In: C. Rollinger and C. Peylo (eds.)
Künstliche Intelligenz, Special Issue on Intelligent Systems and Teleteaching,
1999, 4, 19-25.
Adaptive and Intelligent Technologies
for Web-based Education
Peter Brusilovsky
Carnegie Technology Education and
Human-Computer
Interaction Institute
Carnegie Mellon University
Pittsburgh, PA 15213,
USA
plb@cs.cmu.edu
Abstract: The paper provides a review of adaptive and
intelligent technologies in a context of Web-based distance education. We
analyze what kind of technologies are available right now, how easy they can be
implemented on the Web, and what is the place of these technologies in
large-scale Web-based education.
1 Introduction
Web-based education (WBE) is currently a hot research and
development area. Benefits of Web-based education are clear: classroom
independence and platform independence. Web courseware installed and supported
in one place can be used by thousands of learners all over the world that are
equipped with any kind of Internet-connected computer. Thousands of Web-based
courses and other educational applications have been made available on the Web
within the last five years. The problem is that most of them are nothing more
than a network of static hypertext pages. A challenging research goal is the
development of advanced Web-based educational applications that can offer some
amount of adaptivity and intelligence. These features are important for WBE
applications since distance students usually work on their own (often from
home). An intelligent and personalized assistance that a teacher or a peer
student can provide in a normal classroom situation is not easy to get. In
addition, being adaptive is important for Web-based courseware because it has to
be used by a much wider variety of students than any "standalone" educational
application. A Web courseware that is designed with a particular class of users
in mind may not suit other users.
Since the early days of the Web, a number of research teams
have implemented different kinds of adaptive and intelligent systems for on-site
and distance WBE. The goal of this paper is to provide a brief review of the
work performed so far in his area. The review is centered on different adaptive
and intelligent technologies. We stay on the level of technologies to
provide compatibility with earlier papers on adaptive hypermedia [7] and
Web-based ITS [6]. By adaptive and intelligent technologies we mean essentially
different ways to add adaptive or intelligent functionality to an educational
system. A technology usually could be further dissected into finer grain
techniques and methods, which corresponds to different variations of this
functionality and different ways of its implementation. In the next section we
analyze what kind of technologies are available right now, and how easy they can
be implemented on the Web. After that we discuss what is the place of these
technologies in large-scale Web-based education.
2 Web-based educational systems: a review of technologies
Web-based Adaptive and Intelligent Educational Systems (AIES)
are not an entirely new kind of systems. Historically, almost all Web-based AIES
inherit from two earlier kinds of AIES: intelligent tutoring systems
(ITS) and adaptive hypermedia systems. Most of adaptive and intelligent
technologies applied in Web-based AIES systems were directly adopted from either
the ITS area or the adaptive hypermedia area. As long as Web-based AIES research
get more mature, it will produce original technologies inspired by the Web
context. At least one of these Web-inspired technologies could already be
identified (model matching). This section provides a review of existing
technologies grouped by its origin. For each technology we list existing
Web-based AIES and projects, which implements variations of this technology and
discuss the ways to implement it on the Web.
2.1 ITS technologies in Web-based education
Intelligent tutoring systems is a traditional area of research
that investigates problems of developing AIES [13]. The goal of various ITS is
the use the knowledge about the domain, the student, and about teaching
strategies to support flexible individualized learning and tutoring. A review of
existing intelligent tutoring systems performed by the author in 1990 helped to
identify three core ITS technologies: curriculum sequencing, intelligent
analysis of student's solutions, and interactive problem solving support. All
these technologies were implemented in numerous ITS. Since 1990, only one new
technology (example-based problem solving support) was added to the set to
classify a functionality that was not covered by the core three. While the
proposed set of ITS technologies could be considered subjective and incomplete,
it turned out to be very useful for classifying existing Web-based AIES.
Web-based AIES that use traditional ITS technologies are usually called
Web-based ITS. First Web-based ITS were reported in 1995-1996 [6; 12; 34; 37].
These systems still constitute a rather small stream inside the ITS area.
2.1.1 Curriculum sequencing
The goal of the curriculum sequencing technology (also
referred to as instructional planning technology) is to provide the student with
the most suitable individually planned sequence of knowledge units to learn and
sequence of learning tasks (examples, questions, problems, etc.) to work with.
In other words, it helps the student to find an "optimal path" through the
learning material. The classic example is the BIP system [5]. There are two
essentially different kinds of sequencing: active and passive. Active sequencing
implies a learning goal (a subset of domain concepts or topics to be
mastered). Systems with active sequencing can build the best individual path to
achieve the goal. Passive sequencing (which is also called remediation)
is a reactive technology and does not require an active learning goal. It starts
when the user is not able to solve a problem or answer a question (questions)
correctly. Its goal is to offer the user a subset of available learning
material, which can fill the gap in student's knowledge of resolve a
misconception. For active sequencing systems, it makes sense to distinguish
systems with fixed and adjustable learning goal. Most of existing systems can
guide their students to the fixed learning goal - the whole set of domain
concepts. A few systems with adjustable learning goal let a teacher or a student
to select a subset of the whole set of concepts as the current learning goal. In
most of ITS systems with sequencing it is possible to distinguish two levels of
sequencing: high and low. High-level sequencing or knowledge sequencing
determines next learning subgoal: next concept, set of concepts, topic, or
lesson to be taught. Low-level sequencing or task sequencing determines
next learning task (problem, example, test) within current subgoal. High and low
level sequencing are often performed by different mechanisms. In many ITS
systems only one of these two mechanisms are intelligent, for example, a lesson
is selected by a student, while learning tasks within this lesson are adaptively
selected by the system. Some systems can only manipulate the order of task of
one particular kind: usually problems or questions. In this case it could be
also called problem or question sequencing.
Sequencing is currently the most popular technology in
Web-based AIES. Almost all kinds of sequencing mentioned above were already
implemented on the Web. Active sequencing is a dominated type of sequencing.
Only a few systems (InterBook, PAT-InterBook, CALAT, VC Prolog Tutor, and
Remedial Multimedia System) can perform passive remedial sequencing. Among
active sequencing systems, only a handful of systems such as ELM-ART-II, AST,
ADI, ART-Web, ACE, KBS-Hyperbook, and ILESA are able to perform intelligently
both high and low level sequencing. Others, like Manic, leave a choice of
activity within a topic to the user. Vice versa, some systems, like Medtec,
leave a choice of a topic to the user but can generate an adaptive sequence of
problems within the topic. Most of the systems supports sequencing with fixed
learning goal (equals to the whole course). Only a few systems support
adjustable learning goals enabling a teacher (as in DCG) or a student (as in
InterBook and KBS Hyperbook) to select an individual goal. The student can
choose a goal as a subset of domain concepts (InterBook) or a project (KBS
Hyperbook).
Active sequencing in most of the systems is driven by the
students knowledge (more exactly, by the difference between student's knowledge
and global goal). A few systems and projects, however, experiment with the use
of students’ preferences on the type and media of available learning
material to drive sequencing of tasks within a topic [14; 15; 45]. Two
interesting cases of sequencing could be found in DCG and SIETTE systems. DCG
[49] can perform advanced sequencing of educational material adapted to a
learning goal. However, the sequencing is performed before students start
working with the system producing a static Web-based course. SIETTE [40] is an
example of a Web-based adaptive testing system. The only kind of learning
material it possesses is questions. The only thing it can do is to generate an
adaptive sequence of questions to assess student's knowledge. Systems like
SIETTE are incomplete by their nature and have to be used as components in
distributed Web-based AIES.
While curriculum sequencing could be considered as the oldest
ITS technology (it was implemented in almost all first ITS), for about 20 years
it was a Cinderella among other technologies. Very little attention was devoted
to it. Mainstream ITS research were centered around problem solving support
technologies (which will be analyzed below). Problem solving support was
considered as a main duty of an ITS, while delivery and sequencing of education
material was though to be performed outside the system (usually, by a human
teacher). Naturally, almost no ITS includes educational material itself (other
than a set of problems). The situation with Web-based AIES is very different. In
the context of Web-based education a solid amount of educational material
(usually structured as a hyperspace) is one of the main attractions of an
educational system. In this context (with its "lost in hyperspace" problem),
curriculum sequencing technology becomes very important to guide the student
through the hyperspace of available information. This technology is also natural
and easy to implement on the Web: all knowledge could be located on the server
and all sequencing could be done by a CGI-script. It's not surprising that, it
is not only the oldest, but also the most popular technology of Web-based AIES.
2.1.2 Problem solving support technologies
As it is mentioned above, for many years, problem solving
support was considered as a main duty of an ITS system and a main value of an
ITS technology. We have identified three problem solving support technologies:
intelligent analysis of student solutions, interactive problem solving support,
and example-based problem solving support. All these technologies can help a
student in a process of solving an educational problem, but they do it by
different ways.
Intelligent analysis of student solutions deals with
students' final answers to educational problems no matter how these answers were
obtained. To be considered as intelligent, a solution analyzer has to decide
whether the solution is correct or not, find out what exactly is wrong or
incomplete, and possibly identify which missing or incorrect knowledge may be
responsible for the error (the last functionality is referred as knowledge
diagnosis). Intelligent analyzers can provide the student with extensive error
feedback and update the student model. The classic example is PROUST [Johnson,
1986 #681. As it could be seen from the Tables 1 and 3, a number of Web-based
AIES implement intelligent analysis of student solutions.
Interactive problem solving support is a more recent
and a move powerful technology. Instead of waiting for the final solution, this
technology can provide a student with intelligent help on each step of problem
solving. The level of help can vary: from signaling about a wrong step, to
giving a hint, to executing the next step for the student. The systems which
implement this technology (often referred to as interactive tutors) can
watch the actions of the student, understand them, and use this understanding to
provide help and to update the student model. The classic example is the
LISP-TUTOR [2]. This technology is also represented by a number of Web-based
AIES (Tables 1 and 3).
The example-based problem solving technology is the
newest one. This technology is helping students to solve new problems not by
articulating their errors, but by suggesting them relevant successful problem
solving cases from their earlier experience (it could be examples explained to
them or problems solved by them earlier). An example is ELM-PE [51]. In the Web
context, this technology is implemented in ELM-ART [12] and ELM-ART-II
[53].
In the area of traditional ITS, the interactive problem
solving support technology absolutely dominates. Interactive problem solving
support is an ultimate goal of almost any ITS, while intelligent analysis of
student solutions is often considered imperfect (and example based problem
solving support is too rare to consider as a competitor). Again, the Web context
changes the situation. Both intelligent analysis of student solutions and
example based problem solving support appears to be very natural and useful in
Web context. Both technologies are passive (works by student request) and can be
relatively easy implemented on the Web using a CGI interface. Moreover, an old
standalone AIES, which uses these technologies, could be relatively easy ported
to the Web by implementing a CGI gateway to the old standalone program. It is
not surprising that these technologies were among the first implemented on the
Web. An important benefit of these two technologies in the Web context is their
low interactivity: both usually require only one interaction between browser and
server for a problem solving cycle. This is very important for the case of slow
Internet connection. These technologies can provide intelligent support when a
more interactive technology will be hardly useful. Currently, these technology
dominates in Web context over more powerful and interaction hungry interactive
problem solving support.
Interactive problem solving support technology is the last ITS
technology migrated to the Web. The problem here is that the "fast-track"
approach of implementing Web-based ITS (developing a CGI interface to an older
standalone ITS) used in pioneer systems does not work properly for this
technology. It could be well illustrated by the PAT-Online system [41], which
was probably the first trial to implement interactive problem solving support on
the Web. This system uses a form-based CGI-AppleScript interface to a standalone
Practical Algebra Tutor (PAT) system. Since CGI interface is passive, the Web
version of the system had to provide a "submit" button for the student to get
the feedback from the system. Naturally, it also added another feature, which
was essential for students with a slow Internet connection: a possibility to
request a feedback once after performing several problem solving steps. As a
result, PAT-Online moved to the category of an intelligent problem analyzers,
more exactly, to a subcategory of analyzers that are capable to analyze
incomplete solutions (ELM-ART also belongs to this subcategory). The intelligent
analyzers of this subcategory can be placed between traditional analyzers and
interactive tutors (in Tables 1 and 3 they are marked with keyword "partial",
however, they can't be considered as real interactive tutors).
A real interactive tutor is expected to be not only
interactive, but also active. It should not sleep from one help request to
another, but instead should be able to monitor what the student is doing and
instantly react to errors. It simply can't be implemented with the traditional
server-side CGI interactivity and requires client-side interactivity based on
Java. Java technology has matured very recently. Two years ago the review [8]
named it as a prospective platform for Web-based AIES and mentioned only three
Java-based systems. Now Java provides a reliable solution for Web-based
interactive tutors. To be more exact, Java offers two different solutions. One
solution is a tutor implemented completely in Java. It could be a Java applet
working in a browser, or a Java application. Another solution is a distributed
client-server tutor where a part of functionality is implemented in Java and
works on the client side, and another part works on the server side. The parts
communicate over the Internet. While the pure Java solution looks simpler (just
a new language to build an AIES), the client-server architecture offers a more
attractive choice for developing Web-based tutors. It is a definite choice for
porting a standalone interactive tutor on the Web. D3-WWW-Trainer [20] and
AlgeBrain [1] demonstrate how to re-use the intelligent functionality of an
earlier standalone tutor by changing it to a server-side application and
developing a relatively thin "brainless" Java client that implements interface
functions and communicates with an intelligent server. Event relatively small
newly implemented interactive tutors such as ADIS [50] and ILESA [30], which
could be easily implemented in pure Java, can benefit from client-server
architecture for such reasons as central student modeling. Finally, an overhead
of the client-server approach (the need to have a distributed system) is not
very big since Java naturally supports several ways of client-server
communications - HTTP/CGI, sockets, or RMI/CORBA. We think, that the
client-server architecture will become very popular in the coming years as a
standard way of implementing Web-based interactive tutors and a way to implement
all kinds of highly interactive Web-based AIES. We already see examples of using
it for implementing pen-based interface in WITS-II [27] and an animated
pedagogic agent Vincent in TEMAI [38].
2.2 Adaptive hypermedia technologies in Web-based education
Adaptive hypermedia is a relatively new research area [7].
Adaptive hypermedia systems apply different forms of user models to adapt the
content and the links of hypermedia pages to the user. We distinguish two major
technologies in adaptive hypermedia: adaptive presentation and adaptive
navigation support. Education always was one of the main application areas for
adaptive hypermedia. A number of standalone (i.e., non-Web-based) adaptive
educational hypermedia systems was built between 1990 and 1996. First Web-based
AIES that use adaptive hypermedia technologies were reported in 1996 [12; 17].
Since that the Web has become the primary platform for developing educational
adaptive hypermedia systems.
The goal of the adaptive navigation support technology
is to support the student in hyperspace orientation and navigation by changing
the appearance of visible links. Adaptive navigation support (ANS) can be
considered as a generalization of curriculum sequencing technology in a
hypermedia context. It shares the same goal - to help students to find an
"optimal path" through the learning material. At the same time, adaptive
navigation support has more options than traditional sequencing: it can guide
the students both directly and indirectly. In a WWW context where hypermedia is
a basic organizational paradigm, adaptive navigation support can be used very
naturally and efficiently. There are several known ways to adapt the links [7].
Two examples of ANS-based standalone systems are ISIS-Tutor [10] with adaptive
hiding and adaptive annotation and Hypadapter [24] with adaptive hiding and
adaptive sorting. The three ways that are most popular in Web-based AIES are
direct guidance, adaptive link annotation, and adaptive link hiding.
Direct guidance implies that the system informs the student
which of the links on the current page will drive him or her to the "best" page
in the hyperspace (which page is "best" is decided on the basis of student's
current knowledge and learning goal). Often, if a link to the next best page is
not presented on the current page, the system can generate a dynamic "next"
link. As we can see, adaptive navigation support with direct guidance is almost
equivalent to curriculum sequencing technology. There are some differences
though (in addition to the different origin). A page suggested by a direct
guidance technology is always a page of the existing hyperspace. The student
usually could reach this page in one or several steps without the system
guidance. The guidance just helps the student to realize that this page is
"best" and to get there fast. In an ITS with adaptive sequencing a "page" with
next best task or presentation could be completely generated from system's
knowledge, thus the student has no ways to get to this material others than
using sequencing. Also, direct guidance usually applies a one level sequencing
mechanism (in comparison with two-level sequencing in most ITS): the best page
is simply selected from the 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 the adaptive presentation technology is to
adapt the content of a hypermedia 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.
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 student model matching (or simply model matching) 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 adaptive collaboration support and intelligent class
monitoring. These examples quite differ from each other and probably could
be considered as different technologies within the student model matching
group.
Adaptive collaboration support is a very new adaptive
technology which was developed within last 5 years along with development of
networked educational systems. The goal of adaptive collaboration support is to
use system's knowledge about different students to form a matching group for
different kinds of collaboration. The pioneering non-WBE (i.e., non-Web, or
non-educational) examples of adaptive collaboration support are known for
already a few years. These examples include forming a group for collaborative
problem solving at a proper moment of time [25; 26] or finding the most
competent peer to answer a question about a topic (i.e. finding a person with a
model showing good knowledge of this topic) [31]. Less than two years ago
Brusilovsky [8] predicted that adaptive collaboration support will become a
popular technology. This prediction came true almost immediately. Now we can
list already several real examples of adaptive collaboration support in WBE
context. The group from University of Saskatchevan has extended their original
workplace-oriented peer-help technology developed for PHelpS system [21; 31] to
the WBE context in their Intelligent Helpdesk system [22]. Another similar
system was developed and evaluated in the University of Central Florida [32]. In
addition to that, the group in the University of Duisburg known for their
pioneering work on adaptive collaboration support [25] have recently suggested a
complete framework for implementation of intelligent support techniques for
distributed internet-based education. This framework can naturally support their
original adaptive collaboration support techniques and provides a framework for
exploring other model matching techniques.
Intelligent class monitoring is also based on the ability to
compare records of different students. However, instead of searching for a
match, it search for a mismatch. The goal is to identify the students who have
learning records essentially different from those of their peers. These students
may be different from others in many ways. They cold be progressing too fast, or
too slow, or simply have accessed much less material than others. In any case,
these students need teacher's attention more than others - to challenge those
who can, to provide more explanations for those who can't, and to push those who
procrastinate. In a regular classroom the teacher can simply track students
attendance and activity to find students who need special attention. In a
Web-based classroom, the teacher in the best case has only logging data - tables
with numbers which are very hard to grasp. At the same time, the need to
identify a small subset of students who need help more than others is more
important. In WBE context, communication between teacher and students is usually
more time consuming and a distance teacher simply can't individually address
more than a small subset of the class. The system HyperClassroom [36] provides
an interesting example of using fuzzy mechanisms to identify deadlocked students
in a WBE classroom. At the time of writing, it is the only example of the
intelligent class monitoring technology known to the author.
3 Adaptive and intelligent technologies for large-scale Web-based education
It should be clear to anyone who is familiar with the needs of
Web-based education, that adaptive and intelligent technologies can enhance
different sides of Web-based educational systems. Adaptive presentation can
improve the usability of course material presentation. Adaptive navigation
support and adaptive sequencing can be used for overall course control and for
helping the student in selecting most relevant tests and assignments. Problem
solving support and intelligent solution analysis can significantly improve the
work with assignments providing both interactivity and intelligent feedback
while taking a serious grading load from the teachers’ shoulders. Model
matching technologies can enforce both administration of distance courses and
communication / collaboration between students and teachers.
From another side, adaptive and intelligent technologies have
not found yet their place in "real" virtual classroom, i.e., as a part of real
courseware used by hundreds of distance students. Most of the systems discussed
above are typical "lab" systems, which have never been used for teaching real
distance classes. The rest of them, a handful of systems mainly from ELM-ART and
AHA families, were used in a few relatively small classes. At the same time,
none of the dozens of commercial and "university-grown" Web courseware systems
that are used in hundreds of real distance courses applies adaptive and
intelligent technologies. Does it mean that research and practice in Web-based
education area will never merge together?
The position of the author is the following. Web-based
education itself is relatively young. Until now different companies producing
Web-based education systems were able to compete on the market with their simple
non-adaptive systems. However, a number of research level systems have already
clearly demonstrated the benefits of adaptive and intelligent technologies. As
long as the competition on the market of Web-based educational system will
increase, “being adaptive” or “being intelligent” will
become an important factor for winning the customers. Traditional Web-based
education companies will start to include adaptive and intelligent
functionality. Research teams with solid experience in using adaptive and
intelligent technologies will found startup companies to bring their technology
to the market. The first technologies to be used in commercial systems will
probably be sequencing technologies (page sequencing and question sequencing)
since they match very well to the current structure of Web-based education
systems. Next will come the turn of adaptive navigation support and model
matching. Problem-solving support technologies will stay on research level for
longer, though we could expect the market debut of small Web-based tutors that
are aimed to support teaching a fragment of some subject. I hope that the next
five years will show us a number of examples of commercial-level adaptive and
intelligent systems as well as many new and exciting developments on the
research level.
System
|
Ref.
|
Adaptive sequencing
|
Adaptive navigation support
|
Problem solving support
|
Intelligent solution analysis
|
Adaptive presentation
|
ELM-ART
|
[12]
|
Page
|
Annotation
|
Partial
|
Server
|
Some
|
ELM-ART-II
|
[53]
|
Course, tests
|
Annotation
|
Partial
|
Server
|
Some
|
PAT-InterBook
|
[11]
|
Page,
remedial
|
Annotation
|
Partial
|
Server
|
Some
|
VC Prolog Tutor
|
[39]
|
Task,
remedial
|
|
|
Server
|
|
Table 1 Adaptive and intelligent technologies in
Web-based educational systems that combine adaptive hypermedia and ITS
functionality
System
|
Ref.
|
Adaptive sequencing
|
Adaptive navigation support
|
Adaptive presentation
|
InterBook
|
[9]
|
Page
|
Annotation
|
Some
|
AST
|
[46]
|
Course
|
Annotation
|
Some
|
ADI
|
[43]
|
Course (knowledge+interests)
|
Annotation
|
Some
|
ART-Web
|
[52]
|
Course, tests
|
Annotation
|
Some
|
ACE
|
[45]
|
Course (knowledge+interests)
|
Annotation Hiding
|
Some
|
Remedial Multimedia System
|
[4]
|
Course, remedial
|
Hiding
|
|
PT
|
[28]
|
|
Hiding
|
Yes
|
AHA
|
[16]
|
|
Annotation Hiding
|
Yes
|
WEST-KBNS
|
[18]
|
|
Annotation
|
|
MetaLinks
|
[33]
|
Page
|
|
Some (intro)
|
KBS Hyperbook
|
[23]
|
Course
|
Annotation
|
|
Table 2 Adaptive and intelligent technologies in
Web-based adaptive hypermedia systems
System
|
Ref.
|
Adaptive sequencing
|
Problem solving support
|
Intelligent solution analysis
|
CALAT
|
[35]
|
Course, remedial
|
|
|
Medtec
|
[19]
|
Tasks
|
|
|
Manic
|
[47]
|
Topic
|
|
|
DCG
|
[49]
|
Course
|
|
|
SIETTE
|
[40]
|
Question
|
|
|
ILESA
|
[30]
|
Lesson, problems
|
Server, Java
|
|
PAT-Online
|
[41]
|
|
Partial
|
Server
|
PAT-Java
|
[42]
|
|
Java
|
|
WITS
|
[37]
|
|
|
Server
|
WITS-II
|
[27]
|
|
|
Server, Java
|
Belvedere
|
[48]
|
|
Server, Java
|
|
ADIS
|
[50]
|
|
Server, Java
|
|
(Yang-Akahori)
|
[54]
|
|
|
Server
|
D3-WWW-Trainer
|
[20]
|
|
Server, Java
|
|
AlgeBrain
|
[1]
|
|
Server, Java
|
|
ADELE
|
[44]
|
|
Server, Java
|
|
TEMAI
|
[38]
|
|
Partial
|
Server, Java
|
Table 3 Adaptive and intelligent technologies in
Web-based ITS systems
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