Faculty of Computer Science and Mathematics

Eindhoven University of Technology

Postbus 513, 5600 MB Eindhoven, The Netherlands

tel: +31-40-247 4350

a.i.cristea@tue.nl

In this paper, we extend the automatic authoring
techniques that can be built based on the LAOS model, a five-layer
AHS authoring model. As the LAOS model itself is fairly complex,
although information-rich, an adaptive hypermedia author needs a lot
of system support to be able to populate all its levels with the
corresponding information. Therefore, such automatic authoring
techniques, which are actually automatic transformation (and
interpretation) rules between the different layers of the model, have
been designed. These automatic rules represent, in the area of
adaptive systems, designer-goal oriented adaptation techniques. They
should represent the goal of the designer that is authoring the
hypermedia (such as the pedagogical goal in educational adaptive
hypermedia). Therefore, this paper represents yet another step
towards an adaptive hypermedia (or adaptive course) that ‘writes
itself’. The focus here is on automatic transformation between
the *domain* and a newly introduced *goal and constraints
model*, to show that the effort of introducing this new layer can
be minimal.

Adaptive authoring, adaptive hypermedia, AHS, AHAM, ontologies, MOT, Dexter model

Adaptive hypermedia system (AHS) are becoming
nowadays popular, due to their connection with the W3C and IEEE LTTF
[18] movements towards (*ontology*-based) customization and the
*semantic Web*[24]. The success of commercial adaptive systems
as Firefly, or research AHS as AHA! [15], Interbook [4], TANGOW [6]
and others has pushed AHS forward. Their edge over classical ITS
systems relies on their simplicity: they contain a simple domain -,
user model (usually an overlay - of the domain model), aimed at a
quick response, which is extremely beneficial in the speed-concerned
WWW environment. However, for quite a long while there has been a
lack of powerful authoring tools for adaptive hypermedia [2, 7]. One
of the main reasons was the great (but fruitful) diversity in AHS
implementations, many with implicit models[27].

Here we build on AHAM [27] and on the LAOS model
[12] that allows a more flexible model for adaptive hypermedia
authoring. As authoring of information rich adaptive hypermedia is
difficult and time-consuming, we have added, next to the LAOS model
that allows high flexibility, some methods to bypass the workload for
the adaptive hypermedia author. Here we show, for instance.
(adaptive, adaptable) automatic authoring techniques that can lead to
more powerful AHS authoring tools. Instead of having the author
populate the layers of an adaptive hypermedia model such as LAOS, the
system can take many of the tasks over and perform them with no or
little authoring intervention. Here we are going to highlight some
examples of such *automatic authoring*, as we call it.

The paper is organized as follows. In section 2 we
briefly recall the LAOS model as well as the definitions we need for
the automatic transformations. Section 3 introduces automatic
transformations that can lead to designer adaptation: automatic
adaptation of the designed hypermedia (e.g., courseware) itself to
the designer goal. In section 4 we exemplify some automatic
transformations between two concrete layers, the *domain* and
the *goal and constraints layer*, that are allowed by the LAOS
model, and compute some *flexibility degrees* to show the
expressiveness of the possible transformations and give also some
examples and implementation instances from MOT [13]. In section we
present a short discussions about the benefits and implications of
such automatic, designer goal oriented transformations. Finally,
section 6 draws conclusions.

**Fig. 1. **The
five level authoring model

The LAOS model (figure 1) is a generalized model for
generic adaptive hypermedia authoring and was introduced in [12],
therefore we are not going into many details about it here. It is
based on the AHAM model [27] which in turn extends the Dexter
reference model [17] for the specific field of adaptive hypermedia.
The LAOS model is composed of five components: the *domain model*
(DM), *goal and constraints model* (GM), *user model* (UM),
*adaptation model* (AM) and *presentation model* (PM), as
can be seen in Figure 1.

The idea is based on the book–course or
book–presentation metaphor: generally speaking, when making a
presentation (GM), be it for the Web or not, we base it on one or
more references (DM). Simplifying, a presentation (GM) is based on
one or more books (DM)^{[1]}.
This is why we need an intermediate presentation (GM) layer. The rest
of the layers are shared with the AHAM model, so the motivation of
using them is similar to the motivation of the previous model.

The basic idea is that such a model is easier to
maintain than a small but compact model with all needed information
in the same place. A change in user information will go, for
instance, directly into the *user model* (and might influence
the adaptation model) but has nothing to do with the domain –
so will not influence the *domain -* or *goal and constraints
model*. A presentation style change or update, on the other hand,
will influence only the *goal and constraints model* (if it is a
content related presentation style change) or only the *presentation
model* (if it is a interface related change). So, each type of
information is kept separately from information of other type, thus
allowing maintainability.

Moreover, with the LAOS structure, dynamic
(adaptive) presentation generation becomes possible. The actual
presentation seen by the user can contain both elements of the *goal
and constraints -* as well as elements of the *domain model*
(e.g., for clarification of an explanation based on only the GM, the
other elements/ objects of the respective concept, or the other
concepts related to the current concept, can be referred, via a jump
over one layer). This increases the flexibility and expressivity of
the created adaptive presentations, as we shall see by computing the
flexibility indexes of automatic transformations, for instance^{[2]}.

In the following we are going to list the definitions regarding the different layers that are going to be used in the automatic transformations.

**Definition 1.** We consider a concept map *CM*
of the AHS to be determined by the tuple <C,L>, where C
represents the set of concepts and L the set of links (*CM* ÍCM,
the set of all concept maps of the AHS).

**Definition 2.** A concept *c*ÎC
is defined by the tuple < A_{c},*C*_{c}>
where A_{c} (A_{c}¹Æ)
is a set of attributes and *C*_{c} a set of
sub-concepts.

**Definition 3.** A_{min} is the minimal
set of (standard) attributes^{[3]}
required for each concept to have (A_{c}ÊA_{min}).

**Definition 4.** A concept *c*ÎC
is a composite concept if *C*_{c}¹Æ.

**Definition 5.** A concept *c*ÎC
is an atomic concept if *C*_{c}=Æ.

**Definition 6.** A link *l*ÎL
is a tuple <*c1*, *c2*,*n _{l}*,

**Definition 7.** An attribute aÎA_{c}
is a tuple <*var*,*val*>, where *var* is the
name of the attribute (variable or type) and *val* is the value
(contents) of the attribute^{[4]}.

**Definition 8.** Each concept *c* must be
involved at least in one link *l*. This special relation is
called *hierarchical link* (or link to father concept).
Exception: root concept.

Concept *c* is determined by its identification
*i*Î{1,…,C} (where
C=card(C)) and the attributes of concept* i* are a_{i}[h],
with hÎ{1,…,A* _{i}*}
and A

The goal map *GM* of the AHS is a special *CM*,
as follows.

**Definition 9.** A concept *c*ÎC
in *GM*is defined by the tuple < A_{c},*C*_{c}>
where A_{c} (card(A_{min})=2)^{[5]}
is a set of attributes and *C*_{c} a set of
sub-concepts.

**Definition 10.** A
link *l*ÎL in *GM*
is a tuple <*c1*,*c2*,*n _{l}*,

UM and AM have been described relatively well by AHAM [26].

However, another way of representing the UM is given in [10], where we view the UM as a concept map (CM). In such a way, relations between the variables within the UM can be explicitly expressed as relations in the UM, and do not have to be “hidden” among adaptation rules (figure 2). The components of the concept-based user model are:

a concept map of user variables and their values (UVM)

a history concept map (HM): an overlay model of visited attributes and concepts from the GM and DM (a copy or a pointer to them) with extra historical variables and their respective values attached (e.g., a set of [date of visit, duration] for each visit)

a future concept map (FM): an overlay model of attributes and concepts from the GM and DM to be visited (copy or pointers to them); this map should be in the general case dynamic, i.e., its components (concepts, attributes, links, variables, values) can vary according to the AM(adaptation model) application by the AE (adaptation engine) and the user’s decisions.

**Fig. 2. **The
3-leyered user model in LAOS

We have introduced in [9] a new three-layer
adaptation model (defining *low level assembly-like adaptation
language*, *medium level programming adaptation language* and
*adaptation strategies language*) that we are in the process of
refining and populating, but this is beyond the scope of the present
paper.

The PM has to take into consideration the physical properties and the environment of the presentation and provide the bridge to the actual code generation for the different platforms (e.g., HTML, SMIL [25]).

In [11] we have defined the notion of designer
adaptation (and adaptability), as *adaptation (*and*
adaptability) to *the* design (authoring) goals*. In this
paper we elaborate on the different possibilities of implementing
such adaptation, based on the LAOS adaptive hypermedia authoring
model introduced in [12].

In the following, we will present some automatic transformation possibilities from one layer to the other of the LAOS model, which can be performed in exclusivity by the authoring system, triggered or not by the designer’s (author’s) specific request.

These processes are normally done by hand during adaptive hypermedia design and authoring, and many of them are considered to be domain dependent (and therefore embedded in the domain functionality).

Here we find some patterns that allow us to
generalize and automatically perform these transformations. In the
first case, we talk about *adaptable design generation*, whereas
in the second, we can talk about *adaptive design generation*. A
simple rule system, for instance, can be implemented to make the
choice between adaptability and adaptivity, and then within
adaptivity, among the different adaptive options^{[7]}
presented.

*Adaptivity* implies that the system makes the
inferences about the possible choices, and then takes the decision
that is conforming to its adaptation model. The system then
executes the choice.

In *adaptability*, the inferences about
possible choices, as well as the selections are made by the user, and
the system then executes the choice.

However, a combined version of adaptivity and
adaptability is possible. The system can make the inferences about
possible choices, and then allow the user to make the selection (or
decision). This we call *adaptive adaptability*.

Moreover, we look at the *flexibility index* of
many of the automatic transformations presented in the following
sections, defined as the combinatorial index giving all the
functionalities that can be covered by such transformations.

This section discusses the automatic (adaptive,
adaptable) *goal and constraints model* generation from the
*domain model*, according to some presentation constraints and
goals (e.g., pedagogical strategy or pedagogical technique). This
transformation can be viewed as the first step from *information*
to *knowledge*. This is due to the fact that, as said in [13]
for instance, the *lesson ^{[8]}
level* repeats the information contained in the concept level, now
modeled and grouped based on pedagogical goals.

A very simple way of using the concept attributes can be for the selection of the specific types that are the only ones to go in the goal and constraints model. This transformation has been used for demonstration purposes by the MOT adaptive hypermedia authoring system [13].

*I.e., for A _{min}
={title, keywords, introduction, text, explanation, pattern,
conclusion} (A_{min} =7) as defined in section 2.1, we define
A_{transf} ÍA_{min}
as A_{transf} ={title,
introduction} (A_{transf} =2), as the transfer set from DM to
GM, for implementing a goal-constraints model representing the
elements for the pedagogical goal “introductory presentation”
(e.g., for a beginners course, or for an overview on the whole
material).*

*If A _{transf}
={title, explanation} (A_{transf} =2) we can implement a
goal-constraints model representing the elements for the pedagogical
goal “motivational presentation” (e.g., for a
motivational overview on the whole material, that is to attract
students towards it).*

*As a third example, we mention the selection of
A _{transf} ={title,
text} (A_{transf} =2), with which we can implement a
goal-constraints model for a “motivational presentation”
(e.g., for a motivational overview on the whole material, to attract
students towards it).*

*As a fourth and last example, we mention the
selection of A _{transf}
={title, keywords, pattern, conclusion} (A_{transf}=4) that
implements a goal-constraints model representing the elements of the
pedagogical goal “rehearsal” (e.g., for a summary or
resuming presentation of the whole material, that is to remind
students what they have learned in that lesson, and what the main
important points – patterns - are).*

Obviously, many more such pre-selections can be
done. It is easier to imagine these types of transformations, if we
go back to the book metaphor, and we are trying to construct a
presentation based on a book: first, we are going to select the
material that is going to be presented, as the whole book might be
too long (hence, *constraints*) and its focus might be elsewhere
(hence, *goal*). In this way, we build our *goal and
constraints model*. Next, we are going to focus on the order,
style, etc. of the presentation, which will appear in the *adaptation
model* (and partially in the *presentation model*). Finally,
we will interact with our audience and decide on skipping some parts
or going into more details into others, depending on their reaction
to our story (so, *user model* building and processing).

If we look at the combinatorics of these
transformations, the *flexibility degree*, computing in this
case the number of different lesson materials (so, the number of
different sub-layers in the *goal and constraints model*) that
can be generated automatically just with this simple procedure, is as
follows. The different ways of selecting attributes from a concept C1
are:

where *C(a,b)* are combinations of *a*
elements taken *b* at a time.

This number is flex(1)³87
for A_{min} =7 and represents the number of possible
selections from C1 attributes if we don’t care about the order.
However, because in the *goal and constraints layer* the order
starts being important, as opposed to the *domain layer*, the
actual formula is:

where *P(a,b)* are permutations of *a*
elements taken *b* at a time.

So flex(1)³ 13699, which is a much greater number.

Again, this is just the flexibility degree for one single concept and its extracted attributes. In a hypermedia concept map, there are many concepts. If we consider very simple automatic transformations, such as implemented in MOT [13], where all concepts in a concept map C are transformed in the same way, then these numbers don’t change. However, if we allow concepts to be transformed independently, the flexibility degree will drastically grow.

As said in the link definition for the conceptual
layer (section 2.1), beside the obligatory hierarchical links,
concepts can be involved in several other relations (links), which
are defined by their *start point*, *end point*, *name*
(type) and *weight*. In [11] we have shown that simply by using
the attribute structure of the concepts, and labeling links between
concepts with the name of the attribute that presents some
relatedness, a great number of links can be automatically generated.
However, in the LAOS structure we allow other types of links between
concepts, which may not be automatically generated or related to
attribute types.

These link types can be used to generate new, specific links at the level of the GM model.

A very simple example is the selection of some
selected type of links only, that are to be taken over by the GM
model (e.g., only *name* links).

In MOT, the automatic transformation functions described in section 4.1 go hand in hand with an automatic transformation into a standard, hierarchical, ordered link structure.

In other words, the selected attribute subset will
keep (almost) the same *hierarchical structure* as its DM
source: if a concept *C1* was a sub-concept of concept *C2*,
and, let’s say, we use the transformation of choosing only the
{*title*,*introduction*} attributes; then, *L11=C1.title*
and *L12=C1.introduction* will be sub-concepts of *L21=C2.title*,
and the former attribute *C2.introduction* becomes concept *L22*,
which is also a sub-concept of *L21*. In this way, the
hierarchical link structure in *domain model* is translated into
a hierarchical link structure of the *goal and constraints
model*^{[9]}:

LL21Ê LL22, LL11, LL12

Moreover, concepts in the GM have an *order
relation*, as opposed to concepts in the DM, which are represented
as concept *sets* (so without order within a hierarchical
depth). The solution implemented in MOT is to first list the
(selected) attributes of a DM concept, and then the sub-concepts of
the same concept. In our example case, this means that the order
relationship is:

LL21 > LL22 > LL11 > LL12.

Finally, relations in the GM have a type, which can be hierarchical, as describe before, or {AND/OR}. The latter are relations between elements from the same hierarchical depth. Automatically, all elements at a certain hierarchical depth are transformed in the MOT GM into concepts connected via an ‘AND’ relation:

AND(LL21 , AND(LL22 , LL11 , LL12)).

These can then be manually altered (e.g., into ‘OR’ relations), and added weights, but we are not going into details about this here.

The link-based transformation above is very simple, taking into account just the hierarchical link relations in the DM, but it is useful to illustrate the many different types of links that can be generated in the GM from only such a simple link sub-set. Here, one hierarchical relationship (together with the implicit attribute relationship) at DM level generates 3 relationships at the GM level. Please note that the above transformations don’t take into account the relatedness links. By using these relations we could design an extended version of the three GM links above, as follows.

If, for instance, in the above setting, concept *C1*
is related (e.g., via a ‘text’ attribute relatedness
relation) with *C3* (*link(C1,C3,’text’,’70%’)*),
and we write the new GM concepts resulting as *LL31=C3.title*
and *LL32=C3.introduction*, then we could write an automatic
transformation from *domain -* to *goal and constraints
model* that would generate:

LL21Ê LL22 , LL11, LL12,LL31,LL32

LL21 > LL22 > LL11,LL31 > LL12,LL32

AND(LL21 , AND(LL22 , LL11 , LL12,LL31,LL32)).

It is easy to see that this transformation would integrate in the introductory presentation also all related concepts.

As previously said, MOT is already combining (a primitive version of) the above. However, much more complex and interesting combinations are possible.

The total number of possible combinations is
obviously huge, as for each concept attribute type transformation
there will be different possible link type transformations, making
the total *flexibility degree* a *product of the independent
flexibility degrees of the two transformation types*.

In sections 3, 4 we have shown a small, illustrative number of different types of automatic (adaptive, adaptable) transformation possibilities that can be directly performed by the adaptive hypermedia authoring system, in order to make the task of the author easier. These transformations are based on the data design defined by the LAOS model, which allows a concept-oriented approach for data design, analysis and usage.

It is interesting to note the great number of
different design possibilities these automatic functions permit,
computed in the form of a *flexibility degree*, which shows also
the range of the adaptivity of the final system.

Moreover, although only some example transformations
from the *domain –* to the *goal and constraints model*
have been discussed and analyzed here, more types are possible (such
as *domain -* to *adaptation model*, *goal and
constraints -* to *adaptation model*, etc.). In practice it
is reasonable to expect that these transformations will be parallel.
This combination of all transformations may be leading to a situation
where one transformation may be setting some restrictions on another
one, but most of the time, these multiple transformations together
will generate an increased number of possible functionalities.

We have not extended all the examples or computed the flexibility degree from all the cases, as the space in the paper did not permit it. Instead, we have tried to give some flavor of the different possible automatic transformations, their applicability and their diversity.

In this paper we have introduced different possible
automatic authoring techniques between two specific layers of LAOS, a
five level AHS authoring model with a clear-cut separation of the
representation levels: the *domain model* (DM), the *goal and
constrain model* (GM), the *user model* (UM), the *adaptation
model* (AM) and finally the *presentation model* (PM).

We have previously shown [1] that authoring of adaptive hypermedia is a difficult task, which might be the main impediment that keeps AHS from being wider spread. Therefore we have implemented some goal-oriented automatic authoring techniques in MOT [13] that have the role to help the AHS author and ease the authoring burden. The implementation in MOT is mainly for demonstration purposes at this stage, and has therefore to be further developed.

In the current paper we have worked at the design for such a development from a more general, partially theoretical point of view. We have given a few examples for the automatic transformations, we have introduced and computed the flexibility degree offered by such transformations, and we have discussed the significance and extension possibilities of such transformations.

In this way, we are gradually advancing towards adaptive hypermedia that ‘writes itself’, being therefore adaptive not only to the final AHS user, but also to its designer (or author).

This research is linked to the European Community Socrates Minerva project "Adaptivity and adaptability in ODL based on ICT" (project reference number 101144-CP-1-2002-NL-MINERVA-MPP).

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[1] This is why the GM layer is so dense: from one DM multiple GM versions can be generated.

[2] Please note however, that automatic transformations represent in themselves a restriction on the total flexibility of the system, because they do not add new data, but are based on re-usage of inherent information. The actual flexibility allowed by the LAOS model (given by the combination of all possible elements allowed by LAOS) is therefore much greater.

[3] This is to ‘force’ the authors to give at least some minimal information about the concept they are defining, in order to be make the semantics of the concept machine-readable (minimal ontology-based meta-data tagging).

[4] With values being volatile or not according to AHAM [26].

[5]
Each *GM* concept has only 2 attributes: ‘*name*’*
*and ‘*contents*’.

[6]
Links can be added between any concept of the owned *GM* to any
concept of the whole CM space of
concepts, within *GM *or jumping a level, to the DM.

[7] here, transformations.

[8] an educational-oriented version of the LAOS goal and constraints level.

[9] which can be regarded also as a hierarchical inclusion relation.