Analysis of Employment Competitiveness of College Students Based on Binary Association Rule Extraction Algorithm

Lixia Guo

doi:10.4108/eetsis.5765

Authors

Lixia Guo Xinxiang Vocational and technical College

DOI:

https://doi.org/10.4108/eetsis.5765

Keywords:

BAREA, Employment Competitiveness, college students, IoT

Abstract

Today, assessing competition among college students in the job search is extremely important. However, various methods available are often inaccurate or inefficient when it comes to determining the level of their readiness for work. Conventional techniques usually depend on simplistic measures or miss out on crucial factors responsible for employability. The challenging characteristics of such competitive employment of college students are the lower levels of perceived stress, financing my education, and crucial professional skills. Hence, in this research, the Internet of Things Based on Binary Association Rule Extraction Algorithm (IoT-BAREA) technologies have improved college students' employment competitiveness. IoT-BAREA addresses this situation using a binary association rule extraction algorithm that helps detect significant patterns and relationships in large amounts of data involving student attributes and employment outcomes. IoT-BAREA positions itself as capable of providing insights into features that highly mediate the employability levels among students. This paper closes this gap and recommends a new IoT-BAREA method to help increase accuracy and efficiency in evaluating student employment competitiveness. Specifically, this study uses rigorous evaluation methods such as precision, recall and interaction ratio to determine how well IoT-BAREA predicts students' employability.

References

Li, Y. (2021, May). The Paths of Ideological and Political Education in Improving Competitiveness in Employment of College Students. In 2021 2nd International Conference on Computers, Information Processing and Advanced Education (pp. 168-170).

Zhang, X., Yin, J., Liu, Z., Chen, J., Jiang, T., & Li, C. (2021, November). Research on Measures to Improve the Employment Competitiveness of Students Majoring in Power Based on the PDCA Program. In 2021 2nd International Conference on Information Science and Education (ICISE-IE) (pp. 1414-1418). IEEE.

Shen, Y., Fan, W., Li, M., Lu, Z., & Lu, Q. (2023). Research on the Employment Competitiveness of Overseas Returnees and the Construction of an Evaluation System. In SHS Web of Conferences (Vol. 179, p. 05030). EDP Sciences.

Huazhi, H. E. (2020). Research on Ways to Improve Students' Employment Competitiveness in Higher Vocational Colleges from the Perspective of Craftsman Spirit. The Theory and Practice of Innovation and Enntrepreneurship, 3(3), 176.

Wei, Q., & Qin, X. (2021, May). Research on the Employment Competitiveness of the Local Colleges and Universities of Economics and Management Majors Students in the Background of Internet Plus. In 6th International Conference on Education Reform and Modern Management (ERMM 2021) (pp. 416-419). Atlantis Press.

Vedhathiri, T. (2020). Collaborative dissertation based on the human resources needs of MSMES to improve their competitiveness and to overcome the disruption. Procedia Computer Science, 172, 551-558.

Zhou, F. (2022). Construction of model to enhance college students’ core competitiveness from perspective of employment competition through IoT apps. Wireless Communications and Mobile Computing, 2022.

Xie, T. (2023). Campus IoT system and students’ employment education innovation based on mobile edge computing. Soft Computing, 27(14), 10263-10272.

Ma, X., & Pan, H. (2023). Improving entrepreneurial skills and professional association of college students using big data analysis and IoT. Soft Computing, 27(19), 14253-14267.

Boudreau, K., & Kaushik, N. (2020). The Gender Gap in Tech & Competitive Work Environments? Field Experimental Evidence from an Internet-of-Things Product Development Platform (No. w27154). National Bureau of Economic Research.

Al-Emran, M., Malik, S. I., & Al-Kabi, M. N. (2020). A survey of Internet of Things (IoT) in education: Opportunities and challenges. Toward social internet of things (SIoT): Enabling technologies, architectures and applications: Emerging technologies for connected and smart social objects, 197-209.

Sun, Q., & Wang, Y. (2023, July). Analysis of College Students’ Employment Competitiveness Based on Binary Association Rule Extraction Algorithm. In International Conference on Frontier Computing (pp. 306-311). Singapore: Springer Nature Singapore.

Hsu, P. L., Lai, R., & Chiu, C. C. (2003). The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance. Expert Systems with Applications, 25(1), 51-62.

Kumar, B., Roy, S., Sinha, A., Iwendi, C., & Strážovská, Ľ. (2022). E-commerce website usability analysis using the association rule mining and machine learning algorithm. Mathematics, 11(1), 25.

Yu, H., Zhang, R., & Kim, C. (2023). Intelligent analysis system of college students' employment and entrepreneurship situation: Big data and artificial intelligence-driven approach. Computers and Electrical Engineering, 110, 108823.

Tian, F. (2023). Case Study and Practice of Ideological and Political Education in the Course. Advances in Educational Technology and Psychology, 7(13), 64-71.

Peng, J., & Deng, C. (2022). Research on the relationship between college students' employability and IT skills training based on mixed research methods. Frontiers in Psychology, 13, 1054134.

Gherheș, V., & Obrad, C. (2018). Technical and humanities students’ perspectives on the development and sustainability of artificial intelligence (AI). Sustainability, 10(9), 3066.

Palacios, C. A., Reyes-Suárez, J. A., Bearzotti, L. A., Leiva, V., & Marchant, C. (2021). Knowledge discovery for higher education student retention based on data mining: Machine learning algorithms and case study in Chile. Entropy, 23(4), 485

Luan, H., & Tsai, C. C. (2021). A review of using machine learning approaches for precision education. Educational Technology & Society, 24(1), 250-266.

Supangat, S., Saringat, M. Z. B., Kusnanto, G., &Andrianto, A. (2021). Churn Prediction on Higher Education Data with Fuzzy Logic Algorithm. SISFORMA, 8(1), 22-29.

Jiang, L., Li, Y., & Jiang, C. (2017). Employment competitiveness of college students based on improved AHP. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 5225-5232.

yuan Zhao, Y., & Kerdpitak, C. (2023, May). INFLUENCING Factors Of College Students'employment Competitiveness Based On College Employment Guidance Service System, China. In International Academic Multidisciplinary Research Conference In Munich 2023 (pp. 213-223).