Beschreibung / Inhaltsverzeichnis:
This handbook is for any educator teaching a topic that includes data analysis or computation in order to support learning. It is not just for educators teaching courses in engineering or science, but also data journalism, business and quantitative economics, data-based decision sciences and policy, quantitative health sciences, and digital humanities. It aims to provide an entry point, and a broad overview of Jupyter in education. Whether you are already using Jupyter to teach, you have found learning materials built on Jupyter that piqued your curiosity, or have never heard of Jupyter, the material in this open book can empower you to use this technology in your teaching.
Project Jupyter is a broad collaboration that develops open-source tools for interactive and exploratory computing. The tools include: over 100 computer languages (with a focus on Python), the Jupyter Notebook, JupyterHub, and an ecosystem of extensions contributed by a large community. The Jupyter Notebook has exploded in popularity since late 2014, fueled by its adoption as the favorite environment for doing data science. It has also grown as a platform to use in the classroom, to develop teaching materials, to share lessons and tutorials, and to create computational stories. Notebooks are documents containing text narratives with images and math, combined with executable code (many languages are supported) and the output of that code. This marriage of content and code makes for a powerful new form of data-based communication. Educators everywhere are adopting Jupyter for teaching.
Educators newly adopting Jupyter can be overwhelmed by having to navigate the ecosystem of tools and content. They could study many examples, or consume a myriad of blog posts and videos of talks to distill the patterns of good practices and technical solutions to serve their students best. Several early adopters, having much experience to share, decided to begin collecting this know-how, and share open documentation about using Jupyter for teaching and learning. The result is this open book: a living document that captures the experiences of community members using Jupyter in education.
The Jupyter Community Workshop in Washington, DC (November 2018) began that process, with a book sprint aimed at producing the first version of this handbook. The collaboratively written book consolidates explanations and examples covering key topics, including: what is Jupyter, how to try Jupyter, sharing notebooks with students, locally installing Jupyter, cloud offerings, finding example notebooks, writing lessons in Jupyter, making collections for a course, exporting to other formats with nbconvert, writing textbooks with Jupyter, using Binder and JupyterHub, making assignments and auto-grading, making online courses, teaching with Jupyter in the classroom, active learning and flipped learning pedagogies with Jupyter, and guiding learners to create their own content in Jupyter. This open handbook will grow to encompass all you need to know about Jupyter in teaching and learning.
Materialart:
12
Seiten:
Online-Ressource
URL:
https://jupyter4edu.github.io/jupyter-edu-book/
URL:
https://github.com/jupyter4edu/jupyter-edu-book
Sprache:
Englisch
Anmerkung:
1 Introduction
Acknowledgments
2 Why we use Jupyter notebooks
2.1 Why do we use Jupyter?
2.2 But first, what is Jupyter Notebook?
2.3 Course benefits & anecdotes
2.4 Student benefits
2.5 Instructor benefits
2.6 Conclusions
3 Notebooks in teaching and learning
3.1 Oh the places your notebooks will go!
3.2 Before You Begin…
4 A catalogue of pedagogical patterns
4.1 Introduction
4.2 Shift-Enter for the win
4.3 Fill in the blanks
4.4 Target Practice
4.5 Twiddle, tweak, and frob
4.6 Notebook as an app
4.7 Win-day-one
4.8 Top-down sequence
4.9 Two bites at every apple
4.10 Coding as translation
4.11 Symbolic math over pencil + paper
4.12 Replace analysis with numerical methods
4.13 The API is the lesson
4.14 Proof by example, disproof by counterexample
4.15 The world is your dataset
4.16 Now you try (with different data or process)
4.17 Connect to external audiences
4.18 There can be only one
4.19 Hello, world!
4.20 Test driven development
4.21 Code reviews
4.22 Bug hunt
4.23 Adversarial programming
5 Jupyter Notebook ecosystem
5.1 Language support: kernels
5.2 Using Jupyter notebooks
5.3 Authoring Jupyter notebooks
5.4 Tips and tricks
5.5 Gotchas
6 Getting your class going with Jupyter
6.1 Local installation on students’ or lab computers
6.2 Jupyter on remote servers
6.3 Distribution and collection of materials
6.4 Assessing student learning with Jupyter notebooks
6.5 How do you create Jupyter notebooks for reuse and sharing?
6.6 Jupyter: a 21st Century genre of Open Educational Resources and practices
7 Usage case studies
7.1 Jupyter notebooks in support of scaling for large enrollments
7.2 The “CFD Python” story: guiding learners at their own pace
7.3 Analyzing music with music21
7.4 Interactivity in computer science (high school and middle school)
7.5 Interactive geophysics with Jupyter
7.6 Investigating hurricanes
8 About the authors
8.1 Project lead
8.2 Authors at the sprint
9 Glossary
References
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