| 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 |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# `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
Raw data
{
"_id": null,
"home_page": null,
"name": "nbautoeval",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": "Jupyter, auto-evaluation, exercises",
"author": null,
"author_email": "Thierry Parmentelat <thierry.parmentelat@inria.fr>",
"download_url": "https://files.pythonhosted.org/packages/8b/46/5c9518ac455ef22e4eceb0fbb847d4192889e5c8ba63ddcf21bb7dccea9d/nbautoeval-1.8.0.tar.gz",
"platform": null,
"description": "# `nbautoeval`\n\n`nbautoeval` is a very lightweight python framework for creating **auto-evaluated**\nexercises inside a jupyter (python) notebook.\n\ntwo flavours of exercises are supported at this point\u00a0:\n\n* code-oriented\u00a0: given a text that describes the expectations, students are invited to\n write their own code, and can then see the outcome on teacher-defined data samples,\n compared with the results obtained through a teacher-provided solution, with a visual\n (green/red) feedback\n* quizzes\u00a0: a separate module allows to create quizzes\n\nAt this point, due to lack of knowledge/documentation about open/edx (read: the\nversion running at FUN), there is no available code for exporting the results as\ngrades or anything similar (hence the `autoeval` name).\n\nThere indeed are provisions in the code to accumulate statistics on all\nattempted corrections, as an attempt to provide feedback to teachers.\n\n# Try it on `mybinder`\n\nClick the badge below to see a few sample demos under `mybinder.org` - it's all\nin the `demo-notebooks` subdir.\n\n**NOTE** the demo notebooks ship under a `.py` format and require `jupytext` to be\ninstalled before you can open them in Jupyter.\n\n[](https://mybinder.org/v2/gh/parmentelat/nbautoeval/master?filepath=demo-notebooks)\n\n\n# History\n\nThis was initially embedded into a [MOOC on\npython2](https://github.com/parmentelat/flotpython) that ran for the first time on [the\nFrench FUN platform](https://www.france-universite-numerique-mooc.fr/) in Fall 2014. It\nwas then duplicated into a [MOOC on\nbioinformatics](https://github.com/parmentelat/flotbioinfo) in Spring 2016 where it was\nnamed `nbautoeval` for the first time, but still embedded in a greater git module.\n\nA separate git repo was created in June 2016 from that basis, with the\nintention to be used as a git subtree from these 2 repos (because at\nthe time, adding Python libraries in order to customize the notebook\nruntime on the remote Jupyter platform was a pain)\n\nNow this tool ships as a standalone Python library hosted on pypi.org,\nand so it can easily be added to any docker image\n\n# Installation\n\n```\npip install nbautoeval\n```\n\n# Overview\n\n## code-oriented\n\nCurrently supports the following types of exercises\n * `ExerciseFunction` : the student is asked to write a function\n * `ExerciseRegexp` : the student is asked to write a regular expression\n * `ExerciseGenerator` : the student is asked to write a generator function \n * `ExerciseClass` : tests will happen on a class implementation\n\nA teacher who wishes to implement an exercise needs to write 2 parts :\n\n* One python file that defines an instance of an exercise class; this in a nutshell\n typically involves\n * providing one solution (let's say a function) written in Python\n * providing a set of input data - as an instance of the `Args` dedicated class\n * plus optionnally various tweaks for rendering results\n\n* One notebook that imports this exercise object, and can then take advantage of it to\n write jupyter cells that typically\n * invoke `example()` on the exercise object to show examples of the expected output\n * invite the student to write their own code\n * invoke `correction()` on the exercise object to display the outcome.\n\n## quizzes\n\nHere again there will be 2 parts at work\u00a0:\n\n* The recommended way is to define quizzes in YAML format\u00a0:\n * one YAML file can contain several quizzes - see examples in the `yaml/` subdir\n * and each quiz contain a set of questions\n * grouping questions into quizzes essentially makes sense wrt the maximal number of\n attempts\n * mostly all the pieces can be written in markdown (currently we use `myst_parser`)\n\n* then one invokes `run_yaml_quiz()` from a notebook to display the test\n * this function takes 2 arguments, one to help locate the YAML file\n * one to spot the quiz inside the YAML file\n * run with `debug=True` to pinpoint errors in the source\n \n## results and storage\n\nRegardless of their type all tests have an `exoname` that is used to store information\nabout that specific test; for quizzes it is recommended to use a different name than \nthe quiz name used in `run_yaml_quiz()` so that students cant guess it too easily.\n\nstuff is stored in 2 separate locations\u00a0:\n\n* `~/.nbautoeval.trace` contain one JSON line per attempt (correction or submit)\n* `~/.nbautoeval.storage` for quizzes only, preserves previous choices, number of attempts\n\n# Known issues\n\nsee https://github.com/parmentelat/nbautoeval/issues\n",
"bugtrack_url": null,
"license": "CC-BY-SA-4.0",
"summary": "A mini framework to implement auto-evaluated exercises in Jupyter notebooks",
"version": "1.8.0",
"project_urls": {
"Homepage": "https://github.com/parmentelat/nbautoeval"
},
"split_keywords": [
"jupyter",
" auto-evaluation",
" exercises"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "f0d577cedbe86695f522b7e1e12c992a28799846ebe799e8a0dbaa9e1322e1d1",
"md5": "c6614e80ba11fd4d210b90db77cd4438",
"sha256": "0dd9aed1789bb7e309ea3d35549b95829400b8688bee89f12f46d68d22854c7f"
},
"downloads": -1,
"filename": "nbautoeval-1.8.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "c6614e80ba11fd4d210b90db77cd4438",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 47865,
"upload_time": "2024-12-11T15:51:31",
"upload_time_iso_8601": "2024-12-11T15:51:31.184472Z",
"url": "https://files.pythonhosted.org/packages/f0/d5/77cedbe86695f522b7e1e12c992a28799846ebe799e8a0dbaa9e1322e1d1/nbautoeval-1.8.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "8b465c9518ac455ef22e4eceb0fbb847d4192889e5c8ba63ddcf21bb7dccea9d",
"md5": "78c91cc56305227ca53a36c9a29c9b8f",
"sha256": "55454c8bba081e54ac35f673b79d13ca264c52243f3e800af4d37fb3887afec6"
},
"downloads": -1,
"filename": "nbautoeval-1.8.0.tar.gz",
"has_sig": false,
"md5_digest": "78c91cc56305227ca53a36c9a29c9b8f",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 954635,
"upload_time": "2024-12-11T15:51:33",
"upload_time_iso_8601": "2024-12-11T15:51:33.168579Z",
"url": "https://files.pythonhosted.org/packages/8b/46/5c9518ac455ef22e4eceb0fbb847d4192889e5c8ba63ddcf21bb7dccea9d/nbautoeval-1.8.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-11 15:51:33",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "parmentelat",
"github_project": "nbautoeval",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "nbautoeval"
}
