The research infrastructure for the digital humanities enables multidisciplinary research into learning, human activity and behaviour across faculty borders.
The research infrastructure makes it possible to combine research practices a used in the humanities with measurement techniques used in natural sciences and medicine, including activation level, EGG, ECG, eye-tracking and physiological sensors. It is comprised of the devices and sensors required by the above-mentioned measurement techniques. These are combined with video and audio recording equipment typically used in the humanities, creating an entity that expands beyond current resources in order to collect individual-level data on subjects’ behaviour and reactions.
Data collection and analysis require sufficient data transfer speeds and storage capacity, as well as software designed for the purpose during the analysis stage, and related software expertise. The set of research equipment includes a number of different sensors and storage devices, including subjects’ protocol recording devices and devices used for making recordings of the experiment setting, as well as data pools required by data collection, and software and equipment required by data analysis. Easily movable equipment can be used in laboratory and field conditions alike, and for different target groups. A key goal is to enable simultaneous measurements in increasingly large target groups under similar conditions.
Within the university, the research infrastructure is broadly used in teaching, research and development activities by the different faculties and the Language Centre. This research infrastructure makes it possible to expand the traditional research practices of different disciplines by introducing modern measurement methods that generate data at the level of individuals and communities. Research can focus on, e.g., teaching and learning situations, interaction, customer scenarios and scenarios in simulation teaching. Furthermore, the data generated by the infrastructure is made use of in advanced learning analytics. Learning analytics is one of the key components of a digital leaning environment. It allows for the utilisation of the collected data in the development and analytics of learning and teaching by using methods of machine learning and artificial intelligence.