#1047: An Analytical Approach to Examining Learner Feedback Agency in Social Contexts

Existing literature on Learner Feedback Agency (LFA) highlights its inherent complexity, where it is situational, adaptive to changing contexts; deliberative, shaped by learners’ reasoning rather than just actions; and entangled with structures, shaping each other rather than determined by them. Yet, existing analytical approaches often fall short in capturing LFA’s complexity. This paper proposes the Multi-Agent Interplay Analysis (MAIA) to address this limitation by (1) examining LFA with multi-agent and temporal structures, (2) synchronising behaviour and deliberations with time, and (3) utilising visualisations to examine the interplay of LFA and structure. Through an illustrative example, I demonstrate how MAIA revealed insights into how learners exercise their LFA in social contexts.