Not just a pretty face

A couple of years ago we were dazzled by the release of image recognition tools such as Google Goggles, which, for the first time, allowed regular users to perform instant image-based web searches by using the camera in their hand-held devices. The technology was touted as a Swiss Army knife of visual recognition (applicable to anything from bar codes to restaurant menus in foreign languages).

A bit of road testing soon made it clear that it wasn't great at distinguishing, say, a dalmatian from a poodle, or a Wedgewood plate from a Clarice Cliff. However, it was very good at doing one thing in particular -- identifying individual art works and providing their name and author.  This made it quite a useful app to have in those annoying situations when, flicking through a magazine, you come across a famous painting whose artist you can't quite remember. You simply point your camera at the picture, tap on the "Go" button, and hey presto: title, date and artist.

Museums like the New York Met and the Getty started a series of collaborations with Google, providing metadata for thousands of paintings. Although the app was certainly very handy (and a great talking point in the pub), its pedagogical value was still somewhat limited during those early stages of development -- the best one could hope for was a couple of basic facts about the painting and a list of Google search results (some more relevant than others).

Google Googles at work (Image © by Google).

However, that is changing fast. Educational researchers and IT developers are creating new ways of digging through the data, contextualising, meshing up, inter-linking and reshaping results. It is these developments that can turn visual search technology from a gimmicky app to a powerful educational / research tool.

The University of California Riverside, for example, is developing an ambitious facial recognition project designed to identity individual historical characters portrayed in paintings. The principle is similar to Facebook's infamous facial recognition / photo tagging technology (without the Big Brother implications).

Applied to historical portraits via the Google Goggles infrastructure, this UCR tool is expected to provide answers to questions such as: Who is this person? Where did s/he live? What stage of his/her life is s/he in in this portrait? What was happening in the world at that time? What is his/her facial expression? What other portraits exist of this individual? Who else is in the painting with him/her? What webs of relations can we tease out based on people's associations in different portraits?

UCR's project is still at a very early stage of development and there are plenty of obstacles to overcome before the tool is sufficiently stable and useful. However, it is an encouraging example of the kind of research that is helping us make the crucial transition from visual recognition to visual data mining (and ultimately visual data analysis) in mobile learning.

Mobile learning or drive-by learning?

According to Chinn and Fairlie, in 2001 there were 61 computers per hundred people in North America, but only 0.5 per 100 in South Asia. In response to this imbalance, the One Laptop per Child (OLPC) project took as its mission “to create educational opportunities for the world’s poorest children by providing each child with a rugged, low-cost, low-power, connected laptop with content and software designed for collaborative, joyful, self-empowered learning”.

OLPC’s underlying premise is that the large scale distribution of Information and Communication Technology among the less privileged will tackle problems of accessibility to computers and simultaneously improve IT literacy rates. OLPC thus links access with use and practice, relying on models of self directed learning.

OLPC love. (Image: CC by laihiu.)

The project has been welcomed by various NGOs and HE institutions. However, it has also attracted its fair share of controversy. Nicholas Negroponte, founder of the OLPC project, takes the principle of self-directed learning to its logical extreme. Much to the consternation of some teachers and educational researchers, he has boldly summed up the association between ICT access and use with the phrase “you can give kids XO laptops and just walk away”.

From Negroponte's point of view, the availability of ML resources alone is enough to encourage the development of valuable learning skills among children. These skills, in turn, will have a positive impact on interrelated socio-economic factors, reducing multiple forms of deprivation such as poverty, social exclusion and illiteracy, not just among the children but also within their broader community.

From the point of view of many teachers and educational researchers, Negroponte's model (which has been dubbed "drive-by learning") is deeply flawed. Efficient learning skills are unlikely to materialize out of thin air just because the right set of tools are placed in front of the student. A laptop is a great tool in the classroom, but a poor substitute for a teacher. Some OLPC insiders have recently joined the sceptics by putting a big question mark over the drive-by approach as a result of a damning assessment of failures in the project's implementation across Peru.

The OLPC project is an inspirational and ambitious attempt to reduce the global digital divide and bring socio-economic advantages to deprived communities. The world needs more projects like OLPC, but we also need to work harder to ensure that their underlying pedagogical principles are right.