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Affective state-based framework for e-learning systems

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dc.contributor.author Rodríguez, Juan Antonio
dc.contributor.author Comas, Joaquim
dc.contributor.author Binefa i Valls, Xavier
dc.date.accessioned 2022-07-14T06:38:49Z
dc.date.available 2022-07-14T06:38:49Z
dc.date.issued 2021
dc.identifier.citation Rodríguez JA, Comas J, Binefa X. Affective state-based framework for e-learning systems. In: Villaret M, Alsinet T, Fernández C, Valls A, editors. Artificial intelligence research and development: proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence. Amsterdam: IOS Press; 2021. p. 357-66. DOI: 10.3233/FAIA210155
dc.identifier.uri http://hdl.handle.net/10230/53729
dc.description.abstract Virtual learning and education have become crucial during the COVID19 pandemic, which has forced a rethink by teachers and educators into designing online content and the indirect interaction with students. In an face-to-face class, some visual cues help the teacher recognize the engagement level of students, while the main weakness of the online approach is the lack of feedback that the teacher has about the learning process of the students. In this paper, we introduce a novel framework able to track the learning states, or LS, of the students while they are watching a piece of knowledge-based content. Specifically, we extract four learning states: Interested, Bored, Confused or Distracted. Finally, to demonstrate the system’s capability, we collected a reduced database to analyze the affective state of the subjects. From these preliminary results, we observe abrupt changes in the LS of the audience when there are abrupt changes in the narrative of the video, indicating that well-structured and bounded information is strongly related with the learning behaviour of the students.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IOS Press
dc.relation.ispartof Villaret M, Alsinet T, Fernández C, Valls A, editors. Artificial intelligence research and development: proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence. Amsterdam: IOS Press; 2021.
dc.rights © 2021 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
dc.rights.uri https://creativecommons.org/licenses/by-nc/4.0
dc.title Affective state-based framework for e-learning systems
dc.type info:eu-repo/semantics/bookPart
dc.identifier.doi http://doi.org/10.3233/FAIA210155
dc.subject.keyword Learning states
dc.subject.keyword e-Learning
dc.subject.keyword Deep Learning
dc.subject.keyword Machine Learning
dc.subject.keyword Facial Expression Analysis
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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