How to Use Theory of Change for Adaptive Monitoring, Evaluation and Learning
In my previous post on designing and implementing adaptive MEL frameworks , I discussed the importance of a program’s Theory of Change (TOC) in facilitating understanding among your team and your stakeholders on what change looks like. The biggest challenge facing most programs, however, is that TOC is often mistaken for another form or version of a results chain/log frame/results framework. While they are not mutually exclusive, they are not the same. The important distinction is that the TOC needs to iterate the theory behind the change you are seeking to enable, not just the process. The process is iterated in a results chain that specifies the tangible results you are pursuing driven by the theory. In an adaptive program, your results chain (i.e.: activities and outputs) can change depending on the initial results you are observing and the reflections that are being undertaken about what should happen next. The ‘what comes next’ reflections need to be based on something, and...
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