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Authordc.contributor.authorLamsa, Joni 
Authordc.contributor.authorUribe, Pablo 
Authordc.contributor.authorJiménez, Abelino 
Authordc.contributor.authorCaballero, Daniela 
Authordc.contributor.authorHamalainen, Raija 
Authordc.contributor.authorAraya, Roberto 
Admission datedc.date.accessioned2021-09-22T16:09:36Z
Available datedc.date.available2021-09-22T16:09:36Z
Publication datedc.date.issued2021
Cita de ítemdc.identifier.citationJournal of Learning Analytics Volume 8(1), 113–125. 2021es_ES
Identifierdc.identifier.other10.18608/jla.2021.7118
Identifierdc.identifier.urihttp://repositorio.uchile.cl/handle/2250/182056
Abstractdc.description.abstractScholars have applied automatic content analysis to study computer-mediated communication in computer-supported collaborative learning (CSCL). Since CSCL also takes place in face-to-face interactions, we studied the automatic coding accuracy of manually transcribed face-to-face communication. We conducted our study in an authentic higher-education physics context where computer-supported collaborative inquiry-based learning (CSCIL) is a popular pedagogical approach. Since learners' needs for support in CSCIL vary in the different inquiry phases (orientation, conceptualization, investigation, conclusion, and discussion), we studied, first, how the coding accuracy of five computational models (based on word embeddings and deep neural networks with attention layers) differed in the various inquiry-based learning (IBL) phases when compared to human coding. Second, we investigated how the different features of the best performing computational model improved the coding accuracy. The study indicated that the accuracy of the best performing computational model (differentiated attention with pre-trained static embeddings) was slightly better than that of the human coder (58.9% vs. 54.3%). We also found that considering the previous and following utterances, as well as the relative position of the utterance, improved the model's accuracy. Our method illustrates how computational models can be trained for specific purposes (e.g., to code IBL phases) with small data sets by using pre-trained models.es_ES
Patrocinadordc.description.sponsorshipAcademy of Finland European Commission 292466 318095 Multidisciplinary Research on Learning and Teaching profiles I ANID/PIA/Basal Funds for Excellence FB0003es_ES
Lenguagedc.language.isoenes_ES
Publisherdc.publisherSoc. Learning Analytics Research-Solar, Canadáes_ES
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile*
Link to Licensedc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
Sourcedc.sourceJournal of Learning Analyticses_ES
Keywordsdc.subjectCollaboration analyticses_ES
Keywordsdc.subjectComputational modelses_ES
Keywordsdc.subjectComputer-supported collaborative learninges_ES
Keywordsdc.subjectCSCLes_ES
Keywordsdc.subjectCSCILes_ES
Keywordsdc.subjectDeep networkses_ES
Keywordsdc.subjectInquiry-based learninges_ES
Keywordsdc.subjectWord embeddinges_ES
Títulodc.titleDeep networks for collaboration analytics: promoting automatic analysis of face-to-face interaction in the context of inquiry-based learninges_ES
Document typedc.typeArtículo de revistaes_ES
Catalogueruchile.catalogadorcrbes_ES
Indexationuchile.indexArtículo de publicación ISIes_ES


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Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile