Big Data: Learning vectors and velocities

The most interesting session I attended at Bett 2014 was to hear Stephen Heppell speak in the main arena. Stephen was on top form as usual, and in his avuncular style managed to engage 800 or so delegates effortlessly for 30 minutes. He showcased some of his latest ideas for educational innovation using technology, under the banner of 'Big Data'. Much of his talk, thankfully, has less to do with Big Data, and more to do with how kids can use their devices to capture the data and use it to better understand the world around them.

Heppell showed a dynamic shipping radar map of the English Channel which tracked every sea vessel. Yachting is one of his passions, so it was inevitable that a marine illustration would creep into his presentation somewhere. He showed that you can interrogate the map by locating any vessel, click on it, and discover where it is heading, where it has come from, and what its relative progress is according to its speed, vector and environment. He argued that if we can do this for large vessels such as shipping, why can't we do a similar thing to track student progress. His point was that we plan highly complex systems such as school curricula, and place high stakes on the progress of students, and yet we know so very little about exactly how they do progress. Technology, he suggested could provide the answers through Big Data.

Student work on data logging light and sound levels

I caught up with Stephen and spent some time discussing these ideas with him on his stand at Bett. One of the Big Data projects he showed me involved children using free downloadable light and sound metre apps to measure their environment around them. He showed how this approach to data logging can enable them to understand the impact of loud sounds and different light levels not only on a school environment, but critically, how it affects their own personal learning experience. An extension of this approach saw students working feverishly to log data about their entire ecosystem, including their own motivation levels, facial expressions and body orientations to create maps of how engaged an entire school of students is at any given time. Such applications of big data (in this context a more appropriate label would be learning analytics) are clearly relevant for learning in a technology rich environment. The more information teachers have (possibly displayed as light arrays at the back of a classroom) about their groups of students, the argument goes, the better they will be able to plan, predict and provide for learning needs across a cohort. We need more information about the vectors and velocities of learning.

Some might baulk at this approach and argue that it's more Big Brother than Big Data, but if we don't try we don't know. Personally, I applaud Stephen Heppell's pioneering approach to the application of learning technology in new and innovative ways. It is refreshing, challenging and potentially ground breaking stuff.

Photos by Steve Wheeler


Big Data: Learning vectors and velocities by Steve Wheeler is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.