||Annotations in the Classroom; The Classroom in Annotations
||May 9 2019
||In this talk I want to explore the impact of using Hypothesis in the classroom. What does it mean to read, think, and annotate publicly? How does it change your learning experience as a student? How do you evaluate and assess different annotation styles as a teacher?
As a student I can share my own experience of this new mode of teaching and learning. As a data scientist, I want to give a taste of possible new metrics and measurements based on annotation data. Finally, as a critical scholar I am hoping to explore how this new metrification and monitoring of reading might affect education.
The talk will rely on data outlined in this essay: https://course-journals.lib.sfu.ca/index.php/pdc2018/article/view/240/213
||Data Scavenger Hunts: Learning about Data Together
||May 9 2019
||Daisy Bingham Room
||Data exploration and visualization are a highly accessible gateway activity to learning data science. In this talk, we discuss our experience with "Data Scavenger Hunts" using web apps to democratize data science and make it accessible to a wide variety of audiences. In order to acheive this, we have developed an R package called `burro` that can enable public datasets to be explored together via a sharable web app. In this talk, we talk about our experience with using data scavenger hunts to teach each other interesting things about data. In particular, we share our experiences with exploring the NHANES (National Health Nutirition Examination Survey) data and the insights we have taught each other. We show that this guided and communal data exploration leads to increased confidence and curiosity about data science in Biodata-Club, our learning community. `burro` apps can be deployed by anyone to start conversations about data.
||Spanking and Spreadsheets: Data-driven Sex Journalism
||Jacqueline Nolis & Heather Nolis
||May 9 2019
||When we saw that the Stranger, Seattle’s alternative newspaper, was running a survey on kinks and sexual preferences, we knew we had to get our hands on the data. We convinced the that using machine learning methods on the responses would be a good idea, and then we quickly set out to analyzing them. But we had never written an article for a newspaper before—nor had we worked with data even remotely as dirty. It turns out what makes for a good blog post or technical journal is very different than writing for print, especially for such a sensitive topic. In this talk we will cover how we made sense of the lewd data, the statistical methods we used (and failures we produced), as well as the final results that ended up in our feature article: “There Are Four Kinds of Sex Partners (which one are you).”