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首页> 外文会议>Artificial intelligence: Theories, models and applications >The Support of e-Learning Platform Management by the Extraction of Activity Features and Clustering Based Observation of Users
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The Support of e-Learning Platform Management by the Extraction of Activity Features and Clustering Based Observation of Users

机译：通过提取活动特征和基于聚类的用户观察来支持电子学习平台管理

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摘要

We present an application of data mining in e-learning, where web platform management was supported by the extraction of users' activity features and further by the clusterisation of users' profiles. By this approach we have identified groups of users with a similar activity on e-learning platform and were able to observe their performance. The experiments presented in this paper were performed on the real data coming from Moodle platform. Comparing to the other research in this filed, that focus on the analysis of students, we investigated teachers' behaviour. We have proposed a smoothing model in the form of a dynamic system, that was used to transform the logged events into time series of activities. These series were later used to cluster teachers' performance and to divide them into three groups: active, moderate and passive users. The main aim of our research was to propose and test an data mining based approach to support of e-learning management by an observation of teachers leading to an increase of the process quality.
机译：我们介绍了数据挖掘在电子学习中的应用，其中通过提取用户的活动特征并进一步通过对用户的配置文件进行聚类来支持Web平台管理。通过这种方法，我们确定了在电子学习平台上具有类似活动的用户组，并能够观察他们的表现。本文介绍的实验是在来自Moodle平台的真实数据上进行的。与本研究中的其他重点研究学生的研究相比，我们调查了教师的行为。我们提出了一种动态系统形式的平滑模型，该模型用于将记录的事件转换为活动的时间序列。这些系列后来被用来对教师的表现进行聚类，并将其分为三类：主动，中度和被动用户。我们研究的主要目的是通过对教师的观察来提出和测试一种基于数据挖掘的方法，以支持电子学习管理，从而提高过程质量。

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