基于游戏的学习环境中有效序列模式挖掘的扩展前缀跨度

R. S. Bermudez, Ariel M. Sison, Ruji P. Medina
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引用次数: 3

摘要

本文提出了一个扩展版本的PrefixSpan,作为一个更好的基于游戏的学习环境(GBLE)的顺序模式挖掘。扩展版PrefixSpan在时间区间约束的积分、有值动作的聚类和封闭序列的提取等方面进行了改进。这三个概念是在先前的工作表明PrefixSpan在生成可用于GBLE辅导服务的序列模式方面的局限性之后衍生出来的。对扩展的PrefixSpan进行了性能评价和生成序列模式质量分析两个阶段的评价。评估结果表明,扩展版本在执行时间和生成的序列模式数量方面提供了显著的改进。最后,将其集成到GBLE后,生成的序列模式质量有了显著提高,这表明它提供了更好的辅导服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extended PrefixSpan for Efficient Sequential Pattern Mining in a Game-based Learning Environment
This paper proposed an extended version of PrefixSpan as a better sequential pattern mining for a game-based learning environment (GBLE). The extended version of PrefixSpan evolved on integrating time interval constraints, clustering valued actions and extracting the closed sequences. These three concepts were derived after a previous work showed limitations of PrefixSpan in generating sequence patterns that can be used in tutoring services of a GBLE. The extended PrefixSpan underwent two phases of evaluation, performance evaluation and analyzing the quality of generated sequence patterns. The evaluation results showed that the extended versions provided a significant improvement in terms of execution time and the number of generated sequence patterns. Lastly, it shows significant improvement in the quality of sequence patterns generated as shown in better tutoring service it provided after integrating it to the GBLE.
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