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Automatic Content Analysis of Student Moral Discourse in a Collaborative Learning Activity

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Collaboration Technologies and Social Computing (CollabTech 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12856))

Abstract

In most computer supported collaborative learning activities, the teacher monitors and/or reviews data generated by students and groups as they complete the learning tasks, in order to provide guidance and feedback. Without appropriate technological means that support the processes of collection and selection of students’ generated responses, these duties can result in a high cognitive load for teachers, especially if students generate textual, qualitative content that requires real-time reviewing. In this research we deal with EthicApp, a collaborative application in which this problem is apparent, as students analyze a given ethics case individually and in small groups and deliver written judgements in each phase of the activity. We present a solution to the problem, based on enhancing EthicApp’s teacher’s interface with automated content analysis capabilities. This includes a dashboard that automatically displays students’ most relevant contributions, and cluster visualizations that permit identifying groups of students with similar responses to activity tasks. Validation of the approach was based on a dataset comprising 4,366 comments about an academic ethics case, which were written by 520 students divided into 19 class groups. Expert judgement was applied to evaluate content analysis effectiveness at selecting comments that are both meaningful and representative of students’ different views. More than 80% of comment selections were found valuable, according to experts’ analysis.

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Author information

Authors and Affiliations

  1. Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile

    Claudio Alvarez

  2. Centro de Investigación en Educación, Universidad de los Andes, Santiago, Chile

    Claudio Alvarez

  3. Facultad de Economía y Negocios, Universidad de Chile, Santiago, Chile

    Gustavo Zurita

  4. Departamento de Ciencias de la Computación, Pontificia Universidad Católica de Chile, Santiago, Chile

    Andrés Carvallo

  5. Escuela de Ingeniería y Ciencias, Universidad de Chile, Santiago, Chile

    Pablo Ramírez & Eugenio Bravo

  6. Departamento de Ciencias de la Computación, Universidad de Chile, Santiago, Chile

    Nelson Baloian

Authors
  1. Claudio Alvarez
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  2. Gustavo Zurita
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  3. Andrés Carvallo
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  4. Pablo Ramírez
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  5. Eugenio Bravo
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  6. Nelson Baloian
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Corresponding author

Correspondence to Claudio Alvarez .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain

    Davinia Hernández-Leo

  2. School of Creative Science, Waseda University, Tokyo, Japan

    Reiko Hishiyama

  3. Department of Management Control and Information Systems, University of Chile, Santiago, Chile

    Gustavo Zurita

  4. Human-Computer Interaction Group, University of Trier, Trier, Germany

    Benjamin Weyers

  5. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Alexander Nolte

  6. Academic Center for Computing and Media Studies, Kyoto University, Kyoto, Japan

    Hiroaki Ogata

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Alvarez, C., Zurita, G., Carvallo, A., Ramírez, P., Bravo, E., Baloian, N. (2021). Automatic Content Analysis of Student Moral Discourse in a Collaborative Learning Activity. In: Hernández-Leo, D., Hishiyama, R., Zurita, G., Weyers, B., Nolte, A., Ogata, H. (eds) Collaboration Technologies and Social Computing. CollabTech 2021. Lecture Notes in Computer Science(), vol 12856. Springer, Cham. https://doi.org/10.1007/978-3-030-85071-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-85071-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85070-8

  • Online ISBN: 978-3-030-85071-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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