Computer Interaction Analysis: Toward an Empirical Approach to Understanding User Practice and Eye Gaze in GUI-Based Interaction

Computer Supported Cooperative Work, Volume 20, p.497-528, 2011
Computer Interaction Analysis: Toward an Empirical Approach to Understanding User Practice and Eye Gaze in GUI-Based Interaction
Robert Moore, Elizabeth Churchill
Abstract

Today's personal computers enable complex forms of user interaction. Unlike older mainframe computers that required batch processing, personal computers enable real-time user control on a one-to-one basis.

Such user interaction involves mixed initiative, logic, language and pointing gestures, features reminiscent of interaction with another human. Yet there are also major differences between computer interaction and human interaction, such as computers' inability to stray from scripts or to adapt to the idiosyncrasies of particular recipients or situations.

Given these similarities and differences, can we study computer interaction using methods similar to those for studying human interaction? If so, are the findings from the analysis of human interaction also useful in understanding computer interaction?

In this paper, we explore these questions and outline a novel methodological approach for examining human-computer interaction, which we call "computer interaction analysis." We build on earlier approaches to human interaction with a computer and adapt them to the latest technologies for computer screen capture and eye tracking.

In doing so, we propose a new transcription notation scheme that is designed to represent the interweaving streams of input actions, display events and eye movements. Finally we demonstrate the approach with concrete examples involving the phenomena of placeholding, repair and referential practices.

Another publication from the same category: Machine Learning and Data Science

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Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.

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