Image Analysis at Scale in the Undergraduate Humanities Classroom

Today, Scott Schopieray and I are presenting some of the work we have been experimenting with in the classroom at the Chicago Colloquium on DH and Computer Science. This is work in progress, so feedback is most welcome! Below are our slides as well as the original presentation proposal. Scott has also posted some of his thoughts about this work on his website here.

The Chicago Colloquium on Digital Humanities and Computer Science
November 9-11, 2018
Loyola University Chicago

Image Analysis at Scale in the Undergraduate Humanities Classroom

Kristen Mapes (@kmapesy) and Scott Schopieray (@schopie1)

Slides:

 

Abstract:

We are interested in sharing approaches and examples of teaching computational image analysis to a non-computer science student population in a humanities context. Digital humanities curricula usually include methodological introductions to such topics as text mining and analysis, mapping, network analysis, metadata, preservation, and archival curation. Since 2015, we have incorporated large scale image analysis into introductory digital humanities courses at the undergraduate level.

Incorporating image analysis into the suite of digital humanities methods adds to the possibilities of DH: it makes digitized collections and born digital image and video content available for analysis at scale beyond those currently available for study with text analysis methods. By expanding this potential corpus of material available for study, we also open up digital humanities to more topics that resonate with our students. Teaching digital humanities to undergraduates is a process of eliciting excitement about an expanded methodological toolkit, and including large scale image analysis is a striking way to get students engaged in thinking about corpora, metadata, method, and presentation.

In the Introduction to DH class at the undergraduate level, we have demonstrated the use of ImagePlot (http://lab.softwarestudies.com/p/imageplot.html) and have given students the opportunity to use the software as well. This approach has led several students to pursue a final project using this method (for example: http://smentow2.msu.domains/puremichigan/). This presentation will share how we have used a corpus of Harlem Renaissance art images to tie the software instruction into the content of the course and how we are now extending computational image analysis instruction beyond ImagePlot to incorporate Distant Viewing (https://www.distantviewing.org/) tools into the classroom in early Fall 2018. This extension of image analysis instruction to algorithmic face detection and classification opens up new possibilities for analyzing material in the classroom and engaging in critical conversations about how such programs work in the corpora we create as well as those used in the corporate world.

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