CS295J/Contributions for class 12

A mixed-initiative system for interface design

Owner: Eric

Proposal Overview

Note: click here for flowchart.

We propose a framework for interface evaluation and recommendation that integrates behavioral models and design guidelines from both cognitive science and HCI. Our framework behaves like a committee of specialized experts, where each expert provides its own assessment of the interface, given its particular knowledge of HCI or cognitive science. For example, an expert may provide an evaluation based on the GOMS method, Fitts's law, Maeda's design principles, or cognitive models of learning and memory. An aggregator collects all of these assessments and weights the opinions of each expert based on past accuracy, and outputs to the developer a merged evaluation score and a weighted set of recommendations.

Different users have different abilities and interface preferences. For example, a user at NASA probably cares more about interface accuracy than speed. By passing this information to our committee of experts, we can create interfaces that are tuned to maximize the utility of a particular user type.

We evaluate our framework through a series of user studies. Interfaces passed to our committee of experts receive evaluation scores on a number of different dimensions, such as time, accuracy, and ease of use for novices versus experts. We can compare these predicted scores to the actual scores observed in user studies to evaluate performance. The aggregator can retroactively weight the experts' opinions to determine which weighting would have given the best predictions of user behavior for the given interface, and observe whether that weighting generalizes to other interface evaluations.

Inputs

• The task the user is trying to accomplish
• The GUI he/she is using to perform this task
• The utility a user gets for values of different performance metrics (time, cognitive load, fatigue, etc.)
• The predicted and/or actual trace of a user using this GUI

Outputs

• An evaluation of the GUI, in terms of the individual metric values (i.e. time, cognitive load, etc.), and the overall utility for this as expressed by the utility function.
• Suggested improvements for the GUI, in two forms:
  • Immediate transformations that can be automatically applied to the GUI
  • Higher level suggestions/guidelines that would have to be made by a developer

Contributions

• The creation of a language for abstractly representing user interfaces in terms of the layout of graphical components and the functional relationships between these components.
• A system for generating interaction histories within user interfaces to facilitate individual and collaborative scientific discovery, and to enable researchers to more easily document and analyze user behavior.
• A system that takes user traces and creates a GOMS model that decomposes user actions into various cognitive, perceptual, and motor control tasks.
• Other evaluation methods using various cognitive/HCI models and guidelines
• A design tool that can provide a designer with recommendations for interface improvements. These recommendations can be made for a specific type of user or for the average user, as expressed by a utility function.