Web-based learning through mixed-initiative interactions : design and implimentation

Nantha Kumar Subramaniam, (2012) Web-based learning through mixed-initiative interactions : design and implimentation. In: The 26th Annual Conference of Asian Association of Open Universities (AAOU2012), 16-18 October 2012, Chiba, Japan.

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Abstract

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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