Construction of Learning Path Using Ant Colony Optimization from a Frequent Pattern Graph by Souvik Sengupta, Sandipan Sahu and Ranjan Dasgupta.
Abstract:
In an e-Learning system a learner may come across multiple unknown terms, which are generally hyperlinked, while reading a text definition or theory on any topic. It becomes even harder when one tries to understand those unknown terms through further such links and they again find some new terms that have new links. As a consequence they get confused where to initiate from and what are the prerequisites. So it is very obvious for the learner to make a choice of what should be learnt before what. In this paper we have taken the data mining based frequent pattern graph model to define the association and sequencing between the words and then adopted the Ant Colony Optimization, an artificial intelligence approach, to derive a searching technique to obtain an efficient and optimized learning path to reach to a unknown term.
The phrase “multiple unknown terms, which are generally hyperlinked” is a good description of any location in a topic map for anyone other than its author and other experts in the field it describes.
Although couched in terms of a classroom educational setting, I suspect techniques very similar to these could be used with any topic map interface with users.