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Predicting Engagement in Video Lectures

Bulathwela, S; Pérez-Ortiz, M; Lipani, A; Yilmaz, E; Shawe-Taylor, J; (2020) Predicting Engagement in Video Lectures. In: Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020). (pp. pp. 50-60). Educational Data Mining (EDM) Green open access

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Abstract

The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. We focus on building models to find the characteristics and features involved in contextagnostic engagement (i.e. population-based), a seldom researched topic compared to other contextualised and personalised approaches that focus more on individual learner engagement. Learner engagement, is arguably a more reliable measure than popularity/number of views, is more abundant than user ratings and has also been shown to be a crucial component in achieving learning outcomes. In this work, we explore the idea of building a predictive model for population-based engagement in education. We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement and propose both cross-modal and modality specific feature sets to achieve this task. We further test different strategies for quantifying learner engagement signals. We demonstrate the use of our approach in the case of data scarcity. Additionally, we perform a sensitivity analysis of the best performing model, which shows promising performance and can be easily integrated into an educational recommender system for OERs.

Type: Proceedings paper
Title: Predicting Engagement in Video Lectures
Event: 13th International Conference on Educational Data Mining (EDM 2020)
Open access status: An open access version is available from UCL Discovery
Publisher version: https://educationaldatamining.org/files/conference...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Context-free Engagement, Cold Start, Video lectures, Quality Assurance, Open Education, OER, Personalisation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10100795
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