GenJSONnotebookGrader

Oskar Åsbrink, Kristoffer Torp

2022, Uppsala, Sweden

This project was supported by Combient Mix AB through summer internships at:

Combient Competence Centre for Data Engineering Sciences, Department of Mathematics, Uppsala University, Uppsala, Sweden

This project is a refactoring and repackaging of work contributed by:

• Suparerk Angkawattanawit
• Benny Avelin
• Raazesh Sainudiin
• Tilo Wiklund

Don't hesitate to raise an issue if you encounter errors.

Introduction to the NotebookGrader

This Notebook AutoGrader converts platform-specific notebooks to JSON to be able to run automatic grading of assignments that are then uploaded to Studium using the Canvas API.

This NotebookGrader supports:

1. Jupyter notebooks(.ipynb) with python and SageMath kernels
2. Databricks notebooks(.dbc) with support for Python and Scala.

Overview

To use the NotebookGrader there are a couple of steps to follow: 0. Clone the NotebookGrader,test_course and CanvasInterface repositories. Put them beside each other in the same folder.

1. Create a 'Master' notebook. This is a notebooks which contains cells of different types: lecture cells(optional), problem cells, test cells and solution cells.
2. From the master notebook, you generate the following notebooks
   • Lecture notebook containing instruction cells, lecture cells etc
   • Problem_TEST notebook that the NotebookGrader uses to correct the student submission
   • Problem notebook for students to fill in
3. Run the NotebookGrader which will fetch student submissions from Canvas, grade them and upload the results.

0. Install necessary dependencies

The NotebookGrader will use python_on_whales and databricks-cli. Install using

pip3 install python_on_whales pip3 install databricks-cli

1. Create a Master notebook

The NotebookGrader needs the Master notebook to be in a specific format. For instructions on how to create a master notenook see the readme in Test_course

2. Generate notebooks

For instructions on how to generate notebooks, see the readme in Test_course.

3. Run the NotebookGrader

For instructions on how to run the NotebookGrader, see the readme in NotebookGrader -> AutoGrader