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Concentration Estimation in E-Learning Based on Learner's Facial Reaction to Teacher's Action

Published:17 March 2020Publication History

ABSTRACT

In video-based learning, estimating the level of concentration is important for increasing the efficiency of learning. Facial expressions during learning obtained with a Web camera are often used to estimate concentration because cameras are easy to install. In this work, we focus on how learners react to video contents and propose a new method which is based on the Jaccard coefficient calculated from learner's facial reactions to teacher's actions. We conduct experiments and collect data in a Japanese cram school. Analysis of our collected data shows a weighted-F1 score of 0.57 for four levels of concentration classification, which is higher than the accuracy obtained with the methods based on learner's facial expression alone. The results indicate that our method can be effective for concentration estimation in an actual learning environment.

References

  1. Tadas Baltrusaitis et al. 2018. Openface 2.0: Facial behavior analysis toolkit. In IEEE International Conference on Automatic Face & Gesture Recognition (FG). IEEE, 59--66.Google ScholarGoogle Scholar
  2. Katy Jordan. 2014. Initial trends in enrolment and completion of massive open online courses. The International Review of Research in Open and Distributed Learning 15, 1 (2014).Google ScholarGoogle ScholarCross RefCross Ref
  3. Mirko Raca. 2015. Camera-based estimation of student's attention in class. Technical Report. EPFL.Google ScholarGoogle Scholar
  4. Jacob Whitehill et al. 2014. The faces of engagement: Automatic recognition of student engagementfrom facial expressions. IEEE Trans. Affective Computing 5, 1 (2014), 86--98.Google ScholarGoogle ScholarCross RefCross Ref

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  1. Concentration Estimation in E-Learning Based on Learner's Facial Reaction to Teacher's Action

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    • Published in

      cover image ACM Conferences
      IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces Companion
      March 2020
      153 pages
      ISBN:9781450375139
      DOI:10.1145/3379336

      Copyright © 2020 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 17 March 2020

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      • poster
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      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate683of2,684submissions,25%

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