Research on the synergistic nurturing mechanism of ideological work and student management in colleges and universities under the binary structure

[1] Zhu, G., Zhu, G., & Zhang, J. (2021). Computer simulation of ideological and political teaching under big data of complexity. Complexity, 2021(10), 1-13. Search in Google Scholar

[2] Li, B. (2019). Research on the construction path of the legalized teaching platform for college student management under the internet thinking. Basic & clinical pharmacology & toxicology.(S1), 125. Search in Google Scholar

[3] Zhang, Y. (2019). Exploration of student management mechanism in higher vocational colleges under the coupling model of institutionalization and humanization. Basic & clinical pharmacology & toxicology.(S2), 125. Search in Google Scholar

[4] Zhu, Y., & Zheng, L. (2021). Ideological and political teaching information management based on artificial intelligence and data security model. Journal of Intelligent and Fuzzy Systems(99), 1-11. Search in Google Scholar

[5] Rong, L. (2021). Design of ideological and political multimedia network teaching resources integration system based on wireless network. Scientific Programming. Search in Google Scholar

[6] Jiao, Y., & Liu, Y. (2021). The teaching optimization algorithm mode of integrating mobile cloud teaching into ideological and political courses under the internet thinking mode. Scientific programming(Pt.13), 2021. Search in Google Scholar

[7] Gao, B. (2022). Application of convolutional neural network in emotion recognition of ideological and political teachers in colleges and universities. Scientific Programming. Search in Google Scholar

[8] Wang, Z. C. G. (2015). Investigation of the effective combined method of ideological, political education and psychological health education. International Journal of Technology, Management. Search in Google Scholar

[9] Luo, S. (2022). Construction of situational teaching mode in ideological and political classroom based on digital twin technology. Computers and Electrical Engineering, 101. Search in Google Scholar

[10] Zhou, J., Wei, Z., Jia, F., & Li, W. (2021). Course ideological and political teaching platform based on the fusion of multiple data and information in an intelligent environment. Journal of Sensors. Search in Google Scholar

[11] Xia, T., & Ahmad, M. T. (2022). Method of ideological and political teaching resources in universities based on school-enterprise cooperation mode. Mathematical Problems in Engineering, 2022. Search in Google Scholar

[12] Hongmei, W., & Songlin, T. (2021). Sentiment analysis of students in ideological and political teaching based on artificial intelligence and data mining. Journal of Intelligent and Fuzzy Systems(3), 1-10. Search in Google Scholar

[13] Honglan, L., & Wu, R. (2021). Study on problems and countermeasures of ideological and political teaching in colleges and universities under the background of new media era. Journal of Intelligent and Fuzzy Systems(1), 1-9. Search in Google Scholar

[14] Hong, N., Wang, X., & Wang, Z. (2021). Abnormal access behavior detection of ideological and political moocs in colleges and universities. Mobile Information Systems. Search in Google Scholar

[15] Rueda, L., Benitez, J., & Braojos, J. (2017). From traditional education technologies to student satisfaction in management education: a theory of the role of social media applications. Information & Management, 54(8), 1059-1071. Search in Google Scholar

[16] Yanyan, J. (2017). The research of the information construction of international students management— —under the background of “the belt and road”. Journal of Higher Education. Search in Google Scholar

[17] Li, Y. (2021). Application of intelligent sensor algorithm in student management information fusion. Scientific programming(Pt.14), 2021. Search in Google Scholar

[18] Sylvia, Louisa G.Bui, EricChudnofsky, RanaLakin, LauraMcCarthy, Megan D.Gupta, Carina T.Furbish, KaylaMehta, Darshan H.Denninger, John W.Park, Elyse R.Fricchione, Gregory L.Spencer, Thomas J. (2020). A pilot study of a mind-body stress management program for student veterans. Complementary therapies in clinical practice, 40(1). Search in Google Scholar

[19] Ning, N. (2016). Research on the higher vocational student status management under the perspective of procedural justice. International Journal of Technology, Management. Search in Google Scholar

[20] Gholami, H., Saman, M. Z. M., Sharif, S., Zakuan, N., Abu, F., & Awang, S. R. (2018). Critical success factors of student relationship management. Sustainability, 10(12). Search in Google Scholar

[21] Chen, W., Samuel, R. D. J., & Krishnamoorthy, S. (2021). Computer vision for dynamic student data management in higher education platform. Journal of multiple-valued logic and soft computing(1/3), 36. Search in Google Scholar

[22] Gholami, H., Zakuan, N., Saman, M. Z. M., Sharif, S., & Kohar, U. H. A. (2020). Conceptualizing and operationalizing the student relationship management strategy: towards a more sustainable-based platform. Journal of Cleaner Production, 244, 118707. Search in Google Scholar

[23] Yin, X. H. (2021). Construction of student information management system based on data mining and clustering algorithm. Complexity, 2021(2), 1-11. Search in Google Scholar

[24] Ozek, M. B. (2018). The effects of merging student emotion recognition with learning management systems on learners’ motivation and academic achievements. Computer applications in engineering education, 26(5), 1862-1872. Search in Google Scholar

[25] Moodi, F., & Saadatfar, H. (2022). An improved k-means algorithm for big data. IET Software, 16(1). Search in Google Scholar

[26] Xie, Q., Wang, X., Sun, H., Zhang, Y., & Lu, X. (2021). Ecg signal detection and classification of heart rhythm diseases based on resnet and lstm. Advances in Mathematical Physics, 2021. Search in Google Scholar

[27] Wang, H., Zhang, Y., Liu, W., Gu, X., & Liu, Z. (2021). A novel gcn-based point cloud classification model robust to pose variances. Pattern Recognition(19), 108251. Search in Google Scholar

[28] Zhan, D., Liang, D., Jin, H., & Wu, X. (2022). Mbbos-gcn: minimum bounding box over-segmentation-graph convolution 3d point cloud deep learning model. Journal of Applied Remote Sensing(1), 16. Search in Google Scholar