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

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...

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Main Authors: Sara Khosravi, Haobo Li, Ahsan Raza Khan, Ahmad Zoha, Rami Ghannam
Format: Article
Language:English
Published: Wiley-VCH 2024-01-01
Series:Advanced Sensor Research
Subjects:
Online Access:https://doi.org/10.1002/adsr.202300067
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author Sara Khosravi
Haobo Li
Ahsan Raza Khan
Ahmad Zoha
Rami Ghannam
author_facet Sara Khosravi
Haobo Li
Ahsan Raza Khan
Ahmad Zoha
Rami Ghannam
author_sort Sara Khosravi
collection DOAJ
description 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.
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spelling doaj.art-4b42a2ca53f54af8b11d01ef1e90ac982024-01-12T05:03:10ZengWiley-VCHAdvanced Sensor Research2751-12192024-01-0131n/an/a10.1002/adsr.202300067Exploring the Elusive Mind: A Multimodal Wearable Sensor Solution for Measuring Mind Wandering in University StudentsSara Khosravi0Haobo Li1Ahsan Raza Khan2Ahmad Zoha3Rami Ghannam4James Watt School of Engineering University of Glasgow Glasgow G12 8QQ UKJames Watt School of Engineering University of Glasgow Glasgow G12 8QQ UKJames Watt School of Engineering University of Glasgow Glasgow G12 8QQ UKJames Watt School of Engineering University of Glasgow Glasgow G12 8QQ UKJames Watt School of Engineering University of Glasgow Glasgow G12 8QQ UKAbstract 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.https://doi.org/10.1002/adsr.202300067educationmachine learningmind‐wanderingmultisensorysensor‐fusionwearable devices
spellingShingle Sara Khosravi
Haobo Li
Ahsan Raza Khan
Ahmad Zoha
Rami Ghannam
Exploring the Elusive Mind: A Multimodal Wearable Sensor Solution for Measuring Mind Wandering in University Students
Advanced Sensor Research
education
machine learning
mind‐wandering
multisensory
sensor‐fusion
wearable devices
title Exploring the Elusive Mind: A Multimodal Wearable Sensor Solution for Measuring Mind Wandering in University Students
title_full Exploring the Elusive Mind: A Multimodal Wearable Sensor Solution for Measuring Mind Wandering in University Students
title_fullStr Exploring the Elusive Mind: A Multimodal Wearable Sensor Solution for Measuring Mind Wandering in University Students
title_full_unstemmed Exploring the Elusive Mind: A Multimodal Wearable Sensor Solution for Measuring Mind Wandering in University Students
title_short Exploring the Elusive Mind: A Multimodal Wearable Sensor Solution for Measuring Mind Wandering in University Students
title_sort exploring the elusive mind a multimodal wearable sensor solution for measuring mind wandering in university students
topic education
machine learning
mind‐wandering
multisensory
sensor‐fusion
wearable devices
url https://doi.org/10.1002/adsr.202300067
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