Can Analytics of Speaking Time Serve as Indicators of Effective Team Communication and Collaboration?

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dc.contributor.authorSalinas, Omar
dc.contributor.authorRiquelme, Fabian
dc.contributor.authorMuñoz, Roberto
dc.contributor.authorCechinel, Cristian
dc.contributor.authorMartinez, Roberto
dc.contributor.authorMonsalves, Diego
dc.date.accessioned2022-11-30T02:46:55Z
dc.date.available2022-11-30T02:46:55Z
dc.date.issued2021
dc.description.abstractPeople with effective teamwork skills, such as collaboration or leadership, are highly demanded in the workplace. In turn, educational providers have adopted active learning methodologies, such as collaborative problem-solving. However, the objective evaluation of collaboration at scale still is a challenge. This paper explores the relationship between quantitative measures obtained from automated transcriptions of speech and qualitative indicators of effective collaboration. An omnidirectional microphone and an artificial intelligence algorithm were used to collect speaking data from 20 triads of students discussing and building a concept map. The study focused on validating the potential value of speech recording devices to quantify the dynamics of communication networks by comparing quantitative metrics obtained from them with an established rating scheme for measuring the extent of collaboration. Results showed a relationship between the standard deviations of the speaking times of the participants in each group and the evaluation obtained from the qualitative rubrics of communication and interpersonal relationships. Thus, the extent to which all group members contribute to the discourse can potentially serve as an indicator of effective group work.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameSalinas_Can2021.pdf
dc.identifier.citationOmar Salinas, Fabian Riquelme, Roberto Munoz, Cristian Cechinel, Roberto Martinez, and Diego Monsalves. 2021. Can Analytics of Speaking Time Serve as Indicators of Effective Team Communication and Collaboration? In X Latin American Conference on Human Computer Interaction (CLIHC 2021). Association for Computing Machinery, New York, NY, USA, Article 12, 1–4. https://doi.org/10.1145/3488392.3488404en_ES
dc.identifier.doihttps://doi.org/10.1145/3488392.3488404
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7528
dc.languageen
dc.publisherACM
dc.sourceCLIHC 2021: X Latin American Conference on Human Computer Interaction
dc.subjectMULTIMODALen_ES
dc.subjectLEARNING ANALYTICSen_ES
dc.subjectTEAMWORKen_ES
dc.subjectCOLLABORATION ANALYTICSen_ES
dc.subjectCSCLen_ES
dc.titleCan Analytics of Speaking Time Serve as Indicators of Effective Team Communication and Collaboration?
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Informatica
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