Latent semantic analysis as a tool for learner positioning in learning networks for lifelong learning

Abstract

As we move towards distributed, self-organized learning networks for lifelong learning to which multiple providers contribute content, there is a need to develop new techniques to determine where learners can be positioned in these networks. Positioning requires us to map characteristics of the learner onto characteristics of learning materials and curricula. Considering the nature of the network envisaged, maintaining data on these characteristics and ensuring their integrity are difficult tasks. In this article we review the usability of Latent Semantic Analysis (LSA) to generate a common semantic framework for characteristics of the learner, learning materials and curricula. Although LSA is a promising technique, we identify several research topics that must be addressed before it can be used for learner positioning.

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