Topic Modeling for Makerspace Artifact Analysis
Topic Modeling for Makerspace Artifact Analysis
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DOI：
10.32473/flairs.v34i1.128699
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
David C. Wilson;Johanna Okerlund;Dominique Exley
中科院分区：
文献类型：
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作者：
David C. Wilson;Johanna Okerlund;Dominique Exley
随着制作现象变得更加普遍、多样化和广泛，识别制作活动类型的一般时间或空间趋势变得越来越具有挑战性。对于想要了解创客现象对世界的影响或有兴趣在自己的创客环境中扩大参与的创客空间领导者来说，识别参与者的创客趋势非常重要。本文展示了如何使用 LDA 进行主题建模来分析创客工件，并说明如何使用这些类型的见解来推断创客现象，以及如何为扩大参与范围提供信息。
As the making phenomenon becomes more prevalent, diverse, and vast, it becomes increasingly challenging to identify general temporal or spatial trends in types of making endeavors. Identifying trends in what participants are making is important to makerspace leaders who seek to understand the impact of the making phenomenon on the world or who are interested in broadening participation within their own maker contexts. This paper shows how topic modeling by means of LDA can be used to analyze maker artifacts, and illustrates how these types of insights can be used to make inferences about the making phenomenon, as well as to inform efforts to broaden participation.