Reducing Error Aversion to Support Novice-to-Expert Transitions with FastTap

Alix Goguey 1 Sylvain Malacria 2 Andy Cockburn 3 Carl Gutwin 4
1 LIG Laboratoire d'Informatique de Grenoble - IIHM
LIG - Laboratoire d'Informatique de Grenoble, Inria - Institut National de Recherche en Informatique et en Automatique
2 LOKI - Technology and knowledge for interaction
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : Expert interaction techniques such as gestures or hotkeys are more efficient than traditional WIMP techniques because it is often faster to recall a command than to navigate to it. However, many users seem to be reluctant to switch to expert interaction. We hypothesize the cause might be the aversion of making errors. To test this, we designed two intermediate modes for the FastTap interaction technique, allowing quick confirmation of what the user has retrieved from memory, and quick adjustment if she has made an error. We investigated the impact of these modes and of various error costs in a controlled study (N=36). We found that participants adopted the intermediate modes, that these modes reduced error rate when error cost was high, and that they did not substantially change selection times. However, while it validates the design of our intermediate modes, we found no evidence of greater switch to memory-based interaction, suggesting that reducing the error rate is not sufficient to promote expert use of techniques.
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Alix Goguey, Sylvain Malacria, Andy Cockburn, Carl Gutwin. Reducing Error Aversion to Support Novice-to-Expert Transitions with FastTap. Actes de la 31e conférence francophone sur l'Interaction Homme-Machine (IHM 2019), Dec 2019, Grenoble, France. pp.1:1-10, ⟨10.1145/3366550.3372247⟩. ⟨hal-02381584⟩

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