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- W4386977206 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." @default.
- W4386977206 created "2023-09-23" @default.
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- W4386977206 date "2023-09-20" @default.
- W4386977206 modified "2023-10-17" @default.
- W4386977206 title "TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback" @default.
- W4386977206 doi "https://doi.org/10.48550/arxiv.2309.11527" @default.
- W4386977206 hasPublicationYear "2023" @default.
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