A Toolbox for Modelling Engagement with Educational Videos

Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman Srazali,María Pérez-Ortiz,Emine Yilmaz,John Shawe-Taylor,Sahan Bulathwela

AAAI 2024(2024)

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Abstract
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.
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Key words
AI Education,Educational Recommendation,Lifelong Learning,Engagement Prediction,Dataset
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