The sound of inattention: Predicting mind wandering with automatically derived features of instructor speech

Ian Gliser; Caitlin Mills; Nigel Bosch; Shelby Smith; Daniel Smilek; Jeffrey D. Wammes

doi:10.1007/978-3-030-52237-7_17

The sound of inattention: Predicting mind wandering with automatically derived features of instructor speech

Ian Gliser, Caitlin Mills, Nigel Bosch, Shelby Smith, Daniel Smilek, Jeffrey D. Wammes

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

Lecturing in a classroom environment is challenging - instructors are tasked with maintaining students’ attention for extended periods of time while they are speaking. Previous work investigating the influence of speech on attention, however, has not yet been extended to instructor speech in live classroom lectures. In the current study, we automatically extracted acoustic features from live lectures to determine their association with rates of classroom mind-wandering (i.e., lack of student attention). Results indicated that five speech features reliably predicted classroom mind-wandering rates (Harmonics-to-Noise Ratio, Formant 1 Mean, Formant 2 Mean, Formant 3 Mean, and Jitter Standard Deviation). These speaker correlates of mind-wandering may be a foundation for developing a system to provide feedback in real-time for lecturers online and in the classroom. Such a system may prove to be highly beneficial in developing real-time tools to retain student attention, as well as informing other applications outside of the classroom.

Original language	English (US)
Title of host publication	Artificial Intelligence in Education- 21st International Conference, AIED 2020, Proceedings, Part I
Editors	Ig Ibert Bittencourt, Mutlu Cukurova, Rose Luckin, Kasia Muldner, Eva Millán
Publisher	Springer
Pages	204-215
Number of pages	12
ISBN (Print)	9783030522360
DOIs	https://doi.org/10.1007/978-3-030-52237-7_17
State	Published - 2020
Externally published	Yes
Event	21st International Conference on Artificial Intelligence in Education, AIED 2020 - Ifrane, Morocco Duration: Jul 6 2020 → Jul 10 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12163 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Artificial Intelligence in Education, AIED 2020
Country/Territory	Morocco
City	Ifrane
Period	7/6/20 → 7/10/20

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Acoustics
Attention
Mind-wandering
openSMILE
Speech

Publisher link

10.1007/978-3-030-52237-7_17

Find It

Find It at U of M Libraries

Cite this

Gliser, I., Mills, C., Bosch, N., Smith, S., Smilek, D., & Wammes, J. D. (2020). The sound of inattention: Predicting mind wandering with automatically derived features of instructor speech. In I. I. Bittencourt, M. Cukurova, R. Luckin, K. Muldner, & E. Millán (Eds.), Artificial Intelligence in Education- 21st International Conference, AIED 2020, Proceedings, Part I (pp. 204-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12163 LNAI). Springer. https://doi.org/10.1007/978-3-030-52237-7_17

The sound of inattention: Predicting mind wandering with automatically derived features of instructor speech. / Gliser, Ian; Mills, Caitlin; Bosch, Nigel et al.
Artificial Intelligence in Education- 21st International Conference, AIED 2020, Proceedings, Part I. ed. / Ig Ibert Bittencourt; Mutlu Cukurova; Rose Luckin; Kasia Muldner; Eva Millán. Springer, 2020. p. 204-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12163 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gliser, I, Mills, C, Bosch, N, Smith, S, Smilek, D & Wammes, JD 2020, The sound of inattention: Predicting mind wandering with automatically derived features of instructor speech. in II Bittencourt, M Cukurova, R Luckin, K Muldner & E Millán (eds), Artificial Intelligence in Education- 21st International Conference, AIED 2020, Proceedings, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12163 LNAI, Springer, pp. 204-215, 21st International Conference on Artificial Intelligence in Education, AIED 2020, Ifrane, Morocco, 7/6/20. https://doi.org/10.1007/978-3-030-52237-7_17

Gliser I, Mills C, Bosch N, Smith S, Smilek D, Wammes JD. The sound of inattention: Predicting mind wandering with automatically derived features of instructor speech. In Bittencourt II, Cukurova M, Luckin R, Muldner K, Millán E, editors, Artificial Intelligence in Education- 21st International Conference, AIED 2020, Proceedings, Part I. Springer. 2020. p. 204-215. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-52237-7_17

Gliser, Ian ; Mills, Caitlin ; Bosch, Nigel et al. / The sound of inattention : Predicting mind wandering with automatically derived features of instructor speech. Artificial Intelligence in Education- 21st International Conference, AIED 2020, Proceedings, Part I. editor / Ig Ibert Bittencourt ; Mutlu Cukurova ; Rose Luckin ; Kasia Muldner ; Eva Millán. Springer, 2020. pp. 204-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{55a6e9809ff543f1a48bf7e9ae2cc3ea,

title = "The sound of inattention: Predicting mind wandering with automatically derived features of instructor speech",

abstract = "Lecturing in a classroom environment is challenging - instructors are tasked with maintaining students{\textquoteright} attention for extended periods of time while they are speaking. Previous work investigating the influence of speech on attention, however, has not yet been extended to instructor speech in live classroom lectures. In the current study, we automatically extracted acoustic features from live lectures to determine their association with rates of classroom mind-wandering (i.e., lack of student attention). Results indicated that five speech features reliably predicted classroom mind-wandering rates (Harmonics-to-Noise Ratio, Formant 1 Mean, Formant 2 Mean, Formant 3 Mean, and Jitter Standard Deviation). These speaker correlates of mind-wandering may be a foundation for developing a system to provide feedback in real-time for lecturers online and in the classroom. Such a system may prove to be highly beneficial in developing real-time tools to retain student attention, as well as informing other applications outside of the classroom.",

keywords = "Acoustics, Attention, Mind-wandering, openSMILE, Speech",

author = "Ian Gliser and Caitlin Mills and Nigel Bosch and Shelby Smith and Daniel Smilek and Wammes, {Jeffrey D.}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 21st International Conference on Artificial Intelligence in Education, AIED 2020 ; Conference date: 06-07-2020 Through 10-07-2020",

year = "2020",

doi = "10.1007/978-3-030-52237-7_17",

language = "English (US)",

isbn = "9783030522360",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "204--215",

editor = "Bittencourt, {Ig Ibert} and Mutlu Cukurova and Rose Luckin and Kasia Muldner and Eva Mill{\'a}n",

booktitle = "Artificial Intelligence in Education- 21st International Conference, AIED 2020, Proceedings, Part I",

}

TY - GEN

T1 - The sound of inattention

T2 - 21st International Conference on Artificial Intelligence in Education, AIED 2020

AU - Gliser, Ian

AU - Mills, Caitlin

AU - Bosch, Nigel

AU - Smith, Shelby

AU - Smilek, Daniel

AU - Wammes, Jeffrey D.

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - Lecturing in a classroom environment is challenging - instructors are tasked with maintaining students’ attention for extended periods of time while they are speaking. Previous work investigating the influence of speech on attention, however, has not yet been extended to instructor speech in live classroom lectures. In the current study, we automatically extracted acoustic features from live lectures to determine their association with rates of classroom mind-wandering (i.e., lack of student attention). Results indicated that five speech features reliably predicted classroom mind-wandering rates (Harmonics-to-Noise Ratio, Formant 1 Mean, Formant 2 Mean, Formant 3 Mean, and Jitter Standard Deviation). These speaker correlates of mind-wandering may be a foundation for developing a system to provide feedback in real-time for lecturers online and in the classroom. Such a system may prove to be highly beneficial in developing real-time tools to retain student attention, as well as informing other applications outside of the classroom.

AB - Lecturing in a classroom environment is challenging - instructors are tasked with maintaining students’ attention for extended periods of time while they are speaking. Previous work investigating the influence of speech on attention, however, has not yet been extended to instructor speech in live classroom lectures. In the current study, we automatically extracted acoustic features from live lectures to determine their association with rates of classroom mind-wandering (i.e., lack of student attention). Results indicated that five speech features reliably predicted classroom mind-wandering rates (Harmonics-to-Noise Ratio, Formant 1 Mean, Formant 2 Mean, Formant 3 Mean, and Jitter Standard Deviation). These speaker correlates of mind-wandering may be a foundation for developing a system to provide feedback in real-time for lecturers online and in the classroom. Such a system may prove to be highly beneficial in developing real-time tools to retain student attention, as well as informing other applications outside of the classroom.

KW - Acoustics

KW - Attention

KW - Mind-wandering

KW - openSMILE

KW - Speech

UR - http://www.scopus.com/inward/record.url?scp=85089613532&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85089613532&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-52237-7_17

DO - 10.1007/978-3-030-52237-7_17

M3 - Conference contribution

AN - SCOPUS:85089613532

SN - 9783030522360

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 204

EP - 215

BT - Artificial Intelligence in Education- 21st International Conference, AIED 2020, Proceedings, Part I

A2 - Bittencourt, Ig Ibert

A2 - Cukurova, Mutlu

A2 - Luckin, Rose

A2 - Muldner, Kasia

A2 - Millán, Eva

PB - Springer

Y2 - 6 July 2020 through 10 July 2020

ER -

The sound of inattention: Predicting mind wandering with automatically derived features of instructor speech

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this