A Toolbox for Modelling Engagement with Educational Videos

TL;DR

This paper introduces the PEEKC dataset and TrueLearn library for modeling learner engagement with educational videos, enabling scalable, online, and interpretable models that outperform baselines and support AI-driven personalized education.

Contribution

The work provides a new dataset and an open-source library with humanly-intuitive models for engagement prediction, advancing research in educational data mining and AI-based personalization.

Findings

TrueLearn models outperform baseline models in engagement prediction.

The dataset contains extensive AI-related educational videos.

The library is accessible and well-documented for practitioners.

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.