Digital Teaching Assistant

Setting Up Development Environment

Ubuntu 20.04 LTS

1. Install Python 3.10. pyenv can help with switching among different Python versions.

2. Install poetry and dependencies:

pip install poetry
poetry install

3. Run tests, launch the app:

poetry shell
make test
make flask

4. If you wish to seed the database, run:

poetry shell
make seed # python -m webapp.app --seed

Windows 10

1. Install Python 3.10. Make sure python is added to PATH. You can check this by navigating to System (Control Panel) -> Advanced system settings -> Environment Variables -> System Variables -> PATH -> Edit.

2. Install Chocolatey.

3. Install GNU make:

choco install make

4. Install poetry and dependencies:

pip install poetry
poetry install

5. Run tests, launch the app:

poetry shell
make test
make flask-win

6. If you wish to seed the database, run:

poetry shell
make seed # python -m webapp.app --seed

Acknoledgements

We appreciate all people who contributed to the project. Thanks to @Plintus-bit for designing the logo!

Architecture

The Digital Teaching Assistant system is described in the following papers:

1. Sovietov P.N. Automatic Generation of Programming Exercises // In Proceedings of the 1st International Conference on Technology Enhanced Learning in Higher Education (TELE), 2021, pp. 111-114.

2. Andrianova E.G., Demidova L.A., Sovetov P.N. Pedagogical design of a digital teaching assistant in massive professional training for the digital economy // Russian Technological Journal. 2022, 10 (3), pp. 7-23.

3. Sovietov P.N., Gorchakov A.V. Digital Teaching Assistant for the Python Programming Course // In Proceedings of the 2nd International Conference on Technology Enhanced Learning in Higher Education (TELE), 2022, pp. 272-276.

4. Demidova L.A., Sovietov P.N., Gorchakov A.V. Clustering of Program Source Text Representations Based on Markov Chains // Vestnik of Ryazan State Radio Engineering University. 2022, 81, pp. 51-64.

5. Demidova L.A., Gorchakov A.V. Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines // Algorithms. 2022, 15 (9), p. 329.

6. Gorchakov A.V., Demidova L.A., Sovietov P.N. Automated program text analysis using representations based on Markov chains and Extreme Learning Machines // Vestnik of Ryazan State Radio Engineering University. 2022, 82, pp. 89-103.