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Abstract Much research has been done on the comprehension and development of visual business process models. In related areas such as linguistics and software engineering, researchers have used techniques from neuroscience to study the physiological and neurological processes when working with text in tasks such as program code debugging and natural language understanding. Such techniques have only to a limited degree been used to improve our understanding of visual conceptual models. In this paper, we will present ongoing research on using techniques for collecting biometric data to investigate how we work with visual conceptual models. We will provide results from an experiment collecting biometric data from 57 people as they respond to comprehension questions in connection with process models. The approach, based on techniques used in multi-modal learning analytics (MMLA), investigates how performance on modelling tasks is correlated with biometric data, collecting data in parallel from EEG, eye-tracking, wristbands, and facial expression (through cameras). We find that improved understanding of the performance of modelling tasks can be achieved by using biometric data in a close-to-natural usage situation. Results from the experiment can also be the basis for providing a neuro-adaptive modelling tool. We have just started initial work on this topic, and we present the start of a larger research program in this area in the concluding remarks.
Krogstie et al. (Sat,) studied this question.