Distracted Learning Index

For fun at one of our internal conferences last week I started measuring how many learners in the course were visibly displaying non-course information on a device–in other words, how many participants were multitasking. I initially called this measure the partial-tasking index, but it seems silly to invent another word for multitasking, even if that word is misleading because it implies success doing more than one thing at a time, which is very difficult to do unless the multitasker has achieved automaticity in one of the tasks. One of my colleagues pointed out a parallel to distracted driving, suggesting there should be a distracted learning index.

I took two readings per class of the percentage of multitaskers, then average the two scores. The best multitasking about I saw for a class was 3%. The worst score was 29%.

I should acknowledge here that the correlation between learning and a good distracted learning index score is probably pretty low. Just because learners are not interacting with a device doesn’t mean they are learning. On the other hand, a poor index score probably is indicative of a problem, particularly if the score gets worse during the class. It’s just a data point, an easy one to gather that is interesting to compare against other courses.

One of my fears of taking this measurement at all is that it could be misinterpreted as a call to eliminate connected technology in the classroom (if there are no distractions, learners will be forced to pay attention). It is true that courses at this conference that had electronic participant materials had, on average, more multitasking. However, some courses with electronic materials scored well on the index, so a lack of laptops doesn’t guarantee engagement, particularly since everyone at the conference has a little computer in their pockets that they can pull out whenever they are bored. Besides, technology-enhanced materials have too much upside for me to advocate a return to paper. Also, it wasn’t a fair comparison because the courses with no electronic participant guides were more often the ones with professional keynote-level speakers.

Another interesting point in the data is that large courses (>100 participants) had similar scores on average as small courses (around 30 participants). This is counterintuitive as I’d expect the larger classes to offer a kind anonymity that mighty encourage multitasking. Again, though, this might be an apples-to-oranges comparison as the larger classes tended to feature professional speakers.


2 thoughts on “Distracted Learning Index

  1. Rob Foshay

    IT professionals distinguish between multi-threading and multi-tasking. In multi-threading, the system suspends all tasks but one, works on that until it needs a resource, then suspends the task, swaps it out to RAM, and loads another one into the CPU. So it maximizes the efficiency of the CPU, and was particularly useful when CPU power was the choke point of most computers. But there’s always a performance hit when the swap occurs. In multi-tasking, the CPU is working on tasks from multiple jobs simultaneously (in different stacks or cores), in order to maximize CPU efficiency.

    Conscious processing is closer to multi-threading than multi-tasking. As you know, the performance hit for a brain to swap tasks is quite substantial. On the other hand, we do multi-task with all the unconscious processes involved in language, perception, body functions, movement, emotion, and certain functions of long-term memory management.

    You could think of a brain as having infinite long-term memory, hierarchically arranged, elaborately indexed — but with a 4-bit bus. But most current computer architectures don’t really correspond well to brain architecture (although I’m convinced that my wife’s computer has so many performance problems because of low self-esteem, stemming from the way she talks to it).


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