Schedule for: 25w2027 - Computational Teaching and Learning

What is everyone working on? What do we hope to get out of the workshop? What software are we using? Feel free to drop-in anytime to meet other participants, see what people are working on and get help installing software on your machine.

We have been using Python, Jupyter, Syzygy, GitHub, nbgrader, CanvasAPI and Jupyter Book in various combinations in several undergraduate courses in the Department of Mathematics at UBC since 2015. We will describe our approach to computational teaching and learning including instructional design, open educational resources, auto-graded assessments and TA training.

Meet in TCPL foyer and then we all go into town for dinner.

A brief introduction to BIRS with important logistical information, technology instruction, and opportunity for participants to ask questions.

We will outline design principles for constructing computational learning materials with Python and Jupyter. We will articulate learning goals, identify problems and examples which align with learning goals, explore different levels of scaffolding and how to embed meaningful feedback for students.

We will explore different systems of varying complexity to share, collect, grade and return computational documents. The simplest workflow involves Syzygy, a learning management system (LMS) and manual grading. We will also describe a workflow involving Syzygy, LMS, nbgrader and LMS API to manage, autograde and upload grades and feedback for thousands of students.

Meet in foyer of TCPL to participate in the BIRS group photo. The photograph will be taken outdoors, so dress appropriately for the weather. Please don't be late, or you might not be in the official group photo!

Participants will work in groups based on course topic and experience to design computational documents with Python and Jupyter. Groups will discuss various aspects of notebook design, as well as activity implementation and delivery. Participants will write their own notebooks with the support of their groups and organizers.

Numerical computing has transformed modern science, enabling researchers to analyze vast datasets, simulate complex experiments, and gain insights into intricate systems. Despite its critical role, computing remains underrepresented in most science curricula. In this talk, I will address the pressing need to integrate computational training into physics education and present research exploring challenges across all scales—from institutional structures to individual student understanding. I will discuss how these findings can drive the computational revolution in science education by informing institutional incentives, teaching practices, course activities, and assessment tools. Additionally, I will examine the opportunities and challenges posed by generative artificial intelligence, highlighting ongoing research in this area. This work is supported by Michigan State University’s CREATE for STEM Institute, the National Science Foundation, the Norwegian Agency for Quality Assurance in Education (NOKUT), the Norwegian Research Council, and the Thon Foundation.

Meet in TCPL foyer and then we all go into town for dinner.

Participants will work in groups based on course topic and experience to design computational documents with Python and Jupyter. Groups will discuss various aspects of notebook design, as well as activity implementation and delivery. Participants will write their own notebooks with the support of their groups and organizers.

2-day workshop participants are welcome to use BIRS facilities (TCPL) until 15:00 on Sunday, although participants are still required to checkout of the guest rooms by 11 M. There is no coffee break service on Sunday afternoon, but self-serve coffee and tea are always available in the 2nd floor lounge, Corbett Hall.