krummstab 0.3

Efficiently give feedback on ADAM submissions at University of Basel

Krummstab Feedback Script

The purpose of this script is to automate some of the menial steps involved in marking ADAM submissions.

The system is made up of three components: the central krummstab PyPI project, and two JSON configuration files, config-shared.json and config-individual.json.

The shared config file contains general settings that need to be adapted to the course that is being taught, but should remain static thereafter. Additionally, it lists all students and their team assignment. This part of the file is subject to change during the semester as students drop the course or teams are reassigned. It is important that all tutors have an identical copy of the shared config file, meaning that whenever a tutor makes changes to the file, she or he should share the new version with the others, for example via the Discord server or uploading it to ADAM.

The individual config file contains personal settings that are only relevant to each tutor. These only need to be set once at the beginning of the course.

Depending on the general settings of the shared config file, different command line options may be mandatory. The help option provides information about the script, its subcommands (currently init, collect, combine and send), and their parameters. Once you have completed the one-time setup below, you'll be able to access the help via:

krummstab -h
krummstab <subcommand> -h

In the following I will go over the recommended workflow using the settings of the Foundations of Artificial Intelligence lecture from the spring semester 2023 as an example.

Requirements

• Python 3.10+: I only tested the script with 3.10 and I think it makes use of some new-ish language features, so I cannot guarantee that everything works as expected with older Python versions.

One-Time Setup

📝 I'm assuming a Linux environment in the following. In case you are using macOS, I hope that the following instructions work without major differences. In case you are using Windows, I recommend trying to install a Windows Subsystem for Linux (WSL), which should allow you to follow these instructions exactly. Alternatively you can try to install the necessary software natively, but I don't offer support here.

To get started, create an empty directory where you want to do your marking, in this example the directory will be called ki-fs23-marking:

mkdir ki-fs23-marking

Navigate to this directory, set up a virtual Python environment, and activate it:

cd ki-fs23-marking
python3 -m venv .venv
source .venv/bin/activate

Then you can install Krummstab in this environment:

pip install krummstab

To test the installation, you can print the help string:

krummstab -h

With the script installed, we continue with the config files. You should have gotten a config-shared.json file from the teaching assistant, copy this file into the directory you just created, in our example ki-fs23-marking. Similarly you can copy the config-individual.json file from the tests directory of this repository. Replace the example entries in the individual configurations with your own information; The parameters are explained here. Make sure that the string you enter in the field tutor_name in your individual config exactly matches the existing entry in the tutor_list field of the shared config.

In general, it is important that the all configurations, besides the individual ones you just adjusted, are exactly the same across all tutors, as otherwise submissions may be assigned to multiple or no tutors. If you think that something should be changed in the shared settings, please let the teaching assistant and the other tutors know, so that the configurations remain in sync. This may in particular be necessary if teams change throughout the semester.

In order to work with the script, you will have to call the krummstab command from a command line whose working directory is the one which contains the two config files. If you'd like to keep the config files somewhere else, you'll have to provide the paths to the files with the -s and -i flags whenever you call krummstab.

Marking a Sheet

While the steps above are only necessary for the initial setup, the following procedure applies to every exercise sheet. The first step is always to activate the virtual environment in which we have installed Krummstab. You do this by navigating to the marking directory and using the source command.

cd ki-fs23-marking
source .venv/bin/activate

If you forget this step you'll get an error saying that the krummstab command could not be found.

init

First, download the submissions from ADAM and save the zip file in the marking directory. (It's important that you only download the submissions after the ADAM deadline has passed, so that all tutors have the same, complete pool of submissions.) Our example directory ki-fs23-marking, with Sheet 1.zip being the file downloaded from ADAM, should look like this:

.
├── .venv
├── config-individual.json
├── config-shared.json
└── Sheet 1.zip

We can now finally make the script do something useful by running:

krummstab init -n 4 -t sheet01 "Sheet 1.zip"

This will unzip the submissions and prepare them for marking. The flag -n expects the number of exercises in the sheet, -t is optional and takes the name of the directory the submissions should be extracted to. By default it's the name of the zip file, but I'm choosing to rename it in order to get rid of the whitespace in the directory name. The directory should now look something like this:

.
├── .venv
├── config-individual.json
├── config-shared.json
├── sheet01
│   ├── 12345_Muster_Müller
│   │   ├── feedback
│   │   │   └── feedback_tutor-name.pdf.todo
│   │   └── Sheet1_MaxMuster_MayaMueller.pdf
│   .
│   ├── DO_NOT_MARK_12346_Meier_Meyer
│   │   └── submission_exercise_sheet1.pdf
│   .
│   └── points.json
└── Sheet 1.zip

As you may have guessed, the submissions you need to mark are those without the DO_NOT_MARK_ prefix. Those directories contain the files submitted by the respective team, as well as a directory called feedback, which in turn contains an empty placeholder PDF file and copies of submitted files that are not PDFs (e.g. source files).

The idea is that you can give feedback to non-PDFs by adding your comments to these copies directly, and delete the ones you don't need to comment on. For the PDF feedback you can use whichever tool you like, and overwrite the .pdf.todo placeholder with the resulting output. If this tool adds files to the feedback directory that you do not want to send to the students, you can add their endings to the config file under the ignore_feedback_suffix key. Marking with Xournal++ is supported by default: Simply add the flag -x to the init command above to automatically create the relevant .xopp files.

While writing the feedback, you can keep track of the points the teams get in the file points.json.

collect

Once you have marked all the teams assigned to you and added their points to the points.json file, you can run the next command, where sheet01 is the path to the directory created by the init command:

krummstab collect sheet01

This will create a zip archive in every feedback directory containing the feedback for that team. Additionally, a semicolon-separated list of all points is printed. This can be useful in case you have to paste the points into a shared spreadsheet. The names are there to be able to double-check that the rows match up.

In case you need make changes to the markings and rerun the collection step, use the -r flag to overwrite existing feedback archives. If you are using Xournal++, you can also use the -x flag here to automatically export the .xopp files before collecting the feedback.

combine

This command is only relevant for the exercise marking mode. TODO: Document this.

send

For the static and random marking modes, it is possible to directly send the feedback to the students via e-mail. For this to work you have to be in the university network, which likely means you'll have to connect to the university VPN. You may find the --dry_run option useful, instead of sending the e-mails directly, it only prints them so that you can double-check that everything looks as expected.

Config File Details

Individual Settings

• tutor_name: ID of the tutor, this must match with either an element of tutor_list (for random/exercise) or a key in teams (for static)
• tutor_email: tutor's email address, feedback will be sent via this address
• feedback_email_cc: list of email addresses that will be CC'd with every feedback email, for example the addresses of all tutors
• smtp_url: the url of the smtp server, smtp-ext.unibas.ch by default
• smtp_port: smtp port to connect to, 587 by default
• smtp_user: smtp user, usually the short Unibas account name
• ignore_feedback_suffix: a list of extensions that should be ignored by the collect sub-command; this is useful if the tools you use for marking create files in the feedback folders that you don't want to send to the students

General Settings

• lecture_title: lecture name to be printed in feedback emails
• marking_mode
  • static: student teams are assigned to a tutor who will mark all their submissions
  • random: student teams are randomly assigned to tutors with every sheet, this tutor will mark the team's entire submission
  • exercise: with every sheet, tutors distribute the exercises and only correct those, but for all submissions
• points_per
  • exercise: tutors keep track how many points teams got for every exercise
  • sheet: tutors only keep track of the total number of points per sheet
• min_point_unit: a float denoting the smallest allowed point fraction, for example 0.5, or 1
• tutor_list: list to identify tutors, for example a list of first names
• max_team_size: integer denoting the maximum number of members a team may have

Teams

• teams: depending on the marking_mode teams are structured slightly differently
  • random/exercise: list of teams, each consisting of a list of students, where each student entry is a list of the form [ "first_name", "last_name", "[e-mail]" ]
  • static: similar to before, but teams are not just listed, but assigned to a tutor; this is done via a dictionary where some ID for the tutors (e.g. first names) are the keys, and the values are the list of teams assigned to each tutor

Development

I added some tests that use the pytest framework. Simply install pytest via pip3 install pytest (or pip, not sure what the difference is), and run the command pytest. Currently it tests the init step for the modes static, random, and exercise and the collect step for the first two modes.