A Toolbox for Modelling Engagement with Educational Videos
This work provides tools for personalizing education through AI, targeting researchers and practitioners in educational data mining and learning analytics, but it is incremental as it builds on existing concepts like open learner models.
The authors tackled the problem of modeling learner engagement with educational videos by introducing the PEEKC dataset and TrueLearn Python library, which achieved predictive performance significantly exceeding baseline models.
With the advancement and utility of Artificial Intelligence (AI), personalising education to a global population could be a cornerstone of new educational systems in the future. This work presents the PEEKC dataset and the TrueLearn Python library, which contains a dataset and a series of online learner state models that are essential to facilitate research on learner engagement modelling.TrueLearn family of models was designed following the "open learner" concept, using humanly-intuitive user representations. This family of scalable, online models also help end-users visualise the learner models, which may in the future facilitate user interaction with their models/recommenders. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytics practitioners. The experiments show the utility of both the dataset and the library with predictive performance significantly exceeding comparative baseline models. The dataset contains a large amount of AI-related educational videos, which are of interest for building and validating AI-specific educational recommenders.