Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Visualization of learner's state and learning paths with knowledge structures
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
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To extract learning order dependencies, we propose a method for analyzing growth trajectories. Due to limitations of the ISM-based method, weaker dependencies considered negligible for most learners' growth are likely to be lost. The proposed method, which is based on sequential pattern mining and minimum support, is expected to extract such dependencies. In addition, for complex dependencies, we can uniquely determine learning order by applying a pruning method based on supports. A case study of offshore software development is also discussed to verify the applicability of the proposed method.