Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/9083
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDubovi, Ilana
dc.date.accessioned2023-04-12T20:59:07Z-
dc.date.available2023-04-12T16:15:12Z
dc.date.available2023-04-12T20:59:07Z-
dc.date.issued2022
dc.identifier.citationDubovi, I. (2022). Emotional engagement assessment: Self-reports versus facial expressions. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 969-972). International Society of the Learning Sciences.en
dc.identifier.urihttps://dx.doi.org/10.22318/icls2022.969
dc.identifier.urihttps://repository.isls.org//handle/1/9083-
dc.descriptionShort Paperen
dc.description.abstractCurrent research utilizes self-reports and facial expression recognition analysis to provide a more continuous and objective insight into how students’ emotional engagement unfolds and impacts learning. Analysis of nursing students learning with virtual reality simulation revealed that only the facial expression data channel, compared to self-reports, was sensitive to fluctuations in engagement which varied throughout the different learning session phases. In addition, findings show that learning achievements were negatively associated with facial expressions of anger and positively associated with positive self-reported emotions. Hence, this study demonstrates that the methodology of using multimodal data channels which encompass different types of measures, can provide insights into a more holistic understanding of engagement in learning and learning achievement.en
dc.publisherInternational Society of the Learning Sciences
dc.relation.ispartofurn:ISBN:978-1-7373306-5-3
dc.relation.ispartofProceedings of the 16th International Conference of the Learning Sciences - ICLS 2022, pp. 969-972
dc.subjectLearning Sciencesen
dc.titleEmotional Engagement Assessment: Self-Reports Versus Facial Expressionsen
dc.typeconferencePaper
dc.identifier.doi10.22318/icls2022.969
Appears in Collections:ISLS Annual Meeting 2022

Files in This Item:
File SizeFormat 
ICLS2022_969-972.pdf355.14 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.