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

Llewellyn, Simon; Simons, Christopher; Smith, Jim

Abstract

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.

Citation

Llewellyn, S., Simons, C., & Smith, J. (2020). Using active learning to understand the videoconference experience: A case study. https://doi.org/10.1007/978-3-030-63799-6_30

• 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
• DOI: https://doi.org/10.1007/978-3-030-63799-6_30
• Keywords: Videoconfererence, Active Learning
• Public URL: https://uwe-repository.worktribe.com/output/6969510