Understanding Peer Feedback Contributions Using Natural Language Processing
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Documents
- Understanding Peer Feedback
Final published version, 272 KB, PDF document
Peer feedback has been widely used in computer-supported collaborative learning (CSCL) setting to improve students’ engagement with massive courses. Although the peer feedback process increases students’ self-regulatory practice, metacognition, and academic achievement, instructors need to go through large amounts of feedback text data which is much more time-consuming. To address this challenge, the present study proposes an automated content analysis approach to identify relevant categories in peer feedback based on traditional and sequence-based classifiers using TF-IDF and content-independent features. We use a data set from an extensive course (N = 231 students) in the setting of engineering higher education. In particular, a total of 2,444 peer feedback messages were analyzed. The CRF classification model based on the TF-IDF features achieved the best performance. The results illustrate that the ability to scale up the automatic analysis of peer feedback provides new opportunities for student-improved learning and improved teacher support in higher education at scale.
Original language | English |
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Title of host publication | Responsive and Sustainable Educational Futures - 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings |
Editors | Olga Viberg, Ioana Jivet, Pedro J. Muñoz-Merino, Maria Perifanou, Tina Papathoma |
Number of pages | 16 |
Publisher | Springer |
Publication date | 2023 |
Pages | 399-414 |
ISBN (Print) | 9783031426810 |
DOIs | |
Publication status | Published - 2023 |
Event | Proceedings of the 18th European Conference on Technology Enhanced Learning, ECTEL 2023 - Aveiro, Portugal Duration: 4 Sep 2023 → 8 Sep 2023 |
Conference
Conference | Proceedings of the 18th European Conference on Technology Enhanced Learning, ECTEL 2023 |
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Land | Portugal |
By | Aveiro |
Periode | 04/09/2023 → 08/09/2023 |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 14200 LNCS |
ISSN | 0302-9743 |
Bibliographical note
Publisher Copyright:
© 2023, The Author(s).
- Computer Supported Collaborative Learning, Content Analysis, Higher Education, Natural Language Processing, Peer Feedback
Research areas
ID: 390400320