Individualising Interaction in
Web-based Instructional Systems
in Higher Education

By John Eklund and Peter Brusilovsky
The University of Technology, Sydney

ABSTRACT

In this paper we make the case for the use of adaptive educational systems for the flexible delivery of course materials in higher education. Recognising the importance of individual learner knowledge, adaptive systems are capable of customising courseware, and take part of the role of the human teacher in individualising instruction.

We base our work on a practical model of the role of knowledge in teaching and learning, and demonstrate adaptivity in computer-based educational environments using InterBook, a tool for delivering adaptive textbooks on the World Wide Web.

InterBook uses adaptive annotation technology to augment hyperlinks with some comment which informs users about the current state of the nodes behind the annotated links, and to do this on an individual basis by adapting links through a user-model, an individual record of a student's progress. Adaptive annotation and adaptive navigation support in general are becoming more popular in the context of the use of Web based instructional tools in Higher education.

 

Introduction

As this paper takes its place in the Apple University Consortium's Academic Conference proceedings, it is most likely the only one which makes the case for the use of computer-based adaptivity as an important feature of instructional systems used in higher education.

Since the conference theme of flexible learning relates to the ways in which we can design and implement technology-based learning experiences for a new generation of learners and in a restructured higher education system, the advocation of knowledge-based systems for flexible learning may seem rather inappropriate. Knowledge-based systems have always been seen as directive and prescriptive, as was a common criticism of Intelligent Tutoring Systems from the early 1980s.

However, we need to be reminded that flexible learning design does not mean implementing educational systems which lack content, direction or feedback; just as successful student-centred learning does not in general come about through a hands-off approach to instruction. In fact, by placing the student at the centre of a model of teaching and learning, we are recognising the fundamental importance of individual student characteristics in instructional processes.

We believe that learning systems which recognise student knowledge as a critical feature of instructional design exemplify this approach.

Most Computer Mediated Communication (CMC) tools rely heavily on a human teacher to individualise the lesson through personal email or phone contact between instructor and student. The content is almost always static.

However, many Web-based educational applications are expected to be used by very different groups of users with minimal assistance from a human teacher, and hence there is a need for systems which can themselves adapt to learners with very different backgrounds, prior knowledge and learning goals.

An electronic textbook is one of the most promising varieties of Web-based educational systems, and is appropriate for the delivery of structured textbook-style content. In this paper we describe an approach for developing adaptive electronic textbooks through InterBook, an authoring tool based on this approach which simplifies the development of adaptive electronic textbooks on the Web.

 

Teaching and knowledge

Before InterBook in particular or adaptive systems in general are introduced, it is worthwhile considering issues of teaching and knowledge, and how they are represented in instructional systems. One of the most fundamental and persistent questions in teaching and learning in both real and virtual environments is that of learner control.

Jacobs (1992) in a review of hypermedia and discovery-based learning demonstrates that there is a long history of this issue before the advent of hypertext or computers. How closely should a learner be monitored and directed? What freedoms should they be given to follow their interests and learn of their own accord?

How should their progress be modelled and what kind of intervention strategies should be used to guide them in their learning? Instructional Technologists call it the instructionist verses constructivist debate, in educational computing it is known as the question of the locus of control, while teacher educators see it as a matter of providing trainee teachers with a working knowledge of varying instructional strategies.

Teacher education programs attempt to provide trainee teachers with three types of knowledge. The first of these is domain knowledge, often taught through curriculum studies courses, which provide the student teachers with expertise in their subject area. Mathematics teachers clearly need to have an understanding of algebra or calculus in order to teach it.

Teachers in Primary education programs may wonder why they study calculus at all, if it is never to be taught explicitly in Primary school. The rationale is that it provides the student teachers with a deeper and broader understanding of the subject of Mathematics, which enhances their understanding of how the elements of the subject interrelate, and thus their ability to teach it.

Nonetheless it is generally agreed that domain knowledge is an important attribute for a good teacher.

The second type of knowledge that teachers require is in the area of methodology, an ability to implement various teaching strategies appropriate to the content and the students. Methodology courses explicitly deal with the relationship between domain knowledge and instructional strategies.

Student teachers study the sequencing, organisation and presentation of a lesson, they learn about classroom management, how to implement a role play or organise a debate, and how to identify students with individual difficulties and provide remediation.

They recognise the value of different learning activities, and become practiced at choosing and implementing them.

The third type of knowledge that is especially critical in distinguishing an effective teacher from a novice is a knowledge of the students, both as a group, and as individuals. Experienced teachers know more about what their students know, they are consequently better aware of how they might build on student knowledge. In many ways this student knowledge is like the teacher understanding students' "learning zones" (Salomon, Globerson & Guterman, 1990).

In this sense, experienced teachers have a well developed 'student model', a stereotypical one for an entire class, and later as they become accustomed to individuals in that class, they develop individual student models. They commonly keep these models as either student profiles and class registers, but more importantly as mental models. "John loves painting, he is creative and responds well to praise. He has difficulty with subtraction." This is an example of a mental model that a teacher might have of a student, often expressed on a term report.

Teachers are practitioners of folk-cognitive science, they implicitly know the character of their class and the individuals in it. Good teachers are able to blend these three knowledge types into effective teaching. From feedback provided to them by their students they continually update their group and individual student models, then select from their domain knowledge the appropriate content to be delivered in one or more appropriate instructional strategies.

 

The three types of knowledge (Figure 1) are not mutually exclusive, and combined are commonly termed teaching knowledge and are present in teachers and in computer based learning environments to varying degrees.

Goodyear (1991) provided a comprehensive overview of some of the difficulties and promises in the use of human teachers as models for the design of computer based learning systems. Humans are complex organisms and learning and teaching is a complex knowledge-based endeavour, and with current computer technology we can only mimic and interpret certain simplistic components of it. Research in the field of human-computer interaction is in its infancy, still struggling to extract and define even the most basic elements from human pedagogy.

Goodyear (1991) points to a lack of formal accounts of education and a corresponding lack of expert models as a general flaw with the literature.

 

Knowledge-based systems

The above consideration of teaching knowledge may be extrapolated to virtual learning environments: multimedia learning environments, Web-based instructional systems and common courseware.

Certainly the potential of courseware to interact with the student has improved over the years, largely because designers of software can write for more powerful computers. Entry-level computers can provide excellent graphics, mass hard-disk storage, several megabytes of memory, and sound capabilities. Developments in peripheral devices such as CD ROM provide scope for developers of educational software to encode more complex routines using this more interactive media.

Multimedia has enhanced the cognitive flexibility of computer based learning through its ability to restructure knowledge presentations to meet changing situations. These new features of microcomputers have become available in a very short time, and affordable hardware continues to develop at a startling rate. There is therefore good reason to think that the capabilities of tutorial software written for the next generation of computers will far exceed today's standards, and provide a platform for a far more natural discourse between the student user and the computer.

However, the success of the media as an instructional aid relies as much on its ability to parallel the cognitive processes of the learner through tracing the student's acquisition of knowledge as it does on its ability to present the lessons in a stimulating and interactive way (Spoer, 1994). The recent improvements in computer hardware, interface design and multimedia presentation have not addressed the problem of providing the computer with an understanding of the user, which we maintain is a critical attribute of an effective teacher.

The complex field of user modelling is another new research area that has arisen from attempts to address exactly this problem.

Since microcomputers were introduced into education in the late 1970's one of their primary uses has been as a teaching tool through the use of educational software. Some of the tutorial software, or courseware, over the past decade has been little more than a screen by screen presentation of content with questions which, when correctly answered, progress the student to the next screen of information.

A common complaint, among many others related to implementing tutorial software in educational settings, has been an incapacity to adapt to the particular learning needs of the student user. An experienced teacher would know from the individual responses of the student which aspect of the work requires repetition, how explanations can be restated and what questions to put to the student before additional material can be given.

The teacher can be considered to have developed a cognitive model of the student, that is, an understanding of the learner's current knowledge in the subject domain, and has at his/her disposal a variety of teaching strategies to be deployed depending on student feedback and the nature of the information to be presented. The tutor works with a student model, a teaching model containing strategies for effective teaching, and an understanding of the lesson content including the relationships between the component parts in the knowledge domain, and is able to make adjustments to the flow of the tutorial by responding to the student.

In the current flurry of interest in the application of cognitive science to instruction, it is sometimes forgotten that the original reason to introduce the computer into education was its potential to provide individualised instruction.

Each student comes to a course with different prior knowledge and he or she understands or misunderstands the instruction in his or her unique way. Consequently, each student has different cognitive needs, or so the argument goes, and instruction in which a single teacher says the same thing to thirty students must be necessarily sub optimal.

Instruction needs to be adapted to the individual learner. This cannot be done in the classroom, but because the computer is a one-on-one interactive device, it has the potential to deliver adaptive instruction to a large number of students on an individual basis. This argument was, and still is, the main reason to believe that the computer medium provides some pedagogical power over and above that provided by the paper-and-pencil medium. (Ohlsson, 1993, p. 204)

While it has been generally argued that one of the strongest reasons for using computers in education is their ability to provide individualised instruction in tutor mode (Ohlsson, 1993; Ross, 1984), one of the problems in making computer software more individualised and the student-computer discourse more natural is that as more branching capabilities are required to meet the enormous number of possibilities in student responses, and soon becomes impossible to implement.

This is one of the reasons for the development of intelligent tutoring systems and subsequently adaptive educational systems.

 

Adaptive Educational Systems

While adaptive hypermedia (Brusilovsky, 1996) is a new direction of research within the area of adaptive and user-model based interfaces, the goal of adaptivity has featured in the design of intelligent systems for a considerably longer period. It is worthwhile at this point to ask "What is adaptivity?"

Adaptive hypermedia systems are capable of altering the content or appearance of the hypermedia on the basis of a dynamic understanding of the individual user, to adapt the content or presentation to certain characteristics of the user. In a discussion which took place during 1997 on the Adaptive Hypertext and Hypermedia Discussion forum [HREF4], a common definition of what an adaptive system actually is was generally agreed.

"By adaptive hypermedia systems we mean all hypertext and hypermedia systems which reflect some features of the user in the user model and apply this model to adapt various visible and functional aspects of the system to the user" (after Brusilovsky, 1996).

By "functional aspects" it is meant those components of a system which may not visibly change in an adaptive system. Curriculum sequencing is a good example: The "next" button will not change in appearance but it will take different users to different pages (Schwarz, 1997). Jungmann (1997) prefers to emphasise the role of the user model in an adaptive system in his suggested definition that "...by adaptive hypermedia systems we mean all hypertext and hypermedia systems which reflect some features of the user in the way the information is presented to the user" (Jungmann, 1997).

The noted psychologist Piaget described intelligence as the ability of an organism to assimilate and to adapt its environment, and it is generally accepted that there are organisms which are more or less intelligent than others.

Certainly it is important to regard adaptivity as a continuum: Adaptivity is not a binary state, there are systems which are strongly adaptive and those which exhibit weak adaptivity. For example, a word processing program can be weakly adaptive, as users can set their own preferences, modifying the interface to suit their requirements.

In this case the user model is simply the preferences file, the system gathers information only by the user's directly altering the settings, and this in turn makes identifiable changes to the interface for the individual. At the other end of the scale an Intelligent Tutoring System is typically strongly adaptive, working in a well-structured information space; gathering data about the user's movements and using this information to dynamically modify the presentation and functionality of the system is clearly defined ways.

It is also important to remember that adaptivity is not a technology, but a goal. Adaptivity is a common functional goal of intelligent systems, which are described as intelligent because they employ specific techniques from artificial intelligence, namely the use of a production system which generates new knowledge on the basis of a set of heuristics.

Naturally for adaptive hypermedia systems to model user and expert knowledge they require a means of collecting data about the student's knowledge state, usually by testing, a means of assessing that data and selecting appropriate tutoring strategies, and a means of representing the domain knowledge. It is widely accepted that adaptivity requires the system to maintain a user-model, the terms "user-model based" and "adaptive" are interchangeable in this regard.

In summary, for a system to be called an adaptive hypermedia system it must have the following characteristics:

• Be based on hypertext (or hypermedia)
  
• Have an explicit user-model which records some features of the individual user
  
• Have a domain model, which is a set of relationships between knowledge elements in the information space
  
• Be capable of modifying some visible or functional part of the system on the basis of information contained in the user-model

Adaptivity may be at the content level or at the link level. Content-level adaptivity is the dynamic generation of content based on a user model. Link level adaptivity on the other hand assumes a static content, and the appearance or prominence of the links connecting elements of this hyperspace is altered. This is what is terms adaptive navigation support (Bell, 1997).

Adaptive navigation support includes the use of user model based maps, link sorting, hiding and annotation. Adaptive annotation augments the links with a comment which provides the user with information about the current state of the nodes behind those links (Brusilovsky, Pesin & Zyryanov, 1993; de La Passardiere & Dufresne, 1992; Hohl, Böcker & Gunzenhäuser, 1996; Schwarz, Brusilovsky & Weber, 1996).

This method has been shown to be especially efficient in educational hypermedia (Brusilovsky, and Pesin, 1995; Zeiliger, Reggers & Peeters, 1996; Eklund & Brusilovsky, 1998) and this is the particular technology used in InterBook. Link annotations can be provided in textual form or in the form of visual cues, for example, using different icons, or colours, font sizes, or font types and so forth.

 

InterBook - An adaptive educational system using link annotation

Brusilovsky (1995) originally suggested a concept-based indexing of content which is implemented in InterBook. InterBook was introduced to the literature of adaptive hypermedia at the WebNet96 Conference [HREF2] in a paper by Brusilovsky, Schwarz, and Weber (1996), and even at that time was a sophisticated product.

It had been centred on the ELM-ART-based systems which were developed at The University of Trier in Germany, and it embodies many ELM-ART features, including link annotation using a traffic-light metaphor, curriculum sequencing, a pre-requisite-based domain model, and a somewhat similar interface. Its main departure from the ELM-ART system was domain independence: a total package for authoring and delivering adaptive electronic textbooks on Web with any suitable content, using the Common Lisp Hypermedia Server (CL-HTTP) (Mallery, 1994).

It also used slightly different annotations than ELM-ART, and offered new features such as "teach this page", an idea attributed to Schwarz. As a Web-based computer mediated courseware delivery tool it has no conferencing facilities, and may be best described as an environment in which structured textbooks could be presented in a multiply-navigable interface.

Any knowledge base that contains reasonably specific and identifiable knowledge elements that can be organised hierarchically into sections, subsections and indexed in detail is suitable for delivery through the InterBook system. Technical and software manuals are an excellent example of suitable material. InterBook takes the structures commonly found in such a textbook (such as tables of content, indexes and glossaries) and delivers them on the Web with navigation support, providing individualised assistance to each learner.

All InterBook-served electronic textbooks have a generated table of content, a glossary, and a search interface. In InterBook, the structure of the glossary resembles the pedagogic structure of the domain knowledge in that each node of the domain network is represented by a glossary entry. Likewise each glossary entry corresponds to one of the "domain concepts".

All sections of an electronic textbook are indexed with "domain-model concepts". For each section, a list of concepts related with this section is provided (Figure 2), and is called the spectrum of the section. The spectrum of the section also dictates the role of a concept in the section (each concept can be either an outcome concept or a background concept).

 

The knowledge about the domain and about the textbook content is used by InterBook to serve a well-structured hyperspace. In particular, InterBook generates contextual links between the glossary and the textbook. Links are provided from each textbook section to corresponding glossary entries for each of the involved background or outcome concepts. Similarly from each glossary entry which describes a concept InterBook provides links to all textbook units that can be used to learn this concept.

This means that an InterBook glossary integrates features of an index and a glossary. These links are not stored in an external format but generated 'on the fly', in other words dynamically, by a special module that takes into account the student's current state of knowledge represented by the user model.

InterBook uses coloured bullets and different fonts to provide adaptive navigation support (Figure 3) through link annotation. Annotations are evident in the individual section of the text (in the textbook window) as well as in a local overview map (on the navigation bar).

Wherever a link appears on a page (in the table of content, in the glossary or on a regular page), the font and the colour of the bullet informs the user about the status of the node behind that link. Currently four colours and three fonts are used. A green bullet and bold font means "ready and recommended", i.e. the node is ready-to-be-learned but still not learned and contains some new material.

A red bullet and an italic font warn about a not-ready-to-be-learned node, while white means "clear, nothing new" (ie., all concepts presented on a node are known to the user). Violet is used to mark nodes which have not been annotated by an author. A check mark is added for already visited nodes.

A node is annotated green when all of the pre-requisite concepts for that node have been met. In other words, the particular user has previously visited a node or nodes which have those pre-requisite concepts listed as outcome concepts.

Obviously the initial node will have no pre-requisite concepts, only outcome concepts. A node is annotated red (a not-recommended node) when it contains pre-requisite concepts that have similarly not been met. Outcome concepts are 'met' when a node is simply visited.

 

 

Certainly, in order for this form of markup to be useful, the textbook needs to be used in a particular way: It assumes that the path of a user will be approximately linear (in the sense that 'linear' means the same path through the information space as is the optimal path sequenced by the author). Suppose for example that a learner decides to enter the textbook at section 2 rather than section 1.

They may decide from the headings that this general topic is rather simple and not want to begin at section one. In this case they would receive 'not recommended' annotations throughout their subsequent movement through the hyperspace. This problem, and the fact that InterBook assumes that a page is learned when it is simply accessed, prompted the development of embedded tests in later versions.

Later versions of InterBook integrate all three methods of annotation: history-based (on the basis of where the user has been), prerequisite-based (on the basis of what prerequisite nodes the user has visited), and knowledge-based (on the basis of the user's demonstrated understanding of the content).

The user model in InterBook is initialised from the registration page via a stereotype model, and is modified as the user moves through the information space.

The user model consists of an individual file in a folder called "users", which is updated as the student progresses through the material. It is stored in an internal Lisp format. New work on the system includes the provision of an "interview" to further specify the user model, and embedded testing for knowledge-based navigation support.

The InterBook approach uses two kinds of knowledge: knowledge about the domain being taught (represented in the form of a domain model) and knowledge about the students (represented in the form of individual student models). The domain model serves as a basis for structuring the content of an adaptive ET.

The simplest form of domain model is just a set of domain concepts. What we call 'concepts' are named differently in different research papers - attributes, topics, knowledge elements, objects, learning outcomes, but in all cases they are just elementary pieces of knowledge for the given domain. Depending on the domain and the application area, concepts can vary in granularity. A more advanced form of the domain model is a network, with nodes corresponding to domain concepts and links reflecting several kinds of relationships between concepts.

This network represents the structure of the domain covered by a hypermedia system. The domain model provides a structure for representation of the student's knowledge of the subject.

For each domain model concept, an individual student's knowledge model stores some value which is an estimation of the student knowledge level of this concept. This type of model (called an overlay model) is powerful and flexible: it can independently measure the student's knowledge of different topics. The overlay student model can be kept up-to-date relatively easy.

All student actions (page visits, problem-solving, quizzes answering) are tracked and used to increase or decrease knowledge levels for involved concepts. Another component of the student model is the model of student's learning goals. Each student may have an individually assigned learning goal. A learning goal is just a set of concepts to be learned.

A sequence of assigned learned goals forms an individual order of learning. Adaptive guidance mechanisms will ensure that the student achieves the first learning goal in a sequence, then the second one, and so forth. An individual order of learning is a convenient interface for the teacher to specify an individual syllabus for a student or a class.

It may be done to adapt to special goals or backgrounds of the students, or to a particular textbook. An individual order of learning plus adaptive guidance are the mechanisms which are able to build an unlimited number of personalised adaptive courses from the same course material.

The WWW implementation of InterBook is based on the Common Lisp Hypermedia Server CL-HTTP (Mallery, 1994). CL-HTTP is a fully featured HTTP server completely implemented in Common LISP. CL-HTTP appears to be an optimal platform for our purposes. CL-HTTP offers a Common Gateway Interface to handle incoming URLs.

To enable the server to respond to a particular URL, this URL has to be associated to a response function implemented in LISP. Answering an incoming request, the server recognises a URL, calls an associated function, and passes the received URL and enclosed form values as parameters. In response, the function generates an HTML page.

To do that, it can use a comprehensive library of HTML-generating functions. With such an architecture, CL-HTTP is a very flexible and powerful tool for implementing intelligent applications on WWW.

Since a LISP function is called to handle the request, any interactive or intelligent tool written in LISP can be connected to WWW with the help of CL-HTTP (Brusilovsky, Eklund & Schwarz, 1998).

Authoring an adaptive electronic textbook can be divided into 5 steps. In brief, an Electronic Textbook is prepared as a specially structured Word file and the task is to convert this file into InterBook format. InterBook recognises the structure of the document through the use of headers.

It means that the titles of the highest level sections should have a pre-defined text style "Header 1", the titles of its subsections should have a pre-defined paragraph style "Header 2", and so forth. The title of the textbook should have paragraph style "Title". The result of this step will be a properly structured MS Word file.

The second step in the authoring process then involves concept-based annotation of the Electronic Textbook (ET) to let InterBook know which concepts stand behind each section. This knowledge allows InterBook to help the reader of the ET in several ways, and the result of this step is an annotated structured MS Word file.

An annotation is a piece of text of special style and format inserted at the beginning of each section (between the section header and the first paragraph). Annotations have special character style (hidden + shadowed) which are not visible in the text window to the reader of the ET. For each unit the author provides a set of outcome and background concepts.

In this way, each section is annotated with a set of prerequisite concepts (or terms which exist in other sections which should be read before the current section), and a set of outcome concepts (terms which will be assumed known once the reader has visited the section). The format for the outcome annotation is: (out: concept-name1, concept-name2, etc.) and the format for the background annotation is: (pre: concept-name1, concept-name2, etc.).

Once the annotations are complete the file is saved in RTF format. The RTFtoHTML program (Hector, 1998 [HREF1]) with some special settings is used to convert the ET into HTML format. Then the ".html "extension on the file is manually altered to ".inter" so that it can be recognised by the Interbook system. Lastly, when the InterBook server starts, it parses all InterBook files in its "Texts" folder (i.e. all files with extension .inter) and translates it into the list of section frames. Each unit frame contains the name and type of the unit, its spectrum, and its position in the original HTML file.

The obtained LISP structure is used by InterBook to serve all the available textbooks on WWW providing the advanced navigation and adaptation features. The content which is presented to the student is generated on-the-fly using the knowledge about the textbook, the student model, and HTML fragments extracted from the original HTML file.

These features of InterBook are based on the functionality of the Common Lisp Hypermedia Server (Mallery, 1994).

 

Application of adaptive systems to flexible delivery in higher education

We believe this tool significantly simplifies the design of an adaptive electronic textbook on the Web. It provides full support in preparation and serving such a textbook for authors who need only to be familiar with a word processor. An advanced used who have some knowledge of HTML and LISP programming can use our tool more flexibly.

For example, an author can bypass the first two steps in the authoring process by preparing the textbook directly in HTML format with annotations provided as specially formatted comments. The author can also replace server response functions and HTML generating functions to implement different structure and different "look and feel" of the be requested by a unique URL. As we mentioned above, to enable the server to respond to a particular URL, this URL has to be associated to a response function implemented in LISP which has to generate an HTML page on the fly as an adaptive response.

Brusilovsky, Eklund & Schwarz (1998) made the case for the use of knowledge-based systems in higher education as one which provides a unique solution to the fact that students are entering Web-based instruction with very different knowledge, goals and backgrounds.

Most courseware is aimed at the 'average' student, and does not account for minority groups, such as those with language difficulties, exceptional skill or poor subject knowledge. Accordingly, three steps in courseware development were suggested: First, the development of the content implemented with a variety of instructional strategies such as questions, interactive examples and problems.

Second, the refinement of the materials to suit the requirements of the current student population, and third: the use of adaptive mechanisms which personalise the hyperspace of individual students to account for individual knowledge and preferences. Personalising course materials on these three levels of granularity is not only part of the process of authoring effective courseware, but also critical in utilising the computer as an instructional medium offering some of the benefits of one to one human tutoring.

 

Conclusion

This paper supports the use of knowledge-based systems, in particular adaptive educational systems, for the flexible delivery of course materials in higher education. These systems recognise the importance of individual learner knowledge, as adaptive systems can customise courseware and take part of the role of the human teacher in individualising instruction.

We have based our arguments on a practical model of the role of knowledge in teaching and learning, and demonstrated adaptivity in computer-based educational environments through InterBook, a tool for delivering adaptive textbooks on the World Wide Web. InterBook uses adaptive annotation technology, a form of adaptive navigation support, to augment hyperlinks with a comment which informs users about the current state of the nodes behind the annotated links, and does this on an individual basis by adapting links through a user-model, an individual record of a student's progression through the courseware.

Adaptive annotation and adaptive navigation support in general are becoming more popular on the World Wide Web in the context of the use of Web-based instructional tools in Higher education.

 

References

Bell G (1997) Adaptive navigation within knowledge-based hypermedia. Paper presented at The Flexible Hypertext Workshop: A Workshop Held in Conjunction with The Eighth ACM International Hypertext Conference. (Hypertext'97) Southampton, UK. April 6-11, 1997.

Brusilovsky P & Pesin L (1995) Visual annotation of links in adaptive hypermedia. Proceedings of CHI'95 (Conference Companion). Edited by I. Katz, R. Mack and L. Marks. Denver, May 7-11, 1995, p. 222-223.

Brusilovsky P (1996) Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction Vol. 6, Nos 2-3, p. 87-129.

Brusilovsky P, Eklund J & Schwarz E (1998) Web based education for all: A tool for developing adaptive courseware. Computer Networks and ISDN Systems. Vol. 30. Nos 1-7. p. 291-300. Proceedings of the 7th International World Wide Web Conference (WWW7).

Brusilovsky P, Pesin L & Zyryanov M (1993) Towards an adaptive hypermedia component for an intelligent learning environment. In Human-Computer Interaction, Lecture Notes in Computer Science, Vol. 753, L. J. Bass, J. Gornostaev and C. Unger (eds), Springer-Verlag, Berlin. p. 348-358.

Brusilovsky P, Schwarz E & Weber G (1996) A tool for developing adaptive electronic textbooks on WWW. Proceedings of WebNet'96, World Conference of the Web Society. San Francisco, CA, October 15-19, 1996, p. 64-69.

de La Passardiere B & Dufresne A (1992). Adaptive navigational tools for educational hypermedia. In I. Tomek (Ed.), ICCAL'92, 4-Th International Conference on Computers and Learning . Berlin: Springer-Verlag. p. 555-567.

Eklund J & Brusilovsky P (1998) The Value of Adaptivity in Hypermedia Learning Environments: A Short Review of Empirical Evidence. Paper presented at The 2nd Workshop on Adaptive Hypertext and Hypermedia Held in Conjunction with HYPERTEXT '98: The Ninth ACM Conference on Hypertext and Hypermedia Pittsburgh, PA, USA, June 20-24, 1998.

Goodyear P (1991) Design of intelligent tutoring systems. In Goodyear P(ed.) Teaching knowledge and intelligent tutoring. Ablex Publishing, Norwood, NJ.

Hohl H, Böcker H, Gunzenhäuser R (1996) Hypadapter: An adaptive hypertext system for exploratory learning and programming. User Modeling and user adapted Interaction . Kluwer. Vol. 6, No.s 2-3. p. 131-156.

Jacobs G (1992) Hypermedia and discovery-based learning: a historical perspective. British Journal of Educational technology. Vol. 23 No. 2 p. 113-121.

Jungmann (1997, Feb 11) Re: Definition of adaptivity. Adaptive Hypertext and Hypermedia Discussion List. [Online]. Available email: adaptivehh@uts.edu.au. [1997, Feb 11].

Mallery J (1994) A Common LISP hypermedia server. Proceedings of the First International Conference on the World-Wide Web, May 25, 1994.

Ohlsson S (1993) Impact of cognitive theory on the practice of courseware authoring. Journal of computer assisted learning. Vol. 9, p. 194-221.

Ross S (1984) Matching the Lesson to the Student: Alternative Adaptive Designs for Individualized Learning Systems. Journal of Computer-Based Instruction, Vol. 11, No. 2, p. 42-48.

Salomon G, Globerson T & Guterman E (1990) The computer as a zone of proximal development: Internalising reading-related metacognitions from a reading partner. Journal of Educational Psychology, 81. p. 620-627.

Schwarz E (1997, March 15) Re: Definition of adaptivity. Adaptive Hypertext and Hypermedia Discussion List. [Online]. Available email: adaptivehh@uts.edu.au. [1997, March 15].

Schwarz E, Brusilovsky P & Weber G (1996) World-wide intelligent textbooks. Proceedings of ED-TELECOM'96 - World Conference on Educational Telecommunications. Boston, MA, June 1-22, 1996, p. 302-307.

Spoer (1994) Enhancing the acquisition of conceptual structures through hypermedia. In K McGilly (ed.) Classroom lessons: Integrating Cognitive theory and classroom practice. Bradfort MIT Press, London.

Zeiliger R, Reggers T & Peeters R (1996) Concept-map based navigation in educational hypermedia : a case study. Proceedings of ED-MEDIA'96 - World conference on educational multimedia and hypermedia. Boston, MA, June 17-22, 1996.

 

Hypertext References

[HREF1]
Hector, C. (1998) RTFtoHTML 3.9
http://www.sunpack.com/RTF/

[HREF2]
WebNet96 home page
http://www.aace.org/conf/webnet/

[HREF3]
Jungmann M (1997b) Adaptive hypertext in complex information spaces. Paper presented at The Flexible Hypertext Workshop: A Workshop Held in Conjunction with The Eighth ACM International Hypertext Conference. (Hypertext'97) Southampton, UK. April 6-11, 1997.
http://nero.prakinf.tu-ilmenau.de:80/~jungmann/ws-ht97-html/index.html

[HREF4]
The Adaptive Hypertext and Hypermedia Home Page
http://www.education.edu.au/projects/ah

Return to top

Contact Details
John Eklund
Faculty of Education
The University of Technology,
Sydney NSW Australia
Email: j.eklund@uts.edu.au

Peter Brusilovsky
Human-Computer Interaction Institute
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213, USA
Email: plb@cs.cmu.edu