A Toolbox for Modelling Engagement with Educational Videos
Yuxiang Qiu Karim Djemili Denis Elezi Aaneel Shalman María Pérez-Ortiz Emine Yilmaz John Shawe-Taylor Sahan Bulathwela
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.