Exploring the nature of peer feedback: An epistemic network analysis approach
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Exploring the nature of peer feedback : An epistemic network analysis approach. / Viberg, Olga; Baars, Martine; Mello, Rafael Ferreira; Weerheim, Niels; Spikol, Daniel; Bogdan, Cristian; Gasevic, Dragan; Paas, Fred.
In: Journal of Computer Assisted Learning, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Exploring the nature of peer feedback
T2 - An epistemic network analysis approach
AU - Viberg, Olga
AU - Baars, Martine
AU - Mello, Rafael Ferreira
AU - Weerheim, Niels
AU - Spikol, Daniel
AU - Bogdan, Cristian
AU - Gasevic, Dragan
AU - Paas, Fred
N1 - Publisher Copyright: © 2024 The Author(s). Journal of Computer Assisted Learning published by John Wiley & Sons Ltd.
PY - 2024
Y1 - 2024
N2 - Background Study: Peer feedback has been used as an effective instructional strategy to enhance students' learning in higher education. Objectives: This paper reports on the findings of an explorative study that aimed to increase our understanding of the nature and role of peer feedback in the students' learning process in a computer-supported collaborative learning (CSCL) setting. Exploring what types of feedback are used, and how they relate to each other and are related to academic performance has important implications for students and teachers. Methods: This study was conducted in the higher education setting. It used a dataset consisting of student peer feedback messages (N = 2444) and grades from 231 students who participated in a large engineering course. Using qualitative methods, peer feedback was coded inductively. Epistemic network analysis (ENA) was used to analyse the relation between peer feedback types and performance. Results: Based on the five types of peer feedback (i.e., ‘management’, ‘cognition’ ‘affect’, ‘interpersonal factors’ and ‘suggestions for improvements’), the results of the ENA showed that student feedback categories ‘management’, ‘cognition’ and ‘affect’ were positively related to student performance at the formative assessment phase. Conclusions: The findings and the ENA visualizations also show that ‘suggestions for improvement’ and ‘interpersonal factors’ were not a significant part of student learning in peer assessment and feedback in the studied context.
AB - Background Study: Peer feedback has been used as an effective instructional strategy to enhance students' learning in higher education. Objectives: This paper reports on the findings of an explorative study that aimed to increase our understanding of the nature and role of peer feedback in the students' learning process in a computer-supported collaborative learning (CSCL) setting. Exploring what types of feedback are used, and how they relate to each other and are related to academic performance has important implications for students and teachers. Methods: This study was conducted in the higher education setting. It used a dataset consisting of student peer feedback messages (N = 2444) and grades from 231 students who participated in a large engineering course. Using qualitative methods, peer feedback was coded inductively. Epistemic network analysis (ENA) was used to analyse the relation between peer feedback types and performance. Results: Based on the five types of peer feedback (i.e., ‘management’, ‘cognition’ ‘affect’, ‘interpersonal factors’ and ‘suggestions for improvements’), the results of the ENA showed that student feedback categories ‘management’, ‘cognition’ and ‘affect’ were positively related to student performance at the formative assessment phase. Conclusions: The findings and the ENA visualizations also show that ‘suggestions for improvement’ and ‘interpersonal factors’ were not a significant part of student learning in peer assessment and feedback in the studied context.
KW - computer-supported collaborative learning settings
KW - epistemic network analysis
KW - learning performance
KW - peer feedback
UR - http://www.scopus.com/inward/record.url?scp=85197820526&partnerID=8YFLogxK
U2 - 10.1111/jcal.13035
DO - 10.1111/jcal.13035
M3 - Journal article
AN - SCOPUS:85197820526
JO - Journal of Computer Assisted Learning
JF - Journal of Computer Assisted Learning
SN - 0266-4909
ER -
ID: 398549009