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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
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.
deals with students' final answers to educational
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