User
Modeling in Adaptive Hypermedia Applications
Hongjing Wu,
Geert-Jan Houben[1], Paul De Bra
Department of Computing Science
Eindhoven University of Technology
PO Box 513, 5600 MB Eindhoven
the Netherlands
phone: +31 40 2472733
fax: +31 40 2463992
email: {hongjing,houben,debra}@win.tue.nl
<SMALL>Abstract: A hypermedia application offers its users a lot of freedom to navigate through a large hyperspace. The rich link structure of the hypermedia application can not only cause users to get lost in the hyperspace, but can also lead to comprehension problems because different users may be interested in different pieces of information or a different level of detail or difficulty. Adaptive hypermedia systems (or AHS for short) aim at overcoming these problems by providing adaptive navigation support and adaptive content. The adaptation is based on a user model that represents relevant aspects about the user. The adaptive navigation support and the adaptive content will reduce the orientation problems and the comprehension problems respectively.
The AHS AHA [DC98], that we developed at the Eindhoven
University of Technology, and its predecessors [CD97, DC97], did only support user knowledge at the
page level and not on the level of large abstract concepts, which limits
adaptation to a low knowledge level. Other current AHS appear to suffer from
similar problems. As part of the redesign process for AHA we have developed a
reference model for the architecture of adaptive hypermedia applications, named
AHAM (for Adaptive Hypermedia Application Model) [DHW99], which is an extension of the Dexter hypermedia
reference model [HS90, HS94]. In
AHAM knowledge is represented through hierarchies of large composite abstract
concepts as well as small atomic ones.
AHAM also divides the different aspects of an AHS into a domain
model (DM), a user model (UM) and a teaching model (TM). This division provides a clear separation of
concerns when developing an adaptive hypermedia application.
In this paper, we
specifically concentrate on the user modeling aspects of AHAM, but also describe
how they relate to the domain model and the teaching model. First, we introduce
general concepts involved in adaptive hypermedia applications and their design
and use the AHAM reference model to describe the different aspects, specially
domain model, user model and teaching model. We show how user features are
modeled and how the actual adaptation process is performed. We illustrate this
general approach for user modeling and adaptation by considering the AHS AHA.
Keywords: adaptive hypermedia, user modeling, adaptive
presentation, adaptive navigation, hypermedia reference model
1. Introduction
Hypermedia systems in general and Web-based systems in particular are
becoming increasingly popular as tools for user-driven access to information.
One of the characteristic properties of hypermedia applications is that they
offer users a lot of freedom to navigate through a large hyperspace. By
choosing between hyperlinks users can follow very different paths through the
hyperspace in order to access the contents contained in that hyperspace. Unfortunately,
this rich link structure of the hypermedia application causes some serious
usability problems:
·
A typical
hypermedia system always presents the same links on a page, regardless of the
path a user followed to reach this page.
When the system wants to providing navigational help, it does not know
which part of the link structure is most important for the user. So, if for
example it wants to provide a map of the structure, it does not know exactly
what is relevant for this particular user: the map cannot be simplified by
filtering (or graying) out links that are less relevant for the user. Not having personalized maps is a typical navigation problem of hypermedia applications.
·
Navigation in
ways that the author did not anticipate also causes comprehension problems. For every page the author makes an
assumption about the user’s foreknowledge.
However, there are too many ways to reach a page to make it possible for
an author to anticipate all possible variations in foreknowledge when a user
visits that page. Therefore, a page is always presented in the same way. This often results in users visiting pages
containing a lot of redundant information for them (“too much information”) and
pages that they cannot fully understand because they lack some expected
foreknowledge (“too little information or wrong information”).
Adaptive hypermedia systems (or AHS for short) aim at overcoming these
problems by providing adaptive navigation support and adaptive content. Adaptive hypermedia is a recent area of research
on the crossroad of hypermedia and the area of user-adaptive systems, with
applications in educational applications, on-line information systems, on-line
help systems, information retrieval systems, etc. The goal of this research is
to improve the usability of hypermedia systems by making them
personalized. The personalization or
adaptation is based on a user model that represents relevant aspects
about the user. The system gathers
information about the user by observing the use of the application, and in
particular by observing the browsing behavior of the user. An overview
of systems, methods and techniques for adaptive hypermedia can be found in [B96].
AHA [DC98] is an AHS system developed out of Web-based
courseware for an introductory course on hypermedia at Eindhoven University of
Technology. In AHA knowledge is considered at the same level of abstraction as
the contents: every concept is represented by a page and knowledge represents
whether or not a user knows the concept associated with a page. The user’s
knowledge about a given concept is a binary value: known or not
known.
AHAM (for Adaptive Hypermedia Application Model) [DHW99] is a reference model for the architecture of adaptive
hypermedia applications. It is an extension of the Dexter hypermedia reference
model [HS90, HS94], and evolved partially from a redesign
process of AHA. The central issue in AHAM is the fact that performing both “useful”
and “usable” adaptation in a given application depends on three factors:
·
There must be a domain
model that describes how the information content is structured in terms of
relationships between high level and low level concepts, and that indicates how
these abstract concepts are tied to pages (that can actually be presented).
·
By observing the
user’s behavior a fine-grained user model must be maintained to represent
the user’s preferences, knowledge (related to concepts from the domain model),
goals, navigation history and possibly other relevant aspects.
·
The adaptation of
the presentation of both content and link structure to the user’s preferences
and knowledge level is performed by the system based on some “intelligence“. In
most AHS this intelligence is default (implicit in the system), but AHAM
recognizes that an author can explicitly provide a teaching model
consisting of pedagogical rules.
The key elements in AHAM are thus the domain model (DM), user
model (UM) and teaching model (TM). This division of adaptive hypermedia applications provides a
clear separation of concerns when developing an adaptive hypermedia
application. The main shortcoming in
many current AHS is that these three factors or components are not clearly
separated:
·
The relationship
between pages and concepts is too vague (e.g. [PDS98]) or too strict
(e.g. [DC98]).
·
The pedagogical
rules can not be defined at the conceptual level but only at the page level
(e.g. [DC98], [BSW96a], [BSW96b]).
·
There is a
mismatch between the high level of detail in the user model and the low reliability
of the information on which an AHS must update that user model: for example, do
access times represent reading times?
In this paper, we concentrate on the user modeling aspects of AHAM, but also describe how these aspects relate to the domain model and the teaching model. The rest of the paper is organized as follows. In Section 2 we introduce AHAM as a reference model for the design of hypermedia applications. A more detailed description can be obtained in [DHW99]. Note that some information on authoring support for adaptive hypermedia applications (in the context of AHAM) is available in [WHD99]. In Section 3 we focus on the aspect of user modeling and its influence on the adaptation. We show how user features are represented using attribute/value pairs in the context of AHAM. We describe the role of knowledge values in the representation of the user’s knowledge on concepts from the domain model. We also illustrate how the actual adaptation process is based on the user model and how the maintenance of the user model can be facilitated. As a concrete example of the use of AHAM, Section 4 addresses the specific details of user modelling and adaptation in the AHA system. The way in which the domain model, user model and teaching model are implemented in this specific system are described. Note that this has led to a better insight in the consequences of some original design decisions, which resulted in a number of improvements to the original system. Using this approach we have thus been able to pinpoint some shortcomings in current adaptive hypermedia systems, not just in AHA. Section 5 concludes and indicates some future developments.
2. AHAM, a Dexter-based
Reference Model
The most important aspects of hypermedia applications are the information
nodes and the link structure connecting these nodes. In the Dexter reference
model [HS90, HS94] this is captured in
what Dexter calls the Storage Layer. This layer represents the
application author's view on the application domain in terms of concepts. We
call this view the domain model DM.
In adaptive hypermedia applications the central role of DM is shared
with a user model UM. UM represents the relationship between the user
and DM by keeping track of how much the user knows about each of the concepts
in the application domain.
In
order to perform adaptation (based on DM and UM) the author needs to specify
how the user's knowledge influences the presentation of the information (from
DM). In AHAM this is expressed by means of a teaching model TM
consisting of pedagogical rules: the rules in TM model the explicit
“intelligence” that the author wants the system to use in the adaptation process.
An adaptive engine (as part of the AHS) then uses these rules to manipulate
link anchors (from the Dexter model's Anchoring) and to generate what the Dexter model
calls the Presentation Specifications. Figure 1 shows the global
structure of adaptive hypermedia applications in the AHAM model, just like
Dexter focusing on the Storage Layer.
Figure 1: global structure of
adaptive hypermedia applications.
2.1 The domain model
A component is an abstract notion in an AHS. It is a pair (uid,
cinfo) where uid is a globally unique (object) identifier for the component and
cinfo represents the component’s information that consists of:
·
a set of
attribute-value pairs;
·
a sequence of
anchors (for attaching links);
·
a presentation
specification.
A concept is a component
representing an abstract information item from the application domain. An atomic concept corresponds to a
fragment of information: these are the primitive (non-adaptable) information
units in the model with attribute and anchor values that belong to the
Within-Component Layer. A composite concept has two “additional” attributes:
·
a sequence of
children (concepts);
·
a constructor
function (to denote how the children belong together).
There are a number of constraints. For example, the children of a
composite concept are all atomic concepts (then it is a page (sequence of fragments)
or in typical hypertext terminology a node) or they are all composite
concepts. Figure 2 illustrates a part of a concept hierarchy.
Figure 2: Example concept hierarchy.
An anchor is a pair (aid, avalue), where aid is a unique
identifier for the anchor within the scope of its component and avalue is an
arbitrary value that specifies some “location” within a concept component.
Anchor values of atomic concepts belong to the Within-Component Layer, while anchor
values of composite concepts are identifiers of concepts that belong to that composite.
A specifier is a tuple (uid, aid, dir, pres), where uid is the
identifier of a concept, aid is the identifier of an anchor, dir is a direction
(FROM, TO, BIDIRECT, or NONE), and pres is a presentation specification.
A concept relationship is a component, with two additional
attributes:
·
a sequence of
specifiers;
·
a concept
relationship type.
<!-- should not be necessary.
Netscape bug -->The most
common type of concept relationship is the type link, which corresponds to links in most hypermedia systems (with
typically at least one FROM element and one TO or BIDIRECT element). In AHAM we
consider other types of relationships as well, which play a role in the adaptation,
such as for example relationships of the type prerequisite. When C1 is a prerequisite for C2
it means that the user should read C1 before C2. It does
not mean that there must be a link from C1 to C2. It only
means that the system somehow takes into account that reading about C2
is not desired before some (enough) knowledge about C1 has been
acquired.
Figure 3: Example concept
relationship structure.
Together, the atomic concepts, composite concepts and concept relationships
build the domain model DM of an
application.
2.2 The user model
An AHS associates a number of user model attributes with each
concept component of DM. For every user
the AHS maintains a table-like structure, in which for each concept
the (user-specific) attribute values for that concept are stored. Note that
these attribute values do not necessarily just represent the knowledge level
of a concept. In Section 3 we look at the user model in more detail.
Note that since the user model consists of “named entities” for which we
store a number of attribute/value pairs, there is no reason to limit these
“entities” to pure concepts and their
knowledge level. These concepts can
be used to represent other user features, such as preferences, goals, background
and hyperspace experience. For the AHS (and AHAM) the actual meaning of concepts
is irrelevant.
2.3 The teaching model
The adaptation of the information content and of the link structure is
based on a set of rules. In an application these rules build the
connection between DM, UM and the presentation (specification) to be generated.
Consider the following example:
·
In DM one
expresses that a concept C1 is a prerequisite for concept C2,
and that there is a hypertext link from C1 to C2.
·
In UM one expresses
how much the user knows about concept C1 (and C2).
A
rule is then needed to determine how “relevant” or “desirable” concept C2
is, depending on the knowledge level about C1. Other rules then
express the effect of “relevance” on adaptive
navigation (AN) and adaptive presentation (AP):
·
(AN) One rule
expresses how the relevance of C2 influences the presentation of
links in graphical maps.
·
(AN) Another rule
expresses how the relevance of C2 influences the presentation of
link anchors for links leading to C2.
·
(AP) The
relevance of C2 may influence the presentation of C2
itself. For instance, an undesirable
fragment may be hidden or grayed out.
·
(AP) The
relevance of elements of a composite
may influence their selection or order.
Fragments on a page may be sorted from most relevant to least relevant.
A generic pedagogical rule is a tuple (R, CRT, PH, PR), where R
is a “triggered” rule, CRT is an optional concept relationship type, PH is the
“phase” for the execution of the rule (either pre or post) and PR
is a Boolean “propagate” field which indicates whether this rule may trigger
other rules (to avoid infinite loops). The phase pre is executed during the generation of the page, while post is executed afterwards and is used
for updating the user model. If a generic rule contains more than one concept
it must have a CRT and then applies to all concept relationships of type
CRT. Rules about just one concept do
not have a CRT.
The syntax of the permissible rules depends on the AHS. Generic pedagogical rules are often system-defined,
meaning that an author does not need to specify them. Author-defined rules always
take precedence over (conflicting) system-defined rules. (Note that some AHS do not provide the
possibility for authors to define their own generic pedagogical rules.)
A specific pedagogical rule is a tuple (R, PH, PR), where rule
R uses (concrete) concepts from DM. (PH and PR are as for generic rules.) While specific rules are typically used to
create exceptions to generic rules they can also be used to perform some ad-hoc
adaptation based on concepts for which DM does not provide a relationship. Specific pedagogical rules must always be
defined by the author. They take
precedence over generic rules that would apply to the same concepts.
The teaching model TM of an
AHS is the set of (generic and specific) pedagogical rules.
An AHS does not only have a DM, UM and TM, but also an adaptive engine AE. <!-- should not be necessary.
Netscape bug -->The adaptive engine provides the
implementation dependent aspects while DM, UM and TM describe the information
and adaptation at the conceptual, implementation independent level. It is a
software environment that performs the following functions:
·
It offers generic
page selectors and constructors (used to determine which page to display when
the user follows a link to a composite concept, or how to build a presentation
for a page).
·
It optionally
offers a (very simple programming) language for describing new page selectors
and -constructors.
·
It performs
adaptation by executing the page selectors and constructors (e.g. selecting a
page, selecting fragments, manipulating link anchors).
·
It updates the
user model (instance) each time the user visits a page (by triggering the necessary
pedagogical rules for the post phase).
An adaptive hypermedia application is a 4-tuple (DM, UM, TM,
AE), where DM is a domain model, UM is a user model, TM is a teaching model,
and AE is an adaptive engine.
3. User Modeling and
Adaptation in AHAM
3.1 Representation of user features using (attribute/value)
pairs
By definition adaptive hypermedia applications
reflect some features of the user in the user model. This model expresses various visible aspects of the system that
depend on the user and that are visible to that user. Generally, there are five
user features that are used by existing AHS [B96]:
·
knowledge,
·
user goals,
·
background,
·
hyperspace
experience,
·
preferences.
Almost every adaptive presentation technique relies on the user’s knowledge
as a source of adaptation. The system
has to recognize the changes in the user’s knowledge state and update its user
model accordingly. The user’s knowledge
is often represented by an overlay model (overlay over the concepts from DM) or
a (simpler) stereotype user model. As many adaptation techniques require a
rather fine-grained approach, stereotype models are often too simple to provide
adequate personalization and adaptation.
Overlay models on the other hand are generally hard to initialize.
Acceptable results are often achieved by combining the two kinds of modeling:
starting with stereotype modeling and then moving towards a more fine-grained
overlay model. Using the AHAM definition for user model, it is fairly
straightforward how a user’s knowledge state can be represented by associating
a knowledge value attribute to each
concept.
Apart from the concept’s identifier (which may be just a name) a typical
AHS will store not only a knowledge value for each concept, but also a
read value which indicates whether (and how much) information about
the concept has been read by the user, and possibly some other attribute values
as well. While the model uses a table
representation, implementations of AHS may use different data structures. For instance, a logfile can be used for the read
attribute. The table below illustrates
the (conceptual) structure of a user model for a course on hypermedia.
|
concept
name (uid) |
Knowledge
value |
read |
... |
|
Xanadu |
well learned |
true |
... |
|
KMS |
Learned |
true |
... |
|
WWW-page1 |
well learned |
true |
... |
|
WWW-page2 |
not known |
false |
... |
|
WWW |
Learned |
false |
... |
|
... |
... |
... |
... |
Table
1: Example user model
(instance).
Note that AHAM’s user model UM has enough expressive power to model all
user features that current AHS take into account: the knowledge value of a
concept can be a Boolean, discrete or continuous value depending on the choice
of the author.
The second kind of user feature is the user’s goal. The user’s goal or task is a feature that is
related with the context of the user's working activities rather than with the
user as an individual. It is the most
volatile of all user features. One possible representation of the user’s current
goal uses a set of pairs (Goal,Value), where Value is the probability that Goal
is the current goal of the user. This representation perfectly matches the way
in which AHAM models the user’s state.
Two features of the user that are similar to
the user’s knowledge of the subject but that functionally differ from it, are
the user’s background and the user’s experience in the given hyperspace. By background we mean all the information
related to the user’s previous experience outside
the subject of the hypermedia system.
By user’s experience in the given hyperspace we mean how familiar is the
user with the structure of the hyperspace and how easy can the user navigate in
it. Again, these features can be
modeled in AHAM using concepts’ attribute/value pairs.
For different possible reasons the user can
prefer some nodes and links over others or some parts of a page over
others. This is used most heavily in
information retrieval hypermedia applications.
In fact in most adaptive information retrieval hypermedia applications preferences
are the only information that is stored about the user. These user features
differ from other ones, since in most cases they cannot be deduced by the
system: the user has to inform the system directly or indirectly about the
preferences. AHAM’s concepts’ attribute/value
pairs can again be used to model the user’s preferences.
From the above descriptions we can conclude
that although a user model needs to represent (five) very different aspects of
a user, all of these kinds of aspects can be implemented as sets of concepts
with associated attribute/value pairs.
3.2 Adaptation based on the
user model
The adaptive engine
realizes adaptive presentation and adaptive navigation according to the pedagogical
rules that are either system-defined or written by the author and that depend
on the user model. Below we give a number of examples to show how pedagogical
rules are used to do adaptation. The
syntax used for the rules is arbitrary and only exemplary: AHAM does not prescribe
any specific syntax. Normally every AHS
will provide its own syntax for defining pedagogical rules.
Example 1 For atomic concepts (fragments) let us assume
that the presentation specification is a two-valued (almost Boolean) field,
which is either “show” or “hide”. When
a page is being accessed, the following rule sets the visibility for fragments
that belong to the page, depending on their “relevance” attribute-value.
< access(C) and F IN C.children and
F.relevance = true => F.pres := show,
pre, false >
Here we simplified things, by assuming that we can treat C.children as
if it were a set, whereas it really is a sequence. It is common to execute rules for generating presentation
specifications in the pre phase, as
done in this example.
Example 2 The following rules set the presentation
specification for a specifier that denotes a link (source) anchor depending on
whether the destination of the link is considered relevant and whether the
destination has been read before. For
simplicity we consider a link with just one source and one destination.
< CR.type = link and CR.cinfo.dir[1] = FROM and CR.cinfo.dir[2] = TO and CR.ss[2].uid.relevant = true and CR.ss[2].uid.read = false => CR.ss[1].pres = GOOD, pre, false >
< CR.type = link and CR.cinfo.dir[1] = FROM and CR.cinfo.dir[2] = TO and CR.ss[2].uid.relevant = true and CR.ss[2].uid.read = true => CR.ss[1].pres = NEUTRAL, pre, false >
< CR.type = link and CR.cinfo.dir[1] = FROM and CR.cinfo.dir[2] = TO and CR.ss[2].uid.relevant = false => CR.ss[1].pres = BAD, pre, false>
These rules say that
links to previously unread but “relevant” pages are “GOOD”. Links to previously
read and “relevant” pages are “NEUTRAL” and links to pages that are not “relevant”
are “BAD”. In the AHA system [DC98] this results in the
link anchors being colored blue, purple or black respectively (unless the user
changes these preferences), while in ELM-ART [BSW96a] and
Interbook [BSW96b] the links would be annotated with a
green, yellow or red ball.
3.3 Maintenance of user model
To record the reading history of the user and the evolution of the
user’s knowledge, the system updates the user model based on the observation of
the user’s browsing process. The rules
that the author has defined in TM describe how to keep track of the evolution
of the user’s knowledge.
Example 3 The
following rule expresses that when a page is accessed the “read” user-model
attribute for the corresponding concept is set to true in the post phase:
< access(C) => C.read := true, post,
true >
This rule also says
(through PR being true) that it will trigger other rules that have read on their left-hand side.
Example 4 The
following rule expresses that when a page is “relevant” and it is accessed, the
knowledge value of the corresponding concept becomes “well-learnt” in the pre
phrase. This is somewhat like the
behavior of Interbook [BSW96b].
< access(C) and C.relevant = true =>
C.knowledge := well-learnt, pre, true>
In Interbook, as well as in AHA [DC98], knowledge is
actually updated in the pre
phase. At the end of Section 4 we shall
describe why this option is chosen, and which problems it creates. In general one wishes to have the option to
base some adaptation on the knowledge state before
accessing a page and some adaptation on the knowledge state after reading the page.
4. User Modeling and
Adaptation in the AHA system
AHA [DC98] is a simple adaptive
hypermedia system. We first describe the properties of the version that is
currently being used for two on-line courses and one on-line information kiosk.
At the end of this section we indicate planned changes for the next version of
AHA.
· In AHA the domain model consists of three types of concepts: abstract concepts, fragments and pages. Concepts are loosely associated with (HTML) pages, not with fragments.
· The user model consists of:
q Color preferences for link anchors which the user can customize. (These preferences result in “non-relevant” link anchors to be hidden if their color is set to black, or visibly “annotated” if their color is set to a non-black color, different from that of “relevant” link anchors.)
q For each abstract concept, a Boolean knowledge attribute. (True means the concept is known, false means it is not known.)
q For each page, a Boolean read attribute. (True means the page was read, false means it was not read.) AHA actually logs access and reading times, but they cannot be used in a more sophisticated way in the current version.
· AHA comes with a teaching model containing system-defined generic pedagogical rules. It offers a simple language for creating author-defined specific pedagogical rules (but no author-defined generic rules).
The domain model can only
contain concept relationships of the types that are shown below. The influence
of these relationships on the adaptation and the user model updates is defined
by system-defined generic pedagogical rules. In AHA all rules are executed in
the pre phase and are triggered
directly by a page access, thus eliminating the need for propagation (causing
problems that we describe later).
·
When a page is
accessed, its read attribute in the
user model is updated:
< access(P) => P.read :=
true, pre, false >
·
The relationship
type generates links a page to an
abstract concept. A generates relationship between P and C
means that reading page P generates knowledge about C:
< access(P) => C.knowledge := true, pre, false >
This “generation” of knowledge in AHA is controlled by a structured comment in an HTML page:
<!-- generates readme
-->
This example generates comment denotes that the concept readme becomes known when the page is accessed.
·
The relationship
type requires links a page to a
composite concept which is defined by a Boolean expression of concepts. Although in principle this composite concept
is unnamed, we shall use a “predicate” or “pseudo attribute of the page” to
refer to it: P.requires is used as a
Boolean attribute of which the value is always that of the corresponding
Boolean expression. A requires relationship is implemented
using a structured comment at the top of an HTML page, e.g.:
<!-- requires ( readme
and intro ) -->
This example expresses that this page is only
considered relevant when the concepts readme
and intro are both known. In AHA, links to a page for which requires is false are
considered BAD, and reading such a page does not generate knowledge. Below we give the rules that determine how
the link anchors will be presented.
They are very similar to the rules in Example 2 (Subsection 3.2):
< CR.type = link and CR.cinfo.dir[1] = FROM and CR.cinfo.dir[2] = TO and CR.ss[2].uid.requires = true and CR.ss[2].uid.read = false => CR.ss[1].pres = GOOD, pre, false >
< CR.type = link and CR.cinfo.dir[1] = FROM and CR.cinfo.dir[2] = TO and CR.ss[2].uid.requires = true and CR.ss[2].uid.read = true => CR.ss[1].pres = NEUTRAL, pre, false >
< CR.type = link and CR.cinfo.dir[1] = FROM and CR.cinfo.dir[2] = TO and CR.ss[2].uid.requires = false => CR.ss[1].pres = BAD, pre, false>
·
The relationship
type link only applies to pairs of
pages in AHA.
In AHA structured HTML comments are used for specifying author-defined
specific pedagogical rules about the conditional inclusion of fragments in HTML
pages. With a fragment F we can associate a “pseudo attribute” requires to indicate the condition, just
like for whole pages. The syntax is
illustrated by the following example:
<!-- if ( readme and not
intro ) -->
... here comes the content
of the fragment ...
<!-- else -->
... here is an alternative
fragment ...
<!-- endif -->
AHA only includes fragments when their requires “attribute” is true.
We conclude this section with an illustration of one specific
shortcoming which we have found in both AHA [DC98] and
Interbook [BSW96b]: the “new” knowledge values are
calculated in the pre phase (and in
fact these systems do not support a two-phase approach at all). When a user requests a page, the knowledge
generated by reading this page is already taken into account during the
generation of the page. This has
desirable as well as undesirable side-effects:
·
When links to
other pages become relevant after
reading the current page it makes sense to already annotate the link anchors as
relevant when presenting the page. Once
a page is generated its presentation remains static while the user is reading
it (and rightfully so). The new
knowledge thus needs to be taken into account before the page is actually read.
·
Pages contain
information that becomes relevant or non-relevant depending on the user’s knowledge. In some cases the relevance of a fragment
may depend on the user having read the page that contains this fragment. This means that a fragment may be relevant
the first time a page is visited and non-relevant thereafter, or just the other
way round. By already taking into
account the knowledge before the page is generated for the first time a
different “first time version” becomes impossible to create.
5. Conclusion
We have developed a reference model for adaptive hypermedia
applications, named AHAM. The
description of adaptive applications in terms of AHAM has provided us with valuable
redesign issues for the AHS AHA. The
two most important ones are:
·
The division of
an adaptive hypermedia application into a domain
model, user model, and teaching model provides a clear
separation of concerns and will lead to a better separation of orthogonal parts
of the AHS functionality in the implementation of the next version of AHA. We believe that any system which supports
this separation of concerns will not only result in a cleaner implementation,
but also in a more usable authoring environment.
·
By representing
AHA in the AHAM model we have identified another shortcoming: the lack of a
two-phase application of rules. We
found that this shortcoming is present in other AHS as well.
In this paper we
have focused on the user modeling aspects of AHAM, and have described how they
relate to the domain model and the teaching model. We have shown how user features
are modeled and how the actual adaptation process is performed. We have
illustrated this general approach for user modeling and adaptation by
explicitly considering the AHS AHA. The description of these user model aspects
at an abstract level sets AHAM apart from other descriptions of AHS which are
too closely related to the actual implementation of these AHS.
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