Automating parts of the user interface (UI) design process has been a longstanding challenge. We present an automated technique for optimizing the layouts of mobile UIs. Our method uses gradient descent on a neural network model of task performance with respect to the model's inputs to make layout modifications that result in improved predicted error rates and task completion times. We start by extending prior work on neural network based performance prediction to 2-dimensional mobile UIs with an expanded interaction space. We then apply our method to two UIs, including one that the model had not been trained on, to discover layout alternatives with significantly improved predicted performance. Finally, we confirm these predictions experimentally, showing improvements up to 9.2 percent in the optimized layouts. This demonstrates the algorithm's efficacy in improving the task performance of a layout, and its ability to generalize and improve layouts of new interfaces.
A Dark Pattern (DP) is an interface maliciously crafted to deceive users into performing actions they did not mean to do. In this work, we analyze Dark Patterns in 240 popular mobile apps and conduct an online experiment with 589 users on how they perceive Dark Patterns in such apps. The results of the analysis show that 95% of the analyzed apps contain one or more forms of Dark Patterns and, on average, popular applications include at least seven different types of deceiving interfaces. The online experiment shows that most users do not recognize Dark Patterns, but can perform better in recognizing malicious designs if informed on the issue. We discuss the impact of our work and what measures could be applied to alleviate the issue.
We conducted an online study with 165 participants in which we tested their search efficiency and information recall. We confirm that the visual complexity of a website has a significant negative effect on search efficiency and information recall. However, the search efficiency of those who preferred simple websites was more negatively affected by highly complex websites than those who preferred high visual complexity. Our results suggest that diverse visual preferences need to be accounted for when assessing search response time and information recall in HCI experiments, testing software, or A/B tests.
Delayed display of menu items is a core design component of marking menus, arguably to prevent visual distraction and foster the use of mark mode. We investigate these assumptions, by contrasting the original marking menu design with immediately-displayed marking menus. In three controlled experiments, we fail to reveal obvious and systematic performance or usability advantages to using delay and mark mode. Only in very constrained settings after significant training and only two items to learn did traditional marking menus show a time improvement of about 260~ms. Otherwise, we found an overall decrease in performance with delay, whether participants exhibited practiced or unpracticed behaviour. Our final study failed to demonstrate that an immediately-displayed menu interface is more visually disrupting than a delayed menu. These findings inform the costs and benefits of incorporating delay in marking menus, and motivate guidelines for situations in which its use is desirable.
We introduce a new shortcut interface called KeyMap that is designed to leverage Norman's principle of natural mapping. Rather than displaying shortcut command labels in linear menus, KeyMap displays a virtual keyboard with command labels displayed directly on its keys. A crowdsourced experiment compares KeyMap to Malacria et~al.'s ExposeHK using an extension of their protocol to also test recall. Results show KeyMap users remembered 1 more shortcut than ExposeHK immediately after training, and this advantage increased to 4.5 more shortcuts when tested again after 24 hours. KeyMap users also incidentally learned more shortcuts that they had never practised. We demonstrate how KeyMap can be added to existing web-based applications using a Chrome extension.