<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>This paper uses affective computing and mood measures to calculate the negative emotional status of college students during school closures. The psychological data of university students at the University of H were counted using the affective computing model and psychometric methods, the frequency of their negative emotion words was mined, and the changes of each psychometric index were analyzed in terms of the time dimension, stressor, anxiety status, and emotional state, respectively. The epidemic closure period had a great negative impact on students. From January 20 to December 20, 2021, college students’ positive mood characteristic value declined. The negative mood characteristic value increased, and the negative mood characteristic value was always higher than the positive mood characteristic value. Finally, the negative mood characteristic value of 0.27 was much higher than the positive mood characteristic value of 0.07, and there were various discomforts such as about 40% of people were nervous, depressed, lacked motivation, and had diminished interest; at the same time, feelings of difficulty in accomplishing goals, failure, fear, anxiety, and frustration emerged. The epidemic closure greatly impacted students’ psychology, and students’ psychological problems need to be taken seriously and receive timely guidance.</p>
</abstract>

This paper uses affective computing and mood measures to calculate the negative emotional status of college students during school closures. The psychological data of university students at the University of H were counted using the affective computing model and psychometric methods, the frequency of their negative emotion words was mined, and the changes of each psychometric index were analyzed in terms of the time dimension, stressor, anxiety status, and emotional state, respectively. The epidemic closure period had a great negative impact on students. From January 20 to December 20, 2021, college students’ positive mood characteristic value declined. The negative mood characteristic value increased, and the negative mood characteristic value was always higher than the positive mood characteristic value. Finally, the negative mood characteristic value of 0.27 was much higher than the positive mood characteristic value of 0.07, and there were various discomforts such as about 40% of people were nervous, depressed, lacked motivation, and had diminished interest; at the same time, feelings of difficulty in accomplishing goals, failure, fear, anxiety, and frustration emerged. The epidemic closure greatly impacted students’ psychology, and students’ psychological problems need to be taken seriously and receive timely guidance.

A study on the impact of COVID-19 class suspension on college students’ emotions based on affective computing model

Xi’an University of Science and Technology

Institute of Safety Management and Risk Control

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper uses affective computing and mood measures to calculate the negative emotional status of college students during school closures. The psychological...

A study on the impact of COVID-19 class suspension on college students’ emotions based on affective computing model

Online veröffentlicht: 07. Aug. 2023

Seitenbereich: -

Eingereicht: 30. Sept. 2022

Akzeptiert: 15. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00148

Schlüsselwörter
Psychological data, Psychometric index, Student psychology, Negative emotions

© 2023 Gonglong Shi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A study on the impact of COVID-19 class suspension on college students’ emotions based on affective computing model

Online veröffentlicht: 07. Aug. 2023

Seitenbereich: -

Eingereicht: 30. Sept. 2022

Akzeptiert: 15. Feb. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00148

SchlüsselwörterPsychological data, Psychometric index, Student psychology, Negative emotions

© 2023 Gonglong Shi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Psychological data, Psychometric index, Student psychology, Negative emotions