Peripheral Vision

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引用次数: 36

Abstract

In the dozen years since massive open online courses (MOOCs) have been a part of open-source online learning, the related platforms and technologies have settled out to some degree. This chapter indirectly explores 10 of the most well-known MOOC platforms based on social data from the following sources: large-scale web search data (via Google Correlate), academic research indexing (Google Scholar), social imagery and related image tagging (Google Image Search), crowd-sourced articles from a crowd-sourced encyclopedia (Wikipedia), microblogging data (Twitter), and posts and comments from social networking data (Facebook). This analysis is multimodal, to include text and imagery, and the analyses are enabled by various forms of “distant reading,” including topic modeling, sentiment analysis, and computational text analysis, and manual coding of social imagery. This chapter aims to define MOOC platforms indirectly by their course contents and the user bases (and their social media-based discourses) that have grown up around each.
周边视觉
大规模在线开放课程(MOOCs)作为开源在线学习的一部分,在过去的十几年中,相关的平台和技术已经在一定程度上得到了解决。本章间接探讨了基于以下来源的社交数据的10个最知名的MOOC平台:大规模网络搜索数据(通过Google关联),学术研究索引(Google Scholar),社交图像和相关图像标记(Google image search),来自众包百科全书(Wikipedia)的众包文章,微博数据(Twitter),以及来自社交网络数据(Facebook)的帖子和评论。这种分析是多模态的,包括文本和图像,分析是通过各种形式的“远程阅读”实现的,包括主题建模、情感分析、计算文本分析和社会图像的手动编码。本章旨在通过其课程内容和围绕每个平台成长起来的用户群(及其基于社交媒体的话语)来间接定义MOOC平台。
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