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Visualization of sentiment spread on social networked content: Learning analytics for integrated learning environments

机译：在社交网络内容上传播情绪的可视化：学习综合学习环境的分析

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Social Media has been disrupting traditional technology mediated learning, providing students and educators with unsupervised and informal tools and spaces where authentic learning occurs. Still, the traditional LMS persists as the core element in this context, while lacking additional management, monitoring and analysis tools to handle informal learning and content. In this paper, we present an integrated methodology that combines social network analytics, sentiment analysis and topic categorization to perform social content visualizations and analysis aimed at integrated learning environments. Results provide insights on networked content dimension, type of structure, degree of popularity and degree of controversy, as well as on their educational and functional potential in the field of learning analytics.
机译：社交媒体一直在扰乱传统技术介导的学习，为学生和教育工作者提供无监督和非正式的工具和现实学习的空间。尽管如此，传统的LMS仍然存在于此上下文中作为核心元素，而缺乏额外的管理，监控和分析工具，以处理非正式学习和内容。在本文中，我们提出了一种集成的方法，将社交网络分析，情感分析和主题分类结合在一起，以执行综合学习环境的社交内容可视化和分析。结果提供了对网络内容维度，结构类型，普及程度和争议程度的见解，以及他们在学习分析领域的教育和功能潜力。

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