What does machine learning have to do with human learning? What does big data have to do with small classes? How do we help our students take control of their data in a world of government and corporate surveillance? How can we respond critically and with nuance to calls for data-driven teaching practices? What new methods of disinformation are possible in the age of digital networks? How can we resist them and help our students to do the same?
Participants in this track will learn the basics of data science ― including an introduction to coding ― as it applies to education. Participants will engage ways to help students grow in their data literacy in a variety of contexts, including the combating of disinformation online. Participants will also come away with a more informed and nuanced ability to approach decisions regarding educational technology and institutional data policies. This track is ideal for faculty, students, instructional designers, and administrators ― as well as data scientists and software developers working in educational technology ― interested in learning about the relationship of data science, critical digital pedagogy, and the goals of liberal education. No previous coding or statistics experience necessary.
The readings below will help provide background and context for the work of Data Literacies.
- Streitfeld, David. Teacher Knows if You’ve Done the E-Reading
- Tufekci, Zeynap. Algorithmic Harms Beyond Facebook and Google: Emergent Challenges of Computational Agency
- boyd, danah. Hacking the Attention Economy
- Shaffer, Kris. Trump, Russia, Bots, and Breitbart: tl;dr edition
- Shaffer, Kris. Machine Learning
- Grolemund, Garrett and Hadley Wickham. R for Data Science