Use of multimodal learning analytics and biometric data as a contribution to the development of pedagogical activities in entrepreneurship area

The data captured by multimodal technologies is frequently used for assessing cognitive performance in various tasks. This suggests the potential of using these cues as indicators of underlying cognitive processes in different learning contexts. This research is proposed as an initial approach to identify how biometric devices and multimodal learning analytics contribute to identifying characteristics indicative of an entrepreneurial spirit. The methodological development of the M-DVC conceptual model is proposed for collecting, processing, and converting multimodal learning data, along with the Entrepreneurial Skills Identification Test (ESIT) instrument. The results enable the classification of students' entrepreneurial profiles into three sets. Similarly, the data analysis facilitated the generation of physiological indicators' visualization to identify variations before, during, and after the pedagogical activity. Regarding physiological parameters, the indicators with the highest significance are heart rate and electrodermal activity. These results provide the possibility of integrating a method to measure stress in relation to the responses of the Autonomic Nervous System (ANS) through the physiological response of electrodermal activity (EDA) in educational activities aimed at promoting entrepreneurship development.