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This work describes the TrueLearn Python library, which contains a family ofonline learning Bayesian models for building educational (or more generally,informational) recommendation systems. This family of models was designedfollowing the "open learner" concept, using humanly-intuitive userrepresentations. For the sake of interpretability and putting the user incontrol, the TrueLearn library also contains different representations to helpend-users visualise the learner models, which may in the future facilitate userinteraction with their own models. Together with the library, we include apreviously publicly released implicit feedback educational dataset withevaluation metrics to measure the performance of the models. The extensivedocumentation and coding examples make the library highly accessible to bothmachine learning developers and educational data mining and learning analyticpractitioners. The library and the support documentation with examples areavailable at https://truelearn.readthedocs.io/en/latest.

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