A usability study of physiological measurement in school using wearable sensors

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps

The Investigation of the Relationship Between Emotional Engagement and Creativity

Background - One of the most critical challenges in engineering education is improving students’ divergent thinking skills. Usually, we observe students’ fixating on only one single solution for engineering problems. However, their ability to think outside the box and provide alternative solutions should be developed. Research shows that engagement may foster the development of thoughts and boost creativity. Purpose/Hypothesis – Our aim was to investigate students’ engagement with tasks that inspire different facets of creativity (verbal, numeric, and visual). Considering the role of demographics in student engagement, we explored the relationship between their engagement level and demographic traits such as gender, major, age, grades (GPA), and the languages they know besides their native tongue. Design/Method - We utilized electrodermal activity (EDA) sensors, a well-documented proxy of emotional engagement, to measure students’ engagement level while performing tasks that inspire different facets of creativity (verbal, numeric, and visual). Due to the non-normal distribution of the data, non-parametric statistical tests were conducted considering engagement as a dependent variable and demographic traits as independent variables. Results - Statistically significant differences in students’ engagement when exposed to creativity inspired tasks were observed. However, no association between demographics and engagement levels were detected. Conclusions - The results of the study may support educators in designing the instructional materials considering creativity-inspired activities so that students’ engagement level can be increased. Further, results from this study can inform experimental designs, specifically participant selection, in engagement focused studies

EngageMe: The Design and Implementation of a Reflective Tool for Evaluating Student Engagement

Recently, there has been a growing push to explore the potential of non-cognitive factors in helping students reach their fullest potential. Engagement, one predictor of student achievement, is such a factor. Because the conditions under which engagement is elicited may vary, EngageMe, a visualization tool, has been developed to assist instructors’ efforts to understand student engagement in the learning process. The application attempts to enhance traditional observation methods by utilizing electrodermal activity, a measure of physiological arousal, as a proximal indicator of engagement. An iterative, participatory design process was used to create prototypes of the EngageMe interface. The results of this design process, a study focused on the barriers to adoption of this kind of technology, as well as an exploratory case study are discussed. Finally, implications for future development are presented

Handling Missing Data For Sleep Monitoring Systems

Sensor-based sleep monitoring systems can be used to track sleep behavior on a daily basis and provide feedback to their users to promote health and well-being. Such systems can provide data visualizations to enable self-reflection on sleep habits or a sleep coaching service to improve sleep quality. To provide useful feedback, sleep monitoring systems must be able to recognize whether an individual is sleeping or awake. Existing approaches to infer sleep-wake phases, however, typically assume continuous streams of data to be available at inference time. In real-world settings, though, data streams or data samples may be missing, causing severe performance degradation of models trained on complete data streams. In this paper, we investigate the impact of missing data to recognize sleep and wake, and use regression-and interpolation-based imputation strategies to mitigate the errors that might be caused by incomplete data. To evaluate our approach, we use a data set that includes physiological traces-collected using wristbands-, behavioral data-gathered using smartphones-and self-reports from 16 participants over 30 days. Our results show that the presence of missing sensor data degrades the balanced accuracy of the classifier on average by 10-35 percentage points for detecting sleep and wake depending on the missing data rate. The impu-tation strategies explored in this work increase the performance of the classifier by 4-30 percentage points. These results open up new opportunities to improve the robustness of sleep monitoring systems against missing data

An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations

Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.This research was partly supported by Spanish Ministry of Science, Innovation and Universities through projects PGC2018-096463-B-I00 and PGC2018-102279-B-I00 (MCIU/AEI/FEDER, UE)

Establishing a Link between Electrodermal Activity and Classroom Engagement

Technological and pedagogical advancements over the last three decades have significantly changed how students are taught in the industrial engineering classroom. However, changes in teaching do not necessarily equate to increased learning. How can we determine if classroom teaching methods and activities increase the engagement of students, which then may increase the amount of learning that is taking place? Research indicates that electrodermal activity (EDA) can predict engagement in a classroom setting. Assuming that students learn both better and more when they are engaged, we can use EDA to determine which classroom methods and activities are most effective. We measured students’ EDA in two different industrial engineering courses. Preliminary results indicate that we can correlate classroom activities and methods with student engagement. This paper describes our first steps for establishing a connection between EDA and classroom pedagogy, methods of data collection, results, and lessons learned. We compare our results to previously published literature and identify similarities and differences. This work provides a foundation for using EDA measurements to inform industrial engineering educators about increasing engagement, and consequently learning, in the classroom

Wearable Sensing and Quantified-self to explain Learning Experience

Author's accepted manuscript© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The confluence of wearable technologies for sensing learners and the quantified-self provides a unique opportunity to understand learners’ experience in diverse learning contexts. We use data from learners using Empatica Wristbands and self-reported questionnaire. We compute stress, arousal, engagement and emotional regulation from physiological data; and perceived performance from the self-reported data. We use Fuzzy Set Qualitative Comparative Analysis (fsQCA) to find relations between the physiological measurements and the perceived learning performance. The results show how the presence or absence of arousal, engagement, emotional regulation, and stress, as well as their combinations, can be sufficient to explain high perceived learning performanceacceptedVersio