Abstract
In this chapter:
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Predict the effectiveness of an interface. Is it error-prone?
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Prerequisite: A theoretical account of action control and procedural error
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Implementation as an executable user model in ACT-R (Anderson et al. 2004)
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Together with the user model, UI meta-information can be used to predict error rates for different UI elements.
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Notes
- 1.
Parts of Sects. 6.3 and 6.4 have already been published in Halbrügge and Engelbrecht (2014). Parts of Sects. 6.5 and 6.6 have already been published in Halbrügge et al. (2015b). Parts of Sect. 6.7 have already been published in Halbrügge et al. (2016). Parts of Sect. 6.8 have already been published in Halbrügge and Russwinkel (2016).
- 2.
This is not identical to the concept of task-orientation that is used by some work psychologists (e.g., Ulich et al. 1991).
- 3.
The source code of the model is available on GitHub (doi:10.5281/zenodo.53197).
- 4.
English translations of the actual instructions are given here for reasons of comprehensibility. The original instructions are available at Zenodo (doi:10.5281/zenodo.268596).
- 5.
The source code of the model is available on GitHub (doi:10.5281/zenodo.53198).
- 6.
The dissimilarities are computed by ACT-R based on the number of mismatching information units, here trial and current subgoal. No user-specified similarity function is used by this model.
- 7.
The full instructions are available at Zenodo (doi:10.5281/zenodo.268596).
- 8.
The source code is available on GitHub (doi:10.5281/zenodo.55223).
- 9.
English translations of the actual instructions are given here for reasons of comprehensibility. The original instructions are available at Zenodo (doi:10.5281/zenodo.268596).
- 10.
Unfortunately, Ament et al. (2010) give no results of the secondary task that could be used for comparison.
- 11.
The source code of the combined model is available for download on GitHub (doi:10.5281/zenodo.55224).
- 12.
It is true though that some intrusions could also be regarded as motor slips as these can not be distinguished from intrusions on the phenotypical level.
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calculation: relative risk \(= \frac{\text {OR}}{(1-P_{\text {baseline}}) + (P_{\text {baseline}} \cdot \text {OR})} = \frac{10.24}{0.99 + 0.1024} \approx 9.37\).
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Halbrügge, M. (2018). Explaining and Predicting Sequential Error in HCI with Cognitive User Models. In: Predicting User Performance and Errors. T-Labs Series in Telecommunication Services. Springer, Cham. https://doi.org/10.1007/978-3-319-60369-8_6
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