Bulathwela, Sahan; Perez-Ortiz, Maria; Novak, Erik; Yilmaz, Emine; Shawe-Taylor, John; (2021) PEEK: A Large Dataset of Learner Engagement with Educational Videos. In: Proceedings of the 4th Workshop on Online Recommender Systems and User Modeling (ORSUM 2021), in conjunction with the 15th ACM Conference on Recommender Systems. ORSUM: Amsterdam, The Netherlands.

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Abstract

Educational recommenders have received much less attention in comparison to e-commerce and entertainment-related recommenders, even though efficient intelligent tutors have great potential to improve learning gains. One of the main challenges in advancing this research direction is the scarcity of large, publicly available datasets. In this work, we release a large, novel dataset of learners engaging with educational videos in-the-wild. The dataset, named Personalised Educational Engagement with Knowledge Topics PEEK, is the first publicly available dataset of this nature. The video lectures have been associated with Wikipedia concepts related to the material of the lecture, thus providing a humanly intuitive taxonomy. We believe that granular learner engagement signals in unison with rich content representations will pave the way to building powerful personalization algorithms that will revolutionise educational and informational recommendation systems. Towards this goal, we 1) construct a novel dataset from a popular video lecture repository, 2) identify a set of benchmark algorithms to model engagement, and 3) run extensive experimentation on the PEEK dataset to demonstrate its value. Our experiments with the dataset show promise in building powerful informational recommender systems. The dataset and the support code is available publicly.

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