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Thomas S. Tullis

dblp:20/854 · also Tom Tullis · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
2 papers
Wearable and physiological sensing · 72% User interface design and tools · 28%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing
eye tracking		 0.112010
Generation Y, web design, and eye tracking · Int. J. Hum. Comput. Stud. 2010
User interface design and tools
web site design		 0.012010
Generation Y, web design, and eye tracking · Int. J. Hum. Comput. Stud. 2010
User interface design and tools
menu design		 0.011985
Designing a menu-based interface to an operating system · CHI 1985
User interface design and tools › menu design
menu hierarchy		 0.011985
Designing a menu-based interface to an operating system · CHI 1985

Methods — techniques the papers use, named apart from their topics

eye tracking · 0.1sorting technique · 0.0hierarchical cluster analysis · 0.0controlled experiment · 0.0
YearPublicationVenuePosition
2010 Generation Y, web design, and eye tracking
Soussan Djamasbi, Marisa Siegel, Thomas S. Tullis
 Int. J. Hum. Comput. Stud.3
1985 Designing a menu-based interface to an operating system
abstract
The development of a large menu-based interface to an operating system posed a number of interesting user interface questions. Among those were how to determine the user's view of the relationships among the myriad of functions in the system, and how to reflect those relationships in a menu hierarchy. An experiment utilizing a sorting technique and hierarchical cluster analysis was quite effective in learning the user's perception of the relationships among the system functions. A second experiment comparing a “broad” menu hierarchy to a “deep” menu hierarchy showed that users made significantly fewer inappropriate menu selections with the broad hierarchy.
Thomas S. Tullis
 CHI1