This paper describes the development and evaluation of an affect-aware intelligent support component that is part of a learning environment known as iTalk2Learn. The intelligent support component is able to tailor feedback according to a student's affective state, which is deduced both from speech and interaction. The affect prediction is used to determine which type of feedback is provided and how that feedback is presented (interruptive or non-interruptive). The system includes two Bayesian networks that were trained with data gathered in a series of ecologically-valid Wizard-of-Oz studies, where the effect of the type of feedback and the presentation of feedback on students' affective states was investigated. This paper reports results from an experiment that compared a version that provided affect-aware feedback (affect condition) with one that provided feedback based on performance only (non-affect condition). Results show that students who were in the affect condition were less bored and less off-task, with the latter being statically significant. Importantly, students in both conditions made learning gains that were statistically significant, while students in the affect condition had higher learning gains than those in the non-affect condition, although this result was not statistically significant in this study's sample. Taken all together, the results point to the potential and positive impact of affect-aware intelligent support.

Affective states play a significant role in students' learning behaviour. Positive affective states can enhance learning, whilst negative affective states can inhibit it. This paper describes a Wizard-of-Oz study which investigates whether the way feedback is presented should change according to the affective state of a student, in order to encourage affect change if that state is negative. We presented 'high-interruptive' feedback in the form of pop-up windows in which messages were immediately viewable; or 'low-interruptive' feedback, a glowing light bulb which students needed to click in order to access the messages. Our results show that when students are confused or frustrated high-interruptive feedback is more effective, but when students are enjoying their activity, there is no difference. Based on the results, we present guidelines for adaptively tailoring the presentation of feedback based on students' affective states when interacting with learning environments.

IDEAS (Interface Design Experience for the Autistic Spectrum) is a method for involving children with Autism Spectrum Disorders (ASD) in the technology design process. This paper extends the IDEAS method to enable use with a design team, providing specific added support for communication and collaboration difficulties that may arise. A study to trial this extended method was conducted with two design teams, each involving three children with ASD, in a series of six, weekly design sessions focused on designing a math game. The findings from this study reveal that the children were able to successfully participate in the sessions and collaborate with other children. The findings also highlight the positive experience that involvement in such a process can offer this population.

Security policies are required that protect information from unauthorised access, and also respect challenges users face in creating, and particularly managing, increasing numbers of passwords. This paper investigates real password use in the context of daily life. It presents the results of an empirical study where participants completed a password diary over 7 days, followed by debrief interviews to gain further knowledge and understanding of user behaviour. The results reported relate to how many passwords are in use, the types of passwords participants created, the relationships between different passwords and to sensitive services, how participants retrieved their passwords and finally, the different strategies adopted by users in their management of passwords. The paper concludes by providing a high level set of password guidelines, along with suggestions for mechanisms to support creating, encoding, retrieving and executing multiple passwords.

The performance of document analysis systems significantly depends on knowledge about the application domain that can be exploited in the analysis process. Typically one has to deal with different sources of knowledge like syntactic knowledge, semantic knowledge or strategic knowledge guiding the analysis process. We present a knowledge based document analysis system based on tasks. It allows to model and to interpret structural knowledge about documents and knowledge about the analysis process declaratively in a common framework.