Web-based learning through mixed-initiative interactions : design and implimentation
Mixed-initiative interaction is a naturally-occurring feature of human-human interactions. It characterize by turn-taking, frequent change of focus, agenda and control among the “speakers”. This human-based mixed-initiative interaction can be implemented through a mixed-initiative systems which a...
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| Format: | Conference or Workshop Item |
| Published: |
2012
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| Subjects: | |
| Online Access: | https://library.oum.edu.my/repository/883/1/library-document-883.pdf https://library.oum.edu.my/repository/883/ |
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| Summary: | Mixed-initiative interaction is a naturally-occurring feature of human-human
interactions. It characterize by turn-taking, frequent change of focus, agenda and control
among the “speakers”. This human-based mixed-initiative interaction can be implemented
through a mixed-initiative systems which are a popular approach to building intelligent
systems that can collaborate naturally and effectively with people. Mixed-initiative systems
exhibit various degrees of involvement in regards to the initiatives taken by the user or the
system. In any discourse, the initiative may be shared between either, a learner and a system
agent, or between two independent system agents. Both the parties in question establish and
maintain a common goal and context, and proceed with an interaction mechanism involving
initiative taking that optimizes their progress towards the goal. However, the application of
mixed-initiative interaction in web-based learning is very much limited. In this paper, we
discuss the design and implementation of a web-based learning system through mixedinitiative
system known as JavaLearn. JavaLearn allows the interaction between the system
(in the form of a software agent) and the individual learner. Here, the system supports the
learning through a problem solving activity by demanding active learning behaviour from
the learner with minimal natural language understanding by the agent and embodies the
application-dependent aspects of the discourse. It guides the learner to solve the problem by
giving adaptive advice, hints and engage the learner in the real time interaction in the form
of “conversation”. The principal features of this system are: It is adaptive and are based on
reflection, observation and relation. The system acquires its intelligence through the finite
state machine and rule-based agents. (Abstract by authors) |
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