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  4. Vol 8, Issue 2, 2024

Academic Behavior Analysis and Early Warning System Based on K-Means Algorithm

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DOI: 10.23977/acss.2024.080204 | Downloads: 10 | Views: 101

Author(s)

Feiyang Huang 1, Renteng Li 1, Shuyu Shi 1, Chong Zhang 1

Affiliation(s)

1 College of Artificial Intelligence, North China University of Science and Technology, Tangshan, China

Corresponding Author

Feiyang Huang

ABSTRACT

This paper presents the design and implementation of an academic behavior analysis and warning system based on the K-Means algorithm. The system combines students' historical grades with current behavioral data to construct a predictive and academic warning model, aimed at assisting educators in quickly identifying academic risks and providing adjustment suggestions for students on the academic edge. The system is divided into registration and login modules, administrator modules, and user modules, realizing functions such as identity authentication, permission allocation, and account management. In the model construction phase, K-Means clustering is applied to training samples, and multiple linear regression models for grade prediction are built based on the clustering results. In the testing phase, grades of experimental groups are predicted and error analysis is conducted. Experimental results show that the system has lower prediction errors in the construction of predictive models for intelligent medical engineering majors and higher prediction accuracy for computer science and technology majors. The system also establishes a four-level warning mechanism, represented by red, orange, yellow, and green, to help users intuitively understand their academic situations. Overall, this study provides effective support for student academic development through a K-Means-based grade prediction system, with practical application value.

KEYWORDS

K-Means Algorithm, Multiple Linear Regression, Academic Warning, Grade Prediction, Django

CITE THIS PAPER

Feiyang Huang, Renteng Li, Shuyu Shi, Chong Zhang, Academic Behavior Analysis and Early Warning System Based on K-Means Algorithm. Advances in Computer, Signals and Systems (2024) Vol. 8: 22-29. DOI: http://dx.doi.org/10.23977/acss.2024.080204.

REFERENCES

[1] Huang Fangliang, Xu Huanqing, Shen Tongping, Jin Li, Yu Lei. Design and Experimental Research on Intelligent Learning Effect Early Warning Management System Based on Data Mining. Journal of Tonghua Normal University, 2022, 43(12): 84-89.
[2] Su Jin. Student Academic Early Warning Method Based on Fuzzy Association Rules. Software, 2022, 43(7): 27-29.
[3] Wang Guanbang, Liu Hongyan, Li Jinsong, et al. Research on Student Performance Prediction Method Based on K-Means. Information Technology, 2023, 47(02): 1-6. DOI:10.13274/j.cnki.hdzj.2023.02.001.
[4] Guo Henan. Website Design and Implementation Based on Django and Python Technology. Digital Communication World, 2023(6): 60-62.
[5] Fu Tingyan, Sun Yaping, Liang Rui, Wu Xiaodong, Fan Ting, Zhao Wangxiong, Zhang Ke. Design and Implementation of Radiotherapy Process Management System Based on Django. Modern Hospital, 2023, 23(5): 750-754.
[6] Wang Yufen, Zhao Dandan. Design and Implementation of Web Calculator Based on Bootstrap and Django Framework. Information and Computer, 2023, 35(1): 143-146.

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