Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An information-theoretic perspective of tf—idf measures
Information Processing and Management: an International Journal
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Intelligent web-based learning system with personalized learning path guidance
Computers & Education
Personalized curriculum sequencing utilizing modified item response theory for web-based instruction
Expert Systems with Applications: An International Journal
QoL guaranteed adaptation and personalization in E-learning systems
IEEE Transactions on Education
Identifying patterns in learner's behavior Using Markov chains and n-gram models
CSCC'11 Proceedings of the 2nd international conference on Circuits, Systems, Communications & Computers
A Heuristic Method for Learning Path Sequencing for Intelligent Tutoring System ITS in E-learning
International Journal of Intelligent Information Technologies
Review: Educational data mining: A survey and a data mining-based analysis of recent works
Expert Systems with Applications: An International Journal
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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.