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Research on an early warning model of effectiveness evaluation in ideological and political teaching based on big data

  
Aug 24, 2022

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Gao Hongyan. The way for university libraries to improve reader service ability in the era of big data [J]. journal of library science, 2016(5):116–118. (in Chinese) Search in Google Scholar

Lu Yin, Lian Defu. Big data leads the future of education: from the perspective of achievement prediction [J]. Big data, 2015(2):1–4. (in Chinese) Search in Google Scholar

Wang Fengqin, Li Ying, Zhang Zhengxia. Study on the Prediction of Students’ Learning Success or Failure in the Big Data Era [J]. Computer Engineering and Science, 2016(11):309–312. (in Chinese) Search in Google Scholar

Zhou Xianglin. Educational management reform in the era of big data [J]. Chinese Journal of Education, 2014(10):25–30. (in Chinese) Search in Google Scholar

Zhang Yuecong. Research on the Subject Behavior of IAP Workers in Colleges and Universities in the Era of Big Data [J]. Research on Ideological Education, 2014(12):68–72. (in Chinese) Search in Google Scholar

Yi Suo, Wei Xing. The application of big data in IAP education in colleges and universities [J]. Ideological and theoretical education, 2016(11):72–75. (in Chinese) Search in Google Scholar

Marbouti F, Diefes-Dux H A, Madavan K. Models for early prediction of at-risk students in a course using standards-based grading [J]. Computers & Education, 2016, (103):1–15. Search in Google Scholar

Liu Lijuan, Lin Yuheng, Wang Xiaoqi, Cui Jianfeng. The influence of multi-dimensional characteristics on academic early warning and prediction of college students [J]. Journal of Xiamen University of Technology, 2020, 28(01):54–61. (in Chinese) Search in Google Scholar

Delen D. A comparative analysis of machine learning techniques for student retention management [J].Decision Support Systems, 2010, 49(4):498–506. Search in Google Scholar

Lu Jiaojiao. Innovation of IAP education methods in colleges and universities in the era of big data [D]. Xi ‘an: Shaanxi Normal University, 2016. (in Chinese) Search in Google Scholar

Eszter Hargittai. Is Bigger Always Better Potential Biases of Big Data Derived from Social Network Sites[J]. ANNALS, 2015, (1):63–76. Search in Google Scholar

Deng Guofeng, Pang Zhi. Research on the Application of Big Data Technology in IAP Education in Colleges and Universities [J]. Guangxi Social Sciences, 2018(6):12–16. (in Chinese) Search in Google Scholar

Xu Ruifang, Gao Guoxi. Retrospect and prospect of research on IAP education model [J]. Research on ideological education, 2014(8):25–32. (in Chinese) Search in Google Scholar

Duan Guiqin. Research on student achievement evaluation based on global central clustering algorithm [J]. Intelligent Computer and Application, 2019, 009(001):80–83. (in Chinese) Search in Google Scholar

Li Xu. Research on data analysis of campus network users’ behavior based on clustering technology [Master’s thesis]. Jinan: Shandong Normal University, 2016. (in Chinese) Search in Google Scholar

Zhang Tian. Research on Correlation Analysis of College Students’ Achievement Based on Data Mining [Master’s Thesis]. Beijing: Beijing University of Posts and Telecommunications, 2018. (in Chinese) Search in Google Scholar

Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System [J]. The 22nd ACM SIGKDD International Conference. 2016. Search in Google Scholar

Ren Gaogao, Wang Hongwei. Design and Implementation of University Management Information System Based on Data Mining [J]. Computer Measurement and Control, 2016, 24(10): 255–258. (in Chinese) Search in Google Scholar

Zhou W, Chen M, Yang Z, et al. Real estate risk measurement and early warning based on PSO-SVM[J]. Socio-Economic Planning Sciences, 2020(5):101001. Search in Google Scholar

Xie Hong. Design and implementation of large-scale emergency material dispatching system based on data mining technology [J]. Modern Electronic Technology, 2017(8):57–60. (in Chinese) Search in Google Scholar

Li Z, Xiong X, Wang W, et al. Early warning method for transmission line galloping based on SVM and AdaBoost bi-level classifiers [J]. Iet Generation Transmission & Distribution, 2016. Search in Google Scholar

Xue Yucheng. Research on the evaluation system of school-enterprise cooperation in vocational education based on AHP [J]. Journal of Wuhan Vocational and Technical College, 2021, 20(04):101–105. (in Chinese) Search in Google Scholar

Bayesian global analysis of neutrino oscillation data[J]. Journal of High Energy Physics, 2015, 2015(9):200. Search in Google Scholar

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