Reading Minds: Exploring Relationships between Sensor-Data and Self-Reported Cognitive-Affective States of Learners

Sharanya Lal; Tessa H.S. Eysink; Hannie A. Gijlers; Willem B. Verwey; Bernard P. Veldkamp

Reading Minds: Exploring Relationships between Sensor-Data and Self-Reported Cognitive-Affective States of Learners

Sharanya Lal, Tessa H.S. Eysink, Hannie A. Gijlers, Willem B. Verwey, Bernard P. Veldkamp

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

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Abstract

Cognitive-affective states (CASs) strongly affect learning outcomes. Therefore, learning systems need to be CAS-aware to truly cater to learners' needs. In this study, we explore the possibility of multimodal sensor data as reliable indicators of learner CASs. Data collected will include skin conductance, heart rate, skin temperature and global gaze properties, which will be compared with self-reported CASs of learners. Linear mixed models will be applied and results will be presented at the conference.

Original language	English
Title of host publication	International Collaboration toward Educational Innovation for All
Subtitle of host publication	Overarching Research, Development, and Practices - 16th International Conference of the Learning Sciences, ICLS 2022
Editors	Clark Chinn, Edna Tan, Carol Chan, Yael Kali
Publisher	International Society of the Learning Sciences (ISLS)
Pages	2048-2049
Number of pages	2
ISBN (Electronic)	9781737330653
Publication status	Published - 2022
Event	16th International Conference of the Learning Sciences, ICLS 2022 - Virtual, Online, Japan Duration: 6 Jun 2022 → 10 Jun 2022 Conference number: 16

Publication series

Name	Proceedings of International Conference of the Learning Sciences, ICLS
ISSN (Print)	1814-9316

Conference

Conference	16th International Conference of the Learning Sciences, ICLS 2022
Abbreviated title	ICLS 2022
Country/Territory	Japan
City	Virtual, Online
Period	6/06/22 → 10/06/22

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ICLS2022 ProceedingsFinal published version, 350 MB

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Lal, S., Eysink, T. H. S., Gijlers, H. A., Verwey, W. B., & Veldkamp, B. P. (2022). Reading Minds: Exploring Relationships between Sensor-Data and Self-Reported Cognitive-Affective States of Learners. In C. Chinn, E. Tan, C. Chan, & Y. Kali (Eds.), International Collaboration toward Educational Innovation for All: Overarching Research, Development, and Practices - 16th International Conference of the Learning Sciences, ICLS 2022 (pp. 2048-2049). (Proceedings of International Conference of the Learning Sciences, ICLS). International Society of the Learning Sciences (ISLS).

Lal, Sharanya ; Eysink, Tessa H.S. ; Gijlers, Hannie A. et al. / Reading Minds : Exploring Relationships between Sensor-Data and Self-Reported Cognitive-Affective States of Learners. International Collaboration toward Educational Innovation for All: Overarching Research, Development, and Practices - 16th International Conference of the Learning Sciences, ICLS 2022. editor / Clark Chinn ; Edna Tan ; Carol Chan ; Yael Kali. International Society of the Learning Sciences (ISLS), 2022. pp. 2048-2049 (Proceedings of International Conference of the Learning Sciences, ICLS).

@inproceedings{0944de2678f548398fdef3cd9954688e,

title = "Reading Minds: Exploring Relationships between Sensor-Data and Self-Reported Cognitive-Affective States of Learners",

abstract = "Cognitive-affective states (CASs) strongly affect learning outcomes. Therefore, learning systems need to be CAS-aware to truly cater to learners' needs. In this study, we explore the possibility of multimodal sensor data as reliable indicators of learner CASs. Data collected will include skin conductance, heart rate, skin temperature and global gaze properties, which will be compared with self-reported CASs of learners. Linear mixed models will be applied and results will be presented at the conference.",

author = "Sharanya Lal and Eysink, {Tessa H.S.} and Gijlers, {Hannie A.} and Verwey, {Willem B.} and Veldkamp, {Bernard P.}",

note = "Publisher Copyright: {\textcopyright} ISLS.; 16th International Conference of the Learning Sciences, ICLS 2022, ICLS 2022 ; Conference date: 06-06-2022 Through 10-06-2022",

year = "2022",

language = "English",

series = "Proceedings of International Conference of the Learning Sciences, ICLS",

publisher = "International Society of the Learning Sciences (ISLS)",

pages = "2048--2049",

editor = "Clark Chinn and Edna Tan and Carol Chan and Yael Kali",

booktitle = "International Collaboration toward Educational Innovation for All",

address = "United States",

}

Lal, S, Eysink, THS , Gijlers, HA , Verwey, WB & Veldkamp, BP 2022, Reading Minds: Exploring Relationships between Sensor-Data and Self-Reported Cognitive-Affective States of Learners. in C Chinn, E Tan, C Chan & Y Kali (eds), International Collaboration toward Educational Innovation for All: Overarching Research, Development, and Practices - 16th International Conference of the Learning Sciences, ICLS 2022. Proceedings of International Conference of the Learning Sciences, ICLS, International Society of the Learning Sciences (ISLS), pp. 2048-2049, 16th International Conference of the Learning Sciences, ICLS 2022, Virtual, Online, Japan, 6/06/22.

Reading Minds: Exploring Relationships between Sensor-Data and Self-Reported Cognitive-Affective States of Learners. / Lal, Sharanya; Eysink, Tessa H.S.; Gijlers, Hannie A. et al.
International Collaboration toward Educational Innovation for All: Overarching Research, Development, and Practices - 16th International Conference of the Learning Sciences, ICLS 2022. ed. / Clark Chinn; Edna Tan; Carol Chan; Yael Kali. International Society of the Learning Sciences (ISLS), 2022. p. 2048-2049 (Proceedings of International Conference of the Learning Sciences, ICLS).

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

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T1 - Reading Minds

T2 - 16th International Conference of the Learning Sciences, ICLS 2022

AU - Lal, Sharanya

AU - Eysink, Tessa H.S.

AU - Gijlers, Hannie A.

AU - Verwey, Willem B.

AU - Veldkamp, Bernard P.

N1 - Conference code: 16

PY - 2022

Y1 - 2022

N2 - Cognitive-affective states (CASs) strongly affect learning outcomes. Therefore, learning systems need to be CAS-aware to truly cater to learners' needs. In this study, we explore the possibility of multimodal sensor data as reliable indicators of learner CASs. Data collected will include skin conductance, heart rate, skin temperature and global gaze properties, which will be compared with self-reported CASs of learners. Linear mixed models will be applied and results will be presented at the conference.

AB - Cognitive-affective states (CASs) strongly affect learning outcomes. Therefore, learning systems need to be CAS-aware to truly cater to learners' needs. In this study, we explore the possibility of multimodal sensor data as reliable indicators of learner CASs. Data collected will include skin conductance, heart rate, skin temperature and global gaze properties, which will be compared with self-reported CASs of learners. Linear mixed models will be applied and results will be presented at the conference.

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M3 - Conference contribution

AN - SCOPUS:85145774709

T3 - Proceedings of International Conference of the Learning Sciences, ICLS

SP - 2048

EP - 2049

BT - International Collaboration toward Educational Innovation for All

A2 - Chinn, Clark

A2 - Tan, Edna

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PB - International Society of the Learning Sciences (ISLS)

Y2 - 6 June 2022 through 10 June 2022

ER -

Lal S, Eysink THS , Gijlers HA , Verwey WB , Veldkamp BP. Reading Minds: Exploring Relationships between Sensor-Data and Self-Reported Cognitive-Affective States of Learners. In Chinn C, Tan E, Chan C, Kali Y, editors, International Collaboration toward Educational Innovation for All: Overarching Research, Development, and Practices - 16th International Conference of the Learning Sciences, ICLS 2022. International Society of the Learning Sciences (ISLS). 2022. p. 2048-2049. (Proceedings of International Conference of the Learning Sciences, ICLS).

Reading Minds: Exploring Relationships between Sensor-Data and Self-Reported Cognitive-Affective States of Learners

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