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Cited by 44Online Learning and AnalyticsStudent Assessment and FeedbackEvaluation of Teaching Practices

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

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

Cavalcanti, Anderson Pinheiro, Diego, Arthur, Mello, Rafael Ferreira, Mangaroska, Katerina, Nascimento, André, Freitas, Fred, Gašević, Dragan·2020·Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
DOI 10.1145/3375462.3375477
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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.

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Crossref · 10.1145/3375462.3375477

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8 of 8 references verified0 citation errors · 0 not checkable

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  • Longer comments have been associated with enhanced learning and self-regulation.

    “which have been associated with enhanced learning and self-regulation (Baars et al., 2020; Cavalcanti et al., 2020; Osakwe et al., 2021; Zhu & Carless, 2018).”

    From Impact of AI assistance on student agency (2024) · cites this paper as Cavalcanti et al., 2020

Citations

Cites

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Research works printed but not linked: 36

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Metadata

Authors
Cavalcanti, A. P., Diego, A., Mello, R. F., Mangaroska, K., Nascimento, A., Freitas, F., & Gašević, D.
Affiliations
  • Universidade Federal de Pernambuco (BR)
  • Norwegian University of Science and Technology (NO)
  • Universidade Federal Rural de Pernambuco, Recife, PE, Brazil
  • Monash University (AU)
ORCID
Venue
Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
Publisher
ACM
Published
2020-03-13
Pages
428-437
DOI
10.1145/3375462.3375477
OpenAlex
W3011681407
Cited by
44
References
8
Citations by year
2026: 7 · 2025: 5 · 2024: 10 · 2023: 10 · 2022: 5 · 2021: 6 · 2020: 1
FWCI
5.82
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
Funders
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (88882.347595/2019-01)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Education › Student Assessment and Feedback
  • Social Sciences › Education › Evaluation of Teaching Practices
Keywords
feedback quality, online learning, instructor feedback, student self-regulation, text classification, random forest, text features, student learning progress

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