Exploring the Elusive Mind: A Multimodal Wearable Sensor Solution for Measuring Mind Wandering in University Students

Abstract

Abstract Mind‐wandering is a typical daily phenomenon during which attention shifts from external stimuli to internal trains of thought. It affect students' learning by impairing comprehension, diminishing academic achievement, impeding critical thinking, and encouraging a lack of attention and engagement in classroom activities. This study aims to introduce a new method of detecting and tracking mind wandering in university students. This approach involves using wearable sensors, including galvanic skin response (GSR), photoplethysmography (PPG), and eye‐trackers, along with machine learning techniques. The study provides a proof of concept for this multisensory approach. The association between longer fixation duration and mind wandering, and the influence of an instructor's presence on fixation allocation, and, consequently, the frequency and occurrence of mind wandering is investigated. Furthermore, the feasibility of using eye‐trackers in conjunction with GSR and PPG sensors for detecting mind wandering through a wearable multisensory data collection system is assessed. The wearable multisensory device is evaluated by ten participants (university students, males/females aged between 21‐30). Two distinct machine learning methods, support vector machine (SVM) and gated recurrent unit (GRU), are used as classification models. With sensor fusion, the SVM and GRU models yielded maximum accuracies of 86.53% and 89.86%, respectively. Moreover, participants are observed to fixate on instructors more often, just before instances of mind wandering.

Keywords

• education
• machine learning
• mind‐wandering
• multisensory
• sensor‐fusion
• wearable devices