton, 1988), and formal techniques based on pairgrammars (Chen, 1997) may be easily applied, and are useful to generatecustomised hypertexts (Specht & Opperman, 1998; Thomson et al., 2000).However, these methods rarely capture all the author's initial intentions, andare very limited in their scope for knowledge-based interpretation of docu-ments. Relations between document segments are most often captured at alexical level but the global semantics assigned by the initial author to someparts of text is lost. Today, text analysis methods are not ef®cient enough forrecovering the meaning of a paragraph and its actual purpose in the rhetoricstructure. Most hypertexts only propose structural links and reference links: itis far easier to exhibit a reference relationship between two nodes than toexplicit the exact semantics of this reference relationship. When hypertextsare developed from existing text, the resulting hypertexts traditionally storeinformation more than knowledge.

It is for the above reasons that Nanard et al. (Martin, 1992) emphasise theneed to explicit the authors intentions and capture knowledge structures asthey emerge in the authoring process (Tilley & Whitrey, 1993; Thuring &Haneman, 1995). Nevertheless, the knowledge captured during authoringcan still only be used on that particular hypertext document. Furthermorewhen authoring for multiple views, the various rhetorical effects, in terms ofwhich the information may be viewed, are not always obvious to the subjectexpert.

2 C. GHAOUI AND H. AINSLEY

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The Structure of Learning MaterialFor our work we view the structure of learning material from two differentaspects. One aspect is in terms of the various concepts and relationships foundwithin the subject. There is a need to de®ne knowledge associated with theinformation in hypertext documents. In most classical hypertexts, this knowl-edge is not elicited because inclusion and reference link types are not powerfulenough to express many of the logical relations within a text. Systems usingricher types are most often application-dependent. We will provide a meansfor authors to incorporate concept-relation type structures, without enforcinglimited type semantics that the author may ®nd restrictive.

The second aspect is in terms of learning methods. In our work the term`learning methods' is used to describe the various navigational techniqueswhich a student uses according to the instructional aspects of the subject theywant to learn. A learning method determines the generic instructional aspectsof subject material and the generic order in which they should be displayed inorder to cater for a recognised learning objective. We endeavour to co-ordinatethe learning methods to work with the different concept-relationships ofmathematical subject material, leading to a ¯exible system, capable ofcatering for a variety of different instructional objectives. The section on`̀ Authority learning material for multiple views'' explains the ideas behindstructuring in terms of concepts and relationships, and behind structuring forlearning methods, respectively.

Research ObjectivesThe objective of our research is to develop an authoring tool which has thefollowing features:

1. Has generic application in order to hypertise different documentation fromthe scienti®c domain.

2. Have a knowledge base that can be used to identify complementary infor-mation and generate associations for

2.1. Providing prompts to assist human authoring (hypertising) and upda-ting processes.

2.2. Enable some automatic authoring.

3. Is extensible and adaptable to support authoring for different instructionalneeds.

4. Supports automatic view generation for multiple instructional objectives.5. The ability to hypertise existing learning documentation.

GENERATING MULTIPLE HYPERMEDIA LEARNING 3

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The purpose of this paper is to illustrate the bene®ts of OO methodology forthe authoring of hypertext learning material for generating multiple views.The third section lists each feature of OO that is utilised in our work, withexplanation of a novel approach to it's use for hypertext authoring of learningmaterial. The application of OO for hypertext authoring is not new. The fourthsection lists the weaknesses of some existing OO-based authoring models thatwe will address in our work. The ®fth section explains the ExAM framework,and the OO elements that are used to create an OO knowledge-base forgenerating multiple instructional views of on-line learning material. Finallywe end up with an example of ExAM's use for generating views for learningobjectives.

AUTHORING LEARNING MATERIAL FOR MULTIPLE VIEWS

Conceptual StructuringAny subject learning material is made up of multiple concepts which can bearranged into hierarchies and other structures. For example, the concept of acar can be divided according to it's components and functions, the componentscan be further divided into a power assembly and running assembly. Thus ahierarchy can be formed as follows:

The above example of a hierarchy is formed by using a containmentrelation; the box `car' contains the boxes `components' and `functions'. Manyother kinds of hierarchies and other structures can be produced by usingdifferent operators such as `causes' which produce long chain structures of theform À causes B which causes C', and so on. A functional description of a caruses this structure.

Car

Functions Components

Running assembly Power assembly

car

functionscomponents

running power assembly

Fig. 1. Example of a concept structure using the containment relation.


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Different relations create different structures, and a single relation canproduce multiple structures when applied to different aspects of the samesubject (DeYoung, 1990). Duncan (1989) demonstrates the bene®ts of `facetanalysis' in order to support the retrieval of information according to different`facets' of a subject. This was done (Duncan, 1989) by further identifyingcategories of concepts that are anticipated to be generic across differentsubject domains. This allows structuring by type of concept, and reducesambiguity in concept type de®nition for information retrieval by computersystems and human users. We support the use of link and concept types forde®ning generic structures and paths to cater for different learning objectives.

Learning MethodsThe other consideration when structuring learning material is for learningmethods (Ghaoui, 1995). Each student will have different information require-ments according to the understanding of the subject he/she wants to gain. Wewant to facilitate student navigation and information retrieval according tocommon learning techniques. A number of different learning methods canbeen found described in various sources, they pertain to the aspects of subjectapplication, and the order in which relevant segments be presented to thelearner. The following methods are those which we have chosen to facilitate:

� Concept learning: This method displays the components of a view forreaders who want to identify a particular concept within the learningmaterial. This is done by displaying the concept and it's properties withoutcontrasting information about other concepts.

� Discrimination learning: This method displays the components of a view forreaders who want to investigate discrimination learning in their navigationof a hypertext learning material. This view will disclose material such thatthe learner will have the opportunity to discriminate between relatedconcepts through their different attributes e.g. between a sine and cosinewave forms.

� Rule/principle learning: This method displays the components of a view forreaders who want to investigate the application of rules and/or principle(including mathematical formulas) that are discussed within the hypertextsubject material. Student's will learn how to apply a rule or principle, andthe types of circumstances in which it can be used.

� Problem solving: This method displays the components of a view forreaders who want to investigate the solving of certain types of problems.


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The student will learn how to utilise the correct procedure to solve a giventype of problem.

The aim of this work is to create a ¯exible method of modelling subjectmaterial which will allow us to co-ordinate and apply generic learningmethods to document structures created using generic concept and relationtypes. As well as adhering to the objectives outlined in research objectives ofthe ®rst section.

MOTIVATION FOR OO

OO is a methodology for modelling system architecture at a conceptual andoperational level (Nanard & Nanard, 1991), thus it was decided to discoverwhat bene®ts OO might have to offer hypertext authoring. This sectiondescribes the features of OO and how they can be used to bene®t the authoringof hypermedia learning material.

Everyday objects can be thought of as phenomena that provide a service,usually without making you aware of their composition and inner workings.OO objects can be thought of in the same way. Each OO object has a name,attributes, associations with other objects, and methods that deal objectbehaviour under given circumstances. An OO object can be created torepresent any real world or theoretical object. This adaptability (DeBra &Calvi, 1998) of the OO methodology makes it an ideal method to create ageneric framework to support the authoring of instructional material (Gates,1998; Ainsley & Ghaoui, 2000). Any number of objects can be created tocapture and represent the many generic structures found within instructionalmaterial, such as learning methods including concept hierarchies. Objects canalso be created to represent elements of subject detail, right down to the lowestlevel concept. Such is the ¯exibility of object modelling that it can captureboth generalities and detail to support authoring for a variety of informationrequirements.

Bene®ts of OO Polymorphism and InheritanceIn OO theory, an object not only represents an object ìnstance', but alsorepresents a `class' of objects that share the same characteristics. The idea ofan object `class' allows us to categorise and classify things by their propertiesor behaviour. Any number of object ìnstances' can then be created or deleted,each of which ìnherit' all the characteristics of the `parent' class. In terms of


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hypertext authoring, a lot of work and storage can be saved by categorisingsubject phenomena in this way, and dealing with the categories.

Retrieval of Concept Information Ð Classi®cation of ConceptsConcepts found in subject material are usually applied numerous times underdifferent subject conditions. We can use OO to capture each concept as anobject class and thus have it available for use each time the same phenomenonoccurs. For example, in plant biology, a description of the term `photosynth-esis' may be given which can be encapsulated in a node object by the humanauthor. Every time the concept of `photosynthesis' occurs (e.g. `: :the plantscannot survive without the oxygen required for photosynthesis'), the sameinformation, pertaining to `photosynthesis', can be reused and made available.

By incorporating this feature of OO into hypertext learning material, wecan make any piece of conceptual information available each time thatconcept is referred to, regardless of the reference's navigational whereaboutsin the hypertext. This will provide information retrieval support when astudent ®nds himself/herself with missing or forgotten information that isreferenced within, and required for the understanding of, some new subjectmaterial. If the forgotten/missing subject has been encapsulated within anobject, then that object is ìnstantiated' (possibly as an embedded link)providing the student with instant access to the required information.

Propagation of Knowledge Between Concepts Ð Inheritance

of Shared InformationBy extracting commonalties between concepts, we can identify `parent'concepts which capture only the generic aspects of behaviour that apply toa number of different concepts. This generic conceptual information can thenbe inherited each time a subclass or `child' concept is described, for example,an object for the concept of the `Cosine function' will inherit any existinginformation about `Circular functions'.

In this way generic information, such as under what circumstances a type ofconcept can be applied, or other useful information applicable to certain`types' of concept, can be applied to every child concept of that type/class.Information that might not otherwise be obvious or easily retrievable, is thenautomatically made available. This method, when authoring from scratch, alsosaves authoring time and document space, because the generic informationneed to be authored only once regardless of the number of times it is relevantand needs to be made available.


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Supporting View Generation for Common Learning MethodsÐClassi®cation

of Learning MethodsUsing OO we can create classes of generic learning methods by de®ning, at anabstract level, what types of objects each method consists of and in what order.In this way the system can deal with different document subjects in a genericmanner when generating instructional views. Once all the concepts have beendepicted by the human author, and stored in terms of objects to form thehypertext network, the system can then use the learning method objects tocreate different instructional views for learners. The system tracks the objectswith the relevant class de®nitions to organise the document segments accord-ing to the chosen (class of) instructional method.

Support for Extensibility of Learning Methods Ð Classi®cationof Learning Methods

The classi®cation of learning methods enables a human author to easily createnew learning methods by extending/revising existing learning method objects.Existing learning methods can be reused and updated by a human author,creating new learning method objects without affecting the existing learningmethod objects.

Support for Authoring and Automatic View GenerationÐClassi®cationof Associations

OO supports link typing by allowing us to create link objects which we willcall àssociations'. Different associations have different effects on both thestructure of the document when applied in authoring, and the views createdwhen applied in document navigation. Using OO we can capture thecharacteristics and effects of different associations in respective àssociation'classes. See Sections below.

gradient

negative gradient positive

Fig. 2. Examples of the use of the inheritance relation.


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Reducing Authoring Overhead for Document RevisionsÐClassi®cation

of Subject InformationWe can provide a means for a human author to create OO classes for differentsubjects. Any revisions made to `parent' classes will then propagate to their`sub-' or `child' classes without the author having to make changes to eachìnstance'. For example, when an author is revising a concept de®nition, andthat concept has a number of child concepts all sharing the same information,then an OO system will propagate the revisions to each child concept withoutthe author having to revise them all.

OO Associations

It was mentioned earlier that many kinds of structures can be produced byusing different relations, thus demonstrating different aspects of the subject. InOO methodology, relationships between objects are de®ned as àssociations'.One association type already mentioned is the OO ìnheritance' relation.However, if you want to give an object a changeable attribute such as `colour',using inheritance would lead to a high number of classes. Using OOàssociations' we can de®ne further relations between objects such as A has

B, or A uses B.

AggregationIn OO theory, the aggregate relation is used to de®ne a relation between anaggregate object and it's parts, when there are shared attribute(s). For examplea car is made up of parts e.g. body, wheels, engine, which share attributes suchas speed and direction.

This relation can be used in order to facilitate shared knowledge betweendocument nodes related in this manner. For example all nodes that form part ofthe same mathematical ®eld will share information about that mathematical®eld, see ®gure 3.

Car

Engine Body

Maths for Sports Science

Inequalities Simultaneous Equations Quadratic Functions

Fig. 3. Examples of the use of the aggregation relation.


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Other Association Types

Other association types have been decided upon because of their usefulness ingenerating learner views for some recognised learning objectives. We havede®ned appropriate associations to link between (integrate) document facetsand create various ways of looking at these facets. The ®rst subsection of the®fth section gives a more detailed account of the associations used in ourwork.

Bene®t of OO Modules for Modelling the Integrationof Learning MethodsAn OO `module' is a logical construct for grouping classes, associations andgeneralisations (Rumbaugh, 1991). A module captures one perspective orview of a situation. Referencing the same class in multiple modules is themechanism for binding modules together (Rumbaugh, 1991). The nature ofOO modules forces there to be fewer links between modules (externalbinding) than within modules (internal binding).

The theory behind OO `modules' can be used to model the relationshipsbetween different learning methods. The bene®ts of creating classes oflearning methods were discussed in Sections described above. Now eachlearning method class can be viewed as an OO module. Thus a system withknowledge of different learning-method classes, will be able to determine sub-classes that are shared between learning methods, which will allow integrationof two or more learning methods. In this way, learning methods can be used ontheir own or in conjunction with other learning methods. The bene®ts ofclassifying learning methods discussed still stand.

SummaryThis section describes how OO bene®ts hypertext authoring in terms ofknowledge organisation and knowledge storage, respectively.

including is a

Exponential Function

Function Exponential rules for manipulation of powers

using

rate of change

having

Fig. 4. Example of the use of some association types.


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OO as an Effective Means for De®ning Architectural Elements

and Organising KnowledgeOO allows for the creation of a set of generic architectural elements to buildmodels of any real world or theoretical phenomena. Existing documentationcan be restructured in terms of the elements (concepts, relations etc.) providedby the ExAM model. In this way we are able to capture knowledge represen-tation in order to develop structured hypertext and learner tailored applica-tions. Because of the ¯exibility of OO as a modelling methodology, an OOauthoring system is adept at supporting complex structures. Using OO, we (thesystem developers) have been able to capture generic structural relations fordifferent learning methods and teaching styles. This knowledge base can nowbe used by the system to support authoring for multiple learner objectives.

Every object in an OO model is an instance of a class. A class de®nitionde®nes the attributes and methods of an instance. This `classi®cation' is themechanism for OO generalisation, which provides us with an alternativeapproach to `facet analysis' (Duncan, 1989) for de®ning classi®cations ofdocument segments. We used facet analysis to classify document segments byconcept type (Ainsley & Ghaoui, 1998a; Ainsley et al., 1998b), but it is notrich enough to support classi®cation of document segments according tomultiple attributes. OO generalisation provides plenty of scope to de®nedocument segment classi®cations at any level of abstraction, and for any `wayof looking at' the subject domain.

OO supports the abstraction of multiple facets (in the form of OO classes)of mathematical documentation. This feature will support generic applicationin order to hypertise different material. Support for author-de®ned classes andassociations can be provided, thus enabling the subject expert to extend the setof architectural elements available. This will support extensibility and adapt-ability of the system to cater to different instructional needs.

Ef®ciency of OO for Knowledge Storage and Distribution

OO models are well known for their ef®ciency for knowledge representation.Sub-classes or class instances inherit all of the properties (attributes, associa-tions and methods) that are de®ned for the super/parent class. Building an OOhypertext model using classes with multiple inheritance makes it possible toef®ciently handle knowledge without excessive overhead for the system aswell as for its user. Furthermore OO aggregation allows us to de®ne sub-components of an aggregate, whereby attributes, methods, and/or associationsof the aggregate are shared by the aggregate and its sub-components.


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Using the OO class construct (Rumbaugh, 1991), generic concepts andconceptual relationships can be stored. This knowledge can be used forsubsequent exploration and updating of the hypertext network, and forautomatically producing tailored documents.

Furthermore using the features of generalisation and aggregation we canutilise OO methodology (Rumbaugh, 1991) to support knowledge propagationbetween document segments in the form of object attributes and methods.Certain properties can be associated to node objects by de®ning the propertiesin class(es), and de®ning object attributes that derive from the respectiveclass(es). This feature will facilitate automatic knowledge propagation, forexample, the automatic generation of relations between document segmentsthat share certain properties.

WEAKNESSES OF EXISTING OO-BASED AUTHORING MODELS

Inef®cient Storage of InformationMacWeb (Nanard & Nanard 1991) is a system that elicits knowledge about adocument at the authoring stage. However, the knowledge elicited (semantics)using this method can only be applied to the same type of document i.e., it isnot ¯exible enough to apply to all documents of a subject domain.

The semantics de®ned for propagation of knowledge in systems such asMacWeb (Nanard & Nanard 1991) are limited to the use of inheritancerelations which must be administered by the author. Richer semantics couldsupport automatic linking of document segments according to given attributes.

Lack of Authoring Support for Document StructuringMost existing authoring methodologies require the subject expert to authormaterial from scratch. Furthermore, all existing techniques rely on the humanauthor to determine which segments and links within the knowledge structureneed to be depicted in order for later reader tailoring, and therefore worthcapturing in terms of an OO class. Moreover, the author may only have alimited number of rhetorical effects in mind, whereas the reader may bene®tfrom a different angle on the delivery.

There is no support for the identi®cation of node object attributes (e.g.level, application) in order that document segments can ìnherit' knowledgeaccording to their attributes. Neither is there any automatic identi®cation oflearning hierarchies, or methods of associating generic attributes to nodes todetermine a nodes relevance to learning objectives.


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Systems such as Challenger (Kolbe et al., 1992; Ghaoui, 1995), whichsupport the use of automatic view generation of a hypertext, only do soaccording to the views already de®ned by the author when authoring. Andlearners cannot navigate the resulting hypertext according to a variety oforganisational features, but must use views.

ExAM OBJECTS FOR HYPERTEXT STRUCTURING

The ExAM system uses an OO modelling framework in order to guide thestructuring of learning material to generate multiple instructional views. TheExAM authoring framework is extensible to allow the author to revise the OOmodel used to guide authoring. ExAM uses an OO model to build a knowledgebase as the document is being hypertised, and links together documentsegments that are related according to the model. The process results in aknowledge base that contains information about document segments inaccordance with the knowledge needed to generate multiple views (asdetermined by view generating objects in the ExAM model). The outputfrom the system is in the form of learner paths which constitute meaningfulroutes through segments of the original document that cater for the learnersimmediate learning requirements. The learner interface will provide naviga-tional tools including the facility for the learner to state their learningrequirements.

In the second section we identi®ed two different aspects of structuringlearning material. The ®rst aspect describes conceptual structuring usingconcept and relation types. Then we discussed the need to incorporatestructuring for learning methods. To accommodate the two aspects ofstructuring we devised a number of structure-building objects for incorpora-tion into the ExAM system. The ®rst of these structure-building objectsdescribed here is the between-information (BI) object. BI objects are used tosupport conceptual structuring for information ®ltering. BI objects representhigh level categories of subject material that can be used by the system tocreate a dynamic hierarchical menu for the learner to search for the categoriesof information that interest them. Next we discuss within-information (WI)objects. WI objects represent lower level facets of subject material and areused to capture concept-relation structures for different concept types. BI andWI objects support concept map type structures. LM objects support theaspects of view generation that are concerned with a learner's chosen learning-


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method(s). LM objects are used in conjunction the WI objects for creatingpaths through document segments for different learning requirements.

The objects described in this section realise the bene®ts of OO methodol-ogy for hypertext authoring, as described in the third section. We begin bydescribing a set of generic association objects that capture the semantics ofcommon relations in order to generate concept-relation type structures.Association objects are used within the de®nitions of the other structure-building objects.

AssociationsAssociations capture the semantics of common relations between nodeobjects. The set of association types used is determined by the types ofassociations required in the de®nitions of learning method (LM) and WIobjects. The following list details a number of associations that we judgeuseful for structuring subject material. We have created a set of pre-de®nedassociation objects to demonstrate how our theory can work. These associa-tions can be used by the author for his/her de®nition of new LM and WIobjects, and to create on-the-¯y associations between document segments.

� Aggregate: To link an aggregate object to it's parts, as described in the thirdsection SUPPORTS: Used when document objects form parts of anaggregate object and share knowledge accordingly.

� Is a: We will refer to the OO inheritance association, between parent andchild concepts, as the is a association. This association is used betweennodes and/or attributes when an ìs a' relationship occurs. For example<cosine>is a<trigonometric function>. This link forms an ìnheritance'relation i.e. a node object representing the concept <cosine> will inherit allthe properties and associations of the node object representing the concept<trigonometric function>. The link is only used for relations that imply setmembership.SUPPORTS: This association supports propagation of information in theform of inheritance, whereby subclass concepts inherit conceptual infor-mation from their `parent' concepts.

� Including: This association is used to infer that a node's subject occurswithin the subject of another node i.e., it is included within the charac-teristics of the connected-from node. This link is different from the OOàggregate' relation in that the `parts' do not necessarily share any obviousproperties with the `whole'. For example <Differentiation of Sine functions>


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including <Sine function>, whereby the concept `Sine function' is includedin the characteristics of the concept of `Differentiation of Sine functions'.SUPPORTS: We use the ìncluding' association to determine the compositenodes which are included in the description of a higher level concept (interms of the conceptual hierarchy). This association is used to depict thehierarchical relationships within mathematics for the `learning hierarchies'learning method (LM). It is used when some concepts must be learnedbefore a student can begin to understand a more complex concept thatìncludes' the former.

� Having: This link is used to link two nodes when one is a property of theother i.e., node A has node B.SUPPORTS: Any circumstance when it is desirable to connect a documentobject with other document objects that form a property of the former.

� Using: This link is used to link between nodes where the subject of one nodecan be said to ùse' the subject of the other.SUPPORTS: This link is used to link <rule/principle> concepts to subjectinformation that use rule(s)/principle(s).

� Generating: This link is used to link between a process and an outcome.SUPPORTS: To link a procedure/operation to ®nd a subject, with thedescription of the subject in question.

� Showing: This link is used to link a node object containing subject de®nitionwith other node objects that make up an alternative demonstration or de®ni-tion of the same theoretical subject e.g., when a mathematical model of orillustration by graphics is given about the same subject. This link forms atwo way inheritance relationship as every node related using the showing

link will be referencing segments containing information about the sameconcept.SUPPORTS: This link is used to link between description, examples, andquestions.

Between Information (BI) ObjectsAny subject attribute that represents a set of choices to ®lter subject informa-tion, can be captured as a between-information (BI) object. Each BI objectforms an aggregate of `section' objects. For example, the attribute Òrganism'could be captured as a BI object to ®lter between the sections of biologicalscience subject material (where the sections would be `human', `plant', etc.).The following list represents some BI attributes that we have devised forstructuring mathematical learning material:


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� {Field}Ða set of sections according to pure-mathematical ®elds e.g.algebra, calculus, logic, trigonometry, Geometry.

� {Application}Ða set of real world applications e.g. data analysis, deter-mining an objects mass, determining the speed of an object.

� {Level}Ða set of educational levels as determined by national certi®catese.g. GCSE, A-level.

The set of `section' objects of any BI attribute should cover all possiblesections within the hypertext documentation (for that attribute). Each time anauthor depicts a document segment that belongs to a section that is notincluded in the relevant BI object list, then the system will prompt the authorto create a new `section-object' for it. In this way, the BI attributes of anysegment of subject information should correspond to at least one section-object for the respective BI object. BI attribute-objects are independent of oneanother i.e., the learner's choice of section from one BI attribute-object shouldnot effect the learner's choice between the sections of another BI attribute-object. However, the subject information is available.

Within Information (WI) ObjectsBy identifying some attribute types that describe a document segment'srelevance to different learning methods, we can support the generation ofmultiple learning oriented views. WI objects are aggregates of `concept-type'objects that describe low level information types. They are created for use bydocument segments (see next section on `node' objects) to de®ne a set ofsegment attributes. Duncan's work (Duncan, 1989) demonstrated the bene®tsof identifying a set of `facets' for generating views. We have identi®ed anadditional set of general concept types for structuring mathematical learningmaterial.

� {Duncan's Concept types: Product, Part, Process, Procedure, Agent,Property},

� {Math Concept types: Model, Function, Constraint, Parameter, Procedure,Operation, Rule/Principle, Variable property},

� {Concept types for traditional learning: Description, Example, Question}.

The third set of concept types are de®ned for use with the traditional learningmethod (LM) object (described in Section 5.4). Certain LM objects de®neviews in terms of concept types. Thus, a segment's relevance to any given


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learning method is often determined by the segment's WI concept-typeattribute. The next sub-section on `nodes', describes how documentsegments are associated with the structure-building objects described in thissection.

Each concept-type object de®nition can incorporate an abstract structure ofconcept-relation associations. These relations will de®ne the immediategeneric associations of the concept-type for the subject material. Wheresuch structures are de®ned, they are used in conjunction with learning method(LM) objects (described in section 5.4) which determine their sequence forlearning method paths. In this way we can support the integration of learningmethods with the concept-relation structures of the subject material. Thefollowing subsections give details of each `Mathematical concept-type' alongwith diagrams to illustrate their immediate associations.

Concept-Type `Model'The `model' concept-type is used to depict concepts that are mathematicalmodels, formula i.e., formal symbolic or geometric notation used to model areal-world or theoretical system.

Concept-Type `Rule/Principle'The `rule/principle' concept-type is used to depict concepts that are axiomsand laws that govern the behaviour of mathematical entities.

generating

Variable Property/Parameter

Model

including including

Variable Property/Parameter Operation

including is a having

having Constraint

generating Procedure

Operation

showing

Fig. 5. Demonstrating the use of association types to describe a genericstructure for the `Model' (WI) concept-type.


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Concept-Type `Procedure'The `procedure' concept-type is used to depict concepts that representmathematical procedures/manipulation, usually of mathematical models togain insights into the parameters/properties of those models.

Concept-Type Òperation'

The òperation' concept-type is used to depict mathematical operations thatcan be used on mathematical model(s) or parameter(s)/property(ies).

Procedure

including using

including

using Operation

Rule/Principle

Function Operation

Fig. 6. Demonstrating the use of association types to describe a genericstructure for the `Rule/Principle' (WI) concept-type.

Operation

generating

using

Procedure

including generating

Rule/Principle

Model


Fig. 7. Demonstrating the use of association types to describe a genericstructure for the `Procedure' (WI) concept-type.

Operation generating

using Rule/Principle Procedure

Function

is a

including

Model


generating

Fig. 8. Demonstrating the use of association types to describe a genericstructure for the Òperation' (WI) concept-type.


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Concept-Type `Function'

The `function' concept-type is used to depict concepts that are `Function' is anòperation' therefore, the concept-type `function' inherits all the associationsof òperation'.

Concept-Type `Constraint'

The `constraint' concept-type is used to depict constraints of a model(s)parameter(s)/property(ies). i.e., the initial or given values of models para-meters.

Concept-Type `Variable Property/Parameter'

The `variable property/parameter' concept-type is used to depict concepts thatare properties or parameters of model concepts. The value or state of a variableproperty/parameter varies according to mathematical conditions, and canusually be determined by a mathematical procedure or operation. Examples:gradient of a function, radius of a circle, consistency of a system of linearequations, stationary points of a curve etc.

Operation

Function

is a

Fig. 9. Demonstrating the use of association types to describe a genericstructure for the `Function' (WI) concept-type.

Model

Constraint

having

Fig. 10. Demonstrating the use of association types to describe a genericstructure for the `Rule/Principle' (WI) concept-type.


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Learning Method (LM) ObjectsLearning method (LM) objects are used to capture generic knowledgestructures for different identi®ed learning methods. A LM object determinesthe types of document objects involved in a learning method. They also de®nemethods to generate different orderings for the display of the documentobjects involved in any given learning method. LM objects can be generic, thatis, they can be applied to any subject material, and others are created for usewith speci®c types of subject material. The following is a list of learningmethod objects which we have de®ned to demonstrate the use of our theory.

� Generic LM object's.

� {Learning hierarchies},� {Concept learning},� {Discrimination learning},

� LM's associated with WI object components

� {Traditional: Description, Example/Case Study, Question},� {Problem solving procedure},� {Rule/Principle learning}.

The ®rst generic LM listed here, supports information structuring for thetraditional method of subjecting students to subject description, examples ofthe theory and then questions about the theory to test the student's knowledge.The elements of this LM do not necessarily need to be displayed to the studentin that order. The second LM is in attempt to provide a learning route throughconcept hierarchies. In this way the student can attain those information that


Model

Operation

Procedure

generating

generating

having including

Fig. 11. Demonstrating the use of association types to describe a genericstructure for the `Variable property/parameter' (WI) concept-type.


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needs to be understood as a prerequisite to a current subject, and choices ofinformation that can be regarded as a step on from the current subject. Theremainder of the LM objects listed here, correspond to the learning methodslisted in the second section. The following three subsections describe three ofthe LM objects in more detail:

The `Learning-Hierarchy' LM ObjectThis view is de®ned to determine hierarchical arrangements of concepts.Concept_hierarchy views are used to identify pre-requisite learning for anygiven document node. They can also be used to take students throughprogressive stages of learning according to the concept hierarchies available,to reach a given learning requirement.

Path

1. Use including links to determine the pre-requisite concept nodes of thecurrent concept node;

2. Use including links that enter the current concept node to determine theconcepts that can be learned that use the current concept.

The `Concept-Learning' LM ObjectThis learning method displays a view for readers who want to learn thecharacteristics of a particular concept within the learning material. This isdone by displaying the concept and it's properties without contrasting withother similar concepts.

Path

1. Display concept node (with attribute `description');2. Display alternative `description' objects linked to concept with showing

link;3. Display objects linked to concept with having link;

Description node

including

Fig. 12. Demonstrating the relations used for the `Learning hierarchy' learn-ing-method object.


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4. Display èxample' objects linked to concept with showing link;5. Display `question' objects linked to concept with showing link.

The `Traditional' LM ObjectThis LM object is an aggregate of description, example, and question concept-nodes.

Path 1

1. Display `description' nodes using relevant LM for the concept type;2. Display èxample' nodes linked using showing link;3. Display `question' nodes linked using showing link.

Path 2

4. Display èxample' nodes;5. Display `description' nodes linked using showing link using relevant LM

for the concept type;6. Display `question' nodes linked using showing link.

This method must be used in conjunction with a choice of concept. Thedisplay of a node attributes from any choice of WI concept-type object will beused in conjunction with the associations de®ned for that concept type. Ahuman author or learner can decide in which order they want the threeelements, description, example, and question, to be displayed in the view.

NodesAs suggested in other work (Kolbe et al., 1992; Rumbaugh 1991), the notionof an OO object is well suited to represent the notion of a hypermedia `node'.In the ExAM system, a node object forms a reference to what the human-author determines as being a self-contained information segment within theoriginal documentation. A node object can then be used to encapsulate aconcept, an example, a question, or a description associated with any learningmaterial `section', `learning method' (LM) or WI `concept-type' object. Inorder to author for multiple views, we will determine a set of generic attributesthat correspond to the requirements of the structure-building objects describedin the previous section. A node's attributes enable the system to determine thenode's relevance when generating views and paths for learner's objectives.

We are able to use the àttribute' feature of an OO object to enable thefollowing:


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1. Attribute a node with relevant BI section types. This will de®ne it in termsof the different `general' BI facets of maths learning material.

2. Attribute a node with relevant WI concept-types. This will support thecreation of associations between a `concept' node and related `concept'nodes by attributing it to WI concept types which determine the low-levelconceptual relations within the subject material.

Some node attributes are determined by the BI and WI object de®nitions (asdescribed in sections above). The BI and WI objects are extensible accordingto the human-authors instructional objectives. Thus in the ExAM system, thenode object de®nition needs to be extensible, as the attributes can be increasedat any time according to changes made to BI and WI objects.

Further Bene®ts of the Structure-Building ObjectsBI and WI objects each form aggregates of `section' and `concept-type'objects, respectively. BI and WI objects are abstract OO classes, de®ned toallow us, the system designers, and any human author, to de®ne segmentattributes. The BI object class-de®nition determines the type of knowledgecaptured for the differentiation between subject sections. The WI object class-de®nition determines the type of low-level knowledge captured for thedifferentiation between concept types. Thus the BI and WI class de®nitionsallows us to de®ne the types of generic knowledge that will be available foruse by the system to identify relevant objects for learner views.

Section, concept-type, and LM objects are designed to support the assim-ilation of subject knowledge not made explicit by the original (linear)document structure e.g., information associated with a `section' or learningmethod. These three object classes contain the de®nition of a segment's BI-attribute, WI-concept type, and LM plan, respectively. A section objectcaptures those properties that are common to all subject information withthe same section attribute. A concept-type object captures those properties thatare common to subject information that covers the same type of concept. ALM object captures those properties that are common to hypertext that coversthe same type of learning method. In this way we are able to captureknowledge in the form of object properties, and `propagate' the properties(knowledge) to the appropriate document segments. For example, a section-object representing the ®eld `Geometry' can be associated with a documentsegment that contains a de®nition of Geometry. The section-object forGeometry can also capture any other properties of geometry and propagate


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to all of the objects that have geometry as their `®eld' attribute (i.e. theconcepts that belong to that ®eld).

Using OO methodology, we have been able to create extensible objectclasses and attributes to support the above structure-building objects, whichare readily available to support hypertising.

ExAM AUTHORING

Before authoring with ExAM the subject material should exist in electronicformat. This electronic document will be the initial input. The subject expertwill then be required to determine the segments of the document to beincluded in the hypertext.

Learning documents are traditionally divided into sections, or segments,that cover separate learning objectives. Document segments can be made up ofa number of sub-segments, and the author needs to determine which segmentsof the document should be incorporated into the hypertext. ExAM (ExtensibleAuthoring Model) treats each segment as a self-contained entity. The contextof any segment in the resulting hypertext will be dependent on it's place withinthe view generated for the learner's requests. Thus the context of a segmentshown using the hypertext can be different to the context intended when it wasoriginally authored in the linear document.

Hypertising ProcedureOne of the objectives of this research is to generate multiple instructionalviews of learning material, without the need for the human author to determinewhat knowledge needs to be captured. The system stores knowledge aboutwhat information needs to be captured about the document segments, in theform of the structure-building objects described in Section 5. The system willuse the structure-building objects to determine the number and type of attri-butes of a node object. The information required by every structure-buildingobject needs to be captured for every document segment (node) in order forthe system to generate views involving any of the BI or LM objects.

1. Prompt the human author to determine the document segments as ex-plained at the beginning of this section.

2. Prompt the human author to con®rm the existing BI categories for infor-mation ®ltering, and add to if necessary.


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3. Prompt the human author to con®rm the existing learning methods for viewgeneration and add to if necessary.

4. Prompt the human author to depict the ®rst segment for authoring.5. Use the structure building objects to prompt the author to enter the

knowledge required about the segment. (This will include the type of objectthat represents the segment, the segment name, attributes, and associatedsegments.

6. Repeats step 4. For all identi®ed segments.

Demonstrating the use of ExAM's Structure-Building Objectsto Support HypertisationOnce associations have been instantiated between document segments, forevery node, the system will store the structures that involve that node. Everytime an inheritance association is instantiated from a node, the system promptsthe author to instantiate related nodes according to the types associated in theinherited relationships.

After a concept object has been created to refer to a document segment, thesystem will prompt the author to create associated concept objects as de®nedby the object de®nition of the type of concept object. The traditional LM willthen force the system to prompt the human author to determine the segmentswhich incorporate Èxample' information about the concept. The system willthen propagate the relations de®ned by the concept to the example(s), and usethis information to prompt the author to de®ne the related concepts within theexample segment. See Figure 13.

Concept <A> (Description)

Concept <A> (Example)

Concept <X> (Description)

Concept <Y> (Description)

Concept <X> (Example)

Concept <Y> (Example)

showing

showing showing

having including having including

Fig. 13. The diagram illustrates ExAM's support for authoring by automati-cally generating structural elements. Dotted lines represent systemprompted/generated elements.


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In this way, example concepts ìnherit' structure from the object containingthe concept de®nition. The same methodology works for any other instancesof the traditional LM.

Use of String Matching

Use of String-Matching for the Automatic Propagation

of Concept KnowledgeAfter a concept object has been created to refer to a document segment, thesystem will search for other namesakes of the same concept using stringmatching techniques. Where another namesake of the same concept occurs,the new concepts inherit the information of the original concept object.

Use of String-Matching for the Automatic Generation of Ìncluding' LinksThe system can use string-matching to search the hypertext documentfor instances of names of existing concept-object. When instances of aknown concept occur within the boundaries of a concept de®nition or problemsolving procedure then ExAM infers that concept is included in the parentconcept.

THE LEARNER INTERFACE

The learner will be faced with a list of learning/navigation options. Accordingto the option chosen, the system will display a list of subsequent navigationoptions

The learner can navigate the document using the relational databaseaccording to the BI and WI object types:

� Questions or theory,� List of concepts,� List of problems,� List of rule/principles,� List of applications,� List of ®elds.

The learner can also choose from the four different learning methods. See the®fth section for possible views.


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EXAMPLE

Using course notes from Mathematics for sports science, we are able todemonstrate how ExAM will work. The salient segments of the learningmaterial are as follows:<The straight line>, <line of best ®t>, <pivot point>, <equation of a straightline>, <gradient>, <®nding the gradient of a straight line>, <y intercept>,<residues>, <tutorial questions>.

^ Straight line^ Line of best ®t^ Pivot point^ Equation of a straight line^ Gradient^ Finding the gradient^ The y intercept^ Residues^ Questions

^ Q1 Plot the given data^ Q2 Find pivot point {understand `pivot point' [concept learning]}

DYNAMIC MENU

Search ByAttributes: Level Field Application Learning method

>>Choice: Field ‘Calculus’Search By Attributesof Field ‘Calculus’:

Level Field Application

Learning method

Problem solving Rules/Principles

Concept learning

Discrimination learning

Fig. 14. Portion of the dynamic menu available in the learner interface as anoptional method of navigating the learning material. Using drop-down menus linked to a relational database to impart subject options.


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^ Q3 Find line of best ®t [problem solving]^ Q4 Calculate gradient and y intercept, hence write down the equation of

the line of best ®t [concept][rule/principle]^ Q5 Calculate the residuals for each data point and ®nd the average of

these residuals [concept]^ Q6 Estimate, from your equation, the value of V when T� 100^ Q7 Find the gradient and y intercept of y� 2x�1 [rule/principle]^ Q8 Find the gradient and y intercept of 3(y�2x)� 6 [rule/principle]

The author will establish the BI attributes: that the subject material is of the`®eld' Geometry and àpplication' Sports science. These BI section types willbe attributed to all segments authored in the session, until the author statesotherwise.

The author will probably begin with the segment covering the conceptdescription of a straight line. The author will establish the WI attributes: thatthe segment represents the `description' of a concept, and that the concept-type is `model' as the intention of the document segment is to demonstratehow mathematics can model the behaviour of a straight line.

We have pre-de®ned the following possible relationships for the concept-type `model'.

The system prompts the user to identify related segments according to thisabstract model of relationships as depicted in Figure 15.

For each tutorial question relevant to the desired learning objectives, theauthor identi®es and inputs the corresponding learning method that needs tobe understood in order to answer. In order to answer some of the questionsprevious questions must be answered ®rst, this pre-requirements must beimplied for the system to guide the learners.

model

constraint having showing

having variable parameter/property

is a including

including variable parameter/property

including

function

generating

generating operation

procedure

Fig. 15. Showing the abstract model of the generic relationships for the`model' (WI) concept type.


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From Figure 16 it can be seen that the `line of best ®t' inherits properties of`straight line'. Thus for the purpose of learner navigation, the `line of best ®t'inherits a showing link to èqation of a straight line' and an extra having link to`gradient'.

y intercept residuals

having

finding the gradient of a straight line

straight line

gradient

eqn of a straight line showing

generating

having

having

including

is a showing

line of best fit

Fig. 16. Document model for the scenario developed guided by the relation-ships de®ned by the WI objects.

pre••

••

Navigational Options Current node: GRADIENT --> Attributes Field : Geometry Application: Data Analysis

Module: Sport Science Level: BSc level 1

Gradient To find the gradient, decide which way the line of best fit is sloping. If it slopes down, left to right, it has a negative gradient. If it slopes up, left to right, it has a positive gradient.

Now any where on the line construct a right angled triangle and measure the width and heightThe gradient is height/width.

GRADIENT : CONCEPT LEARNING: Straight Line Property 1 of 1 of Straight Line Node: 3 of 6

-ve gradient

+vegradient

Previous Next Showing (description) straight line: ‘Eqn of a straight line’ Showing (example) straight line: ‘Line of best fit’

SEARCH THE HYPERTEXT USING THE DYNAMIC

Explain Explain

Explain Explain

View -requisite learning for ‘Gradient’

See examples of ‘Gradient

Answer questions on ‘Gradient View subjects involving ‘Gradient’

IEW HISTORY START OVER

height

width

.

MENUE V

Fig. 17. ExAM's Learner interface showing the view for learning the con-cept of a straight line.


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View GenerationThe student chooses `concept learning' with `traditional learning'about `Straight line'. The system displays the following sequence of nodesaccording to the concept learning and traditional learning LM objects (seesect. X):- 1 < straight line > showing 2 < equation of a straight line > having

3 < gradient > showing 4 < line of best ®t > showing 5 < [Q1. . .Q8] >

CONCLUSION

This paper demonstrates the bene®ts of OO modelling for authoring hypertextlearning material. The ExAM model employs the bene®ts of OO modelling byproviding an extensible OO framework which guides and semi-automateshypertising for generating multiple learner views. The most salient contribu-tion of ExAM is it's support for organising and generating hypertext docu-ments in terms of knowledge about the theoretical concepts within the subjectmaterial. Conceptual structures are invariable very intricate and subtle. ExAMfacilitates the incorporation of generic concept structures for generatingmultiple views according to a set of generic learning methods.

The aim of our system is to allow information to be organised in apedagogically relevant manner, supporting a step-by-step learning experience.ExAM demonstrates a model that supports dynamic access to documentsegments such that simple algorithms can select the relevant information in ameaningful sequence. To accomplish this, the knowledge is organised in termsof the attributes of document segments that indicate a segment's relevance to alearner's learning objectives. The lower level objects (BI sections, WIconcept-type, and association objects) are mapped, during hypertising, ontolinear document segments according to the attributes of the segments. Themapping permits ExAM's high level structure-building objects (LM objects)to generate hypertext learner-paths from the segments.

The hypertising process requires input from a subject expert, to whom werefer to as the human author. This input is guided and augmented by ExAM'sOO framework. Normally it would be dif®cult for the author to foresee thedifferent possible views of the knowledge domain. Instead the system candetermine what information needs to be captured in order to support ¯exiblemultiple view generation. This is done using the framework's object descrip-tions, reducing the human-authoring overhead. The following list details thefeatures of ExAM that ful®l our research objectives:


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1. Has the ability to hypertise existing documentation1.1. ExAM provides a set of generic architectural elements using OO

semantics. Existing documentation can be restructured in terms of theelements provided.

2. Has generic application in order to hypertise different material2.1. ExAM supports the abstraction of multiple facets (in the form of OO

classes) of mathematical documentation.2.2. ExAM provides generic and extensible structure building objects to

generate association supporting multiple learning methods and subjectsectioning.

2.3. Provision of a pre-de®ned set of facets and associations that exist inmathematical documentation.

3. Enables some automatic structuring using an internal knowledge base toidentify complementary information and generate associations.3.1. ExAM accomplishes knowledge propagation between document

segments in the form of node attributes. ExAM encourages thedesignation of facet (class) attributes which are inherited to alltheoretical segments belonging to respective facets (OO classes).

3.2. ExAM automatically creates associations between document seg-ments. This is done using the knowledge base developed and theattributes assigned to document segments.

4. Has the ability to automatically generate views to cater for multiple learnerobjectives.4.1. ExAM incorporates a set of learning methods which are administered

for generating multiple views for a set of de®ned learner objectives.5. Provides prompts to guide the authoring procedure.

5.1. ExAM guides the authoring procedure by providing prompts as to theapplication of the architectural elements for structuring the existing`linear' documentation.

6. Is extensible and adaptable to cater for different authoring needs.6.1. ExAM provides support for author de®ned attributes, extending the set

of architectural elements available.6.2. ExAM provides support for author de®ned teaching/learning objec-

tive.

REFERENCES

Ainsley, H., Ghaoui, C., & Whiteley, K. (2000). An OO Model to Generate KnowledgeStructures for Authoring Instructional Hypermedia. EUROMICRO WORKSHOP onMultimedia and Telecommunication, Maastricht, Netherlands, Sept. 4±7, 2000.


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rary

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Ainsley, H., & Ghaoui, C. (1998a). Structuring mathematical hypermedia learning material tocommunicate knowledge. CYBERDESIGN: Media, Communication and DesignPractice, pp 151±162, Paris November 25±27, 1998; In C. Branki & K. Zreik (Eds),EuropIA '98, Europia Productions, France, ISBN 2±909285±11±1.

Ainsley, H., Ghaoui, C., & Whiteley, K. (1998b). Modelling mathematical knowledge usinghypermedia. International Conference on the Teaching of Mathematics, Samos island,Greece, July 3±6, 1998.

Chen, C. (1997). Structuring and visualising the WWW by generalised similarity analysis.Proceedings Hypertext `97, 177±186.

DeBra, P., & Calvi, L. (1998). AHA! An open adaptive hypermedia architecture. The NewReview of Hypermedia and Multimedia, 4, 115±139.

Ghaoui, C. (1995). Authoring and linearizing hypertext for electronic and print publishing. APhD Thesis, Computer Science Department, Liverpool University.

DeYoung, L. (1990). Linking considered harmful. Designing and reading hyperdocuments. 1stEuropean Conference on Hypertext, Cambridge University Press, pp. 238±249.

Duncan, E. B. (1989). Structuring knowledge bases for designers of learning materials.Hypermedia 1(1), 20±33.

Gaines, B. R., & Shaw, M. L. G. (1995). Concept maps as hypermedia components. Inter-national Journal of Human-Computer Studies, 43, 323±361.

Gates, K. F. (1998). Toward an adaptive WWW: A case study in customized hypermedia. TheNew Review of Hypermedia and Multimedia, 4, 88±113.

Kneece, N. S. (1996). An electronic publishing spectrum: A framework for text modules.SIGDOC, ACM.

Kolbe, R. W., Broome, R. W., & Augusteijn M. F. (1992). A dual knowledge representation fora domain-independent intelligent training system. Proceedings ACM 20th AnnualConference on Communications, pp. 205±211.

Martin, J. (1992). Object-Oriented Analysis and Design. UK Prentice Hall.Nanard, J., & Nanard, M. (1991). Using structured types to incorporate knowledge in hypertext

Proceedings Hypertext, 91, 329±341.Rumbaugh, J. (1991). Object-Oriented Modelling and Design. UK Prentice-Hall.Salton, G. (1988). Automatic text indexing using complex identi®ers. Proceedings of ACM

Conference on document processing systems, USA ACM Press.Soderston, C. & Kleid, N. (1996). Concept mapping: A job-performance aid For hypertext

developers. SIGDOC.Specht, M., & Oppermann, R. (1998). ACEÐadaptive courseware environment. The New

Review of Hypermedia and Multimedia, 4, 141±159.Thomson, J., Greer, J., & Cooke, J. (2000). Generating instructional hypermedia with APHID.

ACM Hypertext 2000 Conference.Thuring, M., & Hanemann, J. (1995). Hypermedia and cognition. Communications of the ACM

38(8): 57±66.Tilley, S. R., & Whitney, M. J. (1993). Personalized information structures. SIGDOC, 2, 325±

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Generating Multiple Hypermedia Learning Views Using OO Modelling