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 language | English |
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Title of host publication | LAK 2020 Conference Proceedings |
Editors | Vitomir Kovanović, Maren Scheffel, Niels Pinkwart, Katrien Verbert |
Place of Publication | New York NY USA |
Publisher | Association for Computing Machinery (ACM) |
Pages | 428-437 |
Number of pages | 10 |
ISBN (Electronic) | 9781450377126 |
DOIs | |
Publication status | Published - 2020 |
Event | International Conference on Learning Analytics and Knowledge 2020 - Frankfurt, Germany Duration: 23 Mar 2020 → 27 Mar 2020 Conference number: 10th https://lak20.solaresearch.org (Website) https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/proceedings/10.1145/3375462 (Website) |
Conference
Conference | International Conference on Learning Analytics and Knowledge 2020 |
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Abbreviated title | LAK 2020 |
Country/Territory | Germany |
City | Frankfurt |
Period | 23/03/20 → 27/03/20 |
Internet address |
Keywords
- Content Analysis
- Feedback
- Learning Analytics
- Online learning