Investigating patterns of emotion and expressions using smart learning spaces

The Internet of Things (IoT) is based on the use of interconnected device for data transfer. This paper describes findings from current work that uses a range of sensors that are connected together in collecting biometric data from learners. The research is focused on assessing learners’ state during different learning activities by using different biometric data. The paper investigates certain patterns of emotion, expressions and Galvanic Skin Response (GSR) (i.e. sweat levels) amongst participants. The findings are discussed under the prism of learner classification against a number of criteria including learning styles, project management preference, team profile and personality type. The paper contributes in understanding how we can monitor individuals’ state and behaviour during different learning activities and identify predominant patterns.