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Design of a conceptual knowledge extraction framework for a social learning environment based on Social Network Analysis methods

机译:基于社会网络分析方法的社会学习环境概念性知识提取框架设计

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The advent of social media in education has the potential to foster collaborative learning. Exploring students' interactions on the social media tools is an important research direction, which could bring an insight into the collaborative learning process. Therefore, our aim is to propose a conceptual framework for knowledge extraction and visualization from a social media-based learning environment. In particular, we focus on our in-house platform, called eMUSE, which has been successfully used in a project-based learning scenario. The paper addresses the construction of a social graph starting from students' interactions on the social media tools; the objective is to identify appropriate social network analysis techniques that can answer specific educational needs and integrate them in a conceptual knowledge extraction framework. The basis, rationale and analysis levels of the framework are discussed in the paper.
机译:社交媒体在教育中的出现具有促进协作学习的潜力。在社交媒体工具上探索学生的互动是一个重要的研究方向,可以为协作学习过程带来深刻见解。因此,我们的目标是为从基于社交媒体的学习环境中提取和可视化知识提出一个概念框架。特别是,我们专注于名为eMUSE的内部平台,该平台已成功用于基于项目的学习方案中。本文着重从学生在社交媒体工具上的互动出发,构建社交图。目的是确定可以满足特定教育需求的适当社交网络分析技术,并将其整合到概念性知识提取框架中。本文讨论了该框架的基础,原理和分析级别。

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