Unsupervised mlearning in rural India and illustrations: a narrative of contact points and progress
I was recently doing some research on mlearning in rural communities as I believe that much of what we cover in m4d in developing nations would actually prove beneficial to rural, disadvantaged communities in developed nations. At least I imagine that design practices and pattern language would be much the same and the fluid elements would rest more with cultural norms and community support.
Due to a fairly unsophisticated search string, I stumbled across this article and found it quite a good read as it has application across a number of learning spectrums, including lifelong learning, informal learning, mlearning, gaming, etc. Well worth a look.
- Kumar, A.; Tewari, A; Shroff, G.; Chittamuru, D.; Kam, M. & Canny, J. (2010). An Exploratory Study of Unsupervised Mobile Learning in Rural India. ACM. Human-Computer Interaction Institute, Carnegie Mellon University.
Carnegie Mellon University being who they are, the article takes a decidedly scientific approach to the study involving a 26 week observation of unsupervised mlearning taking place via gaming environments on mobile phones in rural India. The article does a good job of accounting for functional variables like electricity availability, etc., ultimately finding a correlation between mlearning and learning progress. What I found most remarkable was an illustration that on the surface seems so mundane. The illustration above indicates with a scientific eye to detail the nature of community interaction through mobile devices over the course of the study. What is remarkable is that the technology presumably diffuses these connections and makes contact points between communities numerous.
This is what mlearning can do, redefine social communication itself.
Without technology, communication between these communities (or just in general) would take place at pragmatic points of contact indicating need (if not a real desire to interact). These could fall into the following categories:
- Economic (trade, vendors, supply, demand, migrant workers, etc.)
- Agricultural (also economic, but more of a sustenance issue. If you grow this, I don’t have to grow this).
- Familial (marriages across communities)
- Political (unions of convenience and strategic importance, shouldering public works, etc.)
The more points of contact, the better, but the traditional model above presumes a limited number of contacts, defined and acculturated in ways refined by centuries. There are just certain ways in which one interacts with that community. Mlearning changes that a bit, at least for children. They interact within their village and outside their village (granted through the conduits of #8, 10, 15 in image above), creating a new social dynamic, a new, slightly expanded nature of interaction with outsiders. It multiplies the points of contact with another group and the subsequent interaction could possibly indicate positive developments for both parties involved.
A good article, but it really took the illustration to drive that point home to me. I would love to have this type of data for all communities to help visualize this better and direct resources towards improving cross-community connections. I like how the Connected States of America interactive is set up to visualize this across US counties and would love some comparable data from across rural communities and developing nations. Once is always working under the assumption that more connections equal greater flow of knowledge, impact, and subsequent development.