ComPAIR: A New Online Tool Using Adaptive Comparative Judgement to Support Learning with Peer Feedback

Tiffany Potter, Letitia Englund, James Charbonneau, Mark Thomson MacLean, Jonathan Newell, Ido Roll


Peer feedback is a useful strategy in teaching and learning, but its effectiveness particularly in introductory courses can be limited by the relative newness of students to both the body of knowledge upon which they are being asked to provide feedback and the skill set involved in providing good feedback. This paper applies a novel approach to facilitating novice feedback: making use of students’ inherent ability to compare. The ComPAIR application discussed in this article scaffolds peer feedback through comparisons, asking students to choose the “better” of two answers in a series of pairings offered in an engaging online context. In contrast to other peer-feedback approaches that seek to train novices to be able to provide expert feedback (such as calibrated peer review) or to crowdsource grading, ComPAIR focuses upon the benefits to be gained from the critical process of comparison and ranking. The tool design is based on the longstanding psychological principle of comparative judgement, by which novices who may not yet have the compass to assess others’ work confidently can still rank content as “better” with accuracy. Data from 168 students in pilot studies in English, Physics and Math courses at the University of British Columbia are reviewed. Though the use of ComPAIR required little classroom time, students perceived this approach to increase their facility with course content, their ability assess their own work, and their capacity to provide feedback on the work of others in a collaborative learning environment.


peer feedback, answer comparison, adaptive comparative judgement, online teaching tools, collaborative learning

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