(My London learning landscape; original image taken from Simon Narracott)
I am once again employing ideas I gathered from Finnish colleagues and applying them to my own learning scenarios. Great collaborations are like that; they just keep on giving. I wrote a bit before on using Thinglink for making reflective impressions based on certain images (history, emotional affect, etc.). In this instance, partly inspired by Edinspace, I am using them to document my learning geography.
I am using them again to document and, hopefully, begin to reveal the patterns of my learning engagement in a specific geographical space. So the process is simple. Choose one representative image of a location and map various engagements on it. One can use a subway map or a photo or anything that provides some geographical structure and map from there. The two examples I am providing here are from London and Seoul, where I have spent most of my time over the last few years. However, they differ a bit in terms of scope and effect. London is much more local and parochial. My engagements were generally on foot; my radius of geographical activity was smaller, but I suspect of much greater intellectual intensity. Seoul, in contrast, is spread over a greater distance owing to my work commute and the patterns of engagement are staccato and occasionally random. I think mapping activities like this does the following:
- Makes visible learning engagements (merely by mapping them are we reflecting on their importance)
- Reveals patterns of engagements
- Identifies weak or unexplored points of our learning radius
- Identifies gaps in our representational arsenal; I am greatly favoring images over other forms of media (especially in Seoul) and so I want to balance that out a bit.
- Stimulates a search for a comprehensive or holistic impression. I find myself wanting to aggregate these engagements or bits into a cohesive whole. The map itself provides that whole, but I want something more than that. Mapping like this stimulates a search for greater capacity for presentation.
This also represents an interesting method for data collection in mobile learning focusing on the use of mobile technology to create meaning, to generate context, and to transform habitus. If we want to move beyond technological determinism in our use and understanding of mobile learning, we need to generate methods that doesn’t foreground the technology over the context and meaning being generated by the individual. It is my belief that this is one method to do so.