Automatic content analysis of online discussions for cognitive presence: a study of the generalizability across educational contexts

Valter Neto, Vitor Rolim, Anderson Pinheiro Cavalcanti, Rafael Dueire Lins, Dragan Gasevic, Rafael Ferreiramello

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This paper investigates the impact of the use of data from different educational contexts in the automatic classification of online discussion messages according to cognitive presence, an essential construct of the community of inquiry model. In particular, this paper analyzed online discussion messages written in Brazilian Portuguese from two different courses that were from different subject areas (Biology and Technology) and had different teaching presences in the online discussions. The study explored a set of 127 features of online discussion messages and a random forest classifier to automatically recognize the phases of the cognitive presence in online discussion messages. The results showed that the classifier achieved better performance whenever applied to the entire dataset. It reveals that when a classifier is created for a specific course it is not generic enough to be applied to a course from a different field of knowledge. The results also showed the importance of the features that were predictive of the phases of the cognitive presence in the educational context. Based on the findings of this study, future work should adopt the same feature set as used in the current study, but it should train the classifier of the cognitive presence on datasets in subject areas related to the topic of the discussions.

Original languageEnglish
Number of pages14
JournalIEEE Transactions on Learning Technologies
DOIs
Publication statusAccepted/In press - 25 May 2021

Keywords

  • Analytical models
  • Community of inquiry model
  • context analysis
  • Context modeling
  • Feature extraction
  • learning analytics
  • Manuals
  • online discussion
  • Reliability
  • Testing
  • Text mining
  • text mining

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