Day 2: Pedagogy and assessment¶
Important
All workshops and presentations are hosted on Zoom. Please register at UBC CTLT Events to join us!
Once registered, you can visit the Jupyterdays Canvas course shell: https://canvas.ubc.ca/courses/66517```
August 13, 2020¶
Time |
Session Title |
Session Lead |
|---|---|---|
9:00 am |
Welcome |
|
9:15 am |
||
9:30 am |
||
10:45 am |
Break |
|
11:00 am |
||
11:30 am |
Lunch |
|
1:00 pm |
Accessibility and inclusivity in teaching data science |
|
1:30 pm |
||
2:00 pm |
Break |
|
2:15 pm |
||
3:30 pm |
Session ends |
Introduction to nbgrader¶
nbgrader is an application for creating and grading assignments with Jupyter notebook. We will learn how to create assignments with nbgrader and how to write meaningful tests with feedback to evaluate student code. The magic of nbgrader is the autograding! Evaluate student code and give feedback automatically. But there’s a catch… we need to write a custom Python script to move student submissions into the nbgrader folders and then we need another script to reassemble the graded notebooks for uploading back to Canvas. It can be done and it will be fun!
Teaching in an online setting¶
In this session, I will discuss transitioning an introductory data science course online this summer. I will discuss the strategies used, challenges faced, and lessons learned from the experience and share some feedback from student surveys.
Effective pedagogies for teaching data science¶
In this session I will discuss a few tips for teaching data science, especially with Jupyter notebooks:
A problem-first teaching methodology.
Live coding.
My implementation of think-pair-share.
Real-time collaborative documents (perfect for online teaching!).
(Time-permitting) Some thoughts on teaching in general.