Breaking it down, and building it back up again

In this talk we describe the process of moving an introductory data science course online. Specifically, we describe the thought process and the tooling for breaking down course components like lectures, workshops, weekly assignments, and group projects into parts that can be delivered synchronously and asynchronously online.

Transcript

1. Breaking it down, and building it back up again minebocek mine-cetinkaya-rundel cetinkaya.mine@gmail.com bit.ly/talso
2. introds.org 20 credits No prereqs Fall 2020 All online!
3. Course content
4. Fall 2019 Week 1 Welcome & Intro to toolkit Week 2 Data visualisation Week 3 Data wrangling Week 4 Data visualisation & wrangling Week 5 Extracting meaning from data Week 6 Data import & Web scraping & Iteration Week 7 Language of models Week 8 Extending modeling Week 9 Model selection & validation Week 10 Logistic models & quantifying uncertainty Week 11 Ethics & Looking beyond
5. Fall 2020 Week 2 Data visualisation Week 1 Welcome & Intro to toolkit Week 3 Data wrangling Week 4 Data visualisation & wrangling Week 5 Extracting meaning from data Week 7 Data science ethics Week 6 Data import & Web scraping & Iteration Week 8 Linear & logistic regression Week 9 Multiple regression & model selection Week 10 Prediction & model validation Week 11 Quantifying uncertainty & looking beyond no change change Clear and early emphasis on ethics! Reorder for consistent weekly video length
6. Weekly components
7. Fall 2019 Lecture 2 x 50 min Content delivery Sporadic application exercises Lab 1 x 2 hr In teams / R Markdown report on GitHub Semi auto / semi human feedback & marking Individual / R Markdown report on GitHub Sami auto / semi human feedback & marking HW Quiz Individual / learnr code exercises & MC questions Auto feedback & marking (for completion) Self study Office hours Lab finish up Reading ???
8. Videos Pre-recorded for content delivery (~1.25 hrs / week) Weekly syncronous & recorded “State of the IDS” videos (~15 mins / week) Fall 2020 Individual / R Markdown report on GitHub Sami auto / semi human feedback & marking HW pre HW Individual / learnr Auto feedback
9. Fall 2019 HW
10. Fall 2020 pre-HW
11. Fall 2020 HW
12. Videos Pre-recorded for content delivery (~1.25 hrs / week) Weekly syncronous & recorded “State of the IDS” videos (~15 mins / week) Lab 1 x 1 hr In teams, synchronous online, with asynchronous option R Markdown report on GitHub Semi auto / semi human feedback & marking Lab finish up Fall 2020 Individual / R Markdown report on GitHub Sami auto / semi human feedback & marking HW pre HW Individual / learnr Auto feedback
13. Pre-lab exercises During lab: Warm up activity During lab: Business as usual Post-lab finish up Fall 2020 Lab Challenge: support during breakout sessions Challenge: tech and motivation for meeting outside of class
14. Videos Pre-recorded for content delivery (~1.25 hrs / week) Weekly syncronous & recorded “State of the IDS” videos (~15 mins / week) Quiz Individual / learnr code exercises & MC questions Auto feedback & marking (for accuracy) Lab 1 x 1 hr In teams, synchronous online, with asynchronous option R Markdown report on GitHub Semi auto / semi human feedback & marking Lab finish up Fall 2020 Individual / R Markdown report on GitHub Sami auto / semi human feedback & marking HW pre HW Individual / learnr Auto feedback
15. Fall 2020 Quiz
16. Videos Pre-recorded for content delivery (~1.25 hrs / week) Weekly syncronous & recorded “State of the IDS” videos (~15 mins / week) Quiz Individual / learnr code exercises & MC questions Auto feedback & marking (for accuracy) Lab 1 x 1 hr In teams, synchronous online, with asynchronous option R Markdown report on GitHub Semi auto / semi human feedback & marking Lab finish up Fall 2020 Individual / R Markdown report on GitHub Sami auto / semi human feedback & marking HW pre HW Individual / learnr Auto feedback Self study Code along sessions Reading ??? Weekly syncronous & recorded (~45 mins / week) Office hours Shorter but more frequent & at different times of day
17. Post on YouTube, turn on community captioning Challenge: assessing effectiveness
18. Projects
19. Fall 2019 Project Spans ~half semester, in teams In class presentation R Markdown report (executive summary) & GitHub repo Human (tutor, CO, peer) marking, minimal written feedback Fall 2020 Project Spans ~half semester, in teams Zoom presentation or pre-recorded video R Markdown report (executive summary) & GitHub repo Human (tutor, CO, peer) marking, minimal written feedback
20. Photo by Christopher Burns on Unsplash minebocek mine-cetinkaya-rundel cetinkaya.mine@gmail.com bit.ly/talso introds.org datasciencebox.org