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Mining learning-dependency between knowledge units from text

By: Zhaohui Wu; Qinghua Zheng; Yanan Qian; Jun Liu; Lu Jiang;

2011 / Springer Science+Business Media / 1066-8888


Identifying learning-dependency among the knowledge units (KU) is a preliminary requirement of navigation learning. Methods based on link mining lack the ability of discovering such dependencies among knowledge units that are arranged in a linear way in the text. In this paper, we propose a method of mining the learning- dependencies among the KU from text document. This method is based on two features that we found and studied from the KU and the learning-dependencies among them. They are the distributional asymmetry of the domain terms and the local nature of the learning-dependency, respectively. Our method consists of three stages, (1) Build document association relationship by calculating the distributional asymmetry of the domain terms. (2) Generate the candidate KU-pairs by measuring the locality of the dependencies. (3) Use classification algorithm to identify the learning-dependency between KU-pairs. Our experimental results show that our method extracts the learning-dependency efficiently and reduces the computational complexity.