Topic Modeling for Makerspace Artifact Analysis

Authors

  • David Wilson UNC Charlotte
  • Johanna Okerlund University of North Carolina at Charlotte
  • Dominique Exley

DOI:

https://doi.org/10.32473/flairs.v34i1.128699

Keywords:

Makerspace, Topic Modeling, Latent Dirichlet Allocation

Abstract

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.

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Published

2021-04-18

How to Cite

Wilson, D., Okerlund, J., & Exley, D. (2021). Topic Modeling for Makerspace Artifact Analysis. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128699

Issue

Section

Special Track: AI for Internet of Things, Maker Education and Creative Learning