From BELIV 2010

Presentation

Notes

- look at cognitive and personality trait, experience in visualization, familiarity with data, quality of visualization, compatibility, contextual factors

- determine a promising set of measures

q and a

- which factor might be more important than others: spatial, perceptual speed, some potential, not critical

- to what extend related only to infovis? not sure, lots of literature have been done in hci, novelty of vis tool, other metrics not looked at in hci may be more important

- different ways to approach vis, most important to think about simple tests to figure out category of users and test them differently; want to have some category of users or cannot trust results clearly

- do come up with measures, active evaluation is kinda of tricky, may be find correlation and simplify it: max time is 30 minutes

- metrics about metrics, how base level is it (e.g., age is an aggregation)

- how should i redesign studies with these in mind, implications: measure as a covariance as analysis (linear?), treat separate groups differently, blocking, give ppt best configuration that works for s/he, not crazy about idea of adapting vis to people since people are adaptive, some power of statistics

- what does it mean to have metrics, scientific point of view to have different results and different experiment results

- see the limit / best case of vis / interaction, interesting to eliminate people that will take a long time to get to the best case, compare two different interaction, first want to see if there is a different for best case;

- training: multi trial study, takes 10-12 trials

- increase number of participants would factor decrease, but how many ppts

- quality of measures, quick to run to foobar test for x but some may not be good, grain of salt: wisdom for right measures, and guidelines for junior