EDBT 2026 Demo / reviewers in the wild / expert
Laura Pelton
dblp:71/3395
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1
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
1 paper |
Usability and user experience research · 77% Learning and educational technologies · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
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
graphical model development environment · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 | 7 |