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2020 | OriginalPaper | Chapter

Using Active Learning to Understand the Videoconference Experience: A Case Study

Authors : Simon Llewellyn, Christopher Simons, Jim Smith

Published in: Artificial Intelligence XXXVII

Publisher: Springer International Publishing

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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.

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Metadata
Title
Using Active Learning to Understand the Videoconference Experience: A Case Study
Authors
Simon Llewellyn
Christopher Simons
Jim Smith
Copyright Year
2020
Book
Artificial Intelligence XXXVII

Print ISBN: 978-3-030-63798-9

Electronic ISBN: 978-3-030-63799-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63799-6_30

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