Editors:
Part of the book series: Communications in Computer and Information Science (CCIS, volume 1112)
Conference series link(s): ICQE: International Conference on Quantitative Ethnography
Conference proceedings info: ICQE 2019.
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (32 papers)
-
Front Matter
-
Full Papers
-
Front Matter
-
About this book
This book constitutes the refereed proceedings of the First International Conference on Quantitative Ethnography, ICQE 2019, held in Madison, Wisconsin, USA, in October 2019.
It consists of 23 full and 9 short carefully reviewed papers selected from 52 submissions. The contributions come from a diverse range of fields and perspectives, including learning analytics, history, and systems engineering, all attempting to understand the breadth of human behavior using quantitative ethnographic approaches.
Editors and Affiliations
-
University of Wisconsin–Madison, Madison, USA
Brendan Eagan, Amanda Siebert-Evenstone
-
University of Copenhagen, Copenhagen, Denmark
Morten Misfeldt
Bibliographic Information
Book Title: Advances in Quantitative Ethnography
Book Subtitle: First International Conference, ICQE 2019, Madison, WI, USA, October 20–22, 2019, Proceedings
Editors: Brendan Eagan, Morten Misfeldt, Amanda Siebert-Evenstone
Series Title: Communications in Computer and Information Science
DOI: https://doi.org/10.1007/978-3-030-33232-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-33231-0Published: 13 October 2019
eBook ISBN: 978-3-030-33232-7Published: 12 October 2019
Series ISSN: 1865-0929
Series E-ISSN: 1865-0937
Edition Number: 1
Number of Pages: XI, 360
Number of Illustrations: 13 b/w illustrations, 90 illustrations in colour
Topics: Computer Appl. in Social and Behavioral Sciences, Computers and Education, Machine Learning, Computer Communication Networks