Demo Data from EDBT 2026 / reviewers in the wild / expert
Michael McCurdy
dblp:28/465
· DBLP profile ↗
4ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 4 · 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
4 papers |
User interface design and tools · 44% Usability and user experience research · 22% Collaborative and social computing · 19% | |
| Artificial intelligence
1 paper |
Topics — the 3 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
User interface design and tools
prototyping |
0.1 | 1 | 2006 | Breaking the fidelity barrier: an examination of our current characterization of prototypes and an example of a mixed-fidelity success · CHI 2006 |
Usability and user experience research
cognitive modeling |
0.1 | 1 | 2005 | Supporting efficient development of cognitive models at multiple skill levels: exploring recent advances in constraint-based modeling · CHI 2005 |
Learning and educational technologies
skill acquisition |
0.0 | 1 | 2005 | Supporting efficient development of cognitive models at multiple skill levels: exploring recent advances in constraint-based modeling · CHI 2005 |
Methods — techniques the papers use, named apart from their topics
constraint satisfaction · 0.1CPM-GOMS · 0.1experimental evaluation · 0.1graphical model development environment · 0.1quantitative analysis · 0.0qualitative analysis · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Breaking the fidelity barrier: an examination of our current characterization of prototypes and an example of a mixed-fidelity successabstractThis paper presents a summary of the space of commonly-used HCI prototyping methods (low-fidelity to high-fidelity) and asserts that with a better understanding of this space, HCI practitioners will be better equipped to direct scarce prototyping resources toward an effort likely to yield specific results. It presents a set of five dimensions along which prototypes can be planned and characterized. The paper then describes an analysis of this space performed by members of the NASA Ames Human-Computer Interaction Group when considering prototyping approaches for a new set of tools for Mars mission planning and scheduling tools. A description is presented of a prototype that demonstrates design solutions that would have been particularly difficult to test given conventional low- or mid- fidelity prototyping methods. The prototype created was "mixed-fidelity," that is, high-fidelity on some dimensions and low-fidelity on others. The prototype is compared to a preexisting tool being redesigned and to a tool that has been developed using the prototype. Experimental data are presented that show the prototype to be a good predictor of eventual user performance with the final application. Given the relative cost of developing prototypes, it is critical to better characterize the space of fidelity in order to more precisely allocate design and development resources. Michael McCurdy, Christopher Connors, Guy Pyrzak, Bob Kanefsky, Alonso H. Vera |
CHI | 1 |
| 2005 | Supporting efficient development of cognitive models at multiple skill levels: exploring recent advances in constraint-based modelingabstractThis paper presents X-PRT, a new cognitive modeling tool supporting activities ranging from interface design to basic cognitive research. X-PRT provides a graphical model development environment for the CORE constraint-based cognitive modeling engine [7,13,21]. X-PRT comprises a novel feature set: (a) it supports the automatic generation of predictive models at multiple skill levels from a single task-specification, (b) it supports a comprehensive set of modeling activities, and (c) it supports compositional reuse of existing cognitive/perceptual/motor skills by transforming high-level, hierarchical task descriptions into detailed performance predictions. Task hierarchies play a central role in X-PRT, serving as the organizing construct for task knowledge, the locus for compositionality, and the cognitive structures over which the learning theory is predicated. Empirical evidence supports the role of task hierarchies in routine skill acquisition. Irene Tollinger, Richard L. Lewis, Michael McCurdy, Preston Tollinger, Alonso H. Vera, Andrew Howes 0001, Laura Pelton |
CHI | 3 |
| 2004 | A constraint satisfaction approach to predicting skilled interactive cognitionabstractIn this paper we report a new approach to generating predictions about skilled interactive cognition. The approach, which we call Cognitive Constraint Modeling, takes as input a description of the constraints on a task environment, on user strategies, and on the human cognitive architecture and generates as output a prediction of the time course of interaction. In the Cognitive Constraint Models that we have built this is achieved by encoding the assumptions inherent in CPM-GOMS as a set of constraints and reasoning about them using finite domain constraint satisfaction. Alonso H. Vera, Andrew Howes 0001, Michael McCurdy, Richard L. Lewis |
CHI | 3 |
| 2004 | Collaborative knowledge management supporting mars mission scientistsabstractThis paper describes the design and deployment of a collaborative software tool, designed for and presently in use on the Mars Exploration Rovers (MER) 2003 mission. Two central questions are addressed. Does collaborative content like that created on easels and whiteboards have persistent value? Can groups of people jointly manage collaboratively created content? Based on substantial quantitative and qualitative data collected during mission operations, it remains difficult to conclusively answer the first question while there is some positive support for the second question. The MER mission provides a uniquely rich data set on the use of collaborative tools. Irene Tollinger, Michael McCurdy, Alonso H. Vera, Preston Tollinger |
CSCW | 2 |