Using learning analytics to understand student perceptions of peer feedback. (April 2021)

• Record Type: Journal Article
• Authors: Misiejuk, Kamila; Wasson, Barbara; Egelandsdal, Kjetil
• Is Part Of: Computers in human behavior. Volume 117(2021)
• Publication Date: 2021-04
• Subjects: Peer assessment -- Feedback -- Backward evaluation -- Learning analytics; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2020.106658
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: Peer assessment (PA) is the process of students grading and giving feedback to each other's work. Learning analytics is a field focused on analysing educational data to understand and improve learning processes. Using learning analytics on PA data has the potential to gain new insights into the feedback giving/receiving process. This exploratory study focuses on backward evaluation, an under researched aspect of peer assessment, where students react to the feedback that they received on their work. Two aspects are analysed: 1) backward evaluation characteristics depending on student perception of feedback that they receive on their work, and 2) the relationship between rubric characteristics and backward evaluation. A big dataset (N = 7, 660 records) from an online platform called Peergrade was analysed using both statistical methods and Epistemic Network Analysis. Students who found feedback useful tended to be more accepting by acknowledging their errors, intending to revise their text, and praising its usefulness, while students who found the feedback less useful tended to be more defensive by expressing that they were confused about its meaning, critical towards its form and focus, and in disagreement with the claims. Moreover, students mostly suggested feedback improvement in terms of feedback specificity, justification and constructivity, rather than kindness. The paper concludes by discussing the potential and limitations of using LA methods to analyse big PA datasets. Highlights: Students perceiving feedback as extremely useful, express more gratitude. Students request more specific, justified or constructive, rather than kind feedback. Backward evaluation might be used to determine the usefulness of a rubric. Large datasets need context data to facilitate interpretation of peer feedback.