AN APPROACH FOR CONSTRUCTING SUITABLE LEARNING PATH FOR DOCUMENTS OCCASIONALLY COLLECTED FROM INTERNET

摘要

With a faster, more accessible Internet, nowadays people tend to search and learn from Internet for some fragmented knowledge. Usually, a vast amount of documents, homepages or learning objects, will be returned by some powerful search engines with no particular order. Even if they might really be related, a user still has to move forward and backward among the material trying to figure out which page to read first because the user might has had little or no experience in the specific domain. Although a user may have some intuitions about the domain but these intuitions are yet to be connected. This paper proposes a learning path construction approach based on a modified TF-IDF, the ATF-1DF, and the well-known Formal Concept Analysis, the FCA, algorithms. First, the approach constructs a Concept Lattice using keywords extracted by the ATF-IDF from collected documents to form a relationship hierarchy between all the concepts represented by the keywords. It then uses FCA to compute mutual relationships among documents to decide a suitable learning path.