How good is my feedback? a content analysis of written feedback

Anderson Pinheiro Cavalcanti, Arthur Diego, Rafael Ferreira Mello, Katerina Mangaroska, André Nascimento, Fred Freitas, Dragan Gaševic

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

1 Citation (Scopus)

Abstract

Feedback is a crucial element in helping students identify gaps and assess their learning progress. In online courses, feedback becomes even more critical as it is one of the resources where the teacher interacts directly with the student. However, with the growing number of students enrolled in online learning, it becomes a challenge for instructors to provide good quality feedback that helps the student self-regulate. In this context, this paper proposed a content analysis of feedback text provided by instructors based on different indicators of good feedback. A random forest classifier was trained and evaluated at different feedback levels. The results achieved outcomes up to 87% and 0.39 of accuracy and Cohen's κ, respectively. The paper also provides insights into the most influential textual features of feedback that predict feedback quality.

Original languageEnglish
Title of host publicationLAK 2020 Conference Proceedings
EditorsVitomir Kovanović, Maren Scheffel, Niels Pinkwart, Katrien Verbert
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages428-437
Number of pages10
ISBN (Electronic)9781450377126
DOIs
Publication statusPublished - 2020
EventInternational Conference on Learning Analytics and Knowledge 2020 - Frankfurt, Germany
Duration: 23 Mar 202027 Mar 2020
Conference number: 10th
https://lak20.solaresearch.org

Conference

ConferenceInternational Conference on Learning Analytics and Knowledge 2020
Abbreviated titleLAK 2020
CountryGermany
CityFrankfurt
Period23/03/2027/03/20
Internet address

Keywords

  • Content Analysis
  • Feedback
  • Learning Analytics
  • Online learning

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