Analyzing speech data to detect work environment in group activities

Valeria Barzola; Eddo Alvarado; Carlos Loja; Alex Velez; Ivan Silva; Vanessa Echeverria

doi:10.1007/978-3-031-11647-6_69

Analyzing speech data to detect work environment in group activities

Valeria Barzola, Eddo Alvarado, Carlos Loja, Alex Velez, Ivan Silva, Vanessa Echeverria

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

Collaboration is one required skill for the future workforce that requires constant practice and evaluation. However, students often lack formative feedback and support for their collaboration skills during their formal learning. Current technologies for emergent learning due to COVID-19 could make visible digital traces of collaboration to support timely feedback. This work aims to automatically detect the group work environment using speech data captured during group activities. Grounded in literature and students’ perspectives, this work defines and implements three indicators for detecting the work environment namely noise, silence and speech time. Three experts rated two hundred thirty-two video instances lasting 30-secs each to get a group work environment score. We report the results of two machine learning models for detecting the group work environment and briefly reflect on these results.

Original language	English
Title of host publication	Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022 Durham, UK, July 27–31, 2022 Proceedings, Part II
Editors	Maria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	357-361
Number of pages	5
ISBN (Electronic)	9783031116476
ISBN (Print)	9783031116469
DOIs	https://doi.org/10.1007/978-3-031-11647-6_69
Publication status	Published - 2022
Externally published	Yes
Event	International Conference on Artificial Intelligence in Education 2022 - Durham, United Kingdom Duration: 27 Jul 2022 → 31 Jul 2022 Conference number: 23rd https://link.springer.com/book/10.1007/978-3-031-11644-5

Publication series

Name	Lecture Notes in Computer Science
Publisher	Spinger
Volume	13356
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Artificial Intelligence in Education 2022
Abbreviated title	AIED 2022
Country/Territory	United Kingdom
City	Durham
Period	27/07/22 → 31/07/22
Internet address	https://link.springer.com/book/10.1007/978-3-031-11644-5

Keywords

Automatic feedback
Collaboration feedback
Group work environment
Human-centered analytics

Access to Document

10.1007/978-3-031-11647-6_69

Cite this

Barzola, V., Alvarado, E., Loja, C., Velez, A., Silva, I., & Echeverria, V. (2022). Analyzing speech data to detect work environment in group activities. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022 Durham, UK, July 27–31, 2022 Proceedings, Part II (pp. 357-361). (Lecture Notes in Computer Science; Vol. 13356). Springer. https://doi.org/10.1007/978-3-031-11647-6_69

Barzola, Valeria ; Alvarado, Eddo ; Loja, Carlos et al. / Analyzing speech data to detect work environment in group activities. Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022 Durham, UK, July 27–31, 2022 Proceedings, Part II. editor / Maria Mercedes Rodrigo ; Noburu Matsuda ; Alexandra I. Cristea ; Vania Dimitrova. Cham Switzerland : Springer, 2022. pp. 357-361 (Lecture Notes in Computer Science).

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abstract = "Collaboration is one required skill for the future workforce that requires constant practice and evaluation. However, students often lack formative feedback and support for their collaboration skills during their formal learning. Current technologies for emergent learning due to COVID-19 could make visible digital traces of collaboration to support timely feedback. This work aims to automatically detect the group work environment using speech data captured during group activities. Grounded in literature and students{\textquoteright} perspectives, this work defines and implements three indicators for detecting the work environment namely noise, silence and speech time. Three experts rated two hundred thirty-two video instances lasting 30-secs each to get a group work environment score. We report the results of two machine learning models for detecting the group work environment and briefly reflect on these results.",

keywords = "Automatic feedback, Collaboration feedback, Group work environment, Human-centered analytics",

author = "Valeria Barzola and Eddo Alvarado and Carlos Loja and Alex Velez and Ivan Silva and Vanessa Echeverria",

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Barzola, V, Alvarado, E, Loja, C, Velez, A, Silva, I & Echeverria, V 2022, Analyzing speech data to detect work environment in group activities. in MM Rodrigo, N Matsuda, AI Cristea & V Dimitrova (eds), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022 Durham, UK, July 27–31, 2022 Proceedings, Part II. Lecture Notes in Computer Science, vol. 13356, Springer, Cham Switzerland, pp. 357-361, International Conference on Artificial Intelligence in Education 2022, Durham, United Kingdom, 27/07/22. https://doi.org/10.1007/978-3-031-11647-6_69

Analyzing speech data to detect work environment in group activities. / Barzola, Valeria; Alvarado, Eddo; Loja, Carlos et al.

Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022 Durham, UK, July 27–31, 2022 Proceedings, Part II. ed. / Maria Mercedes Rodrigo; Noburu Matsuda; Alexandra I. Cristea; Vania Dimitrova. Cham Switzerland : Springer, 2022. p. 357-361 (Lecture Notes in Computer Science; Vol. 13356).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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Barzola V, Alvarado E, Loja C, Velez A, Silva I, Echeverria V. Analyzing speech data to detect work environment in group activities. In Rodrigo MM, Matsuda N, Cristea AI, Dimitrova V, editors, Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022 Durham, UK, July 27–31, 2022 Proceedings, Part II. Cham Switzerland: Springer. 2022. p. 357-361. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-031-11647-6_69