It is about time we developed some less simplified and more realistic representations of learning cycles. We can do this by situating our thinking about learning in more actor-oriented models of what is happening, versus models that focus on abstract and disembodied processes. And by thinking about multiple actors rather than single individuals going through their own action-reflection cycles.
The state of these learning loops can be investigated empirically, and documented by in an actor x actor matrix. [the actors can be individuals or groups of individuals, as found in each of the units] Cells in the matrix can detail what information has been received from the row actor by the column actor. Most Significant Change monitoring can provide the qualitative details of the information being exchanged between the two actors connected by a given cell. Then the relative importance of the information described in each row can be ranked by the provider (and the column contents ranked by the recipient). We can then examine how consistent the providers and recipients ranking are with each other. We can also compare the participant's views with any formal model the organisation might have about how what and when information should be being exchanged between the actors involved.
If you want to explore this approach further in your own organisation, and want to exchange ideas on how to do so, and the results, let me know Email the Editor