Cumulating the science of HCI: from s-R compatibility to transcription typing
CHI '89 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Identity authentication based on keystroke latencies
Communications of the ACM
Unified theories of cognition
Keystroke dynamics as a biometric for authentication
Future Generation Computer Systems - Special issue on security on the Web
Long Term Human-Computer Interaction: An Exploratory Perspective; With 36 Figures
Long Term Human-Computer Interaction: An Exploratory Perspective; With 36 Figures
The Psychology of Human-Computer Interaction
The Psychology of Human-Computer Interaction
A web-based support environment for software engineering experiments
Nordic Journal of Computing
Collecting Feedback during Software Engineering Experiments
Empirical Software Engineering
Predicting student help-request behavior in an intelligent tutor for reading
UM'03 Proceedings of the 9th international conference on User modeling
SOUPS '06 Proceedings of the second symposium on Usable privacy and security
Difficulties experienced by students in maintaining object-oriented systems: an empirical study
ACE '07 Proceedings of the ninth Australasian conference on Computing education - Volume 66
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Typing has long been studied in psychology and HCI, and strong cognitive models for transcription typing exist. The goal of the present research was to test if there is any correlation between students' keystroking speed and performance while they are programming. We present the results from two studies with computer science students conducted in different contexts. Keystroke timings were recorded while they worked on Java and Ada source code. Quality of their programming work was measured mainly in terms of completeness. In the controlled experiment that lasted six hours, 39 students undertook three change tasks on a 6000 LOC Java application. In the field study, data was collected over 6 weeks from 141 students while they worked unsupervised on Ada programming in first year laboratories. In both cases there were highly significant (P=0.001), moderately strong, negative correlations between speed and coding performance. With additional development, these techniques may have promise for user modelling and assessment as well as in educational diagnostics.