Course overview
Development programmes often track activities, outputs and beneficiary numbers while missing the behavioural, relationship and practice changes that indicate whether a programme is influencing its intended partners. Outcome Mapping addresses this gap by focusing monitoring on boundary partners: the individuals, groups and organisations whose actions a programme seeks to influence directly. It helps teams distinguish contribution from attribution, make assumptions explicit, and use evidence for adaptation rather than reporting alone.
This 10-day instructor-led course teaches the full Outcome Mapping method developed by Sarah Earl, Fred Carden and Terry Smutylo. Participants define vision statements, mission statements and boundary partners; formulate outcome challenges; develop graduated progress markers; build strategy maps; and establish monitoring journals. The course also connects Outcome Mapping with theory of change, indicator design, outcome harvesting, OECD DAC evaluation criteria, learning reviews and donor reporting requirements. Participants learn how to set up proportionate monitoring systems that field teams can maintain.
Teaching combines short technical briefings, worked development and humanitarian cases, facilitated group analysis, monitoring-system simulations and individual application planning. Participants apply each method to a live or realistic programme from the first week, then refine it through peer review and instructor feedback. They leave with a completed Outcome Mapping framework and monitoring package for a programme of their choice, including boundary-partner profiles, progress markers, strategy maps, journals and an implementation plan.
The course is suited to professionals designing, managing, monitoring or evaluating development, humanitarian, governance, health, education, climate and livelihoods programmes. It is particularly useful where programmes seek policy influence, institutional change, community ownership, service-quality improvements or shifts in partner practice.


