To make online learning more productive, software agent technology has been applied to provide services for learners in order to assist them to construct knowledge in constructivist ways. This paper is focused on the application of software agents in assisting learners to dynamically adjust learning processes. Unlike pedagogical agents, the agents in this application do not hold domain knowledge but simply assist learners to get through learning processes by a variety of supportive services. They assist learners to develop personalized preferred learning plans and to guide them to dynamically adjust learning toward their goals. In this article, the online learning process is first investigated, and an approach to assisting learners to dynamically adjust learning is outlined. Then, the structure of the UOL (unit of learning) database that provides links between a practical learning scenario and the required services is explored. A multi-agent architecture for realizing the services is configured, and the roles of the involved agents are described. After that, the related agent algorithms for guiding learners to dynamically adjust learning are described.
To make online learning more productive, software agent technology has been applied to provide services for learners in order to assist them to construct knowledge in constructivist ways. This paper is focused on the application of software agents in assisting learners to dynamically adjust learning processes. Unlike pedagogical agents, the agents in this application do not hold domain knowledge but simply assist learners to get through learning processes by a variety of supportive services. They assist learners to develop personalized preferred learning plans and to guide them to dynamically adjust learning toward their goals. In this article, the online learning process is first investigated, and an approach to assisting learners to dynamically adjust learning is outlined. Then, the structure of the UOL (unit of learning) database that provides links between a practical learning scenario and the required services is explored. A multi-agent architecture for realizing the services is configured, and the roles of the involved agents are described. After that, the related agent algorithms for guiding learners to dynamically adjust learning are described.
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