Academic stress detection on university students during COVID-19 outbreak by using an electronic nose and the galvanic skin response

Acevedo, Cristhian Manuel Durán; Gómez, Jeniffer Katerine Carrillo; Rojas, Camilo Andrés Albarracín

doi:10.1016/j.bspc.2021.102756

Cited by 36 publications

(20 citation statements)

References 50 publications

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“…Wearable technology bridges the communication gap between special students and normal students, and also helps them to integrate into society and participate in various activities in life and learning, providing them with a more inclusive educational environment (Díaz et al, 2016; Mehmood & Lee, 2017; Kalist et al, 2020; Nuguri et al, 2021). Instructional staff, including instructors, course designers, or teaching assistants, came in the second (35%) place to assist them in gaining insights about course design and instructional practices (Edwards et al, 2017; Khakurel et al, 2019; Dong & Li, 2021; Acevedo et al, 2021). Teachers can get a clearer picture of student performance and engagement in classroom activities; instructors can adjust teaching strategies based on student learning feedback; and wearable devices can also promote visualized content delivery for training purposes through offering immersive experiences.…”

Section: Discussionmentioning

confidence: 99%

Learning Analytics Based on Wearable Devices: A Systematic Literature Review From 2011 to 2021

Liu

Ren

Kong

et al. 2022

Journal of Educational Computing Research

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Wearable devices are an emerging technological tool in the field of learning analytics. With the help of wearable technologies, an increasing number of scholars have a strong interest in studying the associations between student data and learning outcomes in different learning environments. This systematic review examines 120 articles published between 2011 and 2021, exploring current research on learning analytics based on wearable devices in detail from both descriptive and content analysis. The descriptive analysis reviewed the included literature in five dimensions: publication times of the reviewed literature, wearable devices and data types used in studies, stakeholders, objectives, and methods involved in the analysis procedure. The content analysis aims to examine the literature covered in terms of three categorical domains of educational objectives: cognitive, affective, and behavioral, to investigate the practical applications and potential issues of learning analytics based on wearable devices. After that, based on the overall research content of the reviewed literature, a framework for learning analytics based on wearable devices is present, and its application process is summarized and analyzed for the reference of related researchers. At last, we summarize the limitations of existing studies and present several recommendations to further promote research and development in this field.

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Section: Discussionmentioning

confidence: 99%

Learning Analytics Based on Wearable Devices: A Systematic Literature Review From 2011 to 2021

Liu

Ren

Kong

et al. 2022

Journal of Educational Computing Research

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“…The use of this signal reached an accuracy of more than 90.00% (Castaldo et al, 2019;Melillo et al, 2011) in the classification and identification of stress in the academic setting. However, there is evidence that the GSR signal could be used in conjunction with pattern recognition models for the identification of academic stress with an accuracy more than 95.00% (Durán Acevedo et al, 2021a;Rodríguez et al, 2020). New emerging methods, such as rPPG in combination with demographic data or e-nose, achieved an accuracy over 96.00% (Durán-Acevedo et al, 2021b;Morales-Fajardo et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…It can be proposed that the accuracy of the pattern recognition algorithm on stress and anxiety detection lies between these values. It is worth mentioning that while more frequently used features are temporal, frequential, and nonlinear HRV signals in stress detection in academia; statistical measures, which are less complicated to compute, of the GSR signal yield better accuracies; above 95.00% (Durán Acevedo et al, 2021a;Rodríguez et al, 2020;Santos et al, 2011). In addition, GSR sensors are less invasive, given that they are placed on the fingers; all this could indicate the need to explore the potential of the GSR signal in stress and anxiety detection.…”

Section: Discussionmentioning

confidence: 99%

Advances and challenges in the detection of academic stress and anxiety in the classroom: A literature review and recommendations

et al. 2022

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In recent years, stress and anxiety have been identified as two of the leading causes of academic underachievement and dropout. However, there is little work on the detection of stress and anxiety in academic settings and/or its impact on the performance of undergraduate students. Moreover, there is a gap in the literature in terms of identifying any computing, information technologies, or technological platforms that help educational institutions to identify students with mental health problems. This paper aims to systematically review the literature to identify the advances, limitations, challenges, and possible lines of research for detecting academic stress and anxiety in the classroom. Forty-four recent articles on the topic of detecting stress and anxiety in academic settings were analyzed. The results show that the main tools used for detecting anxiety and stress are psychological instruments such as self-questionnaires. The second most used method is acquiring and analyzing biological signals and biomarkers using commercial measurement instruments. Data analysis is mainly performed using descriptive statistical tools and pattern recognition techniques. Specifically, physiological signals are combined with classification algorithms. The results of this method for detecting anxiety and academic stress in students are encouraging. Using physiological signals reduces some of the limitations of psychological instruments, such as response time and self-report bias. Finally, the main challenge in the detection of academic anxiety and stress is to bring detection systems into the classroom. Doing so, requires the use of non-invasive sensors and wearable systems to reduce the intrinsic stress caused by instrumentation.

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“…Resnik et al (2013) used direct topic modeling with LDA to generate interpretable, psychologically relevant “topics” that added value in the predictions of clinical assessments. Acevedo et al (2021) used an algorithm to classify academic stress levels during COVID-19.…”

Section: Literature Reviewmentioning

confidence: 99%

A deep learning-based prediction model of college students’ psychological problem categories for post-epidemic era—Taking college students in Jiangsu Province, China as an example

2022

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For a long time, it takes a lot of time and energy for psychological workers to classify the psychological problems of college students. In order to quickly and efficiently understand the common psychological problems of college students in the region for real-time analysis in the post-epidemic era, 2,000 college students’ psychological problems were selected as research data in the community question section of the “Su Xin” application, a psychological self-help and mutual aid platform for college students in Jiangsu Province. First, word segmentation, removal of stop words, establishment of word vectors, etc. were used for the preprocessing of research data. Secondly, it was divided into 9 common psychological problems by LDA clustering analysis, which also combined with previous researches. Thirdly, the text information was processed into word vectors and transferred to the Attention-Based Bidirectional Long Short-Term Memory Networks (AB-LSTM). The experimental results showed that the proposed model has a higher test accuracy of 78% compared with other models.

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Academic stress detection on university students during COVID-19 outbreak by using an electronic nose and the galvanic skin response

Cited by 36 publications

References 50 publications

Learning Analytics Based on Wearable Devices: A Systematic Literature Review From 2011 to 2021

Learning Analytics Based on Wearable Devices: A Systematic Literature Review From 2011 to 2021

Advances and challenges in the detection of academic stress and anxiety in the classroom: A literature review and recommendations

A deep learning-based prediction model of college students’ psychological problem categories for post-epidemic era—Taking college students in Jiangsu Province, China as an example

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