Five Ways New Tagging Systems Improve User Learning and Production
The ASC group has been doing several threads of research around the SparTag.us 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 SparTag.us, MrTaggy as a solution to “tag noise”, and SparTag.us 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, SparTag.us increases tag production and decreases user time relative to standard techniques
- Increase learning and memory
- SparTag.us 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 SpartTag.us increase learning of subject matter. In careful experimental measurements of learning gains in an unfamiliar technical domain, we found that SparTag.us 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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