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The digest generation method of learning process based on learning log data

机译:基于学习 日志 数据中学习 过程的 摘要 生成方法

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摘要

The rapid growth of Information technology enables us to realize not only classical learning environment, i.e., classroom lecture and individual learning environment, but also exploratory learning environment in which learners are required their positive learning activity and collaborative learning environment. In these new learning environments, the hyper structured learning contents are quite general, and its information on hyper structure are important for evaluation of learning activities and contents/curriculum. In this study, we propose the digest generation method of learning activities based on learning log data sets, in which learning object meta-data and information on hyper structure are used effectively. Particularly this proposed method is formalized as a multi-objective optimization problem, so we apply the Genetic Algorithm to solve this. In this paper, we describe proposed method from theoretical point of view and features of "The Digest Generation System" and through some experiments we show validity and effectiveness of proposed method.
机译:信息技术的快速增长使我们不仅能够实现古典学习环境,即课堂讲义和个人学习环境,也是探索性学习环境,其中学习者需要他们积极的学习活动和协作学习环境。在这些新的学习环境中,超结构化学习内容非常一般,其有关超结构的信息对于评估学习活动和内容/课程非常重要。在本研究中,我们提出了基于学习日志数据集的学习活动的消化生成方法,其中有效地使用学习对象元数据和关于超结构的信息。特别是这种提出的方​​法被形式化为多目标优化问题,因此我们应用遗传算法来解决这个问题。在本文中,我们描述了来自“消化生成系统”的理论观点和特征的提出方法,并通过一些实验显示了所提出的方法的有效性和有效性。

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