atlas

Atlas: Adaptive Competency-Based Learning

https://github.com/ls1intum/atlas

Science Score: 52.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Committers with academic emails
  • Institutional organization owner
    Organization ls1intum has institutional domain (ase.cit.tum.de)
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (12.6%) to scientific vocabulary

Keywords

artificial-intelligence education gen-ai generative-ai large-language-models learning machine-learning

Keywords from Contributors

modeling-exercises apollon assessment autograding automatic-feedback computer-science education-technology exam-mode exercises feedback
Last synced: 6 months ago · JSON representation ·

Repository

Atlas: Adaptive Competency-Based Learning

Basic Info
Statistics
  • Stars: 1
  • Watchers: 2
  • Forks: 0
  • Open Issues: 3
  • Releases: 0
Topics
artificial-intelligence education gen-ai generative-ai large-language-models learning machine-learning
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License Code of conduct Citation Security

README.md

Atlas: Adaptive Competency-Based Learning

Atlas is a microservice designed to seamlessly incorporate competency models into Learning Management Systems (LMS). By leveraging cutting-edge machine learning and generative AI (GenAI/LLMs), Atlas empowers educators to create sophisticated competency models, fostering more effective teaching methods and improving student learning outcomes.


Why Atlas?

Competency-based education (CBE) is a proven method for enhancing learning, but its implementation can be complex and time-consuming. Atlas addresses this challenge by automating and simplifying the creation of high-quality competency models, including: - Automatically generated relationships between competencies. - Recommendations for linking competencies to relevant learning activities.


Key Features

  • AI-Powered Competency Models: Generate and recommend sophisticated competency models with minimal effort.
  • Seamless LMS Integration: Works with Artemis and can be adapted to other LMSs.
  • Open Source: Freely accessible to the community under the MIT license.
  • Planned API & Micro Frontend: Future enhancements will allow for broader flexibility and easier interaction.

Installation

Detailed installation instructions can be found in the documentation.


Usage

To get started with Atlas and explore its features, refer to the user guide in the documentation.


Contributing

We welcome contributions to improve Atlas! Please follow the contribution guidelines outlined in the documentation.


Support

For issues or questions, please use the GitHub Issues section of this repository.


License

This project is licensed under the MIT License. See the LICENSE file for details.


Acknowledgments

Atlas is currently integrated with Artemis but is designed to adapt to other LMS platforms, making it versatile for a wide range of educational environments.

Owner

  • Name: TUM Applied Software Engineering
  • Login: ls1intum
  • Kind: organization
  • Location: Munich, Germany

Technical University of Munich - School of Computation, Information and Technology

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you cite this project, please use the following citation."
authors:
  -
    affiliation: "Technical University of Munich"
    family-names: Anzinger
    given-names: Maximilian
    orcid: "0009-0000-7378-6292"
  -
    affiliation: "Technical University of Munich"
    family-names: Krusche
    given-names: Stephan
    orcid: "0000-0002-4552-644X"
title: "Atlas: Adaptive Competency-Based Learning"
version: 0.0.0
date-released: 2024-11-18
license: MIT
repository-code: "https://github.com/ls1intum/atlas"
preferred-citation:
  type: data
  authors:
    -
      affiliation: "Technical University of Munich"
      family-names: Anzinger
      given-names: Maximilian
      orcid: "0009-0000-7378-6292"
    -
      affiliation: "Technical University of Munich"
      family-names: Krusche
      given-names: Stephan
      orcid: "0000-0002-4552-644X"
  title: "Atlas: Adaptive Competency-Based Learning"
  doi: "10.5281/zenodo.14045625"
  year: 2024

GitHub Events

Total
  • Create event: 19
  • Issues event: 34
  • Watch event: 1
  • Delete event: 14
  • Issue comment event: 12
  • Member event: 1
  • Push event: 55
  • Pull request review event: 12
  • Pull request review comment event: 6
  • Pull request event: 32
Last Year
  • Create event: 19
  • Issues event: 34
  • Watch event: 1
  • Delete event: 14
  • Issue comment event: 12
  • Member event: 1
  • Push event: 55
  • Pull request review event: 12
  • Pull request review comment event: 6
  • Pull request event: 32

Committers

Last synced: 10 months ago

All Time
  • Total Commits: 19
  • Total Committers: 5
  • Avg Commits per committer: 3.8
  • Development Distribution Score (DDS): 0.316
Past Year
  • Commits: 19
  • Committers: 5
  • Avg Commits per committer: 3.8
  • Development Distribution Score (DDS): 0.316
Top Committers
Name Email Commits
Maximilian Anzinger 4****r 13
Ufuk Yağmur 3****r 2
Ignacio G. 5****n 2
mend-bolt-for-github[bot] 4****] 1
Arda Karaman 4****0 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 25
  • Total pull requests: 24
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 4 days
  • Total issue authors: 4
  • Total pull request authors: 5
  • Average comments per issue: 0.08
  • Average comments per pull request: 0.42
  • Merged pull requests: 19
  • Bot issues: 4
  • Bot pull requests: 1
Past Year
  • Issues: 25
  • Pull requests: 24
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 4 days
  • Issue authors: 4
  • Pull request authors: 5
  • Average comments per issue: 0.08
  • Average comments per pull request: 0.42
  • Merged pull requests: 19
  • Bot issues: 4
  • Bot pull requests: 1
Top Authors
Issue Authors
  • ufukygmr (8)
  • MaximilianAnzinger (7)
  • ardakaraman0 (6)
  • mend-bolt-for-github[bot] (4)
Pull Request Authors
  • MaximilianAnzinger (25)
  • ufukygmr (4)
  • ardakaraman0 (4)
  • mend-bolt-for-github[bot] (2)
  • ignacio-gn (2)
Top Labels
Issue Labels
documentation (4) Mend: dependency security vulnerability (4) bug (1) github (1)
Pull Request Labels
github (12) documentation (9) ready for review (3) enhancement (2) stale (2)