Collaborative peer feedback and learning analytics: theory-oriented design for supporting class-wide interventions
Erkan Er, Yannis Dimitriadis, Dragan Gašević
Abstract
Although dialogue can augment the impact of feedback on student learning, dialogic feedback is unaffordable by instructors teaching large classes. In this regard, peer feedback can offer a scalable and effective solution. However, the existing practices optimistically rely on students’ discussion about feedback and lack a systematic design approach. In this paper, we propose a theoretical framework of collaborative peer feedback which structures feedback dialogue into three distinct phases and outlines the learning processes involved in each of them. Then, we present a web-based platform, called Synergy, which is designed to facilitate collaborative peer feedback as conceptualised in the theoretical framework. To enable instructor support and facilitation during the feedback practice, we propose a learning analytics support integrated into Synergy. The consolidated model of learning analytics, which concerns three critical pieces for creating impactful learning analytics practices, theory, design and data science, was employed to build the analytics support. The learning analytics support aims to guide instructors’ class-wide actions toward improving students’ learning experiences during the three phases of peer feedback. The actionable insights that the learning analytics support offers are discussed with examples.