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ML Education Research

  1. Fiebrink, Rebecca. ”Machine learning education for artists, musicians, and other creative practitioners.” ACM Transactions on Computing Education (TOCE) 19.4 (2019): 1-32. (https://dl.acm.org/doi/10.1145/3294008)
  2. Yang, Qian, et al. ”Grounding interactive machine learning tool design in how non-experts actually build models.” Proceedings of the 2018 designing interactive systems conference. 2018. (https://dl.acm.org/doi/10.1145/3196709.3196729)
  3. Shapiro, R. Benjamin, and Rebecca Fiebrink. ”Introduction to the special section: Launching an agenda for research on learning machine learning.” ACM Transactions on Computing Education (TOCE) 19.4 (2019): 1-6. (https://dl.acm.org/doi/10.1145/3354136)
  4. Sulmont, Elisabeth, Elizabeth Patitsas, and Jeremy R. Cooperstock. ”What is hard about teaching machine learning to non-majors? Insights from classifying instructors’ learning goals.” ACM Transactions on Computing Education (TOCE) 19.4 (2019): 1-16. (https://dl.acm.org/doi/abs/10.1145/3336124)
  5. Ko, Amy J. “We Need to Learn How to Teach Machine Learning.” Medium, Bits and Behavior, 21 Aug. 2017, https://medium.com/bitsand-behavior/we-need-to-learn-how-to-teach-machine-learningacc78bac3ff8.
  6. Field Guide to Making AI Art Responsibly (https://www.liacoleman.com/the-responsible-ai-art-field-guide)

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