# Teaching with Executable Notebooks

(New chapter started 2023.05.24, work in progress)

## :information_source: Chapter outline

> this is the landing page of this chapter

#### Summary

(What this chapter includes)

* One subchapter of **executable notebooks** applied in teaching
* One subchapter of **case study**

#### Motivation and background

* Disadvantages of traditional classroom of teaching technical subjects (e.g. data science, statistics)
* for teachers
* for learners
* Benefits of executable notebooks in general
* reproducible
* focus on practical skills
* easily accessible (open)
* easy for collaboration (compare with latex beamer)
* ...

(reorganise) Basic data skills (statistics, programming for example) are crucial for modern scientists; yet universities typically focus on theory rather than practical skills. As a consequence, students and researchers coming from non-CS curriculum often find it difficult to learn coding. For people in fields such as medicine, psychology and biology, it can be difficult to get started. Executable notebooks, in addition to popular programming/statistic languages, can be a very good way to teach and learn how to code.

## Executable Notebooks

> this is the first subchapter

### Overview

* Malvika's Sloan AGU proposal
* The Carpentries workshops https://carpentries.org
* Jupyter notebooks

### Teaching and learning with Jupyter
* Using Jupyter Notebooks to teach computational literacy
* Teaching and learning with Jupyter Notebooks

### Quarto for education

Quarto for Academics by Prof. Mine Çetinkaya-Rundel
...

## Case studies and examples

The Carpentries workshops, for example, R for reproducible scientific analysis
Data science courses by Prof. Mine Çetinkaya-Rundel (can contact her)
Transitioning a traditional statistics course using R and Quarto: experience by folks at University of Oslo (Chi among others)
...