An analysis of the use of good feedback practices in online learning courses

Anderson Pinheiro Cavalcanti, Vitor Rolim, Máverick André, Fred Freitas, Rafael Ferreira, Dragan Gasevic

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

3 Citations (Scopus)

Abstract

Feedback is an essential component of any learning experience. It allows students to identify gaps in their learning and improve their self-regulation. However, providing useful feedback is a challenging and time-consuming task. In digital learning environments, this challenge is even more significant due to a large number of students. Thus, this paper reports on the findings of an analysis of the quality of feedback provided by instructors in an online course. The paper also proposes a supervised machine learning algorithm that can identify the presence of good practices in feedback messages sent to students in a digital learning environment. The results reveal the most commonly used kinds of feedback and how to identify them automatically. The results of the study could potentially be used to improve the quality of the feedback provided by instructors in online education.

Original languageEnglish
Title of host publicationProceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019
EditorsMaiga Chang, Demetrios G Sampson, Ronghuai Huang, Alex Sandro Gomes, Nian-Shing Chen, Ig Ibert Bittencourt, Kinshuk , Diego Dermeval, Ibsen Mateus Bittencourt
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages153-157
Number of pages5
ISBN (Electronic)9781728134857
ISBN (Print)9781728134864
DOIs
Publication statusPublished - 2019
EventIEEE International Conference on Advanced Learning Technologies 2019 - Maceio, Brazil
Duration: 15 Jul 201918 Jul 2019
Conference number: 19th
http://www.ic.ufal.br/evento/icalt2019/
https://ieeexplore.ieee.org/xpl/conhome/8809605/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Advanced Learning Technologies 2019
Abbreviated titleICALT 2019
CountryBrazil
CityMaceio
Period15/07/1918/07/19
Internet address

Keywords

  • Feedback
  • Learning Analytics
  • Online learning
  • Text Mining

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