# TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback

arXiv: https://arxiv.org/abs/2309.11527 · HTML: https://mlsanity.com/paper/2309.11527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources](https://mlsanity.com/paper/1911.09471/) (2019)
- [Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System](https://mlsanity.com/paper/1907.01591/) (2019)
- [PEEK: A Large Dataset of Learner Engagement with Educational Videos](https://mlsanity.com/paper/2109.03154/) (2021)
- [Towards an Integrative Educational Recommender for Lifelong Learners](https://mlsanity.com/paper/1912.01592/) (2019)
- [Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution](https://mlsanity.com/paper/2112.02034/) (2021)

## Abstract (arXiv)

This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendation systems. This family of models was designed following the "open learner" concept, using humanly-intuitive user representations. For the sake of interpretability and putting the user in control, the TrueLearn library also contains different representations to help end-users visualise the learner models, which may in the future facilitate user interaction with their own models. Together with the library, we include a previously publicly released implicit feedback educational dataset with evaluation metrics to measure the performance of the models. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytic practitioners. The library and the support documentation with examples are available at https://truelearn.readthedocs.io/en/latest.