Education, Science, Technology, Innovation and Life
Open Access
Sign In

Evaluation of Multi-level Employment Psychological Pressure among College Students Based on Dynamic Optimization Models

Download as PDF

DOI: 10.23977/appep.2024.050114 | Downloads: 22 | Views: 177

Author(s)

Jinjun Zhao 1, Shuo Chen 1

Affiliation(s)

1 Zhejiang Business Technology Institute, Ningbo, Zhejiang, 315012, China

Corresponding Author

Shuo Chen

ABSTRACT

In universities, the primary task of vocational education is to cultivate students' ability to adapt to social work, so employment is very important for college students. They need to think about how to find a job and how to apply what they have learned to their work. Employment is a huge psychological burden for college students, especially in the current labor market where many people are unable to find their ideal jobs, which has a certain impact on their psychology, such as conflicts, anxiety, depression, etc. Psychological problems have a significant impact on their employment and even mental health. Therefore, establishing a correct career choice perspective, cultivating various psychological qualities, and maintaining a good psychological state for college students in their career, as well as making effective adjustments to themselves, plays a very important role in their future. Therefore, this article attempted to use the GA-BP (genetic algorithm-back propagation) algorithm to construct a dynamic optimization model for evaluating the multi-level employment psychological pressure of college students. Firstly, the influencing factors of employment psychological pressure of college students were obtained from a large number of literature, and then the various influencing factors of students were investigated and collected as sample data for the GA-BP algorithm. The experiment showed that the combination of GA algorithm and BP algorithm could achieve the highest accuracy in predicting the employment psychological pressure of college students. The average accuracy of the three training results was 0.9, which was higher than the optimized accuracy of momentum algorithm, LM algorithm, adaptive algorithm, and CG algorithm.

KEYWORDS

Assessment of Employment Stress for College Students, GA Algorithm, BP Algorithm, Psychological Issues

CITE THIS PAPER

Jinjun Zhao, Shuo Chen, Evaluation of Multi-level Employment Psychological Pressure among College Students Based on Dynamic Optimization Models. Applied & Educational Psychology (2024) Vol. 5: 101-109. DOI: http://dx.doi.org/10.23977/appep.2024.050114.

REFERENCES

[1] Sprung J M, Rogers A. Work-life balance as a predictor of college student anxiety and depression. Journal of American college health, 2021, 69(7): 775-782.
[2] Ma Y, Bennett D. The relationship between higher education students' perceived employability, academic engagement and stress among students in China. Education+ Training, 2021, 63(5): 744-762.
[3] Peltz J S, Bodenlos J S, Kingery J N. The role of financial strain in college students' work hours, sleep, and mental health. Journal of American college health, 2021, 69(6): 577-584.
[4] Lin C H, Lu F J H, Chen T W. Relationship between athlete stress and burnout: a systematic review and meta-analysis. International Journal of Sport and Exercise Psychology, 2022, 20(5): 1295-1315.
[5] von Keyserlingk L, Yamaguchi‐Pedroza K, Arum R. Stress of university students before and after campus closure in response to COVID‐19. Journal of community psychology, 2022, 50(1): 285-301.
[6] Song S, Xiong X, Wu X. Modeling the SOFC by BP neural network algorithm. International Journal of Hydrogen Energy, 2021, 46(38): 20065-20077.
[7] Wright L G, Onodera T, Stein M M. Deep physical neural networks trained with backpropagation. Nature, 2022, 601(7894): 549-555.
[8] Zhang M, Wang J, Wu J.Rectified linear postsynaptic potential function for backpropagation in deep spiking neural networks. IEEE transactions on neural networks and learning systems, 2021, 33(5): 1947-1958.
[9] Li X, Wang J, Yang C. Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy. Neural Computing and Applications, 2023, 35(3): 2045-2058.
[10] Ileberi E, Sun Y, Wang Z. A machine learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data, 2022, 9(1): 1-17.
[11] Memon M A, Siddique M D, Mekhilef S. Asynchronous particle swarm optimization-genetic algorithm (APSO-GA) based selective harmonic elimination in a cascaded H-bridge multilevel inverter. IEEE Transactions on Industrial Electronics, 2021, 69(2): 1477-1487.
[12] Kushwaha O S, Uthayakumar H, Kumaresan K. Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA). Environmental Science and Pollution Research, 2023, 30(10): 24927-24948.
[13] Zhang W, Liu J, Yu L. Nonlinear inversion for complex resistivity method based on QPSO-BP algorithm. Open Journal of Geology, 2021, 11(10): 494-508.
[14] Suwinyattichaiporn T, Johnson Z D. The impact of family and friends social support on Latino/a first-generation college students’ perceived stress, depression, and social isolation. Journal of Hispanic Higher Education, 2022, 21(3): 297-314.
[15] Siu O L, Lo B C Y, Ng T K.Social support and student outcomes: The mediating roles of psychological capital, study engagement, and problem-focused coping. Current Psychology, 2023, 42(4): 2670-2679.
[16] Charoensukmongkol P, Phungsoonthorn T. The effectiveness of supervisor support in lessening perceived uncertainties and emotional exhaustion of university employees during the COVID-19 crisis: the constraining role of organizational intransigence. The Journal of general psychology, 2021, 148(4): 431-450.
[17] Haikalis M, Doucette H, Meisel M K. Changes in college student anxiety and depression from pre-to during-COVID-19: Perceived stress, academic challenges, loneliness, and positive perceptions. Emerging Adulthood, 2022, 10(2): 534-545.
[18] Anees R T, Heidler P, Cavaliere L P L. Brain Drain in Higher Education. The impact of job stress and workload on turnover intention and the mediating role of job satisfaction at universities. European Journal of Business and Management Research, 2021, 6(3): 1-8.
[19] Eisapareh K, Nazari M, Kaveh M H. The relationship between job stress and health literacy with the quality of work life among Iranian industrial workers: The moderating role of social support. Current Psychology, 2022, 41(5): 2677-2685.
[20] Babapour A R, Gahassab-Mozaffari N, Fathnezhad-Kazemi A. Nurses'  job stress and its impact on quality of life and caring behaviors: A cross-sectional study. BMC nursing, 2022, 21(1): 1-10.
[21] Hoyt L T, Cohen A K, Dull B. "Constant stress has become the new normal": Stress and anxiety inequalities among US college students in the time of COVID-19. Journal of Adolescent Health, 2021, 68(2): 270-276.
[22] Zhang X R, Sun X, Sun W. Deformation expression of soft tissue based on BP neural network. Intelligent Automation & Soft Computing, 2022, 32(2): 1041-1053.
[23] Khan J, Lee E, Kim K. A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network. CAAI Transactions on Intelligence Technology, 2023, 8(4): 1124-1139.

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.