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首页> 外文期刊>International Journal of Information and Communication Technology Education: An Official Pubblication of the Information Resources Management Association >Discovering Learners' Characteristics Through Cluster Analysis for Recommendation of Courses in E-Learning Environment
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Discovering Learners' Characteristics Through Cluster Analysis for Recommendation of Courses in E-Learning Environment

机译：通过集群分析发现学习者的特征，以了解电子学习环境中的建议

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摘要

With the emergence of the web, traditional learning has changed significantly. Hence, a huge number of ‘e-learning systems' with the advantages of time and space have been created. Currently, many e-learning systems are being used by a large number of academic institutions worldwide which allow different users of the system to perform various tasks based on their goals. However, most of these systems follow a ‘one size fits all' approach where same resources are offered to learners irrespective of their unique learning requirements. Therefore, personalization is required as a part of e-learning systems which offers resources to learners based on their profile. This research aims to perform cluster analyses in order to validate clusters created through a k-means algorithm. The clusters will be used to classify a new learner into its appropriate class and recommend relevant courses. Finally, the accuracy of the recommendation is evaluated using various evaluation metrics. The proposed recommendation system helps learners to improve their academic performance and hence overall learning process as well.
机译：随着网络的出现，传统学习发生了重大变化。因此，已经创建了具有时间和空间优势的大量“电子学习系统”。目前，许多全世界的学术机构正在使用许多电子学习系统，这允许系统的不同用户根据其目标执行各种任务。然而，这些系统中的大多数遵循“一种尺寸适合所有”方法，其中提供给学习者的相同资源，而不管他们独特的学习要求。因此，作为电子学习系统的一部分需要个性化，该系统根据其配置文件为学习者提供资源。该研究旨在执行群集分析，以便验证通过K-Means算法创建的集群。群集将用于将新学习者分类为适当的课程并推荐相关课程。最后，使用各种评估度量评估推荐的准确性。拟议的建议制度有助于学习者提高学业成绩，从而提高整体学习过程。

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