Adaptive and intelligent technologies for web-based education

comparison with two-level sequencing in most ITS): the best page is simply selected from the

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set of acceptable pages using some heuristics. We refer to this way of sequencing as page

sequencing. InterBook and ELM-ART provide good examples of this technology. However, the

difference between these two technologies starts to disappear in the Web context. Web-based

ITS systems are naturally moving to hypermedia platform representing at least some part of the

learning material as a hyperspace. As long as some type of educational material (presentations,

problems, and questions) is represented as a set of nodes in hyperspace, sequencing of it

becomes indistinguishable from direct guidance. To stress this similarity we have represented

adaptive sequencing and adaptive navigation support with direct guidance in the same column of

the tables.

The most popular form of ANS on the Web is annotation. It was used first in ELM-ART

[12] and since that applied in all descendants of ELM-ART such as InterBook, AST, ADI, ACE,

and ART-Web as well as in some other systems such as WEST-KBNS and KBS HyperBook.

ELM-ART and InterBook also use adaptive navigation support by sorting. Another popular

technology is hiding and disabling (a variant of hiding that keeps link visible but does not let the

user to proceed to the page behind the link if this page is not ready to be learned). The options

are either to make the link completely non-functional (nothing happens when the user clicks on

it) as implemented, for example, the Remedial Multimedia System [4] or to show the user a list

of pages to be read before the goal page as done in Albatros [29]. Tables 1 and 2 list all major

systems that use adaptive navigation support and indicates the type of adaptation.

The goal of

page to the user's goals, knowledge and other information stored in the user model. In a system

with adaptive presentation, the pages are not static, but adaptively generated or assembled from

pieces for each user. For example, with several adaptive presentation techniques, expert users

receive more detailed and deep information, while novices receive more additional explanation.

Adaptive presentation is very important in WWW context where the same "page" has to suit to

very different students. Only two Web-based AES implement full-fledged adaptive presentation:

PT [28] and AHA [16]. Both these systems apply a flexible but low-level conditional text

technique. Some other systems use adaptive presentation is special contexts. Medtec [19] is able

to generate adaptive summary of book chapters. MetaLinks can generate a special preface to a

content page depending on where the student came from to this page. ELM-ART, AST,

InterBook and other descendants of ELM-ART use adaptive presentation to provide adaptive

insertable warnings about the educational status of a page. For example, if a page is not ready to

be learned, ELM-ART and AST insert a textual warning at the end of it and InterBook inserts a

warning image in a form of a red bar. A very interesting example of adaptive presentation is

suggested in WebPersona project [3] where an individualized presentation of information in an

educational hypertext is performed by a life-like agent.

the adaptive presentation technology is to adapt the content of a hypermedia

2.3 Web-inspired technologies in Web-based education

The last group of technologies is probably the most exciting one since these technologies

has almost no roots in pre-internet educational systems. Currently this group include only one

technology. We call this technology

because the essence of this technology is the ability to analyze and match student models of

many students at the same time. Traditional adaptive and intelligent educational systems has no

opportunity to explore this technology since they usually work with one student (and one student

model) at a time. On the contrary, in the WBE context this opportunity happens naturally

because student records are usually stored centrally on a server (at least for administrative

reasons). It provides an excellent framework for developing various adaptive and intelligent

technologies that can make some use of matching student models of different students. So far,

we have identified two examples of student model matching, which we call

collaboration support

other and probably could be considered as different technologies within the student model

matching group.

student model matching (or simply model matching)adaptiveand intelligent class monitoring. These examples quite differ from each