roup.
7
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.
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