aes26 International Evaluation Conference

Author: Jess Dart, Clear Horizon
This paper introduces the Collective Noticing Method (CNM) and explores its relevance for measurement, evaluation and learning (MEL). Developed by the author, CNM offers evaluators and changemakers a collaborative way to notice, learn from, and respond to subtle and emerging changes. It is a rigorous yet flexible method with a strong focus on learning and collective sensemaking, well suited to the “messy middle” of systems change and place-based work. It also has potential for community and grassroots work, program portfolios, and research impact tracking.

Collective noticing as a practice has a long history, evident in approaches such as Outcome Harvesting, Most Significant Change (MSC), citizen science, ripple effect monitoring, and context monitoring. More recently, this practice has been enabled and accelerated by emerging digital platforms and AI. Building on these foundations, CNM intentionally harnesses the eyes and ears of those implementing and experiencing an initiative to bring shared attention to what is changing.CNM is inherently participatory, inviting many people to observe, document, and interpret change collectively. It values lived experience as a central source of insight, embraces uncertainty, and supports adaptive action grounded in what is actually emerging. As a decolonising and grounded approach to MEL, CNM shares power by broadening who defines what matters and what counts as evidence. It centres learning-oriented measurement and collective sensemaking, bringing forward subtle signals, relational shifts, and everyday insights that conventional approaches often overlook.

CMN involves impact logging and tracking against agreed ways of working and learnings, whereas Outcome Harvesting focuses on outcomes and tends to be done as a one-off study. It differs from MSC in that CNM uses shorter, multi-perspective logs across diverse categories and evidence types.This session introduces CNM and invites participants to critically explore its benefits, limitations, and the contexts in which it is most—and least—useful.