Enhancing Our Understanding of Business Process Model Comprehension Using Biometric Data

Much research has been done on the comprehension and development of conceptual models. In related areas such as linguistics and software engineering one has taken techniques from neuroscience into use, to study the biological and neurological processes when working with textual knowledge representations in tasks such as program code debugging. The use of such techniques has only to a limited degree been used to improve our understanding of visual conceptual models so far. We will in this paper present ongoing research on the use of techniques collecting biometric data to investigate how we work with visual conceptual models. The approach, which are based on techniques used in multi-modal learning analytics (MMLA), investigates how performance on modeling tasks is correlated with biometric data, collecting data in parallel from EEG, eye-tracking, wristbands, and facial expression (through cameras). We find that good understanding of performance of modeling tasks can be achieved by using biometric data in a natural usage situation. We have just scratched the surface of this topic, and we present the start of a larger research program in this area in the concluding remarks.