Prediction of Final Grade of Advanced Mathematics based on BP Neural Network
DOI:
https://doi.org/10.54097/emdz9943Keywords:
College Students, BP Neural Network, Advanced Mathematics, Grade PredictionAbstract
In recent years, with the continuous expansion of college enrollment, the number of students is increasing year by year, so the scale of classroom teaching is also getting larger and larger, so that it is difficult for teachers to track and understand the learning situation of each student, which affects the quality of teaching to a certain extent. At the same time, every year, a certain number of students in colleges and universities fail the exam, repeat the grade, or even drop out. If these problems cannot be solved in time, they will seriously affect the psychological health and future development of students, and also affect the employment rate of graduates, and thus affect the development of schools. How to predict the situation of students, so that students in the study of trouble, hesitation, timely interference, so that students successfully complete the university study life. Through the analysis of the failed students, it is found that about 80% of the failed students are advanced mathematics. Therefore, this paper will use the machine learning technology based on BP neural network to analyze the scores of senior students in the final exam of advanced mathematics. According to the different scores, the scores will be divided into four categories: excellent, good, medium and failed. Through the establishment of the grade prediction model, the paper predicts the grade of the college students in the final exam of higher mathematics, and then gives the students learning guidance.
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References
[1] Ertel W. Machine Learning and Data Mining. Springer London, 2011. DOI: https://doi.org/10.1007/978-0-85729-299-5_8
[2] Mostow J, Beck J . Some useful tactics to modify, map and mine data from intelligent tutors[J]. Natural Language Engineering, 2006, 12(Pt2):195-208. DOI: https://doi.org/10.1017/S1351324906004153
[3] Hippe J M. A Review of the State of the Art.
[4] Zhou Qing, Mou Chao, Yang Dan. Review on research progress of educational data mining [J]. Journal of Software, 2015, 26(11):17. (in Chinese)
[5] Xiao Wei, Ni Chuanbin, Li Rui. Foreign research on learning prediction based on Data mining: Review and prospect [J]. China Distance Education, 2018(2):9. (in Chinese)
[6] Chen Qinhua. Research on the academic prediction Mechanism of college students under the credit system [J]. Journal of Guangxi Normal University: Philosophy and Social Sciences Edition, 2007(S2):6.(in Chinese)
[7] Wang Zihua, Zhang Baojing, Shi Yuan, et al. Study on the prediction mechanism of College students' Academic difficulties [J]. Journal of College of Adult Education, Hebei University of Technology, 2009(3):4. (in Chinese)
[8] Shang Wei, Champion Luo. Study on academic prediction mechanism of local college students [J]. Contemporary Educational Practice and Teaching Research: Electronic Edition, 2016(9):2. (in Chinese)
[9] He Kui. A study on the construction of academic prediction mechanism for college students under the credit system [J]. Journal of Hubei Adult Education University, 2017, 23(3):4.(in Chinese)
[10] Wu Haifeng, CHENG Yusheng, HU Xuegang. Design of university achievement prediction model based on data mining technology [C]// 2010.(in Chinese)
[11] Wan Xinghuo, Zheng Junling, Jin Yongchao. Academic prediction model based on KPCA and its application [J]. Mathematical Theory and Applications, 2013, 33(4):6.(in Chinese)
[12] Zhang Fusheng, Lu Kaidong, Han Yijia. Research on the Academic Monitoring and Prediction System of College Students Based on Campus Cloud [J]. China Education Informatization: Higher Vocational Education, 2015(5):3.(in Chinese)
[13] Jin Yifu, Wu Tao, Zhang Zishi, et al. Design and Analysis of Academic Prediction System in Big Data Environment [J]. China Audio-Visual Education, 2016(2):5. (in Chinese)
[14] Du Juan and Zhai Sheping. Research on College Student Achievement Prediction System Based on improved Apriori Algorithm [J]. Modern Information Technology, 2018, 2(2):3. (in Chinese)
[15] Cui Qiang, Sun Jiyan. The Application of Big Data in Promoting the Quality of Ideological and Political Education in Colleges and Universities -- Taking Dalian University of Technology Academic Prediction Model as an Example [J]. Journal of College Counselors, 2019, 11(5):5.(in Chinese)
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