A mining-based approach on discovering courses pattern for constructing suitable learning path

Tung Cheng Hsieh, Tzone I. Wang

研究成果: Article同行評審

29 引文 斯高帕斯(Scopus)

摘要

In recent years, browser has become one of the most popular tools for searching information on the Internet. Although a person can conveniently find and download specific learning materials to gain fragmented knowledge, most of the materials are imperfect and have no particular order in the content. Therefore, most of the self-directed learners spend most of time in surveying and choosing the right learning materials collected from the Internet. This paper develops a web-based learning support system that harnesses two approaches, the learning path constructing approach and the learning object recommending approach. With collected documents and a learning subject from a learner, the system first discovers some candidate courses by using a data mining approach based on the Apriori algorithm. Next, the leaning path constructing approach, based on the Formal Concept Analysis, builds a Concept Lattice, using keywords extracted from some selected documents, to form a relationship hierarchy of all the concepts represented by the keywords. It then uses FCA to further compute mutual relationships among documents to decide a suitable learning path. For a chosen learning path, the support system uses both the preference-based and the correlation-based algorithms for recommending the most suitable learning objects or documents for each unit of the courses in order to facilitate more efficient learning for the learner. This e-learning support system can be embedded in any information retrieval system for surfers to do more efficient learning on the Internet.

原文English
頁(從 - 到)4156-4167
頁數12
期刊Expert Systems With Applications
37
發行號6
DOIs
出版狀態Published - 2010 六月 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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