TITLE: Semiotic Engineering Contributions for Designing Online Help Systems

AUTHORS & AFFILIATIONS: Milene Selbach Silveira, Faculdade de Informática, PUCRS & SERG/Departamento de Informática, PUC-Rio, Brazil; Simone Diniz Junqueira Barbosa, SERG/Departamento de Informática, PUC-Rio, Brazil; Clarisse Sieckenius de Souza, SERG/Departamento de Informática, PUC-Rio, Brazil

KEYWORDS: help systems design, communicability utterances, semiotic engineering

 PRESENTER/CONTACT PERSON: Milene Selbach Silveira

CONTACT EMAIL: milene@inf.puc-rio.br

ABSTRACT:

Our goal is to improve the content of help systems and provide better access to it, by giving users opportunities to signal breakdowns during interaction. To this end, we use a semiotic engineering model that explores both direct and indirect messages sent from designers to users via systems' interfaces. A system's interface is viewed as a special kind of message, about how users should exchange messages with the application in order to achieve a range of goals. It represents, implicitly or explicitly, how the designers conceived the application, how they built it, and why. The online help system is an important component thereof, because this is where designers have the best chances to explicitly express their vision. Help systems can be grouped with respect to context independence, interface-context sensitivity, task sensitivity, and even user-profile sensitivity. However, we should also be targeting at user-intent sensitivity, which is notoriously difficult to assess. One approach to hitting this target would be to have the system try to compute users' intentions. Our approach is to allow users to signal breakdowns by choosing among a limited set of predefined utterances. These provide an entry point to a cohesive discourse structure, which contains the application's design rationale, and operational and tactical instructions about how to use it. Coupled with the application's conceptual, task and interaction models, these utterances allow the help system to provide information with increased probability of addressing the user's intentions.

Last modified October 28, 2001 by Scott Tilley.