PyBryt is an auto-assessment Python library for teaching and learning.
- The PyBryt Library is a FREE Open Source Python Library that provides auto assessment of grading submissions. Our goal is to empower students and educators to learn about technology through fun, guided, hands-on content aimed at specific learning goals.
- The PyBryt Library is a Open Source Python Library - focused on the auto assessment and validation of Python coding.
- The PyBryt library has been developed under open source to support learning and training institutions to auto assess the work completed by learners.
- The PyBryt Library will work existing auto grading solution such as Otter Grader, OkPy or Autolab.
Educators and Institutions can leverage the PyBryt Library to integrate auto assessment and reference models to hands on labs and assessments.
- Educators do not have to enforce the structure of the solution;
- Learner practice the design process,code design and implemented solution;
- Meaningful & pedagogical feedback to the learners;
- Analysis of complexity within the learners solution;
- Plagiarism detection and support for reference solutions;
- Easy integration into existing organizational or institutional grading infrastructure.
See the Getting Started page on the pybryt documentation for steps to install and use pybryt for the first time. You can also check the Microsoft Learn interactive modules on Introductions to PyBryt and Advanced PyBryt to learn more about to use the library to autoassess your learners activities.
To run the demos, all demos are located in the demo folder.
First install PyBryt with pip:
pip install pybryt
Simply launch the index.ipynb notebook in each of the directories under demo from Jupyter Notebook, which demonstrates the process of using PyBryt to assess student submissions.
We continuously interact with computerized systems to achieve goals and perform tasks in our personal and professional lives. Therefore, the ability to program such systems is a skill needed by everyone. Consequently, computational thinking skills are essential for everyone, which creates a challenge for the educational system to teach these skills at scale and allow students to practice these skills. To address this challenge, we present a novel approach to providing formative feedback to students on programming assignments. Our approach uses dynamic evaluation to trace intermediate results generated by student's code and compares them to the reference implementation provided by their teachers. We have implemented this method as a Python library and demonstrate its use to give students relevant feedback on their work while allowing teachers to challenge their students' computational thinking skills. Paper available at PyBryt: auto-assessment and auto-grading for computational thinking
@misc{pyles2021pybryt,
title={PyBryt: auto-assessment and auto-grading for computational thinking},
author={Christopher Pyles and Francois van Schalkwyk and Gerard J. Gorman and Marijan Beg and Lee Stott and Nir Levy and Ran Gilad-Bachrach},
year={2021},
eprint={2112.02144},
archivePrefix={arXiv},
primaryClass={cs.HC}
}Please use the citing this repositry on the repo menu or citation.cff file in the root of this repo.
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