Dorrit Billman

dblp:b/DorritBillman · DBLP profile ↗
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8ranked-venue papers
3as first author
0since 2021 · last 2016
0000-0003-3797-344XORCID · corroborated

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

Artificial intelligence and machine learning · 4 · 1 first-authorHuman-computer interaction and ubiquitous computing · 4 · 2 first-authorApplied, interdisciplinary, general and emerging 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
3 papers
User interface design and tools · 50% Design research and methods · 29% Collaborative and social computing · 16%
Artificial intelligence
2 papers
Knowledge representation and reasoning · 90% Learning theory · 10%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Design research and methods › user-centered design
need finding
0.112011
Benefits of matching domain structure for planning software: the right stuff · CHI 2011
Collaborative and social computing › collaborative learning › knowledge construction
sensemaking
0.112007
Medical sensemaking with entity workspace · CHI 2007
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition
scientific knowledge discovery
0.112006
An interactive environment for the modeling and discovery of scientific knowledge · Int. J. Hum. Comput. Stud. 2006
User interface design and tools
interactive modeling
0.012006
An interactive environment for the modeling and discovery of scientific knowledge · Int. J. Hum. Comput. Stud. 2006
Machine learning › Learning theory › statistical learning theory
bias-variance tradeoff
0.011991
Variability Bias and Category Learning · ML 1991
Knowledge, reasoning and agents › Knowledge representation and reasoning
category learning
0.011991
Variability Bias and Category Learning · ML 1991

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

prototype · 0.1needs analysis · 0.1laboratory study · 0.1interactive knowledge modeling · 0.1user study · 0.1software system · 0.1
YearPublicationVenuePosition
2016 Transfer at the Level of Human-Computer System: Problem Solving using Procedure-Automation Software
Dorrit Billman, Debra Schreckenghost, Zachary A. Caddick
CogSci1
2011 Benefits of matching domain structure for planning software: the right stuff
abstract
We investigated the role of domain structure, in designing for software usefulness and usability. We ran through the whole application development cycle, in miniature, from needs analysis through design, implementation, and evaluation, for planning needs of one NASA Mission Control group. Based on our needs analysis, we developed prototype software that matched domain structure better than did the legacy system. We compared our new prototype to the legacy application in a laboratory, high-fidelity analog of the natural planning work. We found large performance differences favoring the prototype, which better captured domain structure. Our research illustrates the importance of needs analysis (particularly Domain Structure Analysis), and the viability of the design process that we are exploring.
Dorrit Billman, Lucia Arsintescu, Michael Feary, Jessica Lee, Asha Smith, Rachna Tiwary
CHI1
2011 Modeling Performance Differences across Systems, Tasks, and Strategies
Jessica Lee, Dorrit Billman
CogSci2
2008 The CACHE Study: Group Effects in Computer-supported Collaborative Analysis
Gregorio Convertino, Dorrit Billman, Peter Pirolli, J. P. Massar, Jeff Shrager
Comput. Support. Cooperative Work.2
2007 Medical sensemaking with entity workspace
abstract
Knowledge workers making sense of a topic divide their time among activities including searching for information, reading, and taking notes. We have built a software system that supports and integrates these activities. To test its effectiveness, we conducted a study where subjects used it to perform medical question-answering tasks. Initial results indicate that subjects could use the system, but that the nature of this use depended on the subject's overall question-answering strategy. Two dominant strategies emerged that we call the Reader and Searcher strategies.
Dorrit Billman, Eric A. Bier
CHI1
2006 An interactive environment for the modeling and discovery of scientific knowledge
Will Bridewell, Javier Nicolás Sánchez, Pat Langley, Dorrit Billman
Int. J. Hum. Comput. Stud.4
1994 Acquiring and Combining Overlapping Concepts
Joel D. Martin, Dorrit Billman
Mach. Learn.2
1991 Variability Bias and Category Learning
Joel D. Martin, Dorrit Billman
ML2