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Establishment and Optimization Algorithm of Fuzzy Index Evaluation System for Course Ideological and Political Teaching

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DOI: 10.23977/trance.2024.060406 | Downloads: 5 | Views: 108

Author(s)

Chengxiu Dong 1

Affiliation(s)

1 Department of Primary Education, Jinan Preschool Education College, Jinan, Shandong, 250307, China

Corresponding Author

Chengxiu Dong

ABSTRACT

In recent years, ideological and political work is not only the necessary premise for schools to adhere to socialist education, but also the main purpose of school moral education. Based on the teaching research of ideological and political course, this paper established five first-level indicators, including teaching objectives, teaching contents, subject objects, course evaluation process and educational evaluation methods. Based on the analytic hierarchy process, this paper analysed the ideological and political teaching structure of the course from many angles. Professional teachers could be targeted in the process of ideological and political teaching of professional courses, with the help of evaluation index system of ideological and political teaching as support and guidance. In addition, this paper also optimized the initial indicators by expert correspondence and analytic hierarchy process to establish the evaluation index system of ideological and political course and determine the weight distribution. Through the analysis of the experimental data, it was found that the α coefficient of each dimension was higher than 0.85, which was qualified after inspection. The Sampling Suitability Quantity (KMO) value was greater than 0.86, indicating that the index system was stable, consistent and valid.

KEYWORDS

Course Ideology and Politics, Fuzzy Evaluation, Analytic Hierarchy Process, Machine Learning

CITE THIS PAPER

Chengxiu Dong, Establishment and Optimization Algorithm of Fuzzy Index Evaluation System for Course Ideological and Political Teaching. Transactions on Comparative Education (2024) Vol. 6: 43-53. DOI: http://dx.doi.org/10.23977/trance.2024.060406.

REFERENCES

[1] Zhou Y. The Application of Curriculum Ideology and Politics in the Training of Judicial Vocational Education Talents. Journal of Higher Education Research, 2022, 3(2): 155-159.
[2] Wang Z, Luo Q, Shouhong X U. Discussion on the Present Situation and Strategies for the Course Ideology and Politics of Organic Chemistry. University Chemistry, 2019, 34(11): 45-50.
[3] Maurer M. Governing policy expansion in a collective skill formation system: the case of vocational education and training for adults in Switzerland. International Journal of Lifelong Education, 2022, 41(2): 133-145.
[4] Carlucci D, Renna P, Izzo C, Schiuma G. Assessing teaching performance in higher education: a framework for continuous improvement. Management Decision, 2019, 57(2): 461-479.
[5] He H, Yan H, Liu W. Intelligent teaching ability of contemporary college talents based on BP neural network and fuzzy mathematical model. Journal of Intelligent and Fuzzy Systems, 2020, 39(9): 1-11.
[6] Fang C. Intelligent online teaching system based on SVM algorithm and complex network. Journal of Intelligent and Fuzzy Systems, 2020, 40(5): 1-11.
[7] Zhang S, Luo G. A model of the teaching quality evaluation based on fuzzy analytic hierarchy process. Revista de la Facultad de Ingenieria, 2017, 32(7): 447-453.
[8] Keegan S. The BMA needs to step up. BMJ, 2020, 113(2): 259-282.
[9] Georgescu I. Computing the Risk Indicators in Fuzzy Systems. Journal of Information Technology Research, 2017, 5(4): 63-84.
[10] Kaya T. Monitoring Brand Performance Based on Household Panel Indicators Using a Fuzzy Rank-based Oreste Methodology. Journal of multiple-valued logic and soft computing, 2018, 31(5-6): 443-467.
[11] Wang J, Liu S, Wang S, Liu Q, Tang J. Multiple Indicators-Based Health Diagnostics and Prognostics for Energy Storage Technologies Using Fuzzy Comprehensive Evaluation and Improved Multivariate Grey Model. IEEE Transactions on Power Electronics, 2021, 36(11): 12309-12320.
[12] Kumar, Divesh, Garg, Chandra, Prakash. Evaluating sustainable supply chain indicators using fuzzy AHP: Case of Indian automotive industry. Benchmarking, 2017, 24(6): 1742-1766.
[13] Teimouri H, Ansari M, Teimouri H, Nasab HH, Ghavagh AR. Assessing Potential of Physical Development with an Emphasis on Geomorphological Indicators Using AHP-FUZZY (Case Study: Estahban City). Asian journal of water, environment and pollution, 2018, 15(2): 115-126.
[14] Jang S W. Stock Forecasting using Fuzzy Neural Networks, Technical Indicators, and Foreign Exchange Rates. International Journal of Advanced Trends in Computer Science and Engineering, 2020, 9(3): 3345-3349.
[15] Kumar D, Garg CP. Evaluating sustainable supply chain indicators using Fuzzy AHP: Case of Indian automotive industry. Benchmarking An International Journal, 2017, 24(6): 1742-1766.
[16] Shatalova AY, Shevchenko I V, Bamadio B, Lebedev KA. Improved five-factor Altman evaluation model credit about the enterprise with economic indicators as fuzzy numbers. Computational Nanotechnology, 2020, 7(1): 72-83.
[17] Shaktawat A, Vadhera S. Sustainability Assessment Considering Socio-environmental and Economic Indicators Using Fuzzy Logic: A Case Study of Indian Hydropower Projects. Asian Journal of Water, Environment and Pollution, 2019, 16(2): 1-7.

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