Real-time learning behavior mining for e-learning

Yen-Hung Kuo, Juei-Nan Chen, Yu-Lin Jeng, Yueh-Min Huang
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引用次数: 6

Abstract

Over the last years, we have witnessed an explosive growth of e-learning. More and more learning contents have been published and shared over the Internet. Therefore, how to progress an efficient learning process becomes a critical issue. This paper proposes a sequential mining algorithm to analyze learning behaviors for discovering frequent sequential patterns. By these patterns, we can provide suggestions for learners to select their interest learning contents. Different to other sequential mining algorithms, this study provides an incrementally method to analyze learning sequencing. More specifically, the mining algorithm in this paper can provide real-time analysis, and then report to learners for selecting learning contents more easily.
面向电子学习的实时学习行为挖掘
在过去的几年里,我们见证了电子学习的爆炸式增长。越来越多的学习内容在互联网上发布和分享。因此,如何推进一个高效的学习过程成为一个关键问题。本文提出了一种序列挖掘算法来分析学习行为,以发现频繁的序列模式。通过这些模式,我们可以为学习者选择自己感兴趣的学习内容提供建议。与其他序列挖掘算法不同,本研究提供了一种增量分析学习序列的方法。更具体地说,本文的挖掘算法可以提供实时分析,然后报告给学习者,以便更容易地选择学习内容。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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