The User Experience and Interaction Research Centre hosts several research projects. Projects are predominantly performed by in-house faculty researchers and students, but also in collaboration with other research and industry partners. Here, we list the most notable ongoing projects. The project listed here also represent some of the application areas, in which we are interested and in general, are connected to research areas pursued elsewhere at the faculty. More projects, especially those done as parts of student final theses can be found here.

Analysis of user feedback in software applications – In the project, we focus on learnability and ergonomics of use of user interfaces in selected domains (internet banking, ordering systems of telecom operators)

Classifying eye tracking data with neural networks – In this project, large and loosely structured volumes of data streaming from sensors of implicit feedback are processed by neural networks, which have the task of detecting patterns in the streams. Using this approach, it is possible to effectively detect unexpected problems in application usability.

Dynamic regions of interest on the Web – The project is focused on creation of a technology for easy collecting of implicit feedback on dynamic web pages (e.g., pages that often change their appearance during a single session). Execution of user studies on such pages in standard conditions is burdensome, as it requires manual resizing and repositioning of areas of interest in eye tracking analytical tools. In this project, we bypass the problem by translation of raw gaze coordinates to logical components of the web pages – the DOM objects.

Automatic measurement of application learnability – Here, methods for automated evaluation of learnability for computer games are developed. We add an extra source of information to traditional playtesting – the eye tracking. The eye tracking is not invasive and can assess the player’s behaviour in more detail. The method compares expected interaction scenarios defined by game designer to real playtest session data, acquired by eye tracker.

Detection of deception in questionnaire filling – During filling of the online questionnaires, problems arise concerning reliability of data in-put by participants and even malicious behaviour. Implicit feedback can help to identify cases, when the user was deliberately or unconsciously providing misleading or false information.

EyeCrowd (employment of eye-tracking in crowdsourcing) – The project goal is to merge two current research areas, the crowdsourcing (the use of wide group of lay people to solve human intelligence tasks) and eye tracking. The motivation to this, sources from the lack of knowledge about how the crowd workers execute their tasks. Employment of eye tracking, as well as other implicit feedback sources, can shed light on the process and increase the potential for its output quality control.