Brusilovsky defines hypermedia as "a set of nodes or hyperdocuments (for the purpose of brevity we will call them "documents") connected by links" to related documents [176]. The user of a hypermedia system accesses documents in a nonlinear fashion. While document linking provides many advantages (e.g. navigational freedom), the complex task of "finding
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http://ltsc.ieee.org, https://ieee-sa.centraldesktop.com/ltsc/ [Accessed: 14-Jun-2015] 10 http://www.adlnet.org [Accessed: 14-Dec-2015]
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one’s way around" and selecting relevant material can pose a considerable challenge for the user, creating the feeling of being "lost in the hyperspace". The user must be facilitated in gaining efficient access to relevant information. Furthermore, each user is unique, having distinct characteristics, goals, preferences, background, and interests making personalisation necessary. Two kinds of hypermedia systems emerged to meet the personalisation requirement:
(a) Adaptable Hypermedia Systems tailor the presentation according to the user's presentation preferences, background, etc. where the user directly provides information, typically via a dialog or questionnaire. Here, the adaptation changes are performed once – at the time of the user's initial interaction.
(b) Adaptive Hypermedia Systems (AHSs) can be defined as systems which can alter their various visible aspects (i.e. their structure, functionality or interface) based on their user model in order to accommodate the differing needs of individuals or groups of users and the changing needs of users over time (this definition combines definitions from [176] and [180]). Adaptive systems provide personalised guidance and adapt the presentation (information, media types, etc.) based on a model of the interaction context (task, user, device, time, place, etc.). A user model profile is gradually developed using implicit inferences based on interaction with the user [181], i.e. it is based on the user's behaviour (browsing actions, page accesses, etc.). Users are unaware of this process in many cases, and apart from initial registration a user is not required to provide any further information.
3.3.1 Architecture and Components
This section provides an overview of a typical AHS structure. All AHS employ a User Model
(UM) built from user knowledge, interests, preferences, goals and objectives, action history,
type, style, skills and capabilities, individual traits, experience and other relevant properties that might be useful for adaptation.
A Domain Model (DM) is a knowledge space that defines the structure and organisation (links, relationships) of the conceptual representation of the application domain (sometimes called a content model). DM is typically a collection of elementary knowledge fragments of various sizes. An Adaptation Engine (AE) applies the UM to adapt the presentation, information content and navigation structure throughout the interaction with the user. An example of an AHS model is the LAOS [182] model given in Figure 3-1 (page 51), where the Adaptation Model (AM) contains the adaptation specification for the course, the Presentation Model (PM) contains information relating to the presentation of the course and Goal and Constraints Model (GM) contains pedagogical and structural information about the content.
In order to answer individual user requests a typical AHS, first retrieves the user model and subsequently retrieves the domain model to perform adaptation of the requested resources. A developer-oriented insight into the internal structure of AHS in education can be found in [183].
Figure 3-1 Five Layers of the LAOS Model [182]
An explicit User Model (UM) is a distinctive component of every adaptive system. The UM captures relevant user features, that are collected either implicitly (e.g. UM is updated based on user-AH system interaction) or explicitly (e.g. system requests direct input from the user). Many AEH systems use learners’ knowledge to perform adaptation. AEH UMs are frequently called
student models and represent users’ existing knowledge within a specific domain. The first AH
systems implemented their user models as group competency-based models (e.g. stereotypes, where a user can move to another group when conditions pertaining to the new group are met). Another approach is to employ a so called weighted overlay model to store information about the learner’s knowledge levels about each domain item (e.g. a binary value: known/not known, qualitative value: good-average-poor, numeric value: 0-100, probability that the user knows the KE: percentage, etc.). Hence, the learner’s knowledge is represented as an overlay of domain knowledge. Today’s models are complex domain/skill matrices [184]. Furthermore, while such models were formerly components of a monolithic learning environment, they are now delivered as a service in line with the current trends towards distributed learning frameworks. For example, such a UM can harvest user data from multiple sources (e.g. learning systems) and may be owned and managed independently. An example is the CUMULATE server [185], [186] which has been successfully incorporated within a tutoring system [187]. Furthermore, there are personalised delivery environments such as WHURLE (Web-based Hierarchical Universal Reactive Learning Environment) [188] that support different user models. WHURLE [189] adapts to visual/textual preferences determined based on an online Inventory of Learning Styles
The domain (knowledge space) model structures and describes the content and serves as the backbone of the AH system. The DM consists of Knowledge Elements (KE) that denote elementary fragments of domain knowledge (e.g. concepts, knowledge items, topics, knowledge elements, learning objectives, learning outcomes). DMs of current systems are of varying complexity ranging from simple set/vector models of unrelated KE (no internal structure) to complex ontology-based networks of interrelated KE. Most frequently used links between KEs are prerequisite links, inhibitor links and semantic links (e.g. IS-A, PART-OF) which lend themselves to adaptation and user modelling techniques.
Adaptation Model (AM) is set of generic and specific adaptation rules for the content adaptation,
navigation adaptation and the user model updates. These rules, for example, can be Condition- Action rules where the rule’s action is performed when its condition becomes true or IF-THEN rules as implemented in LAOS [182].
The Adaptive Engine (AE) tailors content based on the contents of both the DM and UM. The three most popular adaptation technologies include adaptive content selection, adaptive navigation support, and adaptive presentation [191]. AE acts as an interpreter for adaptation rules (in AM) and it is typically implementation-depended, while DM, UM and AM describe the adaptation and content at implementation-independent level. In general AHS interactions AE deploys a number of interfaces [10] for monitoring and controlling system usage as follows: User Event Tracker (e.g. featured in GenericLogDB layer in AHA! [192]) tracks and logs
user interactions (e.g. mouse/keyboard events) with the system, which then can be used for UM updates.
Behaviour Monitor uses data provided by the Event Tracker and applies AM rules to modify the UM.
Registration gathers personal information (e.g. questionnaire/form data) used for the initialisation of the UM. For example ProfileDB layer creates new user profiles in AHA! [192].
Information Delivery Interface produces Web pages (collections of DM units) tailored to the UM based on the feedback from the AM.
Furthermore, there are authoring modules for content management, e.g. ConceptDB layer in AHA! [192] creates/destroys concepts, allows concept/attribute searches and creates the adaptation rules associated with the attribute.
The solutions presented in this thesis could be deployed to extend the AH adaptation process, and hence a brief overview of each adaptation approach is given in Section 3.3.2.
Some existing AHS use a Presentation Model (PM) to provide adaptive presentation support that tailors information presentation to best suit the user’s profile. This approach is particularly useful in educational AHS, where the content presented is adapted to the learner's current knowledge, knowledge growth, progression of competency, goals and other characteristics.
Although techniques for adaptive multimedia presentation exist, the techniques for text adaptation are most studied and used in fully-fledged systems. These techniques can be applied to fragments of information relative to a concept.
3.3.2 Adaptation Approaches
This section provides an overview of different adaptation approaches implemented in AHS. General adaptation issues are presented in Section 3.5. Comprehensive surveys can be found in [176], [193], [194].
Adaptive content selection is performed by restricting current access to learning content. The
content can be changed (when content fragments are inserted, removed, summarised via statistical or linguistic analysis) or (de)emphasised (dimming, sorting, scaling text/images, changing text fonts, scaling segments to suggest relevant/important content fragments).
Adaptive navigation support (link-level adaptation) limits the browsing space to the most
relevant documents by suggesting links or providing adaptive descriptions for visible links. The approaches to adaptive navigation include:
Guidance. Local guidance suggests the next step - the link to the most appropriate node leaving
no other option to the user. Global guidance, aims at finding the shortest navigational path to the most desired information.
Orientation Support provides the user with their “location” in the hyperspace. Local orientation
informs about the nodes directly linked from the current node while Global Orientation informs about the whole hyperspace.
Personalised Views allow the user to organise and manage hyperspace by maintaining a set of
the most relevant links for a particular goal. A number of different techniques for adapting links were identified in [195].
Other approaches to adaptation include (a) structural adaptation that gives the user a spatial representation of the hyperspace environment, which in the educational setting may provide the learner with a sense of their position within the environment and an indication of the size of the environment. Structural aids include overview maps, local maps, filters and indexes; and (b)
historical adaptation where history trails, footprints (logged by the system), landmarks (marked
by the user) and progression cues are used to represent the user's path through the system, which in turn gives the learner a sense of their current progress.
3.3.3 Advantages and Development Trends
AEH systems offer numerous advantages. As Web based learning systems they provide general eLearning advantages, such as interactivity (simulations, experiments, on-line collaboration with other learners and instructors, video conferencing), media-rich content (searchable media rich learning material in different forms and presentation styles), just-in-time delivery, etc. Furthermore, they offer a personalised user-centric experience that boosts learning outcomes.
Despite the advantages, early AEH systems suffered from a number of shortfalls. For example, although educational, early systems ignored well established pedagogical and instructional design principles. An overview of eLearning platforms provided in [196] indicates three evolutionary generations of learning management systems, starting from monolithic, progressing through modular to service-oriented systems. The same applies to AEH, as initial architectures did not separate in many cases the teaching/pedagogical model, content/domain model and adaptation engine [196]. This approach inhibits reusability of teaching material, and forces instructors to opt for a pedagogical approach at design time. Later AEH systems were centralised by nature limiting the extensibility of the system. When distributed AEH emerged, they continued to deal with closed corpus content domain (content created at design time for the system in question) thus limiting system reusability. Third generation, service oriented systems, such as APeLS [197] are highly modularised, supporting addition of new modules.
Brusilovsky, one of the founders of AH “movement” has voiced concerns regarding AH usage [198], claiming that “almost 10 years after the appearance of the first adaptive Web-based educational systems, just a handful are used for teaching real courses, typically in a class led by one of the authors of the adaptive system.” [183, p. 6] and “their inability to meet the needs of practical Web-enhanced education” [183, p. 7]. Almost exclusive focus on adaptive content delivery prevented personalised technologies (e.g. AH systems) from becoming high impact technologies [199]. Slow take-up by learners is due to lack of usability and to the low technical quality of the content delivery (long delays, frequent stoppages, etc.).
Two current trends in AH area can be identified. Firstly, attempting to mimic modern LMS