Designing an effective strategy for developing and utilizing Jupyter Notebooks as part of your teaching methodology can be a challenging task. Here are some best practices to consider when creating your notebooks:
Identify Learning Outcomes¶
Clearly define the learning outcomes you want to achieve using Jupyter Notebooks for your course or module. Consider the following questions:
- Are the students expected to learn a specific programming language?
- Are the students expected to learn a new technology or installation process?
- Will students work in groups or independently to complete assignments?
- Will students work on assignments synchronously during class or asynchronously afterward?
- Will students publish their notebooks as PDFs, HTML websites, dashboards, Jupyter Books, etc.?
- What kind of datasets are required to achieve the desired learning outcomes?
Choose an Ideal Pedagogical Strategy¶
Select a suitable pedagogical approach to deliver your notebook content. Below are some examples of pedagogical strategies you can adapt for your course:
- Direct Instruction: Teacher-led demonstrations and explanations.
- Inquiry-Based Learning: Encourages students to ask questions and explore.
- Collaborative Learning: Students work together to solve problems and complete tasks.
- Flipped Classroom: Students review content outside of class and engage in activities during class time.
Identify Technology Requirements¶
Based on your answers to the questions above, determine the necessary technology and resources, such as software, hardware, and datasets, to support the learning outcomes.
By carefully considering these factors, you can create a structured and effective approach to integrating Jupyter Notebooks into your teaching.
Pedagogical strategies for notebook creation¶
| Pedagogical Approach Name | Course Name | Notebook Example | Pedagogical Approach Description |
|---|---|---|---|
| Cell Execution | Data 101 (Data Engineering) | Lecture 2 Notebook | Students use this notebook to learn how to connect to a Postgres database hosted in a cloud server. The pedagogical approach followed is to do “Shift-Enter” to execute all the cells and in the process understand to work with Postgres database. Students read data from the database, run complex queries against the data to perform analysis |
| Fill in with Code | Data 101 (Data Engineering) | Project 2 Notebook | Students evaluate their understanding by completing the sql query which has been left incomplete as part of the assigment. Through this approach students can test their understanding and identify gaps in their knowledge |
| Translate Mathematics to Code | PMB/MCB/BioE C 146 (Data Science for Biology) | Lecture 8 Notebook | Students learn about dynamic programming in this notebook. They generate fibonacci series and find longest common subsequence using recursive function/dynamic programming and calculate the associated complexity. Here, the students will be codifying mathematical expression to understand the knowledge better |
| Explore APIs | EPS 88 (PyEarth) | Week 2 Assignment | In this notebook, students query a remote server hosted by US Geological Survey Org to get data about areas in the globe where an earth quake has happened and whose magnitude is greater than 5.0. They create a map highlighting those places |
| Hello World | Econ 130 (Economics for Public Policy) | Intro Jupyter notebook | Many courses have this intro notebook where student perform basic operations in Python/R to understand the syntax of the programming language. Instructors spend a week helping students get accustomed to the chosen programming language |
Tips for Notebook Creation¶
- Have a text to code ratio of 3:1
- Explain the ouput of the cell in the text annotation above each cell
- Have an intro Jupyter notebook which focuses on exposing students to syntactical elements of the programming language