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Using active learning to understand the videoconference experience: A case study

Llewellyn, Simon; Simons, Christopher; Smith, Jim


Simon Llewellyn

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Jim Smith
Professor in Interactive Artificial Intelligence


Videoconferencing is becoming ubiquitous, especially so during the COVID-19 pandemic. However, user experience of a videoconference call can be variable. To better understand and classify the performance of videoconference call systems, this paper reports a case study in which active learning - an interactive form of machine learning in which system engineers provide labels for instances of feature data - is applied to videoconference call logs. Investigations reveal that although system engineers have differing videoconference domain knowledge and so provide a wide range of labels, the active learning approach produces promising results in terms of model scale, accuracy and confidence reflecting the subjectivity of engineers’ experience.


Llewellyn, S., Simons, C., & Smith, J. (2020). Using active learning to understand the videoconference experience: A case study.

Conference Name 40th SGAI International Conference on Artificial Intelligence, AI2020
Conference Location Cambridge, UK
Start Date Dec 15, 2020
End Date Dec 17, 2020
Acceptance Date Oct 15, 2020
Online Publication Date Dec 8, 2020
Publication Date Dec 15, 2020
Deposit Date Jan 4, 2021
Publisher Springer Verlag
Volume 12498 LNAI
Pages 386-392
Series Title Lecture Notes in Computer Science
Series Number 12498
Series ISSN 0302-9743
ISBN 9783030637989
Keywords Videoconfererence, Active Learning
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