Privacy-Preserving and Scalable Affect Detection in Online Synchronous Learning

Felix Böttger; Ufuk Cetinkaya; Daniele Di Mitri; Sebastian Gombert; Krist Shingjergji; Deniz Iren; Roland Klemke

doi:10.1007/978-3-031-16290-9_4

Privacy-Preserving and Scalable Affect Detection in Online Synchronous Learning

Felix Böttger^*
, Ufuk Cetinkaya
, Daniele Di Mitri
, Sebastian Gombert
, Krist Shingjergji
, Deniz Iren
, Roland Klemke

^*Corresponding author for this work

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

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Abstract

The recent pandemic has forced most educational institutions to shift to distance learning. Teachers can perceive various non-verbal cues in face-to-face classrooms and thus notice when students are distracted, confused, or tired. However, the students’ non-verbal cues are not observable in online classrooms. The lack of these cues poses a challenge for the teachers and hinders them in giving adequate, timely feedback in online educational settings. This can lead to learners not receiving proper guidance and may cause them to be demotivated. This paper proposes a pragmatic approach to detecting student affect in online synchronized learning classrooms. Our approach consists of a method and a privacy-preserving prototype that only collects data that is absolutely necessary to compute action units and is highly scalable by design to run on multiple devices without specialized hardware. We evaluated our prototype using a benchmark for the system performance. Our results confirm the feasibility and the applicability of the proposed approach.

Original language	English
Title of host publication	Educating for a New Future
Subtitle of host publication	Making Sense of Technology-Enhanced Learning Adoption - 17th European Conference on Technology Enhanced Learning, EC-TEL 2022, Proceedings
Editors	Isabel Hilliger, Pedro J. Muñoz-Merino, Tinne De Laet, Alejandro Ortega-Arranz, Tracie Farrell
Place of Publication	Cham
Publisher	Springer, Cham
Pages	45-58
Number of pages	14
Volume	13450
Edition	1
ISBN (Electronic)	978-3-031-16290-9
ISBN (Print)	978-3-031-16289-3
DOIs	https://doi.org/10.1007/978-3-031-16290-9_4
Publication status	Published - 5 Sept 2022
Event	17th European Conference on Technology Enhanced Learning - Toulouse, France Duration: 12 Sept 2022 → 16 Sept 2022

Publication series

Series	Springer Lecture Notes in Computer Science (LNCS)
Volume	13450
ISSN	0302-9743

Conference

Conference	17th European Conference on Technology Enhanced Learning
Abbreviated title	EC-TEL2022
Country/Territory	France
City	Toulouse
Period	12/09/22 → 16/09/22

Keywords

Action units
Affect detection
Emotion recognition
Privacy
TRUSTWORTHY_AI

Access to Document

10.1007/978-3-031-16290-9_4

2022_bottger_SenseTheClassroom_

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Böttger, F., Cetinkaya, U., Mitri, D. D., Gombert, S., Shingjergji, K., Iren, D., & Klemke, R. (2022). Privacy-Preserving and Scalable Affect Detection in Online Synchronous Learning. In I. Hilliger, P. J. Muñoz-Merino, T. De Laet, A. Ortega-Arranz, & T. Farrell (Eds.), Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption - 17th European Conference on Technology Enhanced Learning, EC-TEL 2022, Proceedings (1 ed., Vol. 13450, pp. 45-58). Springer, Cham. https://doi.org/10.1007/978-3-031-16290-9_4

Böttger, Felix ; Cetinkaya, Ufuk ; Mitri, Daniele Di et al. / Privacy-Preserving and Scalable Affect Detection in Online Synchronous Learning. Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption - 17th European Conference on Technology Enhanced Learning, EC-TEL 2022, Proceedings. editor / Isabel Hilliger ; Pedro J. Muñoz-Merino ; Tinne De Laet ; Alejandro Ortega-Arranz ; Tracie Farrell. Vol. 13450 1. ed. Cham : Springer, Cham, 2022. pp. 45-58 (Springer Lecture Notes in Computer Science (LNCS), Vol. 13450).

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abstract = "The recent pandemic has forced most educational institutions to shift to distance learning. Teachers can perceive various non-verbal cues in face-to-face classrooms and thus notice when students are distracted, confused, or tired. However, the students{\textquoteright} non-verbal cues are not observable in online classrooms. The lack of these cues poses a challenge for the teachers and hinders them in giving adequate, timely feedback in online educational settings. This can lead to learners not receiving proper guidance and may cause them to be demotivated. This paper proposes a pragmatic approach to detecting student affect in online synchronized learning classrooms. Our approach consists of a method and a privacy-preserving prototype that only collects data that is absolutely necessary to compute action units and is highly scalable by design to run on multiple devices without specialized hardware. We evaluated our prototype using a benchmark for the system performance. Our results confirm the feasibility and the applicability of the proposed approach. ",

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Böttger, F, Cetinkaya, U, Mitri, DD, Gombert, S, Shingjergji, K, Iren, D & Klemke, R 2022, Privacy-Preserving and Scalable Affect Detection in Online Synchronous Learning. in I Hilliger, PJ Muñoz-Merino, T De Laet, A Ortega-Arranz & T Farrell (eds), Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption - 17th European Conference on Technology Enhanced Learning, EC-TEL 2022, Proceedings. 1 edn, vol. 13450, Springer, Cham, Cham, Springer Lecture Notes in Computer Science (LNCS), vol. 13450, pp. 45-58, 17th European Conference on Technology Enhanced Learning, Toulouse, France, 12/09/22. https://doi.org/10.1007/978-3-031-16290-9_4

Privacy-Preserving and Scalable Affect Detection in Online Synchronous Learning. / Böttger, Felix; Cetinkaya, Ufuk; Mitri, Daniele Di et al.
Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption - 17th European Conference on Technology Enhanced Learning, EC-TEL 2022, Proceedings. ed. / Isabel Hilliger; Pedro J. Muñoz-Merino; Tinne De Laet; Alejandro Ortega-Arranz; Tracie Farrell. Vol. 13450 1. ed. Cham: Springer, Cham, 2022. p. 45-58 (Springer Lecture Notes in Computer Science (LNCS), Vol. 13450).

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

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N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

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AB - The recent pandemic has forced most educational institutions to shift to distance learning. Teachers can perceive various non-verbal cues in face-to-face classrooms and thus notice when students are distracted, confused, or tired. However, the students’ non-verbal cues are not observable in online classrooms. The lack of these cues poses a challenge for the teachers and hinders them in giving adequate, timely feedback in online educational settings. This can lead to learners not receiving proper guidance and may cause them to be demotivated. This paper proposes a pragmatic approach to detecting student affect in online synchronized learning classrooms. Our approach consists of a method and a privacy-preserving prototype that only collects data that is absolutely necessary to compute action units and is highly scalable by design to run on multiple devices without specialized hardware. We evaluated our prototype using a benchmark for the system performance. Our results confirm the feasibility and the applicability of the proposed approach.

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Böttger F, Cetinkaya U, Mitri DD, Gombert S, Shingjergji K, Iren D et al. Privacy-Preserving and Scalable Affect Detection in Online Synchronous Learning. In Hilliger I, Muñoz-Merino PJ, De Laet T, Ortega-Arranz A, Farrell T, editors, Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption - 17th European Conference on Technology Enhanced Learning, EC-TEL 2022, Proceedings. 1 ed. Vol. 13450. Cham: Springer, Cham. 2022. p. 45-58. (Springer Lecture Notes in Computer Science (LNCS), Vol. 13450). doi: 10.1007/978-3-031-16290-9_4

Privacy-Preserving and Scalable Affect Detection in Online Synchronous Learning

Abstract

Publication series

Conference

Keywords

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