An approach for constructing suitable learning path for documents occasionally collected from Internet

Tung Cheng Hsieh, Ti Kai Chiu, Tzone-I Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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-IDF, 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.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Pages3138-3143
Number of pages6
DOIs
Publication statusPublished - 2008 Dec 29
Event7th International Conference on Machine Learning and Cybernetics, ICMLC - Kunming, China
Duration: 2008 Jul 122008 Jul 15

Publication series

NameProceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
Volume6

Other

Other7th International Conference on Machine Learning and Cybernetics, ICMLC
CountryChina
CityKunming
Period08-07-1208-07-15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Control and Systems Engineering

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