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In the zone: Towards detecting student zoning out using supervised machine learning

Drummond, J and Litman, D (2010) In the zone: Towards detecting student zoning out using supervised machine learning. In: UNSPECIFIED UNSPECIFIED, 306 - 308. ISBN 364213436X, 9783642134364

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Abstract

This paper explores automatically detecting student zoning out while performing a spoken learning task. Standard supervised machine learning techniques were used to create classification models, built on prosodic and lexical features. Our results suggest these features create models that can outperform a Bag of Words baseline. © 2010 Springer-Verlag.

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Details

Item Type: Book Section
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Drummond, J
Litman, Ddlitman@pitt.eduDLITMAN
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 27 August 2010
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 6095 L
Number: PART 2
Page Range: 306 - 308
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-642-13437-1_53
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISBN: 364213436X, 9783642134364
ISSN: 0302-9743
Date Deposited: 05 May 2015 16:24
Last Modified: 21 Dec 2021 12:57
URI: http://d-scholarship.pitt.edu/id/eprint/22935

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