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Jans Aasman

dblp:33/4331 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2002
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1Human-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.

Artificial intelligence
1 paper
Trustworthy machine learning · 44% Information extraction and text analysis · 44% Knowledge representation and reasoning · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › syntactic parsing
chunking
0.011990
Knowledge Level and Inductive Uses of Chunking (EBL) · AAAI 1990
Machine learning › Trustworthy machine learning › interpretability
explanation-based learning
0.011990
Knowledge Level and Inductive Uses of Chunking (EBL) · AAAI 1990
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge level
0.011990
Knowledge Level and Inductive Uses of Chunking (EBL) · AAAI 1990
YearPublicationVenuePosition
2002 DEVAN: a tool for detailed video analysis of user test data
abstract
A tool was developed for structured and detailed analysis of video data from user tests of interactive systems. It makes use of a table format for representing an interaction at multiple levels of abstraction. Interactions are segmented based on threshold times for pauses between actions. Usability problems are found using a list of observable indications for the occurrence of problems. The tool was evaluated by having two analysts apply it to three data sets from user tests on two different products. The segmentation technique proved to yield meaningful segments that helped in understanding the interaction. The interaction table was explicit enough to discuss in detail what had caused the differences in the analysts' lists of usability problems. The results suggested that the majority of differences were caused by unavoidable differences in interpretations of subjects' behaviour and that only minor improvements should be expected by refining the tool.
Arnold Vermeeren, Karin den Bouwmeester, Jans Aasman, Huib de Ridder
Behav. Inf. Technol.3
1990 Knowledge Level and Inductive Uses of Chunking (EBL)
Paul S. Rosenbloom, Jans Aasman
AAAI2