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dc.contributor.authorUribe, Pablo
dc.contributor.authorJiménez, Abelino
dc.contributor.authorAraya, Roberto
dc.contributor.authorLämsä, Joni
dc.contributor.authorHämäläinen, Raija
dc.contributor.authorViiri, Jouni
dc.contributor.editorVittorini, P.
dc.contributor.editorDi Mascio, T.
dc.contributor.editorTarantino, L.
dc.contributor.editorTemperini, M.
dc.contributor.editorGennari, R.
dc.contributor.editorDe la Prieta, F.
dc.date.accessioned2020-09-22T07:34:35Z
dc.date.available2020-09-22T07:34:35Z
dc.date.issued2020
dc.identifier.citationUribe, P., Jiménez, A., Araya, R., Lämsä, J., Hämäläinen, R., & Viiri, J. (2020). Automatic Content Analysis of Computer-Supported Collaborative Inquiry-Based Learning Using Deep Networks and Attention Mechanisms. In P. Vittorini, T. Di Mascio, L. Tarantino, M. Temperini, R. Gennari, & F. De la Prieta (Eds.), <i>Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference</i> (pp. 95-105). Springer International Publishing. Advances in Intelligent Systems and Computing, 1241. <a href="https://doi.org/10.1007/978-3-030-52538-5_11" target="_blank">https://doi.org/10.1007/978-3-030-52538-5_11</a>
dc.identifier.otherCONVID_41677920
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/71837
dc.description.abstractComputer-supported collaborative inquiry-based learning (CSCIL) represents a form of active learning in which students jointly pose questions and investigate them in technology-enhanced settings. Scaffolds can enhance CSCIL processes so that students can complete more challenging problems than they could without scaffolds. Scaffolding CSCIL, however, would optimally adapt to the needs of a specific context, group, and stage of the group's learning process. In CSCIL, the stage of the learning process can be characterized by the inquiry-based learning (IBL) phase (orientation, conceptualization, investigation, conclusion, and discussion). In this presentation, we illustrate the potential of automatic content analysis to find the different IBL phases from authentic groups' face-to-face CSCIL processes to advance the adaptive scaffolding. We obtain vector representations from words using a well-known feature engineering technique called Word Embedding. Subsequently, the classification task is done by a neural network that incorporates an attention layer. The results presented in this work show that the proposed best performing model adds interpretability and achieves a 58.92{\%} accuracy, which represents a 6{\%} improvement compared to our previous work, which was based on topic-models.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherSpringer International Publishing
dc.relation.ispartofMethodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference
dc.relation.ispartofseriesAdvances in Intelligent Systems and Computing
dc.rightsIn Copyright
dc.subject.otheroppimisanalytiikka
dc.subject.otherinquiry based learning
dc.subject.otherdeep neural networks
dc.subject.othernatural language processing
dc.titleAutomatic Content Analysis of Computer-Supported Collaborative Inquiry-Based Learning Using Deep Networks and Attention Mechanisms
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202009225919
dc.contributor.laitosKasvatustieteiden laitosfi
dc.contributor.laitosOpettajankoulutuslaitosfi
dc.contributor.laitosDepartment of Educationen
dc.contributor.laitosDepartment of Teacher Educationen
dc.contributor.oppiaineMatematiikka ja luonnontieteetfi
dc.contributor.oppiaineKasvatustiedefi
dc.contributor.oppiaineMatematiikka ja luonnontieteeten
dc.contributor.oppiaineEducationen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-3-030-52537-8
dc.description.reviewstatuspeerReviewed
dc.format.pagerange95-105
dc.relation.issn2194-5357
dc.type.versionacceptedVersion
dc.rights.copyright© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference in Methodologies and intelligent Systems for Techhnology Enhanced Learning
dc.relation.grantnumber292466
dc.subject.ysotietokoneavusteinen oppiminen
dc.subject.ysoyhteisöllinen oppiminen
dc.subject.ysoluonnollinen kieli
dc.subject.ysosisällönanalyysi
dc.subject.ysotutkiva oppiminen
dc.subject.ysoneuroverkot
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p7221
jyx.subject.urihttp://www.yso.fi/onto/yso/p18727
jyx.subject.urihttp://www.yso.fi/onto/yso/p26762
jyx.subject.urihttp://www.yso.fi/onto/yso/p14612
jyx.subject.urihttp://www.yso.fi/onto/yso/p18173
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-030-52538-5_11
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramResearch profiles, AoFen
jyx.fundinginformationSuomen Akatemia, grant numbers 292466 and 318095.


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