The reality is that we operate in fast evolving environment that need to be considered when implementing our programs or projects. As colleagues pointed out, constraints brought up by COVID-19 for example should push to adapt in order to successful achieve the intended results.
This can be done for example introducing what USAID called " collaborating, learning and adapting (CLA) framework" [CLA Tool Kit Landing | USAID Learning Lab] which involves a set of practices integrated in the program cycle to ensure that programs are coordinated with others, grounded in a strong evidence base, and iteratively adapted to remain relevant throughout the implementation. With this, identified critical assumptions central to a TOC must be periodically tested – which is a central feature of assumption-based planning - and if no longer valid then adaptive management steps employed in response.
It is true that this brings an implementation complexity which also requires the use of new, and still evolving complex-responsive evaluation methods. Under such conditions, there is need to integrate data science* in MEL activities.
Note: Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data (https://en.wikipedia.org/wiki/Data_science).
RE: Can we use an evidence-based, evolving Theory of Change to achieve "local learning” during project design?
Dear all,
The reality is that we operate in fast evolving environment that need to be considered when implementing our programs or projects. As colleagues pointed out, constraints brought up by COVID-19 for example should push to adapt in order to successful achieve the intended results.
This can be done for example introducing what USAID called " collaborating, learning and adapting (CLA) framework" [CLA Tool Kit Landing | USAID Learning Lab] which involves a set of practices integrated in the program cycle to ensure that programs are coordinated with others, grounded in a strong evidence base, and iteratively adapted to remain relevant throughout the implementation. With this, identified critical assumptions central to a TOC must be periodically tested – which is a central feature of assumption-based planning - and if no longer valid then adaptive management steps employed in response.
It is true that this brings an implementation complexity which also requires the use of new, and still evolving complex-responsive evaluation methods. Under such conditions, there is need to integrate data science* in MEL activities.
Note: Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data (https://en.wikipedia.org/wiki/Data_science).
Best,
Janvier