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Predicting Engagement in Video Lectures

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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.

Authors: Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz and John Shawe-Taylor

Published in: Education
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Predicting Engagement in Video Lectures

  1. 1. Predicting Engagement in Video Lectures Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor Department of Computer Science This research is part of the X5GON project funded from the EU’s Horizon 2020 research programme grant No 761758 and partially funded by the EPSRC Fellowship titled ”Task Based Information Retrieval”, under grant No EP/P024289/1.
  2. 2. 2 Predicting Engagement in Video Lectures Introduction Department of Computer Science • Large scale creation of video lectures (MOOCs, OERs etc.) • Better Management → High Quality Learner Experience • Engagement: Contextual and Context-agnostic • Beneficial use-cases: - Part of quality assurance of video lectures - Ranking between videos that has the same topical content - Addresses the cold-start problem Introduction Related Work Methodology Results Conclusion
  3. 3. 3 Predicting Engagement in Video Lectures Research Questions Department of Computer Science Objective: Ranking video lectures based on context-agnostic engagement •RQ1: Encoding learner engagement •RQ2: Predicting engagement using cross-modal features •RQ3: Effect of modality-specific features •RQ4: Feature Influence •RQ5: Addressing cold-start problem •RQ6: Domain-based dynamics Introduction Related Work Methodology Results Conclusion
  4. 4. 4 Predicting Engagement in Video Lectures Related Work • Problem Background: - Learner Engagement → Better learning outcomes [Lan et al. (EDM ‘ 17), Ramesh et al. (AAAI ‘14)] - Rapid creation of educational resources need scalable quality assurance [Pawlowski et. al (2007), Ehlers et al. (2018), Camilleri (2014)] •Other Information Retrieval Domains [Dalip et al, (JDIQ ’11), Bendersky et al. (WSDM ’11)] • Engagement of Video Lectures - Median Normalised Engagement Time (MNET) [Guo et al. (L@S’14)] - Context-agnostic engagement based on [Wu et al. (ICSWM ‘19)] - Watch time used in videos [Covington et al. (RecSys ’16)] Department of Computer Science Introduction Related Work Methodology Results Conclusion
  5. 5. 5 Predicting Engagement in Video Lectures Methodology Department of Computer Science Introduction Related Work Methodology Results Conclusion Objective: Ranking video lectures based on context-agnostic engagement • Multiple ways to refine engagement signals (RQ1) • Large dataset of video lectures (RQ1 - RQ3) • Evaluate cross-modal and modality-specific feature sets (RQ2 – RQ4) • Context-agnostic vs. personalised model performance (RQ5) • Domain-specific vs. domain-agnostic model performance (RQ6)
  6. 6. 6 Predicting Engagement in Video Lectures Engagement Refined Department of Computer Science Introduction Related Work Methodology Results Conclusion Median Normalised Engagement Time (MNET) [Guo et al. (L@S’14)] • Raw: Raw engagement labels • Cleaned: Remove bot-like users • Standardised: Standardise NET to address personal biases of learners • Comparative: Use intra-session lecture pairs to build a relative ranking between the video lectures [Thurstone (1927), Perez-Ortiz and Mantiuk (2017)]
  7. 7. 7 Predicting Engagement in Video Lectures Features Department of Computer Science Introduction Related Work Methodology Results Conclusion
  8. 8. 8 Predicting Engagement in Video Lectures Dataset and Experiments Department of Computer Science Introduction Related Work Methodology Results Conclusion Source: www.videolectures.net •4,063 Recorded research talks in peer-reviewed conferences - Published between September, 1999 and October , 2017 - 155,850 user view events for lectures with 5+ views • Popular Machine Learning models for performance evaluation • SHAP analysis for feature influence [Lundberg and Lee (NeurIPS ‘17)] Data and Code https://github.com/sahanbull/context-agnostic-engagement
  9. 9. 9 Predicting Engagement in Video Lectures Ranking Performance (RQ 1-2) Department of Computer Science Introduction Related Work Methodology Results Conclusion • Pairwise Ranking Accuracy of engagement prediction models with standard error from 5-fold cross validation and cross- modal features. • RR: Ridge Regression, SVR: Support Vector Regression, KRR: Kernelised RR, KSVR: Kernelised SVR, RF: Radom Forest Regression
  10. 10. 10 Predicting Engagement in Video Lectures Ranking Performance (RQ 1-2) Department of Computer Science Introduction Related Work Methodology Results Conclusion • Pairwise Ranking Accuracy of engagement prediction models with standard error from 5-fold cross validation and cross- modal features. • RR: Ridge Regression, SVR: Support Vector Regression, KRR: Kernelised RR, KSVR: Kernelised SVR, RF: Radom Forest Regression
  11. 11. 11 Predicting Engagement in Video Lectures RQ 4-5: Results Department of Computer Science Introduction Related Work Methodology Results Conclusion (a) (b)
  12. 12. 12 Predicting Engagement in Video Lectures Modality and Domain (RQ 3 and 6) Department of Computer Science Introduction Related Work Methodology Results Conclusion Modality Domain
  13. 13. 13 Predicting Engagement in Video Lectures Conclusion Department of Computer Science Introduction Related Work Methodology Results Conclusion • Among the models presented Random Forest Regressor performs best - Models that capture non-linear patterns outperform the linear counterparts • Substantial amount of performance can be achieved using cross-modal features - However, Modality-specific > Cross-modal features • Length of the lecture is the most influential feature • Context-agnostic Engagement → cold-start problem • A domain-agnostic model is sufficient for engagement prediction
  14. 14. 14 Predicting Engagement in Video Lectures Limitations and Future Work Department of Computer Science Introduction Related Work Methodology Results Conclusion • Limitations - Need for more Authority and Topic Coverage features. - Only easily automatable features used. - Majority English, Video lectures. • Future Work - More authority and topic-coverage related features - More sophisticated features and models - Multi-lingual, multi-modal educational resources
  15. 15. 15 Predicting Engagement in Video Lectures References • A. S. Lan, C. G. Brinton, T.-Y. Yang, and M. Chiang. Behavior-based latent variable model for learner engagement. In Proc. of Int. Conf. on Educational Data Mining, 2017. • A. Ramesh, D. Goldwasser, B. Huang, H. Daume III, and L. Getoor. Learning latent engagement patterns of students in online courses. In Proc. of AAAI Conference on Artificial Intelligence, 2014. • Jan M. Pawlowski, Volker Zimmermann, and Imc Ag. Open content: A concept for the future of e-learning and knowledge management?, 2007 • Max Ehlers, Robert Schuwer, and Ben Janssen. Oer in tvet: Open educational resources for skills development, 2018 • Anthony F. Camilleri, Ulf Daniel Ehlers, and Jan Pawlowski. State of the art review of quality issues related to open educational resources (OER), volume 52 S. - JRC Scientific and Policy Reports of Publications Office of the European Union 2014. 2014 • Lane, Andrew (2008). Who puts the education into open educational content? In: Katz, Richard N. ed. The Tower and the Cloud: Higher Education in the Age of Cloud Computing. Boulder, Colorado: Educause, pp. 158–168. • Daniel Hasan Dalip, Marcos Andre Goncalves, Marco Cristo, and Pavel Calado. Automatic assessment of document quality in web collaborative digital libraries. Journal of Data and Information Quality, 2(3), December 2011. Department of Computer Science
  16. 16. 16 Predicting Engagement in Video Lectures References • M. Bendersky, W. B. Croft, and Y. Diao. Quality-biased ranking of web documents. In Proc. of ACM Int. Conf. on Web Search and Data Mining, 2011. • Philip J. Guo, Juho Kim, and Rob Rubin. How video production affects student engagement: An empirical study of MOOC videos. In Proc. of the First ACM Conf. on Learning @ Scale, 2014. • Wu, S., Rizoiu, M.A. and Xie, L., 2018, June. Beyond views: Measuring and predicting engagement in online videos. In Twelfth International AAAI Conference on Web and Social Media. • Paul Covington, Jay Adams, and Emre Sargin. Deep neural networks for youtube recommendations. In Proc. of ACM Conf. on Recommender Systems, 2016. • L. L. Thurstone. A law of comparative judgment. Psychological Review, 34(4):273–286, 1927. • M. Perez-Ortiz and R. K. Mantiuk. A practical guide and software for analysing pairwise comparison experiments, 2017 • Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems. 2017 Department of Computer Science
  17. 17. Predicting Engagement in Video Lectures Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz and John Shawe-Taylor Department of Computer Science This research is part of the X5GON project funded from the EU’s Horizon 2020 research programme grant No 761758and partially funded by the EPSRC Fellowship titled ”Task Based Information Retrieval”, under grant No EP/P024289/1. Contact: m.bulathwela@ucl.ac.uk https://github.com/sahanbull/context-agnostic-engagementCode and Dataset:

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