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

Claudio Alvarez*, Gustavo Zurita, Andrés Carvallo, Pablo Ramírez, Eugenio Bravo, Nelson Baloian

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationCollaboration Technologies and Social Computing - 27th International Conference, CollabTech 2021, Proceedings
EditorsDavinia Hernandez-Leo, Reiko Hishiyama, Gustavo Zurita, Benjamin Weyers, Alexander Nolte, Hiroaki Ogata
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-19
Number of pages17
ISBN (Print)9783030850708
DOIs
StatePublished - 2021
Event27th International Conference on Collaboration Technologies and Social Computing, CollabTech 2021 - Virtual, Online
Duration: 30 Aug 20212 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12856 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Collaboration Technologies and Social Computing, CollabTech 2021
CityVirtual, Online
Period30/08/212/09/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Automated content analysis
  • CSCL
  • Ethics teaching
  • Higher education

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