Deceptive techniques known as dark patterns specifically target online users. Children are particularly vulnerable as they might lack the skills to recognise and resist these deceptive attempts. To be effective, interventions to forewarn and forearm should build on a comprehensive understanding of children’s existing mental models. To this end, we carried out a study with 11- to 12-year-old Scottish children to reveal their mental models of dark patterns. They were acutely aware of online deception, referring to deployers as being ‘up to no good.’ Yet, they were overly vigilant and construed worst-case outcomes, with even a benign warning triggering suspicion. We recommend that rather than focusing on specific instances of dark patterns in awareness raising, interventions should prioritise improving children’s understanding of the characteristics of, and the motivations behind, deceptive online techniques. By so doing, we can help them to develop a more robust defence against these deceptive practices.

One important concept of Ambient Intelligence (AmI) is meeting the user needs unobtrusively. To meet such needs, it is crucial to provide way(s) that will allow users to indicate their preferences to the system and allow users update them easily. As this will be used by the system to reason about their preferences and better align services with users behaviour. The paper aim to identify an effective way to manage, represent and reason with users preferences. A simple interface for managing users' preferences was provided to help manage and resolve some of the complexities in users' preferences. The interface also relied on textual menus for simplicity. Videos demonstration have been provided to show how the interface effectively works with a reasoning system in a smart house, providing different results when there is a change in users' preference(s).

Considering the potential physical limitations of older adults, the naturalness of touch-based gestures as an interaction method is questionable. Assuming touch-based gestures are natural, they should be highly learnable and amenable to enhancement through social interactions. To investigate whether social interactions can enhance the learnability of touch gestures for older adults with low digital literacy, we conducted a study with 42 technology- naïve participants aged 64 to 82. They were paired and encouraged to play two games on an interactive tabletop with the expectation to use the drag gesture to complete the games socially. We then compared these results with a previous study of technology-naïve older adults playing the same games individually. The results of the comparisons show that dyadic interactions had some benefits for the participants in helping them to become comfortable with the drag gesture by negotiation and imitation. Further qualitative analysis suggested that playing pairs generally helped learners to comfortably explore the digital environment using the newly acquired skill.

Touch-based interactions, if intuitive and natural, should be highly learnable. However, the learnability of touch gestures as a computer interaction modality cannot be taken for granted and should be evaluated empirically. This is especially true when technology-naïve older adults are concerned, given the psychological and physical constraints associated with this age range. To investigate this issue, we conducted a study with 17 technology-naïve older adults, whose experience with technology was mostly limited to making calls with mobile phones. Specifically, the participants were presented with three simple digital games on an interactive tabletop surface and asked to play the games on their own with minimal instruction or help. The first two games required the use of a drag gesture whereas the third game required the use of a rotate gesture. The main research question was whether the older adults could learn the gestures effectively and efficiently. A set of variables such as task duration and gesture accuracy were measured. Results showed that the learnability of a drag gesture was relatively high and that the participants could transfer this skill across the games. In contrast, the learnability of a rotate gesture was low and most of the participants failed to demonstrate any improvement over the tasks of the game. These findings partially corroborate those of earlier work. Implications for future work are drawn, especially exploring the potential of social interactions between older adults for enhancing the learning effects.

An evaluation method is proposed based on walkthrough analysis coupled with a taxonomic analysis of observed problems and causes of usability error. The model mismatch method identifies usability design flaws and missing requirements from user errors. The method is tested with a comparative evaluation of two information retrieval products. Different profiles of usability and requirements problems were found for the two products, even though their overall performance was similar.

This paper reports a study of novice subjects using Word 5.1. for the Macintosh. In particular, user errors were analysed. The intention was to investigate the difficulty, that evaluators have in determining root error causes from surface characteristics. Errors made by subjects were examined and classified in phenotype and genotype categories[3]. An error was classified in a genotype category if it was felt that system had failed to support that particular user mental action. The utility of the analysis that was used for analysing usability errors is then discussed.