Welcome to the SECTION
COGNITION, DATA AND EDUCATION (code)
Research at the BMS section of CoDE (Cognition, Data and Education) is centred around data. Data appears at many stages of the scientific endeavour. Part of our group is involved in large scale data collection for assessment, while others are interested in modern forms of data like process data, log data, and social media data, or collect physiological data (e.g., EEG). Then there is a significant group of CoDE researchers more focused on the mining and modelling of data, either interested in a) explanation and statistical inference or in b) prediction.
At CoDE we are mostly interested in the following steps of the empirical cycle: data collection, data analytics and data interpretation.
- Large-scale educational assessment
- Physiology (e.g., EEG)
- Behaviour (e.g., Eye-tracking, bodily movements)
- Log and process data
- Measurement (transformation of raw data into reliable and valid measures)
- Mining (exploration, prediction)
- Modelling (explanation, statistical inference)
- Explainable AI
There are a few distinct application areas where we contribute to science: (1) Health, (2) Learning, and (3) Human Factors, although a lot of projects that cross the boundaries of these application areas. The interface where all three application areas meet we call Behavioural Data Analytics. In this focus group we study the methodology of new ways of data collection, and their implication for data analysis, inference, prediction and fairness.
Apart from our scientific work, we teach courses for most of the BMS educational programmes. Apart from bachelor courses in research methodology and data analysis, we teach bachelor and master courses in data science (text mining, social network analysis, and machine learning).