Dynamic Detection of Novice vs. Skilled Use Without a Task Model

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Presentation transcript:

Dynamic Detection of Novice vs. Skilled Use Without a Task Model Amy Hurst, Scott E. Hudson, Jennifer Mankoff

Summary Motivation: Current applications don’t adapt to user skill level in a dynamic way without a task model Preparation: Collected previous work on specific features (menu selection) Built statistical model of skill level Automatic feature selection

Experiments Created a training set Assigned labels of “novice” or “skilled” Generated features for next study Validated decisions with previous work Tested “live” classifier Qualitatively, a success

Pro “Live” analysis of user skill level allows immediate application feedback appropriate to user skill level Being able to determine skill level without task model is application independent method Non-obtrusive to users but reliable to 90%

Con Not technical, very informal Uninformed of choice of classifier Poor labeling of training data i.e. arbitrarily deciding on “skilled” status at end of task 2 Their model is not applicable to everyone Starting with one feature and ending with 46 Lack of quantitative results