TrueLearn: A Python Library for Personalised Informational
  Recommendations with (Implicit) Feedback
Yuxiang Qiu Karim Djemili Denis Elezi Aaneel Shalman María Pérez-Ortiz Sahan Bulathwela
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.