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

TL;DR

TrueLearn is a Python library that implements Bayesian online learning models for personalized educational recommendations, emphasizing interpretability, user control, and accessibility for developers and practitioners.

Contribution

The paper introduces the TrueLearn library, a novel collection of Bayesian models for personalized recommendations with interpretability features and a supporting educational dataset.

Findings

Library enables personalized educational recommendations

Models are interpretable and user-friendly

Includes evaluation metrics and example implementations

Abstract

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.