Nozomi Kato

THATCamp@CAA: Teaching Art Online

Google Doc Version is HERE.

Participants: Pam Stefanuca, Nathalie Hager, Chinghsin Wu, Tessa Fleming, L W Schermerhorn, Naraelle Hohensee, Peter Chanthanakone, Ian McDermott, Steven Zucker, Beth Harris, John Resig, Ellen Tani and Nozomi Kato (in the order of the email list that I received at the end).

I proposed my “baby session” because I wanted to discuss something small and local (innovative ways of using digital tools in CUNY/NYC art classrooms), but I chose to open up the topic to accommodate the larger audience at CAA. As a result, my session became more general and enjoyable for all participants. However, there were several points that I would have liked to address in my session, so I list them below.

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Note 1: My friend in LaGuardia CC Library sent us the links below:

1) “History of Online Instructions” at CUNY.

2) University-wide “Hybrid Initiative” at CUNY.

#This is where NYC art students who attend city-funded universities stand right now in terms of online instructions.

“Going the Distance: Online Education in the United States, 2011.”

#This last link is something about online instruction in the U. S. more generally to start with.

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Note 2: Lecture video production.
There are advantages and disadvantages of producing video lectures of the instructors themselves. Advantages: the videos can be used to promote themselves as art scholars, their study abroad programs and/or their future courses. Disadvantages: missing opportunities of diversifying their classroom instructions. In NYC, I assign video lectures of African American, Caucasian and Asian scholars to expose my students to different modes of information delivery. This is just an example, but those can add nuances to the contents covered in my art history survey courses. These nuances are valued in NYC/CUNY where I can be asked to teach foundation level courses from Eurocentric, Afrocentric, and/or Japan-centric perspectives. When I taught at CUNY Medgar Evers College in central Brooklyn, for instance, my students watched a video in which an African American priest was discussing Gothic Cathedrals. This is for an art history survey course that begins with ancient Egypt in Africa.

Thanks to everyone who participated in the session! 
It was great to learn about your experiences through our thoughtful and lively discussion.
Please feel free to add more resources and activity ideas to the Google doc (link is below).

Best wishes,

La Tanya S. Autry
@artstuffmatters

PowerPoint: tinyurl.com/nvuhcy4  

Google Doc: tinyurl.com/k6amfjq

Thanks to everyone who participated in THATCamp CAA 2015. We’d like to invite you to offer feedback on your experience here so we can make the next one even better!

Notes and Code from the Creative Coding Workshop can be found here:

bit.ly/ThatCoding101

Thanks!!

What technologies are employed in the classroom. What hardware, software, best practices could be employed in a test classroom space for art faculty.

Examples can include online tools, ipad, tablet, or laptop applications, online collaboration tools, and art making applications.

The goal is to brainstorm tools and applications for testing new teaching and learning ideas before implementing in the classroom…

Find it here: docs.google.com/document/d/1PHnMk06jrQprpaIDYu49gRRQCJJj8txAgyexTIWIItY/edit

Slides and links from the February 10th, 2015 THATCamp CAA session on applying computer vision techniques to art history research, facilitated by John Resig (and at Ukiyo-e.org).

Learn More About Computer Vision

• Session Slides: Applying Computer Vision to Art History
  ejohn.org/files/thatcamp-cv-2015.pdf
• Paper: Using Computer Vision to Increase the Research Potential of Photo Archives
  ejohn.org/research/computer-vision-photo-archives/
• Additional research: ejohn.org/research/
• Syllabus: Brown Computer Vision Course
  cs.brown.edu/courses/csci1430/
  

Computer Vision Software and Libraries

• Optical Character Recognition: Tesseract (Open Source, Unsupervised)
  code.google.com/p/tesseract-ocr/
• Face Matching: OpenBiometrics (Open Source, Supervised)
  openbiometrics.org/
• Image Similarity: imgSeek (Open Source, Unsupervised)
  www.imgseek.net/
• Image Similarity: TinEye’s MatchEngine (Commercial, Unsupervised)
  services.tineye.com/MatchEngine
• Image Categorization: Ersatz (Commercial, Minimally Supervised)
  ersatz1.com/
• General Computer Vision: libCCV (Open Source, Supervised, Requires Coding)
  libccv.org/
• General Computer Vision: OpenCV (Open Source, Supervised, Requires Coding)
  opencv.org/

The doc for this group: bit.ly/ThatCamp_digitools

Link to Google Doc of Notes: docs.google.com/document/d/1NZ9xyFZxZYru62qXI4oyvkD3JWnYjpPf4cYgEK_R8xo/edit?usp=sharing

Let’s work off of this doc to share activities

docs.google.com/document/d/1qLMhJpD87FwSHf26Hy1Ddc0GCHFGcqLAaVZYkKoFHhg/edit