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Predicting Engagement in Video Lectures | | Sahan Bulathwela
; María Pérez-Ortiz
; Aldo Lipani
; Emine Yilmaz
; John Shawe-Taylor
; | | Date: |
31 May 2020 | | 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 context-agnostic 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. | | Source: | arXiv, 2006.0592 | | Services: | Forum | Review | PDF | Favorites | |
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