Five Ways New Tagging Systems Improve User Learning and Production

The ASC group has been doing several threads of research around the tagging/annotation system and the MrTaggy exploratory browser for tags. Elsewhere, I’ve written a series of blog posts summarizing our group’s research about sensemaking with, MrTaggy as a solution to “tag noise”, and as a way of increasing tag production and improving memory.

In combination, the research shows how these systems achieve a number of useful goals:

  • Increase individual tag production rates
    • By lowering the cost-of-effort of entering tags with its Click2Tag technique, increases tag production and decreases user time relative to standard techniques
  • Increase learning and memory
    • users show increased memory for orirginal material when compared to people using more standard tagging techniques
    • MrTaggy compensates of lack of background knowledge with its exploratory user interface.
  • Reduce effects of “tag noise”
    • MrTaggy’s Exploratory UI helps people learn domain vocabulary. People appear to learn more of the vocabulary for a domain because MrTaggy encourages people to attend to tags associated with their queries.
  • Support the transfer of expertise
    • Expert tags in increase learning of subject matter. In careful experimental measurements of learning gains in an unfamiliar technical domain, we found that users explosed to a simulated set of “expert’s tags” showed singificantly more learning than control conditions in which users worked without expert tags.

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