Show Me How You Click, and I’ll Tell You What You Can: Predicting User Competence and Performance by Mouse Interaction Parameters
Automatically detecting and adapting to user competence is a promising approach for advancing human-technology interaction. With the present work, we demonstrate that perceived user competence and performance can be predicted by easily ascertainable low-level mouse interaction parameters with considerable amounts of explained variance. N = 71 users with varying competence interacted with a statistical software while mouse interaction parameters were recorded. Results showed that perceived task competence could best be predicted by clicks per second, maximum mouse velocity, and average duration of pauses > 150 ms (R2 = .39). Perceived system competence could best be predicted by clicks per second, maximum mouse acceleration, and average number of pauses > 150 ms (R2 = .28). Performance could best be predicted by clicks per second, maximum mouse velocity, and average number of pauses > 150 ms (R2 = .50). Results imply that assessing low-level mouse interaction parameters could be a feasible approach for automatic detection of user competence and performance.
KeywordsAdaptive systems User competence Mouse interaction
This research was funded by the European Social Fund and the Free State of Saxony under Grant No. 100269974. We want to thank our student assistants Daniel Götz, Katharina Schulzeck and Sabine Wollenberg for their support in data collection and manuscript preparation.
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