Learning in Activity

Greeno and Engestrom (2014) discuss a framework for examining learning by looking at the activity system in which the learning occurs. This is in contrast to cognitive psychology’s MO of considering only the individual, separate from the context they are situated. Interestingly, the authors contend that their situated perspective is not at odds with the cognitive perspective, rather, that they are simply studying “the same phenomenon at different levels of analysis”.

A focus on activity systems values deep learning over factual recall, and this kind of big picture view acknowledges conceptual understanding as being inherently tied to the settings and activities in which they are employed. The situated perspective of learning allows for better explanations of how these “functional concepts” are learned.


Engestrom’s Activity Systems Model

Building on Engestrom’s model of activity systems, shown here, attention is given to the apparent benefits of expansivity of the Subject’s understanding of the Object of the learning activity. In other words, presenting the purpose of a learning activity as something greater (more expansive) than simple fact recall or skill building, as well as integrating participants’ views on what the purpose should be, are important in developing powerful activity systems.

A particularly vivid spatial analogy is used which likens conceptual domains to physical spaces. Individuals moving about in this space (learning) leave trails, linking their cognitive journey from concept to concept. As they continue to explore the domain space, some of their trails will overlap, creating novel pathways and connections between previously unlinked, or distantly linked concepts. The metaphor is taken a step further by Ingold by relating traditional, narrow-Object activity systems with the concept of transport, the travel lines of which are “typically straight and regular, and intersect only at nodal points of power”, in contrast to wayfarers, whose paths are “typically winding and irregular, yet comprehensively entangled”, much like expansive-Object systems.