Name | nbautoeval JSON |
Version |
1.8.0
JSON |
| download |
home_page | None |
Summary | A mini framework to implement auto-evaluated exercises in Jupyter notebooks |
upload_time | 2024-12-11 15:51:33 |
maintainer | None |
docs_url | None |
author | None |
requires_python | >=3.10 |
license | CC-BY-SA-4.0 |
keywords |
jupyter
auto-evaluation
exercises
|
VCS |
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No requirements were recorded.
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# `nbautoeval`
`nbautoeval` is a very lightweight python framework for creating **auto-evaluated**
exercises inside a jupyter (python) notebook.
two flavours of exercises are supported at this point :
* code-oriented : given a text that describes the expectations, students are invited to
write their own code, and can then see the outcome on teacher-defined data samples,
compared with the results obtained through a teacher-provided solution, with a visual
(green/red) feedback
* quizzes : a separate module allows to create quizzes
At this point, due to lack of knowledge/documentation about open/edx (read: the
version running at FUN), there is no available code for exporting the results as
grades or anything similar (hence the `autoeval` name).
There indeed are provisions in the code to accumulate statistics on all
attempted corrections, as an attempt to provide feedback to teachers.
# Try it on `mybinder`
Click the badge below to see a few sample demos under `mybinder.org` - it's all
in the `demo-notebooks` subdir.
**NOTE** the demo notebooks ship under a `.py` format and require `jupytext` to be
installed before you can open them in Jupyter.
[](https://mybinder.org/v2/gh/parmentelat/nbautoeval/master?filepath=demo-notebooks)
# History
This was initially embedded into a [MOOC on
python2](https://github.com/parmentelat/flotpython) that ran for the first time on [the
French FUN platform](https://www.france-universite-numerique-mooc.fr/) in Fall 2014. It
was then duplicated into a [MOOC on
bioinformatics](https://github.com/parmentelat/flotbioinfo) in Spring 2016 where it was
named `nbautoeval` for the first time, but still embedded in a greater git module.
A separate git repo was created in June 2016 from that basis, with the
intention to be used as a git subtree from these 2 repos (because at
the time, adding Python libraries in order to customize the notebook
runtime on the remote Jupyter platform was a pain)
Now this tool ships as a standalone Python library hosted on pypi.org,
and so it can easily be added to any docker image
# Installation
```
pip install nbautoeval
```
# Overview
## code-oriented
Currently supports the following types of exercises
* `ExerciseFunction` : the student is asked to write a function
* `ExerciseRegexp` : the student is asked to write a regular expression
* `ExerciseGenerator` : the student is asked to write a generator function
* `ExerciseClass` : tests will happen on a class implementation
A teacher who wishes to implement an exercise needs to write 2 parts :
* One python file that defines an instance of an exercise class; this in a nutshell
typically involves
* providing one solution (let's say a function) written in Python
* providing a set of input data - as an instance of the `Args` dedicated class
* plus optionnally various tweaks for rendering results
* One notebook that imports this exercise object, and can then take advantage of it to
write jupyter cells that typically
* invoke `example()` on the exercise object to show examples of the expected output
* invite the student to write their own code
* invoke `correction()` on the exercise object to display the outcome.
## quizzes
Here again there will be 2 parts at work :
* The recommended way is to define quizzes in YAML format :
* one YAML file can contain several quizzes - see examples in the `yaml/` subdir
* and each quiz contain a set of questions
* grouping questions into quizzes essentially makes sense wrt the maximal number of
attempts
* mostly all the pieces can be written in markdown (currently we use `myst_parser`)
* then one invokes `run_yaml_quiz()` from a notebook to display the test
* this function takes 2 arguments, one to help locate the YAML file
* one to spot the quiz inside the YAML file
* run with `debug=True` to pinpoint errors in the source
## results and storage
Regardless of their type all tests have an `exoname` that is used to store information
about that specific test; for quizzes it is recommended to use a different name than
the quiz name used in `run_yaml_quiz()` so that students cant guess it too easily.
stuff is stored in 2 separate locations :
* `~/.nbautoeval.trace` contain one JSON line per attempt (correction or submit)
* `~/.nbautoeval.storage` for quizzes only, preserves previous choices, number of attempts
# Known issues
see https://github.com/parmentelat/nbautoeval/issues
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