A mini framework to implement auto-evaluated exercises in Jupyter notebooks
Project description
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.
History
This was initially embedded into a MOOC on
python2 that ran for the first time on the
French FUN platform in Fall 2014. It
was then duplicated into a MOOC on
bioinformatics in Spring 2016 where it was
named nbautoeval
for the first time, but still embedded in a greater git module.
The current git repo is created in June 2016 from that basis, with the intention to be used as a git subtree from these 2 repos, and possibly others since a few people have proved interested.
Installation
pip install nbautoeval
Overview
code-oriented
Currently supports the following types of exercises
ExerciseFunction
: the student is asked to write a functionExerciseRegexp
: the student is asked to write a regular expressionExerciseGenerator
: the student is asked to write a generator functionExerciseClass
: 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
- 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.
- invoke
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
)
- one YAML file can contain several quizzes - see examples in the
-
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file nbautoeval-1.7.0.tar.gz
.
File metadata
- Download URL: nbautoeval-1.7.0.tar.gz
- Upload date:
- Size: 44.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 53ecd3b8121bd214378294c1dbda342d815fc65796c72276e59ed08293b70113 |
|
MD5 | 3cdfbf4285b05c42f952d8c8ac4c8889 |
|
BLAKE2b-256 | 3f6ef07bd2278329709c2a76fddc6c7d11f9235dd141a9b10ce904f0e07a4fde |
File details
Details for the file nbautoeval-1.7.0-py3-none-any.whl
.
File metadata
- Download URL: nbautoeval-1.7.0-py3-none-any.whl
- Upload date:
- Size: 41.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.6.0.post20200814 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 51105046e5aebeaf6fbf666403714ba080099fb69980c77726902e2dfbd858b9 |
|
MD5 | 2ac43de8510139a5c39b99f59ecf8a1b |
|
BLAKE2b-256 | 980db065d50e3b345198bb7eb47a649e5e047a6502b0cc813fe7683fe93042e7 |